# data[].attributes.description
A learning path for systems biology biocurators which will support the creation and curation of interoperable and reusable models for precise hypotheses and predictions.

# data[].attributes.workflow-content.nodes[].data.name
Biocuration knowledge - biocuration skills

# data[].attributes.workflow-content.nodes[].data.description
This course is a guide to the UniProt resource, including what kinds of information it provides and how to access the data using the UniProt website.

# data[].attributes.workflow-content.nodes[].data.html-description
This course is a guide to the UniProt resource, including what kinds of information it provides and how to access the data using the UniProt website.

# data[].attributes.workflow-content.nodes[].data.name
Complex Portal - reference protein complex resource

# data[].attributes.workflow-content.nodes[].data.description
This training materials provides a brief introduction to EMBL-EBI's Complex Portal: a manually curated, encyclopedic resource of macromolecular complexes from a number of key model organisms.

# data[].attributes.workflow-content.nodes[].data.html-description
This training materials provides a brief introduction to EMBL-EBI's Complex Portal: a manually curated, encyclopedic resource of macromolecular complexes from a number of key model organisms.

# data[].attributes.workflow-content.nodes[].data.name
ChEBI - reference small molecule resource

# data[].attributes.workflow-content.nodes[].data.description
ChEBI - The online chemical dictionary for small molecules
Chemical Entities of Biological Interest (ChEBI) is a user-friendly online chemical dictionary that focuses on the nomenclature, structure, and biological properties of 'small' molecules that may be encountered by anyone working in molecular biology. This course will show you what kind of information is available in ChEBI and how to access and query the database.

# data[].attributes.workflow-content.nodes[].data.html-description
ChEBI - The online chemical dictionary for small molecules Chemical Entities of Biological Interest (ChEBI) is a user-friendly online chemical dictionary that focuses on the nomenclature, structure, and biological properties of 'small' molecules that may be encountered by anyone working in molecular biology. This course will show you what kind of information is available in ChEBI and how to access and query the database.

# data[].attributes.workflow-content.nodes[].data.description
This webinar, part of the biocuration series, will introduce the concept of an ontology, a type of controlled vocabulary that encompasses relationships between entities, and will cover why ontologies are useful for describing biological data and biology. The webinar will also discuss the role of biocurators and the relevance of ontologies to biocuration. 

# data[].attributes.workflow-content.nodes[].data.html-description
This webinar, part of the biocuration series, will introduce the concept of an ontology, a type of controlled vocabulary that encompasses relationships between entities, and will cover why ontologies are useful for describing biological data and biology. The webinar will also discuss the role of biocurators and the relevance of ontologies to biocuration.

# data[].attributes.workflow-content.nodes[].data.description
This course provides an overview of RNAcentral and covers different ways of accessing and using the data in RNAcentral, it is aimed at anyone with an interest in non-coding RNAs. 

An undergraduate level knowledge of basic molecular biology is recommended for this course.

# data[].attributes.workflow-content.nodes[].data.html-description
This course provides an overview of RNAcentral and covers different ways of accessing and using the data in RNAcentral, it is aimed at anyone with an interest in non-coding RNAs.

An undergraduate level knowledge of basic molecular biology is recommended for this course.

# data[].attributes.workflow-content.nodes[].data.name
Ensembl - reference gene and mRNA resource

# data[].attributes.workflow-content.nodes[].data.description
This courses is for anyone who would to learn more about using Ensembl via the browser through webinar videos and exercises. An undergraduate degree in a life science subject would be an advantage. 

# data[].attributes.workflow-content.nodes[].data.html-description
This courses is for anyone who would to learn more about using Ensembl via the browser through webinar videos and exercises. An undergraduate degree in a life science subject would be an advantage.

# data[].attributes.workflow-content.nodes[].data.description
IntAct - Quick tour
This quick tour provides a brief introduction to the EMBL-EBI's molecular interactions database, IntAct.

# data[].attributes.workflow-content.nodes[].data.html-description
IntAct - Quick tour This quick tour provides a brief introduction to the EMBL-EBI's molecular interactions database, IntAct.

# data[].attributes.workflow-content.nodes[].data.description
This course will provide an introduction to molecular interactions, their importance and the methodologies use to generate and capture interaction data.

This course is suitable for those with an undergraduate degree in a life science subject. Background knowledge in protein sequence and structure would be an advantage.

# data[].attributes.workflow-content.nodes[].data.html-description
This course will provide an introduction to molecular interactions, their importance and the methodologies use to generate and capture interaction data.

This course is suitable for those with an undergraduate degree in a life science subject. Background knowledge in protein sequence and structure would be an advantage.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Protein interactions and their importance

# data[].attributes.workflow-content.nodes[].data.description
Biocuration involves the collection, curation and integration of information related to the biological sciences into databases or resources. In this collection, we bring together online tutorials and videos introducing knowledge and skills pivotal to biocuration, including ontologies, programming and text mining. 

# data[].attributes.workflow-content.nodes[].data.html-description
Biocuration involves the collection, curation and integration of information related to the biological sciences into databases or resources. In this collection, we bring together online tutorials and videos introducing knowledge and skills pivotal to biocuration, including ontologies, programming and text mining.

# data[].attributes.workflow-content.nodes[].data.name
Data harmonisation and enrichment using EMBL-EBI ontology tools

# data[].attributes.workflow-content.nodes[].data.description
This video explains how the Zooma annotation tool, the OxO mapping service and the Ontology Lookup Service (OLS) can be used to harmonise and enrich biological and biomedical data.
The EMBL-EBI Ontology Tools can be used by both biological curators and software developers. Biological curators access the tools through their respective websites. REST APIs are available for software developers who want to build systems based on these services.

# data[].attributes.workflow-content.nodes[].data.html-description
This video explains how the Zooma annotation tool, the OxO mapping service and the Ontology Lookup Service (OLS) can be used to harmonise and enrich biological and biomedical data. The EMBL-EBI Ontology Tools can be used by both biological curators and software developers. Biological curators access the tools through their respective websites. REST APIs are available for software developers who want to build systems based on these services.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Data harmonisation and enrichment using EMBL-EBI ontology tools

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video - reMarkable remarkable.com Learn more Annuncio 1 di 2 · 0:12 remarkable.com Salta gli annunci   0:07 / 0:20  Data harmonization and enrichment using EMBL-EBI Ontology Tools

# data[].attributes.workflow-content.nodes[].data.associated-resources[].url
  reMarkable remarkable.com Learn more Annuncio 1 di 2 · 0:12 remarkable.com Salta gli annunci   0:07 / 0:20  Data harmonization and enrichment using EMBL-EBI Ontology Tools

# data[].attributes.workflow-content.nodes[].data.description
Training slides on literature searching skills and tools. Part of biocurators training for the Postgraduate Certificate in Biocuration, developed by the University of Cambridge and the European Bioinformatics Institute (EMBL-EBI).

# data[].attributes.workflow-content.nodes[].data.html-description
Training slides on literature searching skills and tools. Part of biocurators training for the Postgraduate Certificate in Biocuration, developed by the University of Cambridge and the European Bioinformatics Institute (EMBL-EBI).

# data[].attributes.workflow-content.nodes[].data.description
This user guide explain how to perform simple and advance search in SIGNOR 3.0 database and the several options to browse its content and use its tools.

# data[].attributes.workflow-content.nodes[].data.html-description
This user guide explain how to perform simple and advance search in SIGNOR 3.0 database and the several options to browse its content and use its tools.

# data[].attributes.workflow-content.nodes[].data.description
Online tutorial for beginner to explore SABIO-RK (http://sabiork.h-its.org/) database, a manually curated database containing structured information about biochemical reactions and their corresponding kinetics mainly extracted from scientific literature. 

# data[].attributes.workflow-content.nodes[].data.html-description
Online tutorial for beginner to explore SABIO-RK (http://sabiork.h-its.org/) database, a manually curated database containing structured information about biochemical reactions and their corresponding kinetics mainly extracted from scientific literature.

# data[].attributes.workflow-content.nodes[].data.name
SIGNOR - Methods in building and analysing biological networks

# data[].attributes.workflow-content.nodes[].data.description
Learning SBGN PD by redrawing an example diagram that contains most of the SBGN PD symbols within a comparatively small diagram. 1st example: glycolysis - bifunctional enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase. 2nd example: iNOS pathway

# data[].attributes.workflow-content.nodes[].data.html-description
Learning SBGN PD by redrawing an example diagram that contains most of the SBGN PD symbols within a comparatively small diagram. 1st example: glycolysis - bifunctional enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase. 2nd example: iNOS pathway

# data[].attributes.workflow-content.nodes[].data.name
Annotation of SBGN Process Description diagrams

# data[].attributes.workflow-content.nodes[].data.name
Knowledge formalisation in SBGN Process Description diagrams

# data[].attributes.workflow-content.nodes[].data.description
Learning main steps for working with the MINERVA platform: how to request an account, upload a diagram in CellDesigner XML and in SBGN-ML, add comments online, explore drug targets, upload omics dataset for visualisation and  download the map in different formats. Supporting materials are video tutorials, example diagrams and example dataset for visualisation.

# data[].attributes.workflow-content.nodes[].data.html-description
Learning main steps for working with the MINERVA platform: how to request an account, upload a diagram in CellDesigner XML and in SBGN-ML, add comments online, explore drug targets, upload omics dataset for visualisation and download the map in different formats. Supporting materials are video tutorials, example diagrams and example dataset for visualisation.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
The MINERVA Platform: Administrator’s manual

# data[].attributes.workflow-content.nodes[].data.description
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

# data[].attributes.workflow-content.nodes[].data.html-description
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Reconstruction of disease mechanisms: planning, development and maintenance

# data[].attributes.workflow-content.nodes[].data.associated-resources[].url
Learning main steps for working with the MINERVA platform: how to request an account, upload a diagram in CellDesigner XML and in SBGN-ML, add comments online, explore drug targets, upload omics dataset for visualisation and  download the map in different formats. Supporting materials are video tutorials, example diagrams and example dataset for visualisation.

# data[].attributes.workflow-content.nodes[].data.description
The academy contains organized training examples for new WikiPathways authors. The WikiPathways Academy will walk you through the process of becoming a pathway author. You will start with basic terminology and rapidly progress to using the authoring tools. In no time at all, you will be able to take the pathway information trapped in static images and unleash it upon the world as living pathway models!

# data[].attributes.workflow-content.nodes[].data.html-description
The academy contains organized training examples for new WikiPathways authors. The WikiPathways Academy will walk you through the process of becoming a pathway author. You will start with basic terminology and rapidly progress to using the authoring tools. In no time at all, you will be able to take the pathway information trapped in static images and unleash it upon the world as living pathway models!

# data[].attributes.workflow-content.nodes[].data.name
An Introduction to Mathematical Modelling

# data[].attributes.workflow-content.nodes[].data.description
This course provides an introduction to the concepts of modelling biological systems. It is intended for biologists who have no experience in modelling but would like to know how it might apply to their area of research. The course provides a complete background to the history of modelling and the different approaches through which a biological system can be approximated by mathematical methods. The course also provides a practical introduction to the COPASI modelling environment.

# data[].attributes.workflow-content.nodes[].data.html-description
This course provides an introduction to the concepts of modelling biological systems. It is intended for biologists who have no experience in modelling but would like to know how it might apply to their area of research. The course provides a complete background to the history of modelling and the different approaches through which a biological system can be approximated by mathematical methods. The course also provides a practical introduction to the COPASI modelling environment.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
An Introduction to Mathematical Modelling

# data[].attributes.workflow-content.nodes[].data.description
On-line tutorial on COPASI, an open-source software application for creating and solving mathematical models of biological processes such as metabolic networks, cell-signaling pathways, regulatory networks, infectious diseases, and many others.

# data[].attributes.workflow-content.nodes[].data.html-description
On-line tutorial on COPASI, an open-source software application for creating and solving mathematical models of biological processes such as metabolic networks, cell-signaling pathways, regulatory networks, infectious diseases, and many others.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Introduction and functionalities of COPASI

# data[].attributes.workflow-content.nodes[].data.name
Introduction to kinetic modelling and COPASI

# data[].attributes.workflow-content.nodes[].data.description
An introduction to mathematical modelling and some basic terms and concepts. It will also be demonstrated how to use COPASI, a software application for simulation and analysis of biochemical networks and their dynamics.

# data[].attributes.workflow-content.nodes[].data.html-description
An introduction to mathematical modelling and some basic terms and concepts. It will also be demonstrated how to use COPASI, a software application for simulation and analysis of biochemical networks and their dynamics.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Introduction to kinetic modelling and COPASI

# data[].attributes.workflow-content.nodes[].data.name
Introduction to logic modelling and CellCollective

# data[].attributes.workflow-content.nodes[].data.description
An introduction to basic terms and concepts in logic modelling and exploration of CellCollective, an interactive, open and collaborative modeling platform for biological networks.

# data[].attributes.workflow-content.nodes[].data.html-description
An introduction to basic terms and concepts in logic modelling and exploration of CellCollective, an interactive, open and collaborative modeling platform for biological networks.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Introduction to logic modelling and CellCollective

# data[].attributes.workflow-content.nodes[].data.name
Model building from literature and data repositories

# data[].attributes.workflow-content.nodes[].data.description
On-line turorials to learn to encode and simulate ordinary differential equation models of signalling pathways from a recent publication using COPASI. Furthermore, you will learn how to annotate models and re-use pre-existing models from open repositories such as BioModels.

# data[].attributes.workflow-content.nodes[].data.html-description
On-line turorials to learn to encode and simulate ordinary differential equation models of signalling pathways from a recent publication using COPASI. Furthermore, you will learn how to annotate models and re-use pre-existing models from open repositories such as BioModels.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Model building from literature and data repositories

# data[].attributes.workflow-content.nodes[].data.description
An introduction to mathematical modelling and will introduce some basic terms and concepts. It will also be demonstrated how to use COPASI, a software application for simulation and analysis of biochemical networks and their dynamics.

# data[].attributes.workflow-content.nodes[].data.html-description
An introduction to mathematical modelling and will introduce some basic terms and concepts. It will also be demonstrated how to use COPASI, a software application for simulation and analysis of biochemical networks and their dynamics.

# data[].attributes.workflow-content.nodes[].data.name
Reproducibility in systems biology modelling

# data[].attributes.workflow-content.nodes[].data.description
Webinar on the importance of reproducibility in the field of Systems Biology.

# data[].attributes.workflow-content.nodes[].data.html-description
Webinar on the importance of reproducibility in the field of Systems Biology.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Reproducibility in systems biology modelling

# data[].attributes.workflow-content.nodes[].data.description
 BridgeDb is a combination of an application programming interface (API), library, and set of data files for mapping identifiers for identical objects. Identifier mapping databases are available for gene products and metabolites. This Bioconductor Vignette explains how to use BridgeDb in the R environment and describes the basic concepts (identifiers, data sources, mapping databases), and how to use BridgeDb mapping databases for identifier mapping, using several code examples.

# data[].attributes.workflow-content.nodes[].data.html-description
BridgeDb is a combination of an application programming interface (API), library, and set of data files for mapping identifiers for identical objects. Identifier mapping databases are available for gene products and metabolites. This Bioconductor Vignette explains how to use BridgeDb in the R environment and describes the basic concepts (identifiers, data sources, mapping databases), and how to use BridgeDb mapping databases for identifier mapping, using several code examples.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Learn about identifier mapping for bioentities for BridgeDb in R

# data[].attributes.workflow-content.nodes[].data.name
Discovering scientific evidence using Europe PMC SciLite annotation

# data[].attributes.workflow-content.nodes[].data.description
This webinar will demonstrate how to use the power of text mining to find evidence in scientific literature.

# data[].attributes.workflow-content.nodes[].data.html-description
This webinar will demonstrate how to use the power of text mining to find evidence in scientific literature.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Discovering scientific evidence using Europe PMC SciLite annotation

# data[].attributes.workflow-content.nodes[].data.name
Creating a Boolean model from an SBGN Process Description diagram via CaSQ

# data[].attributes.workflow-content.nodes[].data.name
Converting WikiPathways diagrams into computational models for modelling

# data[].attributes.workflow-content.nodes[].data.name
Introduction to pathway modelling with WikiPathways

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Introduction to pathway modelling with WikiPathways

# data[].attributes.target-audience[]
Anyone new to the field of systems biology

# data[].attributes.description
Manual example created as proof of concept for our Carpentree project, created at the #CollabW19 workshop in Loughborough on 3 April 2019

The lessons referred to are: 
- Programming with Python (PP): http://swcarpentry.github.io/python-novice-inflammation/index.html
- Plotting and Programming in Python (PPP): http://swcarpentry.github.io/python-novice-gapminder/
- Programming with R (PR):  http://swcarpentry.github.io/r-novice-inflammation/
- R for Reproducible Scientific Analysis (RRSA): http://swcarpentry.github.io/r-novice-gapminder/

# data[].attributes.workflow-content.nodes[].data.description
Final outputs/ learning outcomes from PPP ep. 13 "Looping Over Data Sets"

# data[].attributes.workflow-content.nodes[].data.html-description
Final outputs/ learning outcomes from PPP ep. 13 "Looping Over Data Sets"

# data[].attributes.workflow-content.nodes[].data.description
Python: PP episode 2; PPP ep. 12 
R: PR ep. 3, 

# data[].attributes.workflow-content.nodes[].data.html-description
Python: PP episode 2; PPP ep. 12 R: PR ep. 3,

# data[].attributes.workflow-content.nodes[].data.description
Python: PPP ep. 7/8
R: PR ep. 1, RRSA ep. 5

# data[].attributes.workflow-content.nodes[].data.html-description
Python: PPP ep. 7/8 R: PR ep. 1, RRSA ep. 5

# data[].attributes.workflow-content.nodes[].data.html-description
R: PR ep. 13 RRSA 4

# data[].attributes.workflow-content.nodes[].data.description
Python: PPP ep. 11, PP ep. 3
R: no lesson mapped

# data[].attributes.workflow-content.nodes[].data.html-description
Python: PPP ep. 11, PP ep. 3 R: no lesson mapped

# data[].attributes.workflow-content.nodes[].data.html-description
Python: PP ep. 1, PPP ep. 2

# data[].attributes.workflow-content.nodes[].data.description
Python: PPP ep. 1 - prerequisite for PP
R: PR ep. 9, RRSA ep. 1

# data[].attributes.workflow-content.nodes[].data.html-description
Python: PPP ep. 1 - prerequisite for PP R: PR ep. 9, RRSA ep. 1

# data[].attributes.workflow-content.nodes[].data.description
This is not part of a lesson, software carpentry lessons expect learners to come with key software installed

# data[].attributes.workflow-content.nodes[].data.html-description
This is not part of a lesson, software carpentry lessons expect learners to come with key software installed

# data[].attributes.description
Basic workflow for ChIP-seq analysis

Topic: [ChIP-seq](http://edamontology.org/topic_3169)

