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Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer...

Publication Type
Conference Paper
Book Title
Proceedings of 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Publication Date
Page Numbers
206 to 215
Publisher Location
New Jersey, United States of America
Conference Name
21st IEEE International Workshop on High Performance Computational Biology
Conference Location
Lyons, France and Virtual, France
Conference Sponsor
Conference Date

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 experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, 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 AlphaFold v.2 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 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over 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.