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Title: AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics
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.  more » « less
Award ID(s):
2032054
PAR ID:
10486490
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; ; « less
Publisher / Repository:
The International Journal of High Performance Computing Applications
Date Published:
Journal Name:
The International Journal of High Performance Computing Applications
Volume:
35
Issue:
5
ISSN:
1094-3420
Page Range / eLocation ID:
432 to 451
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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