# data[].attributes.workflow-content.nodes[].data.description
FastQ file(s) with RNA-seq reads

Input data type : http://edamontology.org/format_1930

There are several types of FastQ files (http://edamontology.org/format_2182 is parent), they differ in how the quality scores are calculated (more details on https://en.wikipedia.org/wiki/FASTQ_format). Nowadays the standard is "Sanger quality scores". Depends on the tool what it can handle (and how), but all current workflows can assume its the standard IMO.
Often the data will be compressed (gzip or bzip2), does not seem to be part of EDAM

# data[].attributes.workflow-content.nodes[].data.html-description
FastQ file(s) with RNA-seq reads

Input data type : http://edamontology.org/format_1930

There are several types of FastQ files (http://edamontology.org/format_2182 is parent), they differ in how the quality scores are calculated (more details on https://en.wikipedia.org/wiki/FASTQ_format). Nowadays the standard is "Sanger quality scores". Depends on the tool what it can handle (and how), but all current workflows can assume its the standard IMO. Often the data will be compressed (gzip or bzip2), does not seem to be part of EDAM

# data[].attributes.workflow-content.nodes[].data.html-description
Quality control of the FastQ input file

# data[].attributes.workflow-content.nodes[].data.description
Quality filtering to remove low quality reads. This is recommended to be quite lenient, to only remove bad quality read data and speed up processing afterwards

# data[].attributes.workflow-content.nodes[].data.html-description
Quality filtering to remove low quality reads. This is recommended to be quite lenient, to only remove bad quality read data and speed up processing afterwards

# data[].attributes.workflow-content.nodes[].data.description
Align reads to the genome. Typically takes a FastQ file as input and produces an alignment file in SAM (text) or BAM (binary) format. 
CRAM is a compression that takes into account the reference genome it aligns to (basically only keep info on the differences), it also has options to bin the quality scores (normally 0 to 41, several options e.g. round up to 5). Not sure how popular this is in practice

# data[].attributes.workflow-content.nodes[].data.html-description
Align reads to the genome. Typically takes a FastQ file as input and produces an alignment file in SAM (text) or BAM (binary) format. CRAM is a compression that takes into account the reference genome it aligns to (basically only keep info on the differences), it also has options to bin the quality scores (normally 0 to 41, several options e.g. round up to 5). Not sure how popular this is in practice

# data[].attributes.workflow-content.nodes[].data.description
Filter based on quality, sort and mark duplicates

Not all operations seem to be in EDAM (mark duplicates ?). The ones I added here are very generic

Takes as input the SAM or BAM and produces also these formats

# data[].attributes.workflow-content.nodes[].data.html-description
Filter based on quality, sort and mark duplicates

Not all operations seem to be in EDAM (mark duplicates ?). The ones I added here are very generic

Takes as input the SAM or BAM and produces also these formats

# data[].attributes.workflow-content.nodes[].data.description
Determines in which genomic regions there is a peak of reads in the ChIP sample in comparison with the control sample. This is interpreted as the region where the bait protein used binds the genome (e.g. transcription factor binding site). The result is a BED file (=text) with the regions. Typically several outputs are generated with the location of the summit, narrow and broad peaks. bigBED is the binary form.

# data[].attributes.workflow-content.nodes[].data.html-description
Determines in which genomic regions there is a peak of reads in the ChIP sample in comparison with the control sample. This is interpreted as the region where the bait protein used binds the genome (e.g. transcription factor binding site). The result is a BED file (=text) with the regions. Typically several outputs are generated with the location of the summit, narrow and broad peaks. bigBED is the binary form.

# data[].attributes.workflow-content.nodes[].data.description
Find the closest feature (e.g. gene) to the peak with closestBed in BEDtools

As operation I used "annotation" which is generic. The sub categories did not really fit IMO.

# data[].attributes.workflow-content.nodes[].data.html-description
Find the closest feature (e.g. gene) to the peak with closestBed in BEDtools

As operation I used "annotation" which is generic. The sub categories did not really fit IMO.

# data[].attributes.workflow-content.nodes[].data.description
Get FASTA file from the sequences underlying the peak and determine motifs. If you investigate the binding site of a transcription factor, the "common denominator" in the sequence motifs can provide insight in the recognition motif of that TF

# data[].attributes.workflow-content.nodes[].data.html-description
Get FASTA file from the sequences underlying the peak and determine motifs. If you investigate the binding site of a transcription factor, the "common denominator" in the sequence motifs can provide insight in the recognition motif of that TF

# data[].attributes.title
ChIP-seq workflow (with intermediate data)

# data[].attributes.description
Basic workflow for ChIP-seq analysis

Topic: [ChIP-seq](http://edamontology.org/topic_3169)

# data[].attributes.workflow-content.nodes[].data.html-description
FastQ file(s) with RNA-seq reads

Format: FastQ

# data[].attributes.workflow-content.nodes[].data.description
Quality control of the FastQ input file

Function: [Sequencing Quality Control](http://edamontology.org/operation_3218)

# data[].attributes.workflow-content.nodes[].data.html-description
Quality control of the FastQ input file

Function: Sequencing Quality Control

# data[].attributes.workflow-content.nodes[].data.description
Quality filtering to remove low quality reads. This is recommended to be quite lenient, to only remove bad quality read data and speed up processing afterwards

Function:

* [Sequence trimming](http://edamontology.org/operation_3192)
* [Sequencing Quality Control](http://edamontology.org/operation_3218)

# data[].attributes.workflow-content.nodes[].data.html-description
Quality filtering to remove low quality reads. This is recommended to be quite lenient, to only remove bad quality read data and speed up processing afterwards

Function:

Sequence trimming

Sequencing Quality Control

# data[].attributes.workflow-content.nodes[].data.description
Align reads to the genome

Function: [Read mapping](http://edamontology.org/operation_3198)

# data[].attributes.workflow-content.nodes[].data.html-description
Align reads to the genome

Function: Read mapping

# data[].attributes.workflow-content.nodes[].data.description
Filter based on quality, sort and mark duplicates

# data[].attributes.workflow-content.nodes[].data.html-description
Filter based on quality, sort and mark duplicates

# data[].attributes.workflow-content.nodes[].data.html-description
Topic: ChIP-seq

Function: Peak calling

# data[].attributes.workflow-content.nodes[].data.description
Find the closest gene to the peak with closestBed in BEDtools

Function: [Annotation](http://edamontology.org/operation_0226)

# data[].attributes.workflow-content.nodes[].data.html-description
Find the closest gene to the peak with closestBed in BEDtools

Function: Annotation

# data[].attributes.workflow-content.nodes[].data.description
Get FASTA file from the sequences underlying the peak and determine motifs

Function: [Sequence motif recognition](http://edamontology.org/operation_0239)

# data[].attributes.workflow-content.nodes[].data.html-description
Get FASTA file from the sequences underlying the peak and determine motifs

Function: Sequence motif recognition

# data[].attributes.workflow-content.nodes[].data.html-description
Format: FastQ

# data[].attributes.workflow-content.nodes[].data.html-description
Formats:

SAM

BAM

CRAM

# data[].attributes.workflow-content.nodes[].data.html-description
Formats:

SAM

BAM

CRAM

# data[].attributes.workflow-content.nodes[].data.html-description
Format: BED

# data[].attributes.workflow-content.nodes[].data.description
FastQ file(s) with RNA-seq reads

Input data type : http://edamontology.org/format_1930

There are several types of FastQ files (http://edamontology.org/format_2182 is parent), they differ in how the quality scores are calculated (more details on https://en.wikipedia.org/wiki/FASTQ_format). Nowadays the standard is "Sanger quality scores". Depends on the tool what it can handle (and how), but all current workflows can assume its the standard IMO.
Often the data will be compressed (gzip or bzip2), does not seem to be part of EDAM

# data[].attributes.workflow-content.nodes[].data.html-description
FastQ file(s) with RNA-seq reads

Input data type : http://edamontology.org/format_1930

There are several types of FastQ files (http://edamontology.org/format_2182 is parent), they differ in how the quality scores are calculated (more details on https://en.wikipedia.org/wiki/FASTQ_format). Nowadays the standard is "Sanger quality scores". Depends on the tool what it can handle (and how), but all current workflows can assume its the standard IMO. Often the data will be compressed (gzip or bzip2), does not seem to be part of EDAM

# data[].attributes.workflow-content.nodes[].data.description
fastq file filtering is an important step when dealing with high-throughput sequencing data, because low-quality sequences can contain unexpected and misleading errors; especially, Illumina sequencing quality tends to drop off at the ends of reads, and the initial nucleotides can also be problematic due to calibration issues, such as trimming issues.

# data[].attributes.workflow-content.nodes[].data.html-description
fastq file filtering is an important step when dealing with high-throughput sequencing data, because low-quality sequences can contain unexpected and misleading errors; especially, Illumina sequencing quality tends to drop off at the ends of reads, and the initial nucleotides can also be problematic due to calibration issues, such as trimming issues.

# data[].attributes.workflow-content.nodes[].data.description
Quantification and analysis of short reads to align the reads to the genome.  QuasR takes a FastQ file as input and produces an alignment file in BAM (binary) format. 

QuasR ‘qAlign’  function generates alignment files in BAM format for all input sequence files against the reference genome. The qAlign function is a wrapper for the bowtie and SpliceMap tools.

# data[].attributes.workflow-content.nodes[].data.html-description
Quantification and analysis of short reads to align the reads to the genome. QuasR takes a FastQ file as input and produces an alignment file in BAM (binary) format.

QuasR ‘qAlign’ function generates alignment files in BAM format for all input sequence files against the reference genome. The qAlign function is a wrapper for the bowtie and SpliceMap tools.

# data[].attributes.workflow-content.nodes[].data.description
Quality check is to estimate quality of the reads, sort and mark duplicates. QuasR samples a random subset of sequences and alignments from each sample or an input file and generates a series of diagnostic plots for estimating data quality by various methods. The QC is performed by the ‘qQCReport’ function of the qAlign package.

# data[].attributes.workflow-content.nodes[].data.html-description
Quality check is to estimate quality of the reads, sort and mark duplicates. QuasR samples a random subset of sequences and alignments from each sample or an input file and generates a series of diagnostic plots for estimating data quality by various methods. The QC is performed by the ‘qQCReport’ function of the qAlign package.

# data[].attributes.workflow-content.nodes[].data.description
Determines in which genomic regions there is a peak of reads in the ChIP sample in comparison with the control sample. This is interpreted as the region where the bait protein used binds the genome (e.g. transcription factor binding site). The result is a BED file (=text) with the regions. Typically several outputs are generated with the location of the summit, narrow and broad peaks. bigBED is the binary form.

# data[].attributes.workflow-content.nodes[].data.html-description
Determines in which genomic regions there is a peak of reads in the ChIP sample in comparison with the control sample. This is interpreted as the region where the bait protein used binds the genome (e.g. transcription factor binding site). The result is a BED file (=text) with the regions. Typically several outputs are generated with the location of the summit, narrow and broad peaks. bigBED is the binary form.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Operation: Nucleic acid sequence analysis

# data[].attributes.workflow-content.nodes[].data.description
Find the closest features (e.g. gene, TSS, TFBSs) to the peak and visualise with ChIPSeeker package.

# data[].attributes.workflow-content.nodes[].data.html-description
Find the closest features (e.g. gene, TSS, TFBSs) to the peak and visualise with ChIPSeeker package.

# data[].attributes.workflow-content.nodes[].data.description
Paired-end overlapping reads are merged into a single longer read - we do not perform assembly.

# data[].attributes.workflow-content.nodes[].data.html-description
Paired-end overlapping reads are merged into a single longer read - we do not perform assembly.

# data[].attributes.workflow-content.nodes[].data.description
Low quality trimming (low quality ends and sequences with > 10% undetermined nucleotides removed). Adapter sequences removed using Biopython SeqIO package.
Sequences < 100 nucleotides in length removed.

# data[].attributes.workflow-content.nodes[].data.html-description
Low quality trimming (low quality ends and sequences with > 10% undetermined nucleotides removed). Adapter sequences removed using Biopython SeqIO package. Sequences < 100 nucleotides in length removed.

# data[].attributes.workflow-content.nodes[].data.description
Prokaryotic rRNA reads are filtered. We use the hidden Markov models to identify rRNA sequences.

# data[].attributes.workflow-content.nodes[].data.html-description
Prokaryotic rRNA reads are filtered. We use the hidden Markov models to identify rRNA sequences.

# data[].attributes.workflow-content.nodes[].data.description
Reads with predicted coding sequences (pCDS) above 60 nucleotides in length.

# data[].attributes.workflow-content.nodes[].data.html-description
Reads with predicted coding sequences (pCDS) above 60 nucleotides in length.

# data[].attributes.workflow-content.nodes[].data.description
Matches are generated against predicted CDS, using a sub set of databases (Pfam, TIGRFAM, PRINTS, PROSITE patterns, Gene3d) from InterPro release 31.0. A summary of Gene Ontology (GO) terms derived from InterPro matches to your sample is provided. It is generated using a reduced list of GO terms called GO slim (version goslim_goa 2012).

# data[].attributes.workflow-content.nodes[].data.html-description
Matches are generated against predicted CDS, using a sub set of databases (Pfam, TIGRFAM, PRINTS, PROSITE patterns, Gene3d) from InterPro release 31.0. A summary of Gene Ontology (GO) terms derived from InterPro matches to your sample is provided. It is generated using a reduced list of GO terms called GO slim (version goslim_goa 2012).

# data[].attributes.workflow-content.nodes[].data.description
16s rRNA are annotated using the Greengenes reference database (default closed-reference OTU picking protocol with Greengenes 13.8 reference with reverse strand matching enabled).

# data[].attributes.workflow-content.nodes[].data.html-description
16s rRNA are annotated using the Greengenes reference database (default closed-reference OTU picking protocol with Greengenes 13.8 reference with reverse strand matching enabled).

# data[].attributes.title
ELIXIR Structural Bioinformatics 1: Getting a structure for a protein of interest

# data[].attributes.description
This training workflow is designed to lead you through the process of finding experimentally-determined structures for a protein you are interested in - or predicting structure information if one is not available.

It will then help guide you through visualising that structure as a prerequisite to moving on to other workflows to explore additional information relating to that structure. These next steps will begin to give you greater understanding of key protein features, domains and functional sites - along with knowledge of how genetic variation impacts on the structure.

This training workflow has been designed to help scientists new to the field of structural biololgy.

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the function of the protein in a structural context.


# data[].attributes.workflow-content.nodes[].data.description
**When you have completed this workflow you will be able to:**

- Use the Protein Data Bank in Europe (**PDBe**) to search for an experimentally-derived structure for a protein of interest to you.
- Explore the annotation provided in the PDBe to learn more about your structure, as well as how to assess if the structure you have is suitable for purpose and of acceptable quality.
- Apply protein structure prediction tools (**Phyre2** and **SWISS-MODEL**) to try to determine predicted structure information for your protein, if no experimental data is available.
- Recognise the limitations of your structure predictions, and options for alternative tools (**genThreader**), or those that may provide potentially useful partial information (**PsiPred**, **MEMSAT** and **DISOPRED**).
- Visualise the structure you have obtained using a simple-to-use web-based viewer (**LiteMol**), and select an appropriate 3D viewing software (LiteMol or others) that you can use to further explore functional or site-specific features of your protein in future.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary *(but in some cases have cost or license implications)* to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.html-description
When you have completed this workflow you will be able to:

Use the Protein Data Bank in Europe (PDBe) to search for an experimentally-derived structure for a protein of interest to you.

Explore the annotation provided in the PDBe to learn more about your structure, as well as how to assess if the structure you have is suitable for purpose and of acceptable quality.

Apply protein structure prediction tools (Phyre2 and SWISS-MODEL) to try to determine predicted structure information for your protein, if no experimental data is available.

Recognise the limitations of your structure predictions, and options for alternative tools (genThreader), or those that may provide potentially useful partial information (PsiPred, MEMSAT and DISOPRED).

Visualise the structure you have obtained using a simple-to-use web-based viewer (LiteMol), and select an appropriate 3D viewing software (LiteMol or others) that you can use to further explore functional or site-specific features of your protein in future.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary (but in some cases have cost or license implications) to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.description
Before starting to explore the structure and function of a protein, it is important to have a good background understanding of the basics of protein structure and composition, and how this is produced from the DNA sequence of a gene.

Included in this box is training support to cover the background of the central dogma of biology (DNA to RNA to Protein), basic properties of amino acids, and the major structural elements of proteins (helices, sheets, turns and coils of secondary structure, tertiary and quaternary structures)

# data[].attributes.workflow-content.nodes[].data.html-description
Before starting to explore the structure and function of a protein, it is important to have a good background understanding of the basics of protein structure and composition, and how this is produced from the DNA sequence of a gene.

Included in this box is training support to cover the background of the central dogma of biology (DNA to RNA to Protein), basic properties of amino acids, and the major structural elements of proteins (helices, sheets, turns and coils of secondary structure, tertiary and quaternary structures)

# data[].attributes.workflow-content.nodes[].data.description
To look for a structure for your protein, we are going to introduce you to the Protein Data Bank in Europe - **the PDBe** - which is hosted a the EMBL European Bioinformatics Institute in Cambridge, UK. This is part of a World-wide database of protein structures which are all freely available to search and use. The overall aims of the PDBe are:

- To provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces.
- To maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data).
- To provide high-quality deposition and annotation facilities for structural data as a wwPDB and EMDataBank deposition site.

*If you need some background information in molecular structures, or the experimental techniques used to derive the structures deposited in the PDBe, please use the grey **Additional Reading** boxes below*

The links below provide access to the PDBe resource and training on how to search for protein structures...
***
>### Get started
>
>Begin by working through the tutorial on ***searching the PDBe*** - the link takes you directly to the relevant section in the training materials. You might want to look at the rest of the linked material, but this is the critical part. 
>
>Use the link to ***the PDBe homepage*** to try searching for the examples you see in the training, or to search for your own protein

# data[].attributes.workflow-content.nodes[].data.html-description
To look for a structure for your protein, we are going to introduce you to the Protein Data Bank in Europe - the PDBe - which is hosted a the EMBL European Bioinformatics Institute in Cambridge, UK. This is part of a World-wide database of protein structures which are all freely available to search and use. The overall aims of the PDBe are:

To provide an integrated resource of high-quality macromolecular structures and related data and make it available to the biomedical community via intuitive user interfaces.

To maintain in-house expertise in all the major structure-determination techniques (X-ray, NMR and EM) in order to stay abreast of technical and methodological developments in these fields, and to work with the community on issues of mutual interest (such as data representation, harvesting, formats and standards, or validation of structural data).

To provide high-quality deposition and annotation facilities for structural data as a wwPDB and EMDataBank deposition site.

