AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics
| Autor: | Shantenu Jha, Julio D.C. Maia, Emilia P. Barros, Matteo Turilli, Abraham C. Stern, John E. Stone, Syma Khalid, Abigail C. Dommer, Surl-Hee Ahn, Lillian T. Chong, Austin Clyde, Zied Gaieb, Heng Ma, Thorsten Kurth, Lorenzo Casalino, John McCalpin, David J. Hardy, Tom Gibbs, Rommie E. Amaro, Anthony T. Bogetti, Arvind Ramanathan, Carlos Simmerling, Anda Trifan, Hyungro Lee, James C. Phillips, Mahidhar Tatineni, Alexander Brace, Lei Huang, Terra Sztain |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Theoretical computer science
Computer science COVID19 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) GPU Future application Computational biology Molecular systems 01 natural sciences Viral infection Article Theoretical Computer Science 03 medical and health sciences TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY 0103 physical sciences Spike (database) computational virology Conformational sampling 030304 developmental biology 0303 health sciences 010304 chemical physics SARS-CoV-2 Spike Protein deep learning weighted ensemble molecular dynamics multiscale simulation Workflow Hardware and Architecture AI HPC Spike (software development) Software |
| Zdroj: | bioRxiv article-version (status) pre article-version (number) 1 The International Journal of High Performance Computing Applications |
| Popis: | We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.ACM Reference FormatLorenzo Casalino1†, Abigail Dommer1†, Zied Gaieb1†, Emilia P. Barros1, Terra Sztain1, Surl-Hee Ahn1, Anda Trifan2,3, Alexander Brace2, Anthony Bogetti4, Heng Ma2, Hyungro Lee5, Matteo Turilli5, Syma Khalid6, Lillian Chong4, Carlos Simmerling7, David J. Hardy3, Julio D. C. Maia3, James C. Phillips3, Thorsten Kurth8, Abraham Stern8, Lei Huang9, John McCalpin9, Mahidhar Tatineni10, Tom Gibbs8, John E. Stone3, Shantenu Jha5, Arvind Ramanathan2∗, Rommie E. Amaro1∗. 2020. AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics. In Supercomputing ’20: International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, New York, NY, USA, 14 pages. https://doi.org/finalDOI |
| Databáze: | OpenAIRE |
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