AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

Casalino, Lorenzo; Dommer, Abigail C.; Gaieb, Zied; Barros, Emilia P.; Sztain, Terra; Ahn, Surl-Hee; Trifan, Anda; Brace, Alexander; Bogetti, Anthony T.; Clyde, Austin; Ma, Heng; Lee, Hyungro; Turilli, Matteo; Khalid, Syma; Chong, Lillian T.; Simmerling, Carlos; Hardy, David J.; Maia, Julio DC; Phillips, James C.; Kurth, Thorsten; Stern, Abraham C.; Huang, Lei; McCalpin, John D.; Tatineni, Mahidhar; Gibbs, Tom; Stone, John E.; Jha, Shantenu; Ramanathan, Arvind; Amaro, Rommie E.

doi:10.1177/10943420211006452

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

Journal Article · Tue Apr 20 00:00:00 EDT 2021 · International Journal of High Performance Computing Applications

DOI:https://doi.org/10.1177/10943420211006452· OSTI ID:1788054

Casalino, Lorenzo ^[1]; Dommer, Abigail C. ^[1]; Gaieb, Zied ^[1]; ^[1]; Sztain, Terra ^[1]; ^[1]; Trifan, Anda ^[2]; ^[3]; Bogetti, Anthony T. ^[4]; Clyde, Austin ^[5]; Ma, Heng ^[3]; Lee, Hyungro ^[6]; Turilli, Matteo ^[6]; Khalid, Syma ^[7]; Chong, Lillian T. ^[4]; Simmerling, Carlos ^[8]; Hardy, David J. ^[9]; Maia, Julio DC ^[9]; Phillips, James C. ^[9]; Kurth, Thorsten ^[10] more »

Univ. of California, San Diego, CA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Pittsburgh, PA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
Rutgers Univ., Piscataway, NJ (United States)
Univ. of Southampton (United Kingdom)
Stony Brook Univ., NY (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States)
NVIDIA Corporation, Santa Clara, CA (United States)
Texas Advanced Computing Center, Austin, TX (United States)
San Diego Supercomputing Center, La Jolla, CA (United States)
Rutgers Univ., Piscataway, NJ (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: Coronavirus CARES Act; National Institutes of Health (NIH); National Science Foundation; USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)

Grant/Contract Number:: SC0012704

OSTI ID:: 1788054

Report Number(s):: BNL--221640-2021-JAAM

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Journal Issue: 5 Vol. 35; ISSN 1094-3420

Publisher:: SAGECopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
AI
COVID19
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multiscale simulation
weighted ensemble

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

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