If you need some background information in molecular structures, or the experimental techniques used to derive the structures deposited in the PDBe, please use the grey Additional Reading boxes below

The links below provide access to the PDBe resource and training on how to search for protein structures...

Get started

Begin by working through the tutorial on searching the PDBe - the link takes you directly to the relevant section in the training materials. You might want to look at the rest of the linked material, but this is the critical part.

Use the link to the PDBe homepage to try searching for the examples you see in the training, or to search for your own protein

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
There are two main parts to making a judgement whether a protein structure you have found is suitable or not. Firstly, you need to consider whether it looks **appropriate for your purposes** - for example: 

- How big is your protein supposed to be and does the structure cover all, or just part of the protein?
- If your protein is a transmembrane channel, and you are interested in the transmembrane part - is this part present in the structure? (they can be hard to crystallise!)
- Is the structure for the right protein - and from the right species? (this might seem obvious, but it is important to check!)
- Also, consider where the protein sample came from. Was it a recombinant mammalian protein expressed in a bacterial system? Can you be sure the structure represents appropriate post-translational modifications?

The second aspect when considering a suitable structure is to understand the **quality and reliability of the model** that has been generated from the experimental data. This comes from the validation report data that is provided from the wwPDB - linked into the PDBe on the right-hand navigation panel and introduced in the training material you saw in the last box. 

This is directly linked below for recap - along with information on the data provided in the wwPDB validation reports.
***
>### Get started
>
>You need to be able to decide whether the structure you have found is suitable for your purposes. If you need to recap the ***PDBe guidance on chain quality***, re-visit the section in the training materials using the link below.
>
>For more detail, follow the link to the ***wwPDB validation report overview***, or for a broader view of assessing whether a structure fits your needs ***watch the PDBe webinar***.

# data[].attributes.workflow-content.nodes[].data.html-description
There are two main parts to making a judgement whether a protein structure you have found is suitable or not. Firstly, you need to consider whether it looks appropriate for your purposes - for example:

How big is your protein supposed to be and does the structure cover all, or just part of the protein?

If your protein is a transmembrane channel, and you are interested in the transmembrane part - is this part present in the structure? (they can be hard to crystallise!)

Is the structure for the right protein - and from the right species? (this might seem obvious, but it is important to check!)

Also, consider where the protein sample came from. Was it a recombinant mammalian protein expressed in a bacterial system? Can you be sure the structure represents appropriate post-translational modifications?

The second aspect when considering a suitable structure is to understand the quality and reliability of the model that has been generated from the experimental data. This comes from the validation report data that is provided from the wwPDB - linked into the PDBe on the right-hand navigation panel and introduced in the training material you saw in the last box.

This is directly linked below for recap - along with information on the data provided in the wwPDB validation reports.

Get started

You need to be able to decide whether the structure you have found is suitable for your purposes. If you need to recap the PDBe guidance on chain quality, re-visit the section in the training materials using the link below.

For more detail, follow the link to the wwPDB validation report overview, or for a broader view of assessing whether a structure fits your needs watch the PDBe webinar.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Watch the "Finding the best data for your needs in the PDBe" webinar

# data[].attributes.workflow-content.nodes[].data.description
In the training workflow you will be looking for experimentally-determined structures of proteins which have been contributed to public databases. However, it is useful to have some idea of how these structured were determined using the three main methodologies we have available today. These are:

- X-ray crstallography
- Nuclear Magnetic Resonance spectroscopy
- Cryogenic Electron Microscopy

This box contains background training material to introduce the concept of each of these methodologies.

# data[].attributes.workflow-content.nodes[].data.html-description
In the training workflow you will be looking for experimentally-determined structures of proteins which have been contributed to public databases. However, it is useful to have some idea of how these structured were determined using the three main methodologies we have available today. These are:

X-ray crstallography

Nuclear Magnetic Resonance spectroscopy

Cryogenic Electron Microscopy

This box contains background training material to introduce the concept of each of these methodologies.

# data[].attributes.workflow-content.nodes[].data.description
In this section you will use **SWISS-MODEL** - a web-based integrated service dedicated to protein structure homology modelling. It guides the user in building protein homology models at different levels of complexity.

Building a homology model comprises four main steps: (i) identification of structural template(s), (ii) alignment of target sequence and template structure(s), (iii) model-building, and (iv) model quality evaluation. These steps require specialised software and integrate up-to-date protein sequence and structure databases. Each of the above steps can be repeated interactively until a satisfying modelling result is achieved.

The training provided below will guide you through the use of the SWISS-MODEL server for predicting a structure for your protein sequence of interest.
***
>### Get started
>
>SWISS-MODEL requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit the **Unit Introduction** for guidance.
>
>The tutorials supplied by the SWISS-MODEL team are **all short videos** - each only a few minutes long. You should take some time to view them all before trying the tool. There is also a TeSS workflow outlining the SWISS-MODEL approach.
>
>Use the link to ***SWISS-MODEL*** to submit the sequence for a protein of your choice and try the tool yourself.

# data[].attributes.workflow-content.nodes[].data.html-description
In this section you will use SWISS-MODEL - a web-based integrated service dedicated to protein structure homology modelling. It guides the user in building protein homology models at different levels of complexity.

Building a homology model comprises four main steps: (i) identification of structural template(s), (ii) alignment of target sequence and template structure(s), (iii) model-building, and (iv) model quality evaluation. These steps require specialised software and integrate up-to-date protein sequence and structure databases. Each of the above steps can be repeated interactively until a satisfying modelling result is achieved.

The training provided below will guide you through the use of the SWISS-MODEL server for predicting a structure for your protein sequence of interest.

Get started

SWISS-MODEL requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit the Unit Introduction for guidance.

The tutorials supplied by the SWISS-MODEL team are all short videos - each only a few minutes long. You should take some time to view them all before trying the tool. There is also a TeSS workflow outlining the SWISS-MODEL approach.

Use the link to SWISS-MODEL to submit the sequence for a protein of your choice and try the tool yourself.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video tutorial 2: Start a new modelling project

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video tutorial 3: Template search results

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video tutorial 4: Quaternary structure analysis

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video tutorial 5: Comparison of template sequences

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video tutorial 6: Modelling results and quality estimation

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Homology modelling with SWISS-MODEL (workflow)

# data[].attributes.workflow-content.nodes[].data.description
The prediction of protein structure is a complicated and, compared to other areas of bioinformatics, an old field. The talk linked below (quite long) is a comprehensive look at the challenges and techniques in predicting protein stuctures; beginning with the basics of protein structure and 2D prediction/characterisation, through to homology methods such as those covered in the workflow above. 

It goes beyond homology approaches to consider more complex ab initio folding methods which are of interest - but beyond the general scope of this training workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
The prediction of protein structure is a complicated and, compared to other areas of bioinformatics, an old field. The talk linked below (quite long) is a comprehensive look at the challenges and techniques in predicting protein stuctures; beginning with the basics of protein structure and 2D prediction/characterisation, through to homology methods such as those covered in the workflow above.

It goes beyond homology approaches to consider more complex ab initio folding methods which are of interest - but beyond the general scope of this training workflow.

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
You will use **Phyre2** - a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. 

Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. 

The training below will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. 
***
>### Get started
>
>Phyre2 requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit the **Unit Introduction** for guidance.
>
>Begin by working through the tutorial ***How to use Phyre2***. When you have done that you should review the material on **Interpreting Phyre results**. If you want some more background on how Phyre works, you should view the short **Introduction to Phyre** video. 
>
>Use the link to ***Phyre2*** to submit the sequence for a protein of your choice and try the tool yourself.

# data[].attributes.workflow-content.nodes[].data.html-description
You will use Phyre2 - a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools.

Phyre2 uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence.

The training below will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality.

Get started

Phyre2 requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit the Unit Introduction for guidance.

Begin by working through the tutorial How to use Phyre2. When you have done that you should review the material on Interpreting Phyre results. If you want some more background on how Phyre works, you should view the short Introduction to Phyre video.

Use the link to Phyre2 to submit the sequence for a protein of your choice and try the tool yourself.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Watch the introduction to Phyre2 video

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.name
Getting structure information for your protein

# data[].attributes.workflow-content.nodes[].data.description
Please begin this module by clicking on the purple **Start Here** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this module by clicking on the purple Start Here box

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.name
Visualising your structures in 3D with LiteMol & EzMol

# data[].attributes.workflow-content.nodes[].data.description
Now you have a structure you will want to be able to view and explore it in 3D. **LiteMol** is a web tool for visualization of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey **Additional Reading** box below the topic section.

**However**, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the **Additional Reading** box below.

**Alternatively**, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive **EzMol** tool linked below.
***
>### Get started
>
>If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short **Basic use of standalone LiteMol** video - and using the link to the **Standalone LiteMol** tool to try it for yourself.
>
>If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the **tutorials labelled from the PDBe** below.

# data[].attributes.workflow-content.nodes[].data.html-description
Now you have a structure you will want to be able to view and explore it in 3D. LiteMol is a web tool for visualization of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey Additional Reading box below the topic section.

However, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the Additional Reading box below.

Alternatively, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive EzMol tool linked below.

Get started

If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short Basic use of standalone LiteMol video - and using the link to the Standalone LiteMol tool to try it for yourself.

If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the tutorials labelled from the PDBe below.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: Basic use of standalone LiteMol

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: How to use LiteMol at the PDBe

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Introducing LiteMol (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing extra annotation (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing sequence and structure interactively (PDBe video)

# data[].attributes.workflow-content.nodes[].data.description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. *Similarly, most are commercial ($$) software, and some only free for limited or academic use.*

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.html-description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. Similarly, most are commercial ($$) software, and some only free for limited or academic use.

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.name
Exploring predictions of protein functional sites

# data[].attributes.workflow-content.nodes[].data.description
The next workflow in the series - **ELIXIR Structural Bioinformatics 2: Exploring protein domains and functional sites** - introduces how to search (using query protein sequences or structures) for functional families and domains - as well as how to predict surface or topological sites of possible interaction between proteins and their ligands.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different domains, function or features.

It will help guide you through exploring these tools and interpreting the predicted functional information to give you greater understanding of key protein features and sites - and lead to the next workflow in the series on exploring knowledge of how genetic variation may impact on the structure/function.

# data[].attributes.workflow-content.nodes[].data.html-description
The next workflow in the series - ELIXIR Structural Bioinformatics 2: Exploring protein domains and functional sites - introduces how to search (using query protein sequences or structures) for functional families and domains - as well as how to predict surface or topological sites of possible interaction between proteins and their ligands.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different domains, function or features.

It will help guide you through exploring these tools and interpreting the predicted functional information to give you greater understanding of key protein features and sites - and lead to the next workflow in the series on exploring knowledge of how genetic variation may impact on the structure/function.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
ELIXIR Structural Bioinformatics 2: Exploring protein domains and functional sites

# data[].attributes.workflow-content.nodes[].data.description
Imagine that you have carried out some experiments, and the end result is a protein that you are now interested in learning more about. You might have identified this protein because it is the product of a gene upregulated in a transcriptomic experiment. Maybe you have used protein arrays, or proteomics to identify interesting proteins - or perhaps it is key in the production of an interesting molecule from a metabolomic experiment. Perhaps you just read about it's role in a biological function or disease in the literature, and want to investigate more.

Regardless of how the protein has come to your attention, it is likely that the first information you know about it is the name of the gene that encodes it, the protein name and/or the DNA or amino acid sequence.

Sometimes this is enough to be able to further explore the presence of the protein in different samples. Name and sequence can be enough to look for it in other experiments, or design antibodies to detect it in cell/tissue samples or assays. 

A common scenario is to have found an interesting protein whilst studying Human disease - with a view towards investigating this as a drug target. To do this we need more than just name and sequence - we ideally need structure so that we can consider functional sites and how genetic variation might impact on structure and function.

**This Unit of the workflow will help you with the first steps of finding a structure for your protein** 

# data[].attributes.workflow-content.nodes[].data.html-description
Imagine that you have carried out some experiments, and the end result is a protein that you are now interested in learning more about. You might have identified this protein because it is the product of a gene upregulated in a transcriptomic experiment. Maybe you have used protein arrays, or proteomics to identify interesting proteins - or perhaps it is key in the production of an interesting molecule from a metabolomic experiment. Perhaps you just read about it's role in a biological function or disease in the literature, and want to investigate more.

Regardless of how the protein has come to your attention, it is likely that the first information you know about it is the name of the gene that encodes it, the protein name and/or the DNA or amino acid sequence.

Sometimes this is enough to be able to further explore the presence of the protein in different samples. Name and sequence can be enough to look for it in other experiments, or design antibodies to detect it in cell/tissue samples or assays.

A common scenario is to have found an interesting protein whilst studying Human disease - with a view towards investigating this as a drug target. To do this we need more than just name and sequence - we ideally need structure so that we can consider functional sites and how genetic variation might impact on structure and function.

This Unit of the workflow will help you with the first steps of finding a structure for your protein

# data[].attributes.workflow-content.nodes[].data.description
The first Unit took you through trying to find an experimentally-determined structure for your protein of interest. This is generally the best option for getting a useful structure - but there is a good chance that there wasn't a structure available for you!

Structural coverage of the Human proteome is not complete, and for other organisms can be very sparse. 

**Stop to consider the following:**

- Did you find a PDB structure for your protein?
- Is it from the right species?
- Is the structure complete, or at least from the part of the protein you are interested in?
- Does the quality look to be acceptable?

**If the answer to all of these is "YES"** then you do not need to use the tools in this unit (although you might find them interesting) - proceed to **Unit 3** - *Visualising your structure*

**If the answer to any of these is "No"** then you could either review **Unit 1** and look for alternative structures, or continue with this Unit, **Unit 2** - *Predicting Protein Structure* 

This Unit offers to alternative and complementary approaches to **predicting structure** of your protein based on its homology to proteins where structures are known: **Phyre2** from Imperial College London and **SWISS-MODEL** from the Swiss Institute of Bioinformatics.

**You can use either of these for your prediction - or both and compare the results...**

***These tools predict a structure from sequence - you will need to have the sequence of your protein available***
***
>### Get started
>
>The first step is to find a sequence for the protein you are interested in.
>
>There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)
>
>Three key resources are introduced below:
>
>UniProtKB is a freely available comprehensive resource for protein sequence and annotation data.
>
>Ensembl imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects.
>
>The Protein Data Bank (PDB) archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.
>
>**It does not matter which database you use to find the sequence of your protein! If you haven't used any before, perhaps begin with UniProt**

# data[].attributes.workflow-content.nodes[].data.html-description
The first Unit took you through trying to find an experimentally-determined structure for your protein of interest. This is generally the best option for getting a useful structure - but there is a good chance that there wasn't a structure available for you!

Structural coverage of the Human proteome is not complete, and for other organisms can be very sparse.

Stop to consider the following:

Did you find a PDB structure for your protein?

Is it from the right species?

Is the structure complete, or at least from the part of the protein you are interested in?

Does the quality look to be acceptable?

If the answer to all of these is "YES" then you do not need to use the tools in this unit (although you might find them interesting) - proceed to Unit 3 - Visualising your structure

If the answer to any of these is "No" then you could either review Unit 1 and look for alternative structures, or continue with this Unit, Unit 2 - Predicting Protein Structure

This Unit offers to alternative and complementary approaches to predicting structure of your protein based on its homology to proteins where structures are known: Phyre2 from Imperial College London and SWISS-MODEL from the Swiss Institute of Bioinformatics.

You can use either of these for your prediction - or both and compare the results...

These tools predict a structure from sequence - you will need to have the sequence of your protein available

Get started

The first step is to find a sequence for the protein you are interested in.

There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)

Three key resources are introduced below:

UniProtKB is a freely available comprehensive resource for protein sequence and annotation data.

Ensembl imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects.

The Protein Data Bank (PDB) archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

It does not matter which database you use to find the sequence of your protein! If you haven't used any before, perhaps begin with UniProt

# data[].attributes.workflow-content.nodes[].data.description
If you have reached Unit 3 of this training workflow, then you should either have found a suitable experimentally-determined structure for your protein in Unit 1, or successfully predicted a suitable structure in Unit 2.

This Unit will introduce you to **LiteMol** and **EzMol** as 3D molecular viewers so that you can visualise and explore your structures further - and **JalView** which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.html-description
If you have reached Unit 3 of this training workflow, then you should either have found a suitable experimentally-determined structure for your protein in Unit 1, or successfully predicted a suitable structure in Unit 2.

This Unit will introduce you to LiteMol and EzMol as 3D molecular viewers so that you can visualise and explore your structures further - and JalView which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.description
This training workflow is designed to lead you through the process of finding experimentally-determined structures for a protein you are interested in - or predicting structure information if one is not available. 

It will then help guide you through visualising that structure as a prerequisite to moving on to other workflows to explore additional information relating to that structure. These next steps will begin to give you greater understanding of key protein features, domains and functional sites - along with knowledge of how genetic variation impacts on the structure. 

The training workflow layout *mimics* the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

**Who is it for?**

This training workflow has been designed to help scientists new to the field of structural biololgy. 

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the function of the protein in a structural context. 


Before you start the learning Units, please review the **Learning Outcomes** box below

# data[].attributes.workflow-content.nodes[].data.html-description
This training workflow is designed to lead you through the process of finding experimentally-determined structures for a protein you are interested in - or predicting structure information if one is not available.

It will then help guide you through visualising that structure as a prerequisite to moving on to other workflows to explore additional information relating to that structure. These next steps will begin to give you greater understanding of key protein features, domains and functional sites - along with knowledge of how genetic variation impacts on the structure.

The training workflow layout mimics the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

Who is it for?

This training workflow has been designed to help scientists new to the field of structural biololgy.

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the function of the protein in a structural context.

