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Published October 18, 2021 | Submitted
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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

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.

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.

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Created:
August 20, 2023
Modified:
October 23, 2023