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Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.

Authors :
Trifan, Anda
Gorgun, Defne
Salim, Michael
Li, Zongyi
Brace, Alexander
Zvyagin, Maxim
Ma, Heng
Clyde, Austin
Clark, David
Hardy, David J
Burnley, Tom
Huang, Lei
McCalpin, John
Emani, Murali
Yoo, Hyenseung
Yin, Junqi
Tsaris, Aristeidis
Subbiah, Vishal
Raza, Tanveer
Liu, Jessica
Source :
International Journal of High Performance Computing Applications. Nov2022, Vol. 36 Issue 5/6, p603-623. 21p.
Publication Year :
2022

Abstract

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g. cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10943420
Volume :
36
Issue :
5/6
Database :
Academic Search Index
Journal :
International Journal of High Performance Computing Applications
Publication Type :
Academic Journal
Accession number :
160183735
Full Text :
https://doi.org/10.1177/10943420221113513