Before you start the learning Units, please review the Learning Outcomes box below

# data[].attributes.workflow-content.nodes[].data.name
Citations for tools included in this workflow

# data[].attributes.workflow-content.nodes[].data.description
**PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.**\
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.\
***Nucleic acids research*** Volume 44 (2016) p.D385-95\
DOI: [10.1093/nar/gkv1047](https://dx.doi.org/10.1093/nar/gkv1047)

**Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive.**\
Burley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S.\
***Methods in molecular biology*** Volume 1607 (2017) p.627-641\
DOI: [10.1007/978-1-4939-7000-1_26](https://dx.doi.org/10.1007/978-1-4939-7000-1_26)

**Validation of Structures in the Protein Data Bank.**\
Gore S, Sanz García E, Hendrickx PMS, Gutmanas A, Westbrook JD, Yang H, Feng Z, Baskaran K, Berrisford JM, Hudson BP, Ikegawa Y, Kobayashi N, Lawson CL, Mading S, Mak L, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Peisach E, Sahni G, Sekharan MR, Sen S, Shao C, Smart OS, Ulrich EL, Yamashita R, Quesada M, Young JY, Nakamura H, Markley JL, Berman HM, Burley SK, Velankar S, Kleywegt GJ.\
***Structure*** (2017) p.1916-1927\
DOI: [10.1016/j.str.2017.10.009](https://dx.doi.org/10.1016/j.str.2017.10.009)

**UniProt: the universal protein knowledgebase**\
The UniProt Consortium\
***Nucleic Acids Research.*** Volume 45 (2017) D158-D169\
DOI: [10.1093/nar/gky092](https://doi.org/10.1093/nar/gky092)

**Ensembl 2018.**\
Zerbino DR, Achuthan P, Akanni W, Amode M.R, Barrell D, Bhai J, Billis K, Cummins C, Gall A, García Giro´n C, Gil L, Gordon L, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, To JK, Laird MR, Lavidas I, Liu Z, Loveland JE, Maurel T, McLaren W, Moore B, Mudge J, Murphy DN, Newman V, Nuhn M, Ogeh D, Ong CK, Parker A, Patricio M, Riat HS, Schuilenburg H, Sheppard D, Sparrow H, Taylor K, Thormann A, Vullo A, Walts B, Zadissa A, Frankish A, Hunt SE, Kostadima M, Langridge N, Martin FJ, Muffato M, Perry E, Ruffier M, Staines DM, Trevanion SJ, Aken BL, Cunningham F, Yates A, Flicek P.\
***Nucleic Acids Research*** Volume 46 (2018) Pages D754–D761\
DOI: [10.1093/nar/gkx1098](https://dx.doi.org/10.1093/nar/gkx1098)

**The Phyre2 web portal for protein modeling, prediction and analysis.**\
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE.\
***Nature Protocols*** Volume 10 (2015) p.845\
DOI: [10.1038/nprot.2015.053](https://dx.doi.org/10.1038/nprot.2015.053)

**SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information.**\
Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Cassarino TG, Bertoni M, Bordoli L, Schwede T.\
***Nucleic Acids Research*** Volume 42 (2014) W252-W258\
DOI: [10.1093/nar/gku340](https://dx.doi.org/10.1093/nar/gku340)

**Scalable web services for the PSIPRED Protein Analysis Workbench.** \
Buchan DWA, Minneci F, Nugent TCO, Bryson K, Jones DT.\
***Nucleic Acids Research*** Volume 41 (2013) W340-W348.\
DOI: [10.1093/nar/gkt381](https://dx.doi.org/10.1093/nar/gkt381)

**Protein secondary structure prediction based on position-specific scoring matrices.**\
Jones DT. ***Journal of Molecular Biology*** Volume 292 (1999) p.195-202.\
DOI: [10.1006/jmbi.1999.3091](https://dx.doi.org/10.1006/jmbi.1999.3091)

**pGenTHREADER and pDomTHREADER: New Methods For Improved Protein Fold Recognition and Superfamily Discrimination.**\
Lobley A, Sadowski MI, Jones DT.\
***Bioinformatics*** Volume 25 (2009) p.1761-1767.\
DOI: [10.1093/bioinformatics/btp302](https://dx.doi.org/10.1093/bioinformatics/btp302)

***Transmembrane protein topology prediction using support vector machines.***\
Nugent T, Jones DT.\
***BMC Bioinformatics*** Volume 10 (2009) 159. Epub\
DOI: [10.1186/1471-2105-10-159](https://dx.doi.org/10.1186/1471-2105-10-159)

**The I-TASSER Suite: Protein structure and function prediction.**\
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y.\ 
***Nature Methods*** Volume 12 (2015) p.7-8.\
DOI: [doi:10.1038/nmeth.3213](https://dx.doi.org/doi:10.1038/nmeth.3213)

**Protein structure prediction and analysis using the Robetta server.**\
Kim DE, Chivian D, Baker D.\
***Nucleic Acids Research*** Volume 32 (2004) W526-31.\
DOI: [10.1093/nar/gkh468](https://dx.doi.org/10.1093/nar/gkh468)

**LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.**\
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.\
***Nature Methods*** Volume 14 (2017) p.1121-1122\
DOI: [10.1038/nmeth.4499](https://dx.doi.org/10.1038/nmeth.4499)

**EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.**\
Reynolds CR, Islam SA, Sternberg MJE.\
***Journal of Molecular Biology*** (2018) Epub.\
DOI: [10.1016/j.jmb.2018.01.013](https://dx.doi.org/10.1016/j.jmb.2018.01.013)

**Jalview Version 2-a multiple sequence alignment editor and analysis workbench.**\
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.\
***Bioinformatics*** Volume 25 (2009) p.1189-1191.\ 
DOI: [10.1093/bioinformatics/btp033](https://dx.doi.org/10.1093/bioinformatics/btp033)

# data[].attributes.workflow-content.nodes[].data.html-description
PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.
Nucleic acids research Volume 44 (2016) p.D385-95
DOI: 10.1093/nar/gkv1047

Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive.
Burley SK, Berman HM, Kleywegt GJ, Markley JL, Nakamura H, Velankar S.
Methods in molecular biology Volume 1607 (2017) p.627-641
DOI: 10.1007/978-1-4939-7000-1_26

Validation of Structures in the Protein Data Bank.
Gore S, Sanz García E, Hendrickx PMS, Gutmanas A, Westbrook JD, Yang H, Feng Z, Baskaran K, Berrisford JM, Hudson BP, Ikegawa Y, Kobayashi N, Lawson CL, Mading S, Mak L, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Peisach E, Sahni G, Sekharan MR, Sen S, Shao C, Smart OS, Ulrich EL, Yamashita R, Quesada M, Young JY, Nakamura H, Markley JL, Berman HM, Burley SK, Velankar S, Kleywegt GJ.
Structure (2017) p.1916-1927
DOI: 10.1016/j.str.2017.10.009

UniProt: the universal protein knowledgebase
The UniProt Consortium
Nucleic Acids Research. Volume 45 (2017) D158-D169
DOI: 10.1093/nar/gky092

Ensembl 2018.
Zerbino DR, Achuthan P, Akanni W, Amode M.R, Barrell D, Bhai J, Billis K, Cummins C, Gall A, García Giro´n C, Gil L, Gordon L, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, To JK, Laird MR, Lavidas I, Liu Z, Loveland JE, Maurel T, McLaren W, Moore B, Mudge J, Murphy DN, Newman V, Nuhn M, Ogeh D, Ong CK, Parker A, Patricio M, Riat HS, Schuilenburg H, Sheppard D, Sparrow H, Taylor K, Thormann A, Vullo A, Walts B, Zadissa A, Frankish A, Hunt SE, Kostadima M, Langridge N, Martin FJ, Muffato M, Perry E, Ruffier M, Staines DM, Trevanion SJ, Aken BL, Cunningham F, Yates A, Flicek P.
Nucleic Acids Research Volume 46 (2018) Pages D754–D761
DOI: 10.1093/nar/gkx1098

The Phyre2 web portal for protein modeling, prediction and analysis.
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE.
Nature Protocols Volume 10 (2015) p.845
DOI: 10.1038/nprot.2015.053

SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information.
Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Cassarino TG, Bertoni M, Bordoli L, Schwede T.
Nucleic Acids Research Volume 42 (2014) W252-W258
DOI: 10.1093/nar/gku340

Scalable web services for the PSIPRED Protein Analysis Workbench.
Buchan DWA, Minneci F, Nugent TCO, Bryson K, Jones DT.
Nucleic Acids Research Volume 41 (2013) W340-W348.
DOI: 10.1093/nar/gkt381

Protein secondary structure prediction based on position-specific scoring matrices.
Jones DT. Journal of Molecular Biology Volume 292 (1999) p.195-202.
DOI: 10.1006/jmbi.1999.3091

pGenTHREADER and pDomTHREADER: New Methods For Improved Protein Fold Recognition and Superfamily Discrimination.
Lobley A, Sadowski MI, Jones DT.
Bioinformatics Volume 25 (2009) p.1761-1767.
DOI: 10.1093/bioinformatics/btp302

Transmembrane protein topology prediction using support vector machines.
Nugent T, Jones DT.
BMC Bioinformatics Volume 10 (2009) 159. Epub
DOI: 10.1186/1471-2105-10-159

The I-TASSER Suite: Protein structure and function prediction.
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y.\ Nature Methods Volume 12 (2015) p.7-8.
DOI: doi:10.1038/nmeth.3213

Protein structure prediction and analysis using the Robetta server.
Kim DE, Chivian D, Baker D.
Nucleic Acids Research Volume 32 (2004) W526-31.
DOI: 10.1093/nar/gkh468

LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.
Nature Methods Volume 14 (2017) p.1121-1122
DOI: 10.1038/nmeth.4499

EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.
Reynolds CR, Islam SA, Sternberg MJE.
Journal of Molecular Biology (2018) Epub.
DOI: 10.1016/j.jmb.2018.01.013

Jalview Version 2-a multiple sequence alignment editor and analysis workbench.
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.
Bioinformatics Volume 25 (2009) p.1189-1191.\ DOI: 10.1093/bioinformatics/btp033

# data[].attributes.workflow-content.nodes[].data.description
**Stop to consider the following:**

- Did you generate a structure from your protein sequence?
- Is the structure complete, or at least from the part of the protein you are interested in?
- Does the quality look to be acceptable?
- Have you tried both methods presented here and do your models agree?

**If the answer to all of these is "YES"** then proceed to **Unit 3** - *Visualising your structure*

**If the answer to any of these is "No"** then you may want to consider some additional tools - although specific training on their use is not provided here:


**Option 1:** The **PsiPred** server includes several tools that might be of use. The first of these is an alternative to Phyre2 and SWISS-MODEL - called **genThreader**. You could try this to compare the results to those you may already have already obtained.

If your full-structure modelling is poor or inconclusive, the PsiPred server also contains tools that can help you look at secondary structure domains (**PsiPred**), predict or identify transmembrane sections of your protein (**MEMSAT**), or identify likely regions that are structurally disordered (**DISOPRED**). Guidance on the use of these is beyond the scope of this workflow, but some help and tutorial material is provided on the server website - and linked in the materials section below.

**Option 2:** You could try submitting your sequence to the **I-TASSER** server linked below. This method also relies on finding structural similarity in the PDB, and can take a could of days to provide a result. More information about I-TASSER and its performance can be found on the server website. *The I-TASSER server is for Non-Commerical use only*

**Option 3:** You may want to try to explore the options for *ab initio* folding of your protein. This is technically challenging and computationally difficult - but fairly simple to try using the **Robetta** server linked below. Be warned - this can take quite a while (days to weeks) to provide you with a result! *The Robetta server is for Non-Commerical use only*

Your first step forward could be to explore the lecture on protein structure prediction in the "Background Information" box below - and pay particular attention to the section on folding prediction. You might also want to consider finding an expert in protein structure to collaborate with.

# data[].attributes.workflow-content.nodes[].data.html-description
Stop to consider the following:

Did you generate a structure from your protein sequence?

Is the structure complete, or at least from the part of the protein you are interested in?

Does the quality look to be acceptable?

Have you tried both methods presented here and do your models agree?

If the answer to all of these is "YES" then proceed to Unit 3 - Visualising your structure

If the answer to any of these is "No" then you may want to consider some additional tools - although specific training on their use is not provided here:

Option 1: The PsiPred server includes several tools that might be of use. The first of these is an alternative to Phyre2 and SWISS-MODEL - called genThreader. You could try this to compare the results to those you may already have already obtained.

If your full-structure modelling is poor or inconclusive, the PsiPred server also contains tools that can help you look at secondary structure domains (PsiPred), predict or identify transmembrane sections of your protein (MEMSAT), or identify likely regions that are structurally disordered (DISOPRED). Guidance on the use of these is beyond the scope of this workflow, but some help and tutorial material is provided on the server website - and linked in the materials section below.

Option 2: You could try submitting your sequence to the I-TASSER server linked below. This method also relies on finding structural similarity in the PDB, and can take a could of days to provide a result. More information about I-TASSER and its performance can be found on the server website. The I-TASSER server is for Non-Commerical use only

Option 3: You may want to try to explore the options for ab initio folding of your protein. This is technically challenging and computationally difficult - but fairly simple to try using the Robetta server linked below. Be warned - this can take quite a while (days to weeks) to provide you with a result! The Robetta server is for Non-Commerical use only

Your first step forward could be to explore the lecture on protein structure prediction in the "Background Information" box below - and pay particular attention to the section on folding prediction. You might also want to consider finding an expert in protein structure to collaborate with.

# data[].attributes.workflow-content.nodes[].data.name
Visualising sequence-based features with JalView

# data[].attributes.workflow-content.nodes[].data.description
**Jalview** is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

**JalView** is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.
***
>### Get started
>
>**JalView** has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the **Getting started with JalView** videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.
>
>After watching these videos you can try **JalView** for yourself by clicking the link below to the **JalView Launcher**
>
>If you want to explore the more advanced features of the tool, there is also a link to the **JalView youtube channel** which contains a comprehensive set of video tutorials.

# data[].attributes.workflow-content.nodes[].data.html-description
Jalview is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

JalView is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.

Get started

JalView has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the Getting started with JalView videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.

After watching these videos you can try JalView for yourself by clicking the link below to the JalView Launcher

If you want to explore the more advanced features of the tool, there is also a link to the JalView youtube channel which contains a comprehensive set of video tutorials.

# data[].attributes.title
ELIXIR Structural Bioinformatics 2: Exploring protein domains and functional sites

# data[].attributes.description
This training workflow introduces how to search (using query protein sequences or structures) for protein domains, conservation and protein family information, surface and topological interfaces, and possible ligand binding sites.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different domains, function or features.

It will help guide you through exploring these tools and interpreting the predicted functional information to give you greater understanding of key protein features and sites - and lead to the next workflow in the series on exploring knowledge of how genetic variation may impact on the structure/function.

# data[].attributes.workflow-content.nodes[].data.description
### Some additional information for CATH

Sillitoe, I., Dawson, N., Thornton, J., & Orengo, C. (2015). The history of the CATH structural classification of protein domains. Biochimie, 119, 209-217.

Das, S., Lee, D., Sillitoe, I., Dawson, N. L., Lees, J. G., & Orengo, C. A. (2015). Functional classification of CATH superfamilies: a domain-based approach for protein function annotation. Bioinformatics, 31(21), 3460-3467.

Das, S., Sillitoe, I., Lee, D., Lees, J. G., Dawson, N. L., Ward, J., & Orengo, C. A. (2015). CATH FunFHMMer web server: protein functional annotations using functional family assignments. Nucleic acids research, 43(W1), W148-W153.

**CATH** is a classification of protein structures downloaded from the Protein Data Bank. CATH groups together protein structural domains which are thought to share a common ancestor within a **Homologous superfamily**. **CATH Functional Families** or **FunFams** are a collection of protein domains within a superfamily that share a common function. 

CATH FunSites are groups of amino acid residues that are highly conserved within the FunFam alignment and are most likely to be functionally important.These can include catalytic sites or allosteric sites for enzymes or binding sites for ligands, metal
ions, nucleic acids or interacting with other proteins. The conserved residues in all FunFam alignments have been found to be significantly enriched in known catalytic residues (p < 1.5 x 10−30) and ligand-binding sites (p < 7 x 10−22). 

Conservation scores for each residue position for all CATH FunFam alignments have been calculated using Scorecons (Valdar, 2002). Each position in an alignment is assigned a conservation score between 0 and 1, where a score of 0 indicates no conservation at that position and a score of 1 indicates that the residue at that position is completely conserved. Residues having scores ≥ 0.7 are generally considered to be highly conserved.

Conservation analysis can be done for only highly informative FunFam alignments. An alignment which comprises evolutionary distant relatives is considered to be highly informative that provides more discriminating conservation scores (Bartlett et al., 2002). Scorecons also provides a Diversity of Position Score (DOPS) between 0 and 100 that considers the number of different conservation scores in an alignment and the relative frequency of each score, reflecting the information content of an alignment. We consider FunFam alignments that have a DOPS score >=70 as highly informative.

### And a link to the InterPro FAQ - below

# data[].attributes.workflow-content.nodes[].data.html-description
Some additional information for CATH

Sillitoe, I., Dawson, N., Thornton, J., & Orengo, C. (2015). The history of the CATH structural classification of protein domains. Biochimie, 119, 209-217.

Das, S., Lee, D., Sillitoe, I., Dawson, N. L., Lees, J. G., & Orengo, C. A. (2015). Functional classification of CATH superfamilies: a domain-based approach for protein function annotation. Bioinformatics, 31(21), 3460-3467.

Das, S., Sillitoe, I., Lee, D., Lees, J. G., Dawson, N. L., Ward, J., & Orengo, C. A. (2015). CATH FunFHMMer web server: protein functional annotations using functional family assignments. Nucleic acids research, 43(W1), W148-W153.

CATH is a classification of protein structures downloaded from the Protein Data Bank. CATH groups together protein structural domains which are thought to share a common ancestor within a Homologous superfamily. CATH Functional Families or FunFams are a collection of protein domains within a superfamily that share a common function.

CATH FunSites are groups of amino acid residues that are highly conserved within the FunFam alignment and are most likely to be functionally important.These can include catalytic sites or allosteric sites for enzymes or binding sites for ligands, metal ions, nucleic acids or interacting with other proteins. The conserved residues in all FunFam alignments have been found to be significantly enriched in known catalytic residues (p < 1.5 x 10−30) and ligand-binding sites (p < 7 x 10−22).

Conservation scores for each residue position for all CATH FunFam alignments have been calculated using Scorecons (Valdar, 2002). Each position in an alignment is assigned a conservation score between 0 and 1, where a score of 0 indicates no conservation at that position and a score of 1 indicates that the residue at that position is completely conserved. Residues having scores ≥ 0.7 are generally considered to be highly conserved.

Conservation analysis can be done for only highly informative FunFam alignments. An alignment which comprises evolutionary distant relatives is considered to be highly informative that provides more discriminating conservation scores (Bartlett et al., 2002). Scorecons also provides a Diversity of Position Score (DOPS) between 0 and 100 that considers the number of different conservation scores in an alignment and the relative frequency of each score, reflecting the information content of an alignment. We consider FunFam alignments that have a DOPS score >=70 as highly informative.

And a link to the InterPro FAQ - below

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Frequently Asked Questions about InterPro

# data[].attributes.workflow-content.nodes[].data.description
This training workflow introduces how to search (using query protein sequences or structures) for protein domains, conservation and protein family information, surface and topological interfaces, and possible ligand binding sites.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different domains, function or features.

It will help guide you through exploring these tools and interpreting the predicted functional information to give you greater understanding of key protein features and sites - and lead to the next workflow in the series on exploring knowledge of how genetic variation may impact on the structure/function.

The training workflow layout mimics the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

**Who is it for?**

What is the number one question people always have about their protein?

