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.