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Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

Trifan, Anda and Gorgun, Defne and Li, Zongyi and Brace, Alexander and Zvyagin, Maxim and Ma, Heng and Clyde, Austin and Clark, David and Salim, Michael and Hardy, David J. and Burnley, Tom and Huang, Lei and McCalpin, John and Emani, Murali and Yoo, Hyenseung and Yin, Junqi and Tsaris, Aristeidis and Subbiah, Vishal and Raza, Tanveer and Liu, Jessica and Trebesch, Noah and Wells, Geoffrey and Mysore, Venkatesh and Gibbs, Thomas and Phillips, James and Chennubhotla, S. Chakra and Foster, Ian and Stevens, Rick and Anandkumar, Anima and Vishwanath, Venkatram and Stone, John E. and Tajkhorshid, Emad and Harris, Sarah A. and Ramanathan, Arvind (2021) Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20211015-222208091

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.10.09.463779DOIDiscussion Paper
ORCID:
AuthorORCID
Li, Zongyi0000-0003-2081-9665
Huang, Lei0000-0002-1923-227X
Tajkhorshid, Emad0000-0001-8434-1010
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. We thank the Argonne Leadership Computing Facility supported by the DOE under DE-AC02-06CH11357, the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory supported by the DOE under Contract DE-AC05-00OR22725, and the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory supported by the DOE under Contract No. DE-AC02-05CH11231. 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 MCB-20024). 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). 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 used resources, services, and support from the COVID-19 HPC Consortium (https://covid19-hpc-consortium.org/), a private-public effort uniting government, industry, and academic leaders who are volunteering free compute time and resources in support of COVID-19 research. The authors have declared no competing interest.
Group:COVID-19
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-AC02-06CH11357
Department of Energy (DOE)DE-AC05-00OR22725
Department of Energy (DOE)DE-AC02-05CH11231
NSFMCB-20024
NIHP41-GM104601
Department of Energy (DOE)DE-SC0019323
Department of Energy (DOE)17-SC-20-SC
Coronavirus CARES ActUNSPECIFIED
Subject Keywords:multi-resolution simulations, SARS-CoV-2, COVID19, HPC, AI
DOI:10.1101/2021.10.09.463779
Record Number:CaltechAUTHORS:20211015-222208091
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20211015-222208091
Official Citation:Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action Anda Trifan, Defne Gorgun, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, Michael Salim, David J. Hardy, Tom Burnley, Lei Huang, John McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Thomas Gibbs, James Phillips, S. Chakra Chennubhotla, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, Arvind Ramanathan bioRxiv 2021.10.09.463779; doi: https://doi.org/10.1101/2021.10.09.463779
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111485
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:18 Oct 2021 17:17
Last Modified:18 Oct 2021 17:18

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