What it does! What is the function of the protein you are investigating? Sometimes, we do not know the answer to that, at least not initially. Genomic and metagenomic sequencing projects have provided us with several million protein sequences, around 40% of which will be of unknown function. This number will only increase over time, so we need to develop ways to determine the structures of these proteins and their functions, either by experimentation or by using computational techniques.

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the function of the protein in a structural context. 

Before you start the learning Units, please review the **Learning Outcomes** box below

# data[].attributes.workflow-content.nodes[].data.html-description
This training workflow introduces how to search (using query protein sequences or structures) for protein domains, conservation and protein family information, surface and topological interfaces, and possible ligand binding sites.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different domains, function or features.

It will help guide you through exploring these tools and interpreting the predicted functional information to give you greater understanding of key protein features and sites - and lead to the next workflow in the series on exploring knowledge of how genetic variation may impact on the structure/function.

The training workflow layout mimics the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

Who is it for?

What is the number one question people always have about their protein?

What it does! What is the function of the protein you are investigating? Sometimes, we do not know the answer to that, at least not initially. Genomic and metagenomic sequencing projects have provided us with several million protein sequences, around 40% of which will be of unknown function. This number will only increase over time, so we need to develop ways to determine the structures of these proteins and their functions, either by experimentation or by using computational techniques.

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the function of the protein in a structural context.

Before you start the learning Units, please review the Learning Outcomes box below

# data[].attributes.workflow-content.nodes[].data.description
**When you have completed this workflow you will be able to:**

- Use a range of resources (**PDBe**, **UniProt**, **Ensembl**, **or the previous workflow in this series**) to search for sequence or an experimentally-derived structure for a protein of interest to you.
- Explore the functional family and domain information available from **CATH-gene3D** and **InterPro**.
- Apply surface and topology prediction tools to explore accessible surface and interfaces between proteins (**Pops**, **PopsComp**,  and **Protein Contact Atlas**), and to explore potentially-interesting pockets and pores (**DEPTH**, and **MOLEonline**).
- Use site prediction tools to explore possible ligand binding sites (**3DLigandSite**) and specific sites of nuceotide binding (**Nucleos**).
- Visualise the domains or sites you have obtained using simple-to-use viewers: for structure, **LiteMol**; and for sequence, **JalView**.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary *(but in some cases have cost or license implications)* to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.html-description
When you have completed this workflow you will be able to:

Use a range of resources (PDBe, UniProt, Ensembl, or the previous workflow in this series) to search for sequence or an experimentally-derived structure for a protein of interest to you.

Explore the functional family and domain information available from CATH-gene3D and InterPro.

Apply surface and topology prediction tools to explore accessible surface and interfaces between proteins (Pops, PopsComp, and Protein Contact Atlas), and to explore potentially-interesting pockets and pores (DEPTH, and MOLEonline).

Use site prediction tools to explore possible ligand binding sites (3DLigandSite) and specific sites of nuceotide binding (Nucleos).

Visualise the domains or sites you have obtained using simple-to-use viewers: for structure, LiteMol; and for sequence, JalView.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary (but in some cases have cost or license implications) to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.name
Exploring domains and predictions of protein functional sites

# data[].attributes.workflow-content.nodes[].data.description
Please begin this module by clicking on the purple **Start Here** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this module by clicking on the purple Start Here box

# data[].attributes.workflow-content.nodes[].data.name
Unit 2. Identifying functional domains and families from sequence

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
The **CATH** Search by Sequence tool (link given below) can be used to search for:
- **CATH Functional Families or FunFams** that gives important clues about the function and functional sites of your protein
- **CATH structural domains** that predicts the 3D structure your protein adopts

*Since we are interested in looking at predicted functional sites for your protein, we will be looking at the search results for CATH Functional Families*. 

To submit your protein of interest to the CATH Search by Sequence tool, you will need for your protein of interest, either the **UniProt sequence identifier** or the **protein sequence in FASTA format**.

The search results for the **CATH Functional Families** (FunFams) for your protein displays **significant domain matches** across your protein sequence that matches a CATH FunFam, ranked by E-value of the matches.

If a region of your protein sequence provides a **highly significant match** to a particular CATH FunFam, then there is a good chance they share **similar function and functional sites**.

To investigate functional sites for your protein, it is important to **identify the best FunFam match for each constituent domain** in your protein and look at these matches individually.
***
>### Get started
>
>CATH-gene3D requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. 
>
>You should **visit the tutorial** showing you how to search CATH and view the results of the search first.
>
>When you have looked through that material, use the **link to the CATH tool** below to try a search with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.html-description
The CATH Search by Sequence tool (link given below) can be used to search for:

CATH Functional Families or FunFams that gives important clues about the function and functional sites of your protein

CATH structural domains that predicts the 3D structure your protein adopts

Since we are interested in looking at predicted functional sites for your protein, we will be looking at the search results for CATH Functional Families.

To submit your protein of interest to the CATH Search by Sequence tool, you will need for your protein of interest, either the UniProt sequence identifier or the protein sequence in FASTA format.

The search results for the CATH Functional Families (FunFams) for your protein displays significant domain matches across your protein sequence that matches a CATH FunFam, ranked by E-value of the matches.

If a region of your protein sequence provides a highly significant match to a particular CATH FunFam, then there is a good chance they share similar function and functional sites.

To investigate functional sites for your protein, it is important to identify the best FunFam match for each constituent domain in your protein and look at these matches individually.

Get started

CATH-gene3D requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance.

You should visit the tutorial showing you how to search CATH and view the results of the search first.

When you have looked through that material, use the link to the CATH tool below to try a search with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Tutorial - How to Search CATH for FunFams

# data[].attributes.workflow-content.nodes[].data.name
Unit 3. Surface and topological features from structure

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.name
Protein interfaces with Pops & Protein Contact Atlas

# data[].attributes.workflow-content.nodes[].data.description
Two tools are provided in this topic that can help you explore the surface and potential interaction interfaces of your protein:

- **Pops** and **PopsComp**: can be used to compute solvent accessible surface areas of proteins and nucleic acids, complexes and their components.

- **Protein Contact Atlas**: can provide structural insights through visualisation and analysis of non-covalent contacts.

Calculating the solvent-accessible surface of a protein begins to give you some idea of the regions that might be available for interaction with other proteins or macromolecular structures. **Pops** will provide you with a numerical analysis of atomic or residue-level solvent-accessibility that could be used to highlight accessible regions on a structural model. **PopsComp** will do similar for protein complexes.

Alternatively, **Protein Contact Atlas** will provide a calculation - with visualisations - for positions of residue contact within a single protein, or complex structure. This can give an indication of key and core structural residues, as well as suggesting those that might be more surface-available. 

***
>### Get started
>
>Both **Pops (PopsComp)** and **Protein Contact Atlas** requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. You will need either a **PDBe** identifier, or a predicted structure file to upload to the tools.
>
>You should review the **training materials** which give some guidance on how to use **Pops** and **Protein Contact Atlas** first - these will also show you the kind of results you can expect. Depending on your interest, you might not need/want to use both tools.
>
>When you have looked through that material, use the **link to XXXX** links below to visit the tool you need and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.html-description
Two tools are provided in this topic that can help you explore the surface and potential interaction interfaces of your protein:

Pops and PopsComp: can be used to compute solvent accessible surface areas of proteins and nucleic acids, complexes and their components.

Protein Contact Atlas: can provide structural insights through visualisation and analysis of non-covalent contacts.

Calculating the solvent-accessible surface of a protein begins to give you some idea of the regions that might be available for interaction with other proteins or macromolecular structures. Pops will provide you with a numerical analysis of atomic or residue-level solvent-accessibility that could be used to highlight accessible regions on a structural model. PopsComp will do similar for protein complexes.

Alternatively, Protein Contact Atlas will provide a calculation - with visualisations - for positions of residue contact within a single protein, or complex structure. This can give an indication of key and core structural residues, as well as suggesting those that might be more surface-available.

Get started

Both Pops (PopsComp) and Protein Contact Atlas requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance. You will need either a PDBe identifier, or a predicted structure file to upload to the tools.

You should review the training materials which give some guidance on how to use Pops and Protein Contact Atlas first - these will also show you the kind of results you can expect. Depending on your interest, you might not need/want to use both tools.

When you have looked through that material, use the link to XXXX links below to visit the tool you need and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.name
Citations for tools included in this workflow

# data[].attributes.workflow-content.nodes[].data.description
**PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.**\
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.\
***Nucleic acids research*** Volume 44 (2016) p.D385-95\
DOI: [10.1093/nar/gkv1047](https://dx.doi.org/10.1093/nar/gkv1047)

**UniProt: the universal protein knowledgebase**\
The UniProt Consortium\
***Nucleic Acids Research.*** Volume 45 (2017) D158-D169\
DOI: [10.1093/nar/gky092](https://doi.org/10.1093/nar/gky092)

**Ensembl 2018.**\
Zerbino DR, Achuthan P, Akanni W, Amode M.R, Barrell D, Bhai J, Billis K, Cummins C, Gall A, García Giro´n C, Gil L, Gordon L, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, To JK, Laird MR, Lavidas I, Liu Z, Loveland JE, Maurel T, McLaren W, Moore B, Mudge J, Murphy DN, Newman V, Nuhn M, Ogeh D, Ong CK, Parker A, Patricio M, Riat HS, Schuilenburg H, Sheppard D, Sparrow H, Taylor K, Thormann A, Vullo A, Walts B, Zadissa A, Frankish A, Hunt SE, Kostadima M, Langridge N, Martin FJ, Muffato M, Perry E, Ruffier M, Staines DM, Trevanion SJ, Aken BL, Cunningham F, Yates A, Flicek P.\
***Nucleic Acids Research*** Volume 46 (2018) Pages D754–D761\
DOI: [10.1093/nar/gkx1098](https://dx.doi.org/10.1093/nar/gkx1098)

**CATH: an expanded resource to predict protein function through structure and sequence.**\
Dawson NL, Lewis TE, Das S, Lees JG, Lee D, Ashford P, Orengo CA, Sillitoe I.\
***Nucleic Acids Research*** Volume 45(1) (2017) p.D289–D295.\
DOI: [10.1093/nar/gkw1098](https://dx.doi.org/10.1093/nar/gkw1098)

**InterPro in 2017 — beyond protein family and domain annotations.**\ 
Finn RD, Attwood TK, Babbitt PC, Bateman A, Bork P, Bridge AJ, Chang H, Dosztányi Z, El-Gebali S, Fraser M, Gough J, Haft D, Holliday GL, Huang H, 
Huang X, Letunic I, Lopez R, Lu S, Marchler-Bauer A, Mi H, Mistry J, Natale DA, Necci M, Nuka G, Orengo CA, Park Y, Pesseat S, Piovesan D, Potter SC, 
Rawlings ND, Redaschi N, Richardson L, Rivoire C, Sangrador-Vegas A, Sigrist C, Sillitoe I, Smithers B, Squizzato S, Sutton G, Thanki N, Thomas PD, 
Tosatto SCE, Wu CH, Xenarios I, Yeh L, Young S, Mitchell AL.\
***Nucleic Acids Research*** Volume 45 (2017) p.D190–D199.\
DOI: [10.1093/nar/gkw1107](https://dx.doi.org/10.1093/nar/gkw1107)

**POPSCOMP: an automated interaction analysis of biomolecular complexes.**\
Kleinjung J and Fraternali F.\
***Nucleic Acids Research*** Volume 33 (2005) p.W342-W346.\
DOI: [10.1093/nar/gki369](https://dx.doi.org/10.1093/nar/gki369)

**Visualization and analysis of non-covalent contacts using the Protein Contacts Atlas.**\
Kayikci M, Venkatakrishnan AJ, Scott-Brown J, Ravarani CNJ, Flock T, Babu MM.\
***Nature Structural & Molecular Biology*** Volume 25(2) (2018) p.185-184.\
DOI: [10.1038/s41594-017-0019-z](https://dx.doi.org/10.1038/s41594-017-0019-z)

**Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins.**\
Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS.\ 
***Nucleic Acids Research*** Volume 41 (2013) p.W314-W321.\ 
DOI: [10.1093/nar/gkt503](https://dx.doi.org/10.1093/nar/gkt503)

**MOLEonline: a web-based tool for analyzing channels, tunnels and pores (2018 update).**\
Pravda L, Sehnal D, Toušek D, Navrátilová V, Bazgier V, Berka K, Vařeková RS, Koča J, Otyepka M.\
***Nucleic Acids Research*** Volume 46 (2018) p.W368–W373.\ 
DOI: [10.1093/nar/gky309](https://dx.doi.org/10.1093/nar/gky309)

**3DLigandSite: predicting ligand-binding sites using similar structures.**\
Wass MN, Kelley LA , Sternberg MJ.\
***Nucleic Acids Research*** Volume 38 (2010) p.W469-W473.\ 
DOI: [10.1093/nar/gkq406](https://dx.doi.org/10.1093/nar/gkq406)

**Identification of nucleotide-binding sites in protein structures: a novel approach based on nucleotide modularity.**\
Parca L, Gherardini PF, Truglio M, Mangone I, Ferré F, Helmer-Citterich M, Ausiello G.\   
***Plos ONE*** Volume 7(11) (2012) e50240.\
DOI: [10.1371/journal.pone.0050240](https://dx.doi.org/10.1371/journal.pone.0050240)

**LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.**\
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.\
***Nature Methods*** Volume 14 (2017) p.1121-1122\
DOI: [10.1038/nmeth.4499](https://dx.doi.org/10.1038/nmeth.4499)

**EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.**\
Reynolds CR, Islam SA, Sternberg MJE.\
***Journal of Molecular Biology*** (2018) Epub.\
DOI: [10.1016/j.jmb.2018.01.013](https://dx.doi.org/10.1016/j.jmb.2018.01.013)

**Jalview Version 2-a multiple sequence alignment editor and analysis workbench.**\
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.\
***Bioinformatics*** Volume 25 (2009) p.1189-1191.\ 
DOI: [10.1093/bioinformatics/btp033](https://dx.doi.org/10.1093/bioinformatics/btp033)

# data[].attributes.workflow-content.nodes[].data.html-description
PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.
Nucleic acids research Volume 44 (2016) p.D385-95
DOI: 10.1093/nar/gkv1047

UniProt: the universal protein knowledgebase
The UniProt Consortium
Nucleic Acids Research. Volume 45 (2017) D158-D169
DOI: 10.1093/nar/gky092

Ensembl 2018.
Zerbino DR, Achuthan P, Akanni W, Amode M.R, Barrell D, Bhai J, Billis K, Cummins C, Gall A, García Giro´n C, Gil L, Gordon L, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, To JK, Laird MR, Lavidas I, Liu Z, Loveland JE, Maurel T, McLaren W, Moore B, Mudge J, Murphy DN, Newman V, Nuhn M, Ogeh D, Ong CK, Parker A, Patricio M, Riat HS, Schuilenburg H, Sheppard D, Sparrow H, Taylor K, Thormann A, Vullo A, Walts B, Zadissa A, Frankish A, Hunt SE, Kostadima M, Langridge N, Martin FJ, Muffato M, Perry E, Ruffier M, Staines DM, Trevanion SJ, Aken BL, Cunningham F, Yates A, Flicek P.
Nucleic Acids Research Volume 46 (2018) Pages D754–D761
DOI: 10.1093/nar/gkx1098

CATH: an expanded resource to predict protein function through structure and sequence.
Dawson NL, Lewis TE, Das S, Lees JG, Lee D, Ashford P, Orengo CA, Sillitoe I.
Nucleic Acids Research Volume 45(1) (2017) p.D289–D295.
DOI: 10.1093/nar/gkw1098

InterPro in 2017 — beyond protein family and domain annotations.\ Finn RD, Attwood TK, Babbitt PC, Bateman A, Bork P, Bridge AJ, Chang H, Dosztányi Z, El-Gebali S, Fraser M, Gough J, Haft D, Holliday GL, Huang H, Huang X, Letunic I, Lopez R, Lu S, Marchler-Bauer A, Mi H, Mistry J, Natale DA, Necci M, Nuka G, Orengo CA, Park Y, Pesseat S, Piovesan D, Potter SC, Rawlings ND, Redaschi N, Richardson L, Rivoire C, Sangrador-Vegas A, Sigrist C, Sillitoe I, Smithers B, Squizzato S, Sutton G, Thanki N, Thomas PD, Tosatto SCE, Wu CH, Xenarios I, Yeh L, Young S, Mitchell AL.
Nucleic Acids Research Volume 45 (2017) p.D190–D199.
DOI: 10.1093/nar/gkw1107

POPSCOMP: an automated interaction analysis of biomolecular complexes.
Kleinjung J and Fraternali F.
Nucleic Acids Research Volume 33 (2005) p.W342-W346.
DOI: 10.1093/nar/gki369

Visualization and analysis of non-covalent contacts using the Protein Contacts Atlas.
Kayikci M, Venkatakrishnan AJ, Scott-Brown J, Ravarani CNJ, Flock T, Babu MM.
Nature Structural & Molecular Biology Volume 25(2) (2018) p.185-184.
DOI: 10.1038/s41594-017-0019-z

Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins.
Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS.\ Nucleic Acids Research Volume 41 (2013) p.W314-W321.\ DOI: 10.1093/nar/gkt503

MOLEonline: a web-based tool for analyzing channels, tunnels and pores (2018 update).
Pravda L, Sehnal D, Toušek D, Navrátilová V, Bazgier V, Berka K, Vařeková RS, Koča J, Otyepka M.
Nucleic Acids Research Volume 46 (2018) p.W368–W373.\ DOI: 10.1093/nar/gky309

3DLigandSite: predicting ligand-binding sites using similar structures.
Wass MN, Kelley LA , Sternberg MJ.
Nucleic Acids Research Volume 38 (2010) p.W469-W473.\ DOI: 10.1093/nar/gkq406

Identification of nucleotide-binding sites in protein structures: a novel approach based on nucleotide modularity.
Parca L, Gherardini PF, Truglio M, Mangone I, Ferré F, Helmer-Citterich M, Ausiello G.\
Plos ONE Volume 7(11) (2012) e50240.
DOI: 10.1371/journal.pone.0050240

LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.
Nature Methods Volume 14 (2017) p.1121-1122
DOI: 10.1038/nmeth.4499

EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.
Reynolds CR, Islam SA, Sternberg MJE.
Journal of Molecular Biology (2018) Epub.
DOI: 10.1016/j.jmb.2018.01.013

Jalview Version 2-a multiple sequence alignment editor and analysis workbench.
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.
Bioinformatics Volume 25 (2009) p.1189-1191.\ DOI: 10.1093/bioinformatics/btp033

# data[].attributes.workflow-content.nodes[].data.description
Proteins can often be grouped into families that have shared function, based on similarity of their amino acid sequences. Similarly, functional parts of proteins - or domains - will show high sequence similarity with parts of other proteins that carry out the same function.

