Application of Deep-learning Architectures for Accurate Protein Structure Prediction
From Isabelle Hanlon
Abstract:Since the advent of structural biology, the atomic coordinates of only a fraction of the billions of known protein sequences have been solved experimentally. This fact was recently highlighted during 2021 with the celebration of the 50 years of the Protein Databank (PDB). Advances in bioinformatics and molecular modelling have supplemented the dearth of experimentally-determined structures, offering reasonable predictions in cases where close structural homologues are already known. The application of deep-learning algorithms in recent years has greatly improved the predictive power of de novo computational methods, with contenders such as AlphaFold and trRosetta nearing experimental atomic accuracy. This seminar provides a concise introduction to the recently released AlphaFold 2 algorithm for highly accurate protein prediction, opening an exciting new chapter in structural biology and rational/semi-rational protein engineering. Following its open-source release, features of both AlphaFold 2 and RoseTTAFold have been assimilated into a freely-accessible online notebook (ColabFold) capable of multimeric prediction, including both homo- and hetero- assemblies. The seminar will cover the application of such neural network algorithms to study several protein targets including: an elusive mycobacterial enzyme; an unusual transaminase-reductase fusion involved in natural product biosynthesis; and a selection of machine-generated sequence homologues of medically relevant human enzymes.