AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.
AI
COVID19
GPU
HPC
SARS-CoV-2
computational virology
deep learning
molecular dynamics
multiscale simulation
weighted ensemble
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
20 Nov 2020
20 Nov 2020
Historique:
pubmed:
26
11
2020
medline:
26
11
2020
entrez:
25
11
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 33236007
doi: 10.1101/2020.11.19.390187
pmc: PMC7685317
pii:
doi:
Types de publication
Preprint
Langues
eng
Commentaires et corrections
Type : UpdateIn
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