Understanding whether your protein is part of a family of proteins where functional information is known about different members of that family; or knowing that your protein has a domain with known function - can help you begin to understand what your protein might do.

**This Unit** introduces two useful tools to help you identify functional families and domain information for your protein of interest - **CATH-gene3D** and **InterPro**
***
>### Get started
>
>The tools in this Unit **require** the submission of a **protein sequence** - so you must have this sequence available. If you have not got the sequence for your protein please re-visit **Unit 1** for guidance 

# data[].attributes.workflow-content.nodes[].data.html-description
Proteins can often be grouped into families that have shared function, based on similarity of their amino acid sequences. Similarly, functional parts of proteins - or domains - will show high sequence similarity with parts of other proteins that carry out the same function.

Understanding whether your protein is part of a family of proteins where functional information is known about different members of that family; or knowing that your protein has a domain with known function - can help you begin to understand what your protein might do.

This Unit introduces two useful tools to help you identify functional families and domain information for your protein of interest - CATH-gene3D and InterPro

Get started

The tools in this Unit require the submission of a protein sequence - so you must have this sequence available. If you have not got the sequence for your protein please re-visit Unit 1 for guidance

# data[].attributes.workflow-content.nodes[].data.description
Although we can learn a lot about proteins from conservation of functional domains at sequence level, if we have structural data for a protein we can begin to explore how the 3-dimensional shape of the protein influences its function.

**This Unit** introduces useful tools that can be used to analyse a protein structure and identify regions that may contribute to surface binding to other proteins or macromolecular structures (**Pops** & **Protein Contact Atlas**) - or regions that may form the pores and/or channels of membrane transport proteins (**MOLEonline**) or potential sites of ligand interaction (**DEPTH**).
***
>### Get started
>
>The tools in this Unit **require** the submission of a **protein structure** - so you must have this structure available. If you have not got a structure for your protein please re-visit **Unit 1** for guidance 

# data[].attributes.workflow-content.nodes[].data.html-description
Although we can learn a lot about proteins from conservation of functional domains at sequence level, if we have structural data for a protein we can begin to explore how the 3-dimensional shape of the protein influences its function.

This Unit introduces useful tools that can be used to analyse a protein structure and identify regions that may contribute to surface binding to other proteins or macromolecular structures (Pops & Protein Contact Atlas) - or regions that may form the pores and/or channels of membrane transport proteins (MOLEonline) or potential sites of ligand interaction (DEPTH).

Get started

The tools in this Unit require the submission of a protein structure - so you must have this structure available. If you have not got a structure for your protein please re-visit Unit 1 for guidance

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
The tools (and the training to go with them) in this workflow require either a 3D structure for your protein, or simply the protein sequence.

* The Tools in **Unit 2** can be used with only **sequence**-level information.

* The Tools in **Units 3 & 4** require you to have **structural data** for your protein.

The Task boxes in this Unit help you to obtain the sequence and structure data for your protein that will enable you to make use of the other Units in this workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
The tools (and the training to go with them) in this workflow require either a 3D structure for your protein, or simply the protein sequence.

The Tools in Unit 2 can be used with only sequence-level information.

The Tools in Units 3 & 4 require you to have structural data for your protein.

The Task boxes in this Unit help you to obtain the sequence and structure data for your protein that will enable you to make use of the other Units in this workflow.

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
The first step is to find a sequence for the protein you are interested in. 

There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)

Three key resources are introduced below: 

- **UniProtKB** is a freely available comprehensive resource for protein sequence and annotation data.

- **Ensembl** imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects. 

- The **Protein Data Bank (PDB)** archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

***
>### Get started
>
>*It does not matter which database you use to find the sequence of your protein! If you haven't used any of these before, perhaps start with **UniProt*** 
>
>You should **visit the Quick Tour** online training for a tool first - all tools can be simply searched with a protein or gene name.
>
>When you have looked through that material, use the **link to the xxxx tool** below to try a search for your protein sequence.

# data[].attributes.workflow-content.nodes[].data.html-description
The first step is to find a sequence for the protein you are interested in.

There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)

Three key resources are introduced below:

UniProtKB is a freely available comprehensive resource for protein sequence and annotation data.

Ensembl imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects.

The Protein Data Bank (PDB) archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

Get started

It does not matter which database you use to find the sequence of your protein! If you haven't used any of these before, perhaps start with UniProt

You should visit the Quick Tour online training for a tool first - all tools can be simply searched with a protein or gene name.

When you have looked through that material, use the link to the xxxx tool below to try a search for your protein sequence.

# data[].attributes.workflow-content.nodes[].data.description
You are currently using the second training workflow in the Structural Bioinformatics series. Workflow 1 specifically deals with how to find a protein structure, or to try and predict one if necessary. Therefore, if you have not already worked through the materials included in that workflow, it would be useful to do that now if you wish to make use of any of the tools in this workflow that require structural information on your protein (**Unit 3 & 4**)

***
>### Get started
>
>Click on the link below to **ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest** - the workflow will open in a new window/tab so that you don't lose your place in this one! 

# data[].attributes.workflow-content.nodes[].data.html-description
You are currently using the second training workflow in the Structural Bioinformatics series. Workflow 1 specifically deals with how to find a protein structure, or to try and predict one if necessary. Therefore, if you have not already worked through the materials included in that workflow, it would be useful to do that now if you wish to make use of any of the tools in this workflow that require structural information on your protein (Unit 3 & 4)

Get started

Click on the link below to ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest - the workflow will open in a new window/tab so that you don't lose your place in this one!

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest

# data[].attributes.workflow-content.nodes[].data.description
**InterPro** is a resource that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites.

**InterPro** provides functional analysis of proteins by classifying them into families and predicting domains and important sites - combining protein signatures from a number of member databases into a single searchable resource.

**InterPro** is used by research scientists interested in the large-scale analysis of whole proteomes, genomes and metagenomes, as well as researchers seeking to characterise individual protein sequences.
***
>### Get started
>
>InterPro requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. 
>
>You should visit the **InterPro Quick Tour** showing you how to search InterPro and view the results of the search first.
>
>You might also want to look through the material in the other tutorials which will give further information on how to understand the results that InterPro gives, and the kind of data it uses
>
>When you are happy to try a search, use the **link to InterPro** below to try a search with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.html-description
InterPro is a resource that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites.

InterPro provides functional analysis of proteins by classifying them into families and predicting domains and important sites - combining protein signatures from a number of member databases into a single searchable resource.

InterPro is used by research scientists interested in the large-scale analysis of whole proteomes, genomes and metagenomes, as well as researchers seeking to characterise individual protein sequences.

Get started

InterPro requires the submission of a protein sequence - so the first step is to find the sequence for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance.

You should visit the InterPro Quick Tour showing you how to search InterPro and view the results of the search first.

You might also want to look through the material in the other tutorials which will give further information on how to understand the results that InterPro gives, and the kind of data it uses

When you are happy to try a search, use the link to InterPro below to try a search with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
InterPro: Functional and structural analysis of protein sequences

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
InterPro: A case study of 3 protein family building methodologies

# data[].attributes.workflow-content.nodes[].data.name
Pockets and Pores with DEPTH & MOLEonline

# data[].attributes.workflow-content.nodes[].data.description
Two tools are provided in this topic that can help you determine where the surface of your protein forms a channel or pore - possibly through a membrane - or where the surface forms a potential pocket - that could form a ligand binding site:

- **MOLEonline**: can be used to compute pores and channels, including transmembrane pores and channels, that your protein may form.

- **DEPTH**: can provide insights into where your protein surface may form pocket areas, that may indicate ligand binding sites or could be used as possible novel ligand (ie drug) target sites.

If you have some information about your protein to suggest that it might have a transmembrane domain and act as a channel or pore, you can use **MOLEonline** to try and define the structure of the channel through the protein. This would be useful if you need to understand the residues in the protein that can interact with molecules that have to pass through the channel for example.

Alternatively, if you are looking for a possible ligand binding site on the surface of a globular protein (or the globular domain of a transmembrane protein), you can try to use **DEPTH** to identify regions on the surface that form a pocket - this could be the natural site of ligand interaction, or offer the opportunity to design small molecules that could bind in this position.  

***
>### Get started
>
>Both **MOLEonline** and **DEPTH** require the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. You will need either a **PDBe** identifier, or a predicted structure file to upload to the tools.
>
>You should review the **training materials** which give some guidance on how to use **MOLEonline** and **DEPTH** first - these will also show you the kind of results you can expect. Depending on your interest, you might not need/want to use both tools.
>
>When you have looked through that material, use the **link to XXXX** links below to visit the tool you need and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.html-description
Two tools are provided in this topic that can help you determine where the surface of your protein forms a channel or pore - possibly through a membrane - or where the surface forms a potential pocket - that could form a ligand binding site:

MOLEonline: can be used to compute pores and channels, including transmembrane pores and channels, that your protein may form.

DEPTH: can provide insights into where your protein surface may form pocket areas, that may indicate ligand binding sites or could be used as possible novel ligand (ie drug) target sites.

If you have some information about your protein to suggest that it might have a transmembrane domain and act as a channel or pore, you can use MOLEonline to try and define the structure of the channel through the protein. This would be useful if you need to understand the residues in the protein that can interact with molecules that have to pass through the channel for example.

Alternatively, if you are looking for a possible ligand binding site on the surface of a globular protein (or the globular domain of a transmembrane protein), you can try to use DEPTH to identify regions on the surface that form a pocket - this could be the natural site of ligand interaction, or offer the opportunity to design small molecules that could bind in this position.

Get started

Both MOLEonline and DEPTH require the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance. You will need either a PDBe identifier, or a predicted structure file to upload to the tools.

You should review the training materials which give some guidance on how to use MOLEonline and DEPTH first - these will also show you the kind of results you can expect. Depending on your interest, you might not need/want to use both tools.

When you have looked through that material, use the link to XXXX links below to visit the tool you need and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.name
Unit 4. Identify ligand binding sites from structure

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
An important part of protein function is often the binding of small molecule ligands. 

Whilst tools introduced in **Unit 3** may help identify regions/interfaces suitable for large-scale interaction with other macromolecules, **this Unit** introduces useful tools that can be used to analyse a protein structure and identify regions where smaller ligands bind - both general ligand binding by comparison with known ligand binding sites (**3DLigandSite**), and specifically the binding of nucelotide ligands (**Nucleos**).
***
>### Get started
>
>The tools in this Unit **require** the submission of a **protein structure** - so you must have this structure available. If you have not got a structure for your protein please re-visit **Unit 1** for guidance 

# data[].attributes.workflow-content.nodes[].data.html-description
An important part of protein function is often the binding of small molecule ligands.

Whilst tools introduced in Unit 3 may help identify regions/interfaces suitable for large-scale interaction with other macromolecules, this Unit introduces useful tools that can be used to analyse a protein structure and identify regions where smaller ligands bind - both general ligand binding by comparison with known ligand binding sites (3DLigandSite), and specifically the binding of nucelotide ligands (Nucleos).

Get started

The tools in this Unit require the submission of a protein structure - so you must have this structure available. If you have not got a structure for your protein please re-visit Unit 1 for guidance

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
Many proteins bind to ligands as part of their biological function, sometimes resulting in a change to the ligand (ie catalysis) and sometimes to the protein itself (ie activation). Whilst you might have already tried to use **DEPTH** to identify a potential surface pocket - particularly useful if you are trying to target your protein by designing a new drug perhaps - this pocket may not be an actual ligand binding site.

**3DLigandSite** is an automated method for the prediction of ligand binding sites - not just pockets. Users submit a protein structure which is then used to search a structural library to identify homologous structures with bound ligands. These ligands are superimposed onto the protein structure to predict a ligand binding site.
***
>### Get started
>
>**3DLigandSite** requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. You will need either a structure file downloaded from the **PDBe**, or a predicted structure file to upload to the tools.
>
>You should review **How to use 3DLigandSite** first, which give some guidance on how to use the tool, and will also show you the kind of results you can expect. 
>
>When you have looked through that material, use the **link to 3DLignadSite** below to visit the tool and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.html-description
Many proteins bind to ligands as part of their biological function, sometimes resulting in a change to the ligand (ie catalysis) and sometimes to the protein itself (ie activation). Whilst you might have already tried to use DEPTH to identify a potential surface pocket - particularly useful if you are trying to target your protein by designing a new drug perhaps - this pocket may not be an actual ligand binding site.

3DLigandSite is an automated method for the prediction of ligand binding sites - not just pockets. Users submit a protein structure which is then used to search a structural library to identify homologous structures with bound ligands. These ligands are superimposed onto the protein structure to predict a ligand binding site.

Get started

3DLigandSite requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance. You will need either a structure file downloaded from the PDBe, or a predicted structure file to upload to the tools.

You should review How to use 3DLigandSite first, which give some guidance on how to use the tool, and will also show you the kind of results you can expect.

When you have looked through that material, use the link to 3DLignadSite below to visit the tool and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.description
Nucelotides are an important ligand for many proteins, and include Kinases (that bind ATP), and G proteins (that bind GTP). Nucleotides can be bound in a large number of different conformations by protein so that their binding site is not always easily detectable. 

**Nucleos** is a webserver for the identification of nucleotide-binding sites based on the concept of nucleotide modularity - identifying binding sites for nucleotide modules (namely the nucleobase, the carbohydrate and the phosphate) and then combining them in order to build the complete binding sites for different types of nucleotides (e.g. ADP or FAD).

If you have information to suggest that you protein of interest might require the binding of nucleotides, then using the **Nucelos** tools might help you identify where this binding event happens in the protein structure.
***
>### Get started
>
>**Nucleos** requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit **Unit 1** for guidance. You will need either a **PDBe** identifier, or a predicted structure file to upload to the tools.
>
>You should review the **Overview to using Nucleos** first, which give some guidance on how to use the tool, and will also show you the kind of results you can expect. 
>
>When you have looked through that material, use the **link to Nucleos** below to visit the tool and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.html-description
Nucelotides are an important ligand for many proteins, and include Kinases (that bind ATP), and G proteins (that bind GTP). Nucleotides can be bound in a large number of different conformations by protein so that their binding site is not always easily detectable.

Nucleos is a webserver for the identification of nucleotide-binding sites based on the concept of nucleotide modularity - identifying binding sites for nucleotide modules (namely the nucleobase, the carbohydrate and the phosphate) and then combining them in order to build the complete binding sites for different types of nucleotides (e.g. ADP or FAD).

If you have information to suggest that you protein of interest might require the binding of nucleotides, then using the Nucelos tools might help you identify where this binding event happens in the protein structure.

Get started

Nucleos requires the submission of a protein structure - so the first step is to find the structure for the protein you are interested in. If you do not have that already, re-visit Unit 1 for guidance. You will need either a PDBe identifier, or a predicted structure file to upload to the tools.

You should review the Overview to using Nucleos first, which give some guidance on how to use the tool, and will also show you the kind of results you can expect.

When you have looked through that material, use the link to Nucleos below to visit the tool and try a search with your protein structure.

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
If you have reached Unit 5 of this training workflow, then you should have some information about domains or sites on your protein that might represent interesting function. The tools in **this Unit** can be used to visualise that information on the 3D structure or sequence of your protein.

This Unit will introduce you to **LiteMol** and **EzMol** as 3D molecular viewers so that you can visualise and explore your structures further - and **JalView** which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.html-description
If you have reached Unit 5 of this training workflow, then you should have some information about domains or sites on your protein that might represent interesting function. The tools in this Unit can be used to visualise that information on the 3D structure or sequence of your protein.

This Unit will introduce you to LiteMol and EzMol as 3D molecular viewers so that you can visualise and explore your structures further - and JalView which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.name
Visualising your structure in 3D with LiteMol & EzMol

# data[].attributes.workflow-content.nodes[].data.description
If you have structure-based information you will want to be able to view and explore it in 3D. **LiteMol** is a web tool for visualisation of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey **Additional Reading** box below the topic section.

**However**, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the **Additional Reading** box below.

**Alternatively**, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive **EzMol** tool linked below.
***
>### Get started
>
>If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short **Basic use of standalone LiteMol** video - and using the link to the **Standalone LiteMol** tool to try it for yourself.
>
>If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the **tutorials labelled from the PDBe** below.

# data[].attributes.workflow-content.nodes[].data.html-description
If you have structure-based information you will want to be able to view and explore it in 3D. LiteMol is a web tool for visualisation of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey Additional Reading box below the topic section.

However, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the Additional Reading box below.

Alternatively, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive EzMol tool linked below.

Get started

If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short Basic use of standalone LiteMol video - and using the link to the Standalone LiteMol tool to try it for yourself.

If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the tutorials labelled from the PDBe below.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: Basic use of standalone LiteMol

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: How to use LiteMol at the PDBe

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Introducing LiteMol (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing extra annotation (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing sequence and structure interactively (PDBe video)

# data[].attributes.workflow-content.nodes[].data.name
Visualising sequence-based features with JalView

# data[].attributes.workflow-content.nodes[].data.description
**Jalview** is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

**JalView** is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.
***
>### Get started
>
>**JalView** has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the **Getting started with JalView** videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.
>
>After watching these videos you can try **JalView** for yourself by clicking the link below to the **JalView Launcher**
>
>If you want to explore the more advanced features of the tool, there is also a link to the **JalView youtube channel** which contains a comprehensive set of video tutorials.

# data[].attributes.workflow-content.nodes[].data.html-description
Jalview is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

JalView is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.

Get started

JalView has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the Getting started with JalView videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.

After watching these videos you can try JalView for yourself by clicking the link below to the JalView Launcher

If you want to explore the more advanced features of the tool, there is also a link to the JalView youtube channel which contains a comprehensive set of video tutorials.

# data[].attributes.workflow-content.nodes[].data.description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. *Similarly, most are commercial ($$) software, and some only free for limited or academic use.*

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.html-description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. Similarly, most are commercial ($$) software, and some only free for limited or academic use.

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.description
The next workflow in the series, **ELIXIR Structural Bioinformatics 3: Predicting the effect of mutation on protein function** builds on the previous training workflows in this series on building protein models and identifying functional domains or ligand binding sites - and introduces methods to predict how mutations can impact on protein function.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different mutation effects.

It will help guide you through exploring these tools and interpreting the predicted effect information to give you greater understanding of how variability in your protein may impact on function.

# data[].attributes.workflow-content.nodes[].data.html-description
The next workflow in the series, ELIXIR Structural Bioinformatics 3: Predicting the effect of mutation on protein function builds on the previous training workflows in this series on building protein models and identifying functional domains or ligand binding sites - and introduces methods to predict how mutations can impact on protein function.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different mutation effects.

