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

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics
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.
AI, COVID19, GPU, HPC, SARS-CoV-2, computational virology, deep learning, molecular dynamics, multiscale simultion, weighted ensemble
Casalino, Lorenzo
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Dommer, Abigail C.
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Gaieb, Zied
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Barros, Emilia P.
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Sztain, Terra
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Ahn, Surl-Hee
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Trifan, Anda
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Brace, Alexander
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Bogetti, Anthony T.
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Clyde, Austin
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Ma, Heng
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Lee, Hyungro
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Turilli, Matteo
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Khalid, Syma
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Chong, Lillian T.
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Simmerling, Carlos
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Hardy, David J
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Maia, Julio D C
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Phillips, James C
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Kuth, Thorsten
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Stern, Abraham C
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Huang, Lei
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McCalpin, John D
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Tatineni, Mahidhar
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Gibbs, Tom
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Stone, John E
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Jha, Shantenu
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Ramanathan, Arvind
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Amaro, Rommie E.
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Casalino, Lorenzo
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Dommer, Abigail C.
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Gaieb, Zied
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Barros, Emilia P.
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Sztain, Terra
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Ahn, Surl-Hee
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Trifan, Anda
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Brace, Alexander
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Bogetti, Anthony T.
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Clyde, Austin
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Ma, Heng
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Lee, Hyungro
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Turilli, Matteo
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Khalid, Syma
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Chong, Lillian T.
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Simmerling, Carlos
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Hardy, David J
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Maia, Julio D C
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Phillips, James C
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Kuth, Thorsten
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Stern, Abraham C
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Huang, Lei
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McCalpin, John D
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Tatineni, Mahidhar
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Gibbs, Tom
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Stone, John E
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Jha, Shantenu
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Ramanathan, Arvind
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Amaro, Rommie E.
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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 D C, Phillips, James C, Kuth, Thorsten, Stern, Abraham C, Huang, Lei, McCalpin, John D, Tatineni, Mahidhar, Gibbs, Tom, Stone, John E, Jha, Shantenu, Ramanathan, Arvind and Amaro, Rommie E. (2021) AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. The International Journal of High Performance Computing Applications. (doi:10.1177/10943420211006452).

Record type: Article

Abstract

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.

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More information

e-pub ahead of print date: 20 April 2021
Published date: 20 April 2021
Additional Information: Funding Information: The authors thank D. Maxwell, B. Messer, J. Vermaas, and the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725. We also thank the Texas Advanced Computing Center Frontera team, especially D. Stanzione and T. Cockerill, and for compute time made available through a Director’s Discretionary Allocation (NSF OAC-1818253). We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357. NAMD and VMD are funded by NIH P41-GM104601. The NAMD team thanks Intel and M. Brown for contributing the AVX-512 tile list kernels. Anda Trifan acknowledges support from a DOE CSGF (DE-SC0019323). Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by NIH GM132826, NSF RAPID MCB-2032054, an award from the RCSA Research Corp., a UC San Diego Moore’s Cancer Center 2020 SARS-COV-2 seed grant, to R.E.A. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US DOE Office of Science and the National Nuclear Security Administration. Research was supported by the DOE through the National Virtual Biotechnology Laboratory, a consortium of DOE national laboratories focused on response to COVID-19, with funding from the Coronavirus CARES Act. This work was supported by the NIH (1R01GM115805-01) to L.T.C. Publisher Copyright: © The Author(s) 2021. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: AI, COVID19, GPU, HPC, SARS-CoV-2, computational virology, deep learning, molecular dynamics, multiscale simultion, weighted ensemble

Identifiers

Local EPrints ID: 450827
URI: http://eprints.soton.ac.uk/id/eprint/450827
PURE UUID: ec3440ad-3fd9-45ca-a5de-a8a5a31d2833
ORCID for Syma Khalid: ORCID iD orcid.org/0000-0002-3694-5044

Catalogue record

Date deposited: 13 Aug 2021 16:30
Last modified: 14 Aug 2021 01:42

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Contributors

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

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