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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
Anda Trifan; Defne Gorgun; Zongyi Li; Alexander Brace; Maxim Zvyagin; Heng Ma; Austin R Clyde; David A Clark; Michael Salim; David Hardy; Tom Burnley; Lei Huang; John McCalpin; Murali Emani; Hyunseung Yoo; Junqi Yin; Aristeidis Tsaris; Vishal Subbiah; Jessica Liu; Noah Trebesch; Geoffrey Wells; Venkatesh Mysore; Tom Gibbs; James Phillips; S. Chakra Chennubhotla; Ian Foster; Rick Stevens; Anima Anandkumar; Venkatram Vishwanath; John E. Stone; Emad Tajkhorshid; Sarah A. Harris; Arvind Ramanathan.
  • Anda Trifan; University of Illinois Urbana-Champaign
  • Defne Gorgun; University of Illinois Urbana-Champaign
  • Zongyi Li; California Institute of Technology
  • Alexander Brace; University of Chicago
  • Maxim Zvyagin; Argonne National Laboratory
  • Heng Ma; Argonne National Laboratory
  • Austin R Clyde; Argonne National Laboratory
  • David A Clark; NVIDIA Inc.
  • Michael Salim; Argonne National Laboratory
  • David Hardy; University of Illinois Urbana-Champaign
  • Tom Burnley; Science and Technology Facilities Council
  • Lei Huang; Texas Advanced Computing Center, University of Texas
  • John McCalpin; Texas Advanced Computing Center, University of Texas
  • Murali Emani; Argonne National Laboratory
  • Hyunseung Yoo; Argonne National Laboratory
  • Junqi Yin; Oak Ridge National Laboratory
  • Aristeidis Tsaris; Oak Ridge National Laboratory
  • Vishal Subbiah; Cerebras Inc.
  • Jessica Liu; Cerebras Inc.
  • Noah Trebesch; University of Illinois Urbana-Champaign
  • Geoffrey Wells; University College of London
  • Venkatesh Mysore; NVIDIA Inc.
  • Tom Gibbs; NVIDIA Inc.
  • James Phillips; University of Illinois Urbana-Champaign
  • S. Chakra Chennubhotla; University of Pittsburgh
  • Ian Foster; Argonne National Laboratory
  • Rick Stevens; Argonne National Laboratory
  • Anima Anandkumar; California Institute of Technology
  • Venkatram Vishwanath; Argonne National Laboratory
  • John E. Stone; University of Illinois Urbana-Champaign
  • Emad Tajkhorshid; University of Illinois at Urbana-Champaign
  • Sarah A. Harris; University of