Computational structural biology in the era of deep learning.

Abstract

By the time of the BPS conference, it will be almost three years since CASP14 results showed the AlphaFold2 method from DeepMind is able to model protein structures with an accuracy that in many cases is competitive with the best experiments. Since then, there has been an explosion of work applying deep learning and related methods to other problems in computational structural biology as well as further development of single protein approaches. In this talk I'll examine where the field has now advanced to, drawing primarily on the results of the most recent CASP (CASP15, results of which will be public at the end of 2022). In particular, what, if any, are the remaining limitations for high accuracy modeling of single protein structures? How close are protein complexes models to the accuracy achieved for single proteins? Have the methods been successively extended to modeling RNA structures? Are protein-organic ligand complexes treatable? How far have we progressed towards generating ensembles of conformations, not just single structures? How seriously should we take the estimated accuracy of models? I’ll also attempt to relate progress to the astonishing diversification of deep learning methods that has occurred and what the results tell us about how the methods work: is it all just dumb pattern recognition, or are principles of protein structure learned and applied?