It will help guide you through exploring these tools and interpreting the predicted effect information to give you greater understanding of how variability in your protein may impact on function.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
ELIXIR Structural Bioinformatics 3: Predicting the effect of mutation on protein function

# data[].attributes.title
ELIXIR Structural Bioinformatics 3: Predicting the effect of mutation on protein function

# data[].attributes.description
This workflow builds on the previous training workflows in this series on building protein models and identifying functional domains or ligand binding sites - and introduces methods to predict how mutations can impact on protein function.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide information about different mutation effects.

It will help guide you through exploring these tools and interpreting the predicted effect information to give you greater understanding of how variability in your protein may impact on function.

# data[].attributes.workflow-content.nodes[].data.name
Finding a sequence and structure data for your protein

# data[].attributes.workflow-content.nodes[].data.description
The first step is to find a sequence for the protein you are interested in. 

There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)

Three key resources are introduced below: 

- **UniProtKB** is a freely available comprehensive resource for protein sequence and annotation data.

- **Ensembl** imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects. 

- The **Protein Data Bank (PDB)** archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

***
>### Get started
>
>*As most of the tools you will use in this workflow need either a sequence or a UniProt identifier, it will be best to start with **UniProt*** 
>
>You should visit the **UniProt Quick Tour** online training for a tool first - and when you have done that, use the **link to UniProt** to try a search for your protein.
>
>*The links to the PDBe are provided, along with introductory tutorials, as an alternative to identify known protein structures. Similarly, you might find it interesting to visit the link to workflow 1 in this series: ***ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest****

# data[].attributes.workflow-content.nodes[].data.html-description
The first step is to find a sequence for the protein you are interested in.

There are different ways of doing this depending on what starting information you have. This can include any of the following: protein name, gene name, DNA sequence, accession numbers etc.)

Three key resources are introduced below:

UniProtKB is a freely available comprehensive resource for protein sequence and annotation data.

Ensembl imports genome sequences from consortia which keeps us consistent with many other bioinformatics projects.

The Protein Data Bank (PDB) archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

Get started

As most of the tools you will use in this workflow need either a sequence or a UniProt identifier, it will be best to start with UniProt

You should visit the UniProt Quick Tour online training for a tool first - and when you have done that, use the link to UniProt to try a search for your protein.

The links to the PDBe are provided, along with introductory tutorials, as an alternative to identify known protein structures. Similarly, you might find it interesting to visit the link to workflow 1 in this series: ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
ELIXIR Structural Bioinformatics 1: getting a structure for a protein of interest

# data[].attributes.workflow-content.nodes[].data.description
This workflow builds on the previous training workflows in this series on building protein models and identifying functional domains or ligand binding sites - and introduces methods to predict how mutations can impact on protein function.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide predictions about how mutations in your protein sequence may impact on the protein structure or function.

The training workflow layout mimics the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

**Who is it for?**

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the impact of sequence-level mutation on protein structure and function. 

Before you start the learning Units, please review the **Learning Outcomes** box below

# data[].attributes.workflow-content.nodes[].data.html-description
This workflow builds on the previous training workflows in this series on building protein models and identifying functional domains or ligand binding sites - and introduces methods to predict how mutations can impact on protein function.

It is designed to lead you through the process of finding sequence or structure information for your protein of interest, and using it as a search query to investigate several different online tools - each able to provide predictions about how mutations in your protein sequence may impact on the protein structure or function.

The training workflow layout mimics the experimental process you would go through to achieve this task - but with each step providing explanations and links to key tools and training support to enable you to carry out those steps.

Who is it for?

Typical users are expected to be wet-lab biologists, or even bioinformaticians unfamiliar with this aspect of computational biology, who have identified a protein of interest from their research - and who wish to explore the impact of sequence-level mutation on protein structure and function.

Before you start the learning Units, please review the Learning Outcomes box below

# data[].attributes.workflow-content.nodes[].data.description
When you have completed this workflow you will be able to:

- Use a range of resources (**PDBe**, **UniProt** or **the previous workflow in this series**) to search for sequence or an experimentally-derived structure for a protein of interest to you.
- Use tools to predict the effect of sequence variation on protein function and stability from sequence (**SIFT**, **FATHMM**, **PolyPhen** and **SusPect**), and from structure (**SDM** and **SAAPpred**).
- Search databases of mutation effect on structure for particular use-cases - SNP effect of domains and complexes, and particular effects on the Titin protein (**PinSNPs** and **TitinDB**).
- Visualise the domains or sites you have obtained using simple-to-use viewers: for structure, LiteMol; and for sequence, JalView.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary (but in some cases have cost or license implications) to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.html-description
When you have completed this workflow you will be able to:

Use a range of resources (PDBe, UniProt or the previous workflow in this series) to search for sequence or an experimentally-derived structure for a protein of interest to you.

Use tools to predict the effect of sequence variation on protein function and stability from sequence (SIFT, FATHMM, PolyPhen and SusPect), and from structure (SDM and SAAPpred).

Search databases of mutation effect on structure for particular use-cases - SNP effect of domains and complexes, and particular effects on the Titin protein (PinSNPs and TitinDB).

Visualise the domains or sites you have obtained using simple-to-use viewers: for structure, LiteMol; and for sequence, JalView.

Along the way you will also be pointed towards other background material which will help you understand the theoretical basis of the techniques you are learning to use, as well as other interesting tools that are complementary (but in some cases have cost or license implications) to those presented in the main workflow which you might like to explore.

# data[].attributes.workflow-content.nodes[].data.name
Predicting effect of mutation on protein function

# data[].attributes.workflow-content.nodes[].data.description
Please begin this module by clicking on the purple **Start Here** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this module by clicking on the purple Start Here box

# data[].attributes.workflow-content.nodes[].data.name
Unit 2. Prediction of mutation effects from sequence

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
**SIFT** works purely on the basis of sequence and is one of the oldest and most popular methods. Its performance is far lower than modern methods.

SIFT predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations

The **SIFT tool** does not provide any training materials, and so an example has been set out below for you to try. This example was provided by Dr Andrew Martin at UCL
***
>### Get started
>Click on the **link to the SIFT tool** to open SIFT in a new tab/window
>
>Under **'Single Protein Tools'**, click **'SIFT Sequence'**
>
>Under **'Paste in your protein query sequence'**, paste the sequence:
>
>sp|P11413|G6PD_HUMAN Glucose-6-phosphate 1-dehydrogenase OS=Homo sapiens
MAEQVALSRTQVCGILREELFQGDAFHQSDTHIFIIMGASGDLAKKKIYPTIWWLFRDGL
LPENTFIVGYARSRLTVADIRKQSEPFFKATPEEKLKLEDFFARNSYVAGQYDDAASYQR
LNSHMNALHLGSQANRLFYLALPPTVYEAVTKNIHESCMSQIGWNRIIVEKPFGRDLQSS
DRLSNHISSLFREDQIYRIDHYLGKEMVQNLMVLRFANRIFGPIWNRDNIACVILTFKEP
FGTEGRGGYFDEFGIIRDVMQNHLLQMLCLVAMEKPASTNSDDVRDEKVKVLKCISEVQA
NNVVLGQYVGNPDGEGEATKGYLDDPTVPRGSTTATFAAVVLYVENERWDGVPFILRCGK
ALNERKAEVRLQFHDVAGDIFHQQCKRNELVIRVQPNEAVYTKMMTKKPGMFFNPEESEL
DLTYGNRYKNVKLPDAYERLILDVFCGSQMHFVRSDELREAWRIFTPLLHQIELEKPKPI
PYIYGSRGPTEADELMKRVGFQYEGTYKWVNPHKL
>
>Under **'Enter the substitutions of interest [format]:'**, enter **'S106C'**
>
>Click **'Submit Query'**
>Once the result is returned, click **'Predictions of substitutions entered'**
>
>_Note that there are dire warnings about having to wait 20 minutes -
							in practice for this query you are likely to get a result back in a
							couple of minutes!_
>
>Now you can try using SIFT with your own protein sequence

# data[].attributes.workflow-content.nodes[].data.html-description
SIFT works purely on the basis of sequence and is one of the oldest and most popular methods. Its performance is far lower than modern methods.

SIFT predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring nonsynonymous polymorphisms and laboratory-induced missense mutations

The SIFT tool does not provide any training materials, and so an example has been set out below for you to try. This example was provided by Dr Andrew Martin at UCL

Get started

Click on the link to the SIFT tool to open SIFT in a new tab/window

Under 'Single Protein Tools', click 'SIFT Sequence'

Under 'Paste in your protein query sequence', paste the sequence:

sp|P11413|G6PD_HUMAN Glucose-6-phosphate 1-dehydrogenase OS=Homo sapiens MAEQVALSRTQVCGILREELFQGDAFHQSDTHIFIIMGASGDLAKKKIYPTIWWLFRDGL LPENTFIVGYARSRLTVADIRKQSEPFFKATPEEKLKLEDFFARNSYVAGQYDDAASYQR LNSHMNALHLGSQANRLFYLALPPTVYEAVTKNIHESCMSQIGWNRIIVEKPFGRDLQSS DRLSNHISSLFREDQIYRIDHYLGKEMVQNLMVLRFANRIFGPIWNRDNIACVILTFKEP FGTEGRGGYFDEFGIIRDVMQNHLLQMLCLVAMEKPASTNSDDVRDEKVKVLKCISEVQA NNVVLGQYVGNPDGEGEATKGYLDDPTVPRGSTTATFAAVVLYVENERWDGVPFILRCGK ALNERKAEVRLQFHDVAGDIFHQQCKRNELVIRVQPNEAVYTKMMTKKPGMFFNPEESEL DLTYGNRYKNVKLPDAYERLILDVFCGSQMHFVRSDELREAWRIFTPLLHQIELEKPKPI PYIYGSRGPTEADELMKRVGFQYEGTYKWVNPHKL

Under 'Enter the substitutions of interest [format]:', enter 'S106C'

Click 'Submit Query' Once the result is returned, click 'Predictions of substitutions entered'

Note that there are dire warnings about having to wait 20 minutes - in practice for this query you are likely to get a result back in a couple of minutes!

Now you can try using SIFT with your own protein sequence

# data[].attributes.workflow-content.nodes[].data.name
Unit 3. Prediction of mutation effects using structural information

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
This tool is a combination of SAAPdap/SAAPpred - the Single Amino Acid Polymorphism data analysis pipeline and predictor

The **SAAPpred tool** does not provide any training materials, and so an example has been set out below for you to try. This example was provided by Dr Andrew Martin at UCL (as is SAAPpred)

For comparison with the sequence-based tools in **Unit 2**, the training provided here uses the same example protein: 

_You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia_
***
>### Get started
>Click on the **link to the SAAPpred tool** to open SAAPpred in a new tab/window
>
> - Under **'UniProt Accession'**, enter the accession (**'P11413'**)
>
> - Under **'Native Residue'**, enter **'ser'**
>
> - Under **'Residue Number'**, enter **'106'**
>
> - Under **'Mutant Residue'**, enter **'cys'**
>
> - Press **'Submit'**
>
>The analysis progress will appear on the same page which will then change to the results page
>
> - Click **'ser106cys'**
>
>You will now see a summary of the likely local structural effects of the mutation in different structures of the same protein
>
> - Click **'Predict Pathogenicity'**
>
>Now you can try using SAAPpred with your own protein ID

# data[].attributes.workflow-content.nodes[].data.html-description
This tool is a combination of SAAPdap/SAAPpred - the Single Amino Acid Polymorphism data analysis pipeline and predictor

The SAAPpred tool does not provide any training materials, and so an example has been set out below for you to try. This example was provided by Dr Andrew Martin at UCL (as is SAAPpred)

For comparison with the sequence-based tools in Unit 2, the training provided here uses the same example protein:

You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia

Get started

Click on the link to the SAAPpred tool to open SAAPpred in a new tab/window

Under 'UniProt Accession', enter the accession ('P11413')

Under 'Native Residue', enter 'ser'

Under 'Residue Number', enter '106'

Under 'Mutant Residue', enter 'cys'

Press 'Submit'

The analysis progress will appear on the same page which will then change to the results page

Click 'ser106cys'

You will now see a summary of the likely local structural effects of the mutation in different structures of the same protein

Click 'Predict Pathogenicity'

Now you can try using SAAPpred with your own protein ID

# data[].attributes.workflow-content.nodes[].data.name
Citations for tools included in this workflow

# data[].attributes.workflow-content.nodes[].data.description
**UniProt: the universal protein knowledgebase**\
The UniProt Consortium\
***Nucleic Acids Research.*** Volume 45 (2017) D158-D169\
DOI: [10.1093/nar/gky092](https://doi.org/10.1093/nar/gky092)

**PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.**\
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.\
***Nucleic acids research*** Volume 44 (2016) p.D385-95\
DOI: [10.1093/nar/gkv1047](https://dx.doi.org/10.1093/nar/gkv1047)

**SIFT web server: predicting effects of amino acid substitutions on proteins.**\
Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC; 
***Nucleic Acids Research*** Volume 40 (2012) p.W452–W457.\
DOI: [10.1093/nar/gks539](https://dx.doi.org/10.1093/nar/gks539)

**Predicting the Functional, Molecular and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models.**\
Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt, TR.\
***Human Mutation*** Volume 34 (2013) p.57-65.\
DOI: [10.1002/humu.22225](https://dx.doi.org/10.1002/humu.22225)

**A method and server for predicting damaging missense mutations.**\
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR.\
***Nature Methods*** Volume 7(4) (2010) p.248-249.\
DOI: [10.1038/nmeth0410-248](https://dx.doi.org/10.1038/nmeth0410-248)

**SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features.**\
Yates CM, Filippis I, Kelley LA, Sternberg MJE.\
***Journal of Molecular Biology*** Volume 426(14) (2014) p.2692-2701.\
DOI: [10.1016/j.jmb.2014.04.026](https://dx.doi.org/10.1016/j.jmb.2014.04.026)

**The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations.**\
Al-Numair NS, Martin ACR.\
***BMC Genomics*** Volume 14(3) (2013) p.S4.\
DOI: [10.1186/1471-2164-14-S3-S4](https://dx.doi.org/10.1186/1471-2164-14-S3-S4)

**SDM: a server for predicting effects of mutations on protein stability.**\
Pandurangan AP, Ochoa-Montaño B, Ascher DB, Blundell TL.\
***Nucleic Acids Research*** Volume 45(W1) (2017) p.W229–W235.\
DOI: [10.1093/nar/gkx439](https://dx.doi.org/10.1093/nar/gkx439)

**Anatomy of protein disorder, flexibility and disease-related mutations.**\
Lu H-C, Chung SS, Fornili A, Fraternali F.\
***Frontiers in Molecular Biosciences*** Volume 2 (2015) p.47.\
DOI: [10.3389/fmolb.2015.00047](https://dx.doi.org/10.3389/fmolb.2015.00047)

**TITINdb-a computational tool to assess titin's role as a disease gene.**\
Laddach A, Gautel M, Fraternali F.\
***Bioinformatics*** Volume 33(21) (2017) p.3482-3485.\
DOI: [10.1093/bioinformatics/btx424](https://dx.doi.org/10.1093/bioinformatics/btx424)

**LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.**\
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.\
***Nature Methods*** Volume 14 (2017) p.1121-1122\
DOI: [10.1038/nmeth.4499](https://dx.doi.org/10.1038/nmeth.4499)

**EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.**\
Reynolds CR, Islam SA, Sternberg MJE.\
***Journal of Molecular Biology*** (2018) Epub.\
DOI: [10.1016/j.jmb.2018.01.013](https://dx.doi.org/10.1016/j.jmb.2018.01.013)

**Jalview Version 2-a multiple sequence alignment editor and analysis workbench.**\
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.\
***Bioinformatics*** Volume 25 (2009) p.1189-1191.\ 
DOI: [10.1093/bioinformatics/btp033](https://dx.doi.org/10.1093/bioinformatics/btp033)

# data[].attributes.workflow-content.nodes[].data.html-description
UniProt: the universal protein knowledgebase
The UniProt Consortium
Nucleic Acids Research. Volume 45 (2017) D158-D169
DOI: 10.1093/nar/gky092

PDBe: improved accessibility of macromolecular structure data from PDB and EMDB.
Velankar S, van Ginkel G, Alhroub Y, Battle GM, Berrisford JM, Conroy MJ, Dana JM, Gore SP, Gutmanas A, Haslam P, Hendrickx PM, Lagerstedt I, Mir S, Fernandez Montecelo MA, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Sanz-García E, Sen S, Slowley RA, Wainwright ME, Deshpande MS, Iudin A, Sahni G, Salavert Torres J, Hirshberg M, Mak L, Nadzirin N, Armstrong DR, Clark AR, Smart OS, Korir PK, Kleywegt GJ.
Nucleic acids research Volume 44 (2016) p.D385-95
DOI: 10.1093/nar/gkv1047

SIFT web server: predicting effects of amino acid substitutions on proteins.
Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC; Nucleic Acids Research Volume 40 (2012) p.W452–W457.
DOI: 10.1093/nar/gks539

Predicting the Functional, Molecular and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models.
Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt, TR.
Human Mutation Volume 34 (2013) p.57-65.
DOI: 10.1002/humu.22225

A method and server for predicting damaging missense mutations.
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR.
Nature Methods Volume 7(4) (2010) p.248-249.
DOI: 10.1038/nmeth0410-248

SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features.
Yates CM, Filippis I, Kelley LA, Sternberg MJE.
Journal of Molecular Biology Volume 426(14) (2014) p.2692-2701.
DOI: 10.1016/j.jmb.2014.04.026

The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations.
Al-Numair NS, Martin ACR.
BMC Genomics Volume 14(3) (2013) p.S4.
DOI: 10.1186/1471-2164-14-S3-S4

SDM: a server for predicting effects of mutations on protein stability.
Pandurangan AP, Ochoa-Montaño B, Ascher DB, Blundell TL.
Nucleic Acids Research Volume 45(W1) (2017) p.W229–W235.
DOI: 10.1093/nar/gkx439

Anatomy of protein disorder, flexibility and disease-related mutations.
Lu H-C, Chung SS, Fornili A, Fraternali F.
Frontiers in Molecular Biosciences Volume 2 (2015) p.47.
DOI: 10.3389/fmolb.2015.00047

TITINdb-a computational tool to assess titin's role as a disease gene.
Laddach A, Gautel M, Fraternali F.
Bioinformatics Volume 33(21) (2017) p.3482-3485.
DOI: 10.1093/bioinformatics/btx424

LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.
Sehnal D, Deshpande M, Vařeková RS, Mir S, Berka K, Midlik A, Pravda L, Velankar S, Koča J.
Nature Methods Volume 14 (2017) p.1121-1122
DOI: 10.1038/nmeth.4499

EzMol: A Web Server Wizard for the Rapid Visualization and Image Production of Protein and Nucleic Acid Structures.
Reynolds CR, Islam SA, Sternberg MJE.
Journal of Molecular Biology (2018) Epub.
DOI: 10.1016/j.jmb.2018.01.013

Jalview Version 2-a multiple sequence alignment editor and analysis workbench.
Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ.
Bioinformatics Volume 25 (2009) p.1189-1191.\ DOI: 10.1093/bioinformatics/btp033

# data[].attributes.workflow-content.nodes[].data.description
Small changes, even single amino acids, can have a significant impact on the structure or function of a protein. However, if such changes occur in regions of the protein which are not involved in a core function (such as an active site) or are structural (perhaps in a disordered region), then they may be tolerated.

