This webinar is by the ELIXIR 3D-BioInfo, chaired by Dr. Anastassia Andreevna Vorobieva with two presentations:

                Dr. Charlotte Miton

                Michael Smith Laboratories,  
                    University of British Columbia,
                    Canada

            Title: Causes and Consequences of Epistasis in Protein Evolution and Design


                Dr. Possu Huang

                Stanford University

                 

            Title: Protein Design for Epitope-specific Molecular Recognition

Abstract: Causes and Consequences of Epistasis in Protein Evolution and Design

Proteins are complex molecular machines, composed of highly interconnected amino acid networks. Protein evolution may require the alteration of these intramolecular networks, to foster novel functions. While there have been considerable advances, our ability to optimize protein functions in the laboratory remains limited. One reason for these shortcomings may be the cryptic role played by epistasis, i.e., the dependency of mutational effects on the genetic context, during the rewiring of intramolecular networks. By permitting or restricting access to certain mutations over the course of evolution, epistasis shapes evolutionary pathways, influencing outcomes. Deciphering its biophysical basis is essential if we wish to understand, predict and design proteins.
In this talk, I will discuss various molecular and biophysical origins of mutational epistasis, and illustrate how this phenomenon can lead to the bifurcation of evolutionary pathways, in which two trajectories segregate and become incompatible over time. Our results illuminate how rapidly, but gradually, distinct molecular outcomes can arise in nature and the laboratory.

Abstract: Protein Design for Epitope-specific Molecular Recognition

The growing need for antibodies with customized specificity provides a rich environment for engineering efforts. In recent years, despite having streamlined experimental pipelines, the fundamental math requiring extensive libraries and screen campaigns to get an initial binding signal remains unchanged. A major advancement would be to directly design in silico an epitope-specific binder from scratch, providing a signal for potential optimization by artificial evolution. We have observed several key advantages in neural network approaches over existing methods. By leveraging the unique properties of generative neural networks, we developed a model for immunoglobulin 3D structures, with which diverse structures can be modeled with unprecedented speed. We extended it to a purely deep learning-based protein-protein interface design pipeline that optimize not only spatial orientations but fully-flexible protein structures on the fly to desired epitopes. This novel strategy explores neural network’s capabilities in modeling dynamic structures, and preliminary experimental results on multiple targets support the plausibility of in silico design of epitope-specific antibodies.

Previous webinars from the ELIXIR 3D-BioInfo Community:

Computer Aided Drug Design (CADD) 
Protein Engineering 
Nucleic Acid Tools

For more information on the ELIXIR 3D-BioInfo Community: link