  Deep learning has contributed to major advances in the prediction of protein
structure from sequence, a fundamental problem in structural bioinformatics.
With predictions now approaching the accuracy of crystallographic resolution in
some cases, and with accelerators like GPUs and TPUs making inference using
large models rapid, fast genome-level structure prediction becomes an obvious
aim. Leadership-class computing resources can be used to perform genome-scale
protein structure prediction using state-of-the-art deep learning models,
providing a wealth of new data for systems biology applications. Here we
describe our efforts to efficiently deploy the AlphaFold2 program, for
full-proteome structure prediction, at scale on the Oak Ridge Leadership
Computing Facility's resources, including the Summit supercomputer. We
performed inference to produce the predicted structures for 35,634 protein
sequences, corresponding to three prokaryotic proteomes and one plant proteome,
using under 4,000 total Summit node hours, equivalent to using the majority of
the supercomputer for one hour. We also designed an optimized structure
refinement that reduced the time for the relaxation stage of the AlphaFold
pipeline by over 10X for longer sequences. We demonstrate the types of analyses
that can be performed on proteome-scale collections of sequences, including a
search for novel quaternary structures and implications for functional
annotation.