If you are studying the function of a protein it is important to understand whether there are known mutations (perhaps in disease) or variation at a sequence level between population groups. The tasks in the previous workflow in this series, **Structural Bioinformatics 2: Exploring protein domains and functional sites** may enable you to locate your variation in the context of structure or functional domains.

The tools introduced here enable you to predict the effect of that mutation on the protein - in terms of the impact on structural stability. You will first try **SIFT** as an example - which is an old method, but commonly used and can be found integrated into other prediction approaches.

You will then be introduced to some newer methods (**FATHMM**, **PolyPhen** and **SusPect**). When exploring the effect of a mutation on your protein it will probably be informative to try them all.
***
>### Get started
>
>The tools in this Unit **require** the submission of a **protein sequence** or **Uniprot ID** - so you must have this  available. If you have not got this information for your protein please re-visit **Unit 1** for guidance 

# data[].attributes.workflow-content.nodes[].data.html-description
Small changes, even single amino acids, can have a significant impact on the structure or function of a protein. However, if such changes occur in regions of the protein which are not involved in a core function (such as an active site) or are structural (perhaps in a disordered region), then they may be tolerated.

If you are studying the function of a protein it is important to understand whether there are known mutations (perhaps in disease) or variation at a sequence level between population groups. The tasks in the previous workflow in this series, Structural Bioinformatics 2: Exploring protein domains and functional sites may enable you to locate your variation in the context of structure or functional domains.

The tools introduced here enable you to predict the effect of that mutation on the protein - in terms of the impact on structural stability. You will first try SIFT as an example - which is an old method, but commonly used and can be found integrated into other prediction approaches.

You will then be introduced to some newer methods (FATHMM, PolyPhen and SusPect). When exploring the effect of a mutation on your protein it will probably be informative to try them all.

Get started

The tools in this Unit require the submission of a protein sequence or Uniprot ID - so you must have this available. If you have not got this information for your protein please re-visit Unit 1 for guidance

# data[].attributes.workflow-content.nodes[].data.description
Similar to the topics in the previous **Unit 2**, the tools introduced here enable you to predict the effect of a mutation on your protein, but this time rely on structural information being available. 

You will then be introduced to **SAAPpred** and **SDM**. When exploring the effect of a mutation on your protein it will probably be informative to try both.
***
>### Get started
>
>The tools in this Unit **require structure** and the submission of a **PDB ID** or **Uniprot ID** - so you must have this available. If you have not got this information for your protein please re-visit **Unit 1** for guidance 

# data[].attributes.workflow-content.nodes[].data.html-description
Similar to the topics in the previous Unit 2, the tools introduced here enable you to predict the effect of a mutation on your protein, but this time rely on structural information being available.

You will then be introduced to SAAPpred and SDM. When exploring the effect of a mutation on your protein it will probably be informative to try both.

Get started

The tools in this Unit require structure and the submission of a PDB ID or Uniprot ID - so you must have this available. If you have not got this information for your protein please re-visit Unit 1 for guidance

# data[].attributes.workflow-content.nodes[].data.description
**SDM** is a computational method to predict the change in protein stability upon the introduction of a single mutation. It is a knowledge-based approach that uses conformationally constrained environment-dependent amino acid substitution tables to predict the change in the protein stability between wildtype and mutant protein.

For comparison with the sequence-based tools in **Unit 2**, the training provided here uses the same example protein (provided by Dr Andrew Martin at UCL): 

_You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia_
***
>### Get started
>First - Click on the **How to use SDM** link below to see a brief overview of what the tool presents.
>
>Now try running this example: Click on the **link to the SDM tool** to open SDM in a new tab/window
>
>Click **'Predict'** at the top of the page
>
> - Under **'Single Mutation'** and **'Provide a 4-letter PDB code'**, enter the PDB code for the sequence (**1QKI**)
>
> - Under **'Mutation'**, enter **'S106C'**
> - Under **'Mutation chain'**, enter **'A'**
>
>Click **'Run SDM'**
>
>Now you can try using SDM with your own protein ID

# data[].attributes.workflow-content.nodes[].data.html-description
SDM is a computational method to predict the change in protein stability upon the introduction of a single mutation. It is a knowledge-based approach that uses conformationally constrained environment-dependent amino acid substitution tables to predict the change in the protein stability between wildtype and mutant protein.

For comparison with the sequence-based tools in Unit 2, the training provided here uses the same example protein (provided by Dr Andrew Martin at UCL):

You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia

Get started

First - Click on the How to use SDM link below to see a brief overview of what the tool presents.

Now try running this example: Click on the link to the SDM tool to open SDM in a new tab/window

Click 'Predict' at the top of the page

Under 'Single Mutation' and 'Provide a 4-letter PDB code', enter the PDB code for the sequence (1QKI)

Under 'Mutation', enter 'S106C'

Under 'Mutation chain', enter 'A'

Click 'Run SDM'

Now you can try using SDM with your own protein ID

# data[].attributes.workflow-content.nodes[].data.name
A selection of more modern prediction tools

# data[].attributes.workflow-content.nodes[].data.description
In the previous topic box you were introduces to **SIFT**, an old but common tool for the prediction of mutation effect. Here you will be introduced to three more modern tools: **FATHMM**, **PolyPhen2** and **SusPect**.

They are similar to SIFT in the way that you use them, and so for comparison the training provided here uses the same example protein you used with SIFT: 

_You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia_

***
>### Get started
>
>First, click on the link to the **Tutorial: 3 mutation predictors** to work through the short tutorial. All of the instructions are provided for you in the tutorial pages.
>
>When you have done this, you can use the **link to XXX** below to try each tool with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.html-description
In the previous topic box you were introduces to SIFT, an old but common tool for the prediction of mutation effect. Here you will be introduced to three more modern tools: FATHMM, PolyPhen2 and SusPect.

They are similar to SIFT in the way that you use them, and so for comparison the training provided here uses the same example protein you used with SIFT:

You will use the G6PD protein (UniProtKB accession: P11413; name: G6PD_HUMAN, RefSeq: NP_001035810.1), and analyze the mutation S106->C. This is a known pathogenic mutation causing favism and classified by the WHO as a Class I mutation: Severe deficiency (<10% activity) with chronic (nonspherocytic) hemolytic anemia

Get started

First, click on the link to the Tutorial: 3 mutation predictors to work through the short tutorial. All of the instructions are provided for you in the tutorial pages.

When you have done this, you can use the link to XXX below to try each tool with your protein sequence.

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
If you have reached Unit 5 of this training workflow, then you should have some information about the positions and impact of mutations on structure. The tools in **this Unit** can be used to visualise that information on the 3D structure or sequence of your protein.

This Unit will introduce you to **LiteMol** and **EzMol** as 3D molecular viewers so that you can visualise and explore your structures further - and **JalView** which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.html-description
If you have reached Unit 5 of this training workflow, then you should have some information about the positions and impact of mutations on structure. The tools in this Unit can be used to visualise that information on the 3D structure or sequence of your protein.

This Unit will introduce you to LiteMol and EzMol as 3D molecular viewers so that you can visualise and explore your structures further - and JalView which can be used to visualise a lot of different sequence-based (and some structure) elements. Alternative software solutions will also be mentioned in the Background box.

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.name
Visualising your structures in 3D with LiteMol & EzMol

# data[].attributes.workflow-content.nodes[].data.description
Now you have a structure you will want to be able to view and explore it in 3D. **LiteMol** is a web tool for visualization of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey **Additional Reading** box below the topic section.

**However**, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the **Additional Reading** box below.

**Alternatively**, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive **EzMol** tool linked below.
***
>### Get started
>
>If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short **Basic use of standalone LiteMol** video - and using the link to the **Standalone LiteMol** tool to try it for yourself.
>
>If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the **tutorials labelled from the PDBe** below.

# data[].attributes.workflow-content.nodes[].data.html-description
Now you have a structure you will want to be able to view and explore it in 3D. LiteMol is a web tool for visualization of molecular structures, particularly macromolecules. It is designed to be user-friendly yet flexible, interactive, and very fast even for extremely large protein structures. LiteMol can be run directly from your web browser.

LiteMol is the embedded viewer at the PDBe - you can visualise structures you find at the PDBe directly in LiteMol. The LiteMol viewer is also available to run as a standalone web tool from its webpage - which you can use to load pdb files of your own (for example the results of a structure prediction)

The tutorials and tool links below should enable you to use LiteMol for either of these options. If you want to explore alternative visualisation software, take a look at the grey Additional Reading box below the topic section.

However, you may find that LiteMol is not feature-rich enough for you, and therefore you might want to explore the more complex desktop viewers and modelling suites linked in the Additional Reading box below.

Alternatively, you may consider that LiteMol is more than you need - perhaps if you just wish to highlight some interesting residues in your structure and generate an image for example. In that case you should take a look at the very intuitive EzMol tool linked below.

Get started

If you have produced a structure file and saved it to your computer from the modelling tools, you can visualise that structure in LiteMol by first viewing the short Basic use of standalone LiteMol video - and using the link to the Standalone LiteMol tool to try it for yourself.

If you are working with a structure that you found at the PDBe, then you can either download that structure and use the standalone viewer - or you can explore the structure in the embedded LiteMol viewer at the PDBe website. For this use, please take a look at the tutorials labelled from the PDBe below.

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: Basic use of standalone LiteMol

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Video Tutorial: How to use LiteMol at the PDBe

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Introducing LiteMol (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing extra annotation (PDBe video)

# data[].attributes.workflow-content.nodes[].data.associated-resources[].title
Optional: Viewing sequence and structure interactively (PDBe video)

# data[].attributes.workflow-content.nodes[].data.name
Visualising sequence-based features with JalView

# data[].attributes.workflow-content.nodes[].data.description
**Jalview** is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

**JalView** is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.
***
>### Get started
>
>**JalView** has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the **Getting started with JalView** videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.
>
>After watching these videos you can try **JalView** for yourself by clicking the link below to the **JalView Launcher**
>
>If you want to explore the more advanced features of the tool, there is also a link to the **JalView youtube channel** which contains a comprehensive set of video tutorials.

# data[].attributes.workflow-content.nodes[].data.html-description
Jalview is a free, open source program developed for the interactive editing, analysis and visualisation of multiple sequence alignments. It can also work with sequence annotation, secondary structure information, phylogenetic trees and 3D molecular structures. It might be of interest to you if you had to predict your protein structure or you have only managed to make predictions of secondary structure elements.

JalView is an application you can launch from the web or install locally. It is designed for the kind of in-depth sequence analysis necessary when exploring new protein or RNA sequence families to understand how their sequence relates to structure and function.

It can also access sequence, alignment, 3D structure and annotation databases, and web services for multiple alignment, protein disorder and secondary structure prediction and functional analysis - complimenting the tools that have been introduced in this workflow.

Get started

JalView has a lot of features, but you may not need all of them to review your protein information. The best place to start for a basic introduction to the tool and how to launch it on your Desktop is the Getting started with JalView videos below - this link has 5 short videos totalling 20mins, which will introduce you to using JalView.

After watching these videos you can try JalView for yourself by clicking the link below to the JalView Launcher

If you want to explore the more advanced features of the tool, there is also a link to the JalView youtube channel which contains a comprehensive set of video tutorials.

# data[].attributes.workflow-content.nodes[].data.description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. *Similarly, most are commercial ($$) software, and some only free for limited or academic use.*

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.html-description
There are a number of molecular structure viewers available, the majority of which require local installation to your computer. Some are cross platform and are available on Mac, Linux and Windows - but others will be OS-specific. Similarly, most are commercial ($$) software, and some only free for limited or academic use.

Unfortunately it is beyond the scope of this training workflow to cover all of these details for the different viewers listed below - and you should ensure that you consider installation platform and/or commercial use/cost implications should you chose to explore any of these further.

# data[].attributes.workflow-content.nodes[].data.description
Please begin this stage by clicking on the **Unit Introduction** box

# data[].attributes.workflow-content.nodes[].data.html-description
Please begin this stage by clicking on the Unit Introduction box

# data[].attributes.workflow-content.nodes[].data.description
The tools (and the training to go with them) in this workflow require either a 3D structure to be available for your protein, or simply the protein sequence or a UniProt identifier.

* The Tools in **Unit 2** and **Unit 4** can be used with only **sequence** or **UniProt ID**-level information.

* The Tools in **Unit 3** require you to have **structural data** available for your protein.

The Task boxes in this Unit help you to obtain the sequence and structure data for your protein that will enable you to make use of the other Units in this workflow.

# data[].attributes.workflow-content.nodes[].data.html-description
The tools (and the training to go with them) in this workflow require either a 3D structure to be available for your protein, or simply the protein sequence or a UniProt identifier.

The Tools in Unit 2 and Unit 4 can be used with only sequence or UniProt ID-level information.

The Tools in Unit 3 require you to have structural data available for your protein.

The Task boxes in this Unit help you to obtain the sequence and structure data for your protein that will enable you to make use of the other Units in this workflow.

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
This Unit introduces some resources that try to address specific effects of mutation, as examples of use-cases. Both can be used to explore from the start-point of disease (Titin-related in the first case, and Cancer in the second)

You will be introduced to **TITINdb** - not a predictor of effect, but a searchable database of effects of mutation in the sequence of the Titin protein and how this relates to disease. 

You will then be introduced to **PinSNPs**, a predictor of the effect that mutation in one protein may have on a wider interaction network of proteins associated with it.
***
>### Get started
>
>To use the protein search of **PinSNPs** requires the submission of a **Uniprot ID** - so you must have this available. If you have not got this information for your protein please re-visit **Unit 1** for guidance 
>
>Other browsing or searching options for either resource can be carried out by selecting disease options or particular SNP variants to explore.

# data[].attributes.workflow-content.nodes[].data.html-description
This Unit introduces some resources that try to address specific effects of mutation, as examples of use-cases. Both can be used to explore from the start-point of disease (Titin-related in the first case, and Cancer in the second)

You will be introduced to TITINdb - not a predictor of effect, but a searchable database of effects of mutation in the sequence of the Titin protein and how this relates to disease.

You will then be introduced to PinSNPs, a predictor of the effect that mutation in one protein may have on a wider interaction network of proteins associated with it.

Get started

To use the protein search of PinSNPs requires the submission of a Uniprot ID - so you must have this available. If you have not got this information for your protein please re-visit Unit 1 for guidance

Other browsing or searching options for either resource can be carried out by selecting disease options or particular SNP variants to explore.

# data[].attributes.workflow-content.nodes[].data.description
Please click on the individual **topic** boxes

# data[].attributes.workflow-content.nodes[].data.html-description
Please click on the individual topic boxes

# data[].attributes.workflow-content.nodes[].data.description
**TITINdb** is a web application which integrates titin structure, sequence, isoform, variant and disease information.

Titin is the largest protein in the body, each titin molecule spanning half a muscle sarcomere, with the longest inferred complete (IC) isoform consisting of 35991 amino acids. Recently titin has emerged as an important disease gene, with variants associated with myopathies such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) and hereditary myopathy with early respiratory failure (HMERF).

This application allows disease associated nsSNVs from the literature and population nsSNVs from the 1000 genomes project and gnomAD database to be visualised on structure. Additionally the impact these nsSNVs has been predicted computationally using both structure and sequence based methods.
***
>### Get started
>
>You should review the **video tutorials** linked below first.
>
>You can then visit the **Link to TITINdb** to explore the disease or SNP-associated data available. 
>
>Further information on how the tool works and a glossary of terms (particularly disease acronyms) is also provided below for reference. 

# data[].attributes.workflow-content.nodes[].data.html-description
TITINdb is a web application which integrates titin structure, sequence, isoform, variant and disease information.

Titin is the largest protein in the body, each titin molecule spanning half a muscle sarcomere, with the longest inferred complete (IC) isoform consisting of 35991 amino acids. Recently titin has emerged as an important disease gene, with variants associated with myopathies such as hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM) and hereditary myopathy with early respiratory failure (HMERF).

This application allows disease associated nsSNVs from the literature and population nsSNVs from the 1000 genomes project and gnomAD database to be visualised on structure. Additionally the impact these nsSNVs has been predicted computationally using both structure and sequence based methods.

Get started

You should review the video tutorials linked below first.

You can then visit the Link to TITINdb to explore the disease or SNP-associated data available.

Further information on how the tool works and a glossary of terms (particularly disease acronyms) is also provided below for reference.

# data[].attributes.workflow-content.nodes[].data.description
**PinSnps** is a tool for interactive analysis of large-scale Protein-Protein Interaction Networks, visualisation of associated 3D domain structures and mapping of SNPs, Functional Sites and Post-Translational Modifications.

Searching for a protein will provide you with information about variation mapped to structure where available, but also how the protein is related to others in protein-protein interaction network data. 

If information is available on structure, mutation effects at the interface between interactin protein pairs can be explored. (This has been calculated using **PopsComp** - which is a tool included in the second workflow of this series ****Structural Bioinformatics 2: Exploring protein domains and functional sites**)
***
>### Get started
>
>You should review the **Help on searching PinSNPs** material linked below first.
>
>You can then visit the **Link to PinSNPs** to explore the using your protein of interest (you will need a **Uniprot ID**) - or the available SNP or Cancer-related data. 

# data[].attributes.workflow-content.nodes[].data.html-description
PinSnps is a tool for interactive analysis of large-scale Protein-Protein Interaction Networks, visualisation of associated 3D domain structures and mapping of SNPs, Functional Sites and Post-Translational Modifications.

Searching for a protein will provide you with information about variation mapped to structure where available, but also how the protein is related to others in protein-protein interaction network data.

If information is available on structure, mutation effects at the interface between interactin protein pairs can be explored. (This has been calculated using PopsComp - which is a tool included in the second workflow of this series **Structural Bioinformatics 2: Exploring protein domains and functional sites)

Get started

You should review the Help on searching PinSNPs material linked below first.

You can then visit the Link to PinSNPs to explore the using your protein of interest (you will need a Uniprot ID) - or the available SNP or Cancer-related data.

# meta.available-facets.target-audience[].value
Anyone aiming at employing protein structure modeling in their projects or integrating protein structure modeling in their analysis pipelines

# meta.available-facets.target-audience[].value
Anyone new to the field of systems biology

# meta.available-facets.licence[].value
Creative Commons Attribution 4.0 International

# meta.available-facets.authors[].value
Kornobis, Cabellos, Aguilar, Frias-Lopez, Rozas, Marco & Zardoya