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DTSTAMP:20260705T150636Z
UID:dcd776cb-6194-4d0a-88e3-2662cbd52e98
DTSTART:20220614T090000Z
DTEND:20220614T170000Z
DESCRIPTION:AlphaFold is an AI system developed by DeepMind that predicts p
 rotein 3D structure from its amino-acid sequence. It’s been a year since
  the AlphaFold software and ‘AlphaFold Protein Structure Database’ wer
 e made publicly available for users to explore and investigate their prote
 in of interest.\n\nWe are running a webinar series to mark this occasion b
 y highlighting [the impact of AlphaFold on training and research in life s
 ciences](https://www.ebi.ac.uk/training/events/impact-of-alphafold-on-trai
 ning-and-research-in-life-sciences/). \n\nThis is the first webinar of th
 e series where the speaker will describe and discuss the **scope and visio
 n of AlphaFold. **\n\n**Speakers:**\n\n**Kathryn Tunyasuvunakool** - Mach
 ine learning models have the potential to become core tools in biology\, a
 s recent progress in protein structure prediction illustrates. In this web
 inar I gave an overview of AlphaFold: how the system works\, how to obtain
  protein structure predictions\, and how to analyse them. I then reviewed 
 some ways in which the system has been built upon\, and discussed how to e
 valuate AlphaFold for a new application.\n\n**Randy Read** - ​​Few are
 as of structural biology have been untouched by the recent dramatic increa
 ses in the power and accuracy of computational modelling of protein struct
 ure. These changes have been wrought by the current version of AlphaFold\,
  with RoseTTAFold not far behind. Experimental structural biology is still
  needed to resolve ambiguities in the predicted structures and to verify t
 he details\, but the availability of high-quality models is removing many 
 of the bottlenecks in the experiments. Even without an experimental struct
 ure\, the new models are sufficient to generate interesting hypotheses tha
 t can be tested experimentally\, such as assessing how variants associated
  with genetic disease actually cause disease. Limitations in the models co
 uld potentially be addressed by adding explicit physics and chemistry to t
 he pattern recognition used in the current algorithms\, and by actively ex
 ploiting even limited experimental observations.\n\n**Sergey Ovchinnikov**
  - I discussed the impact of AlphaFold on structural bioinformatics by hi
 ghlighting a few large scale efforts and structure-search tools developed 
 to characterise the AlphaFold models.
LOCATION:\, 
SUMMARY:Scope and vision of AlphaFold
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/scope-and-vision-alphaf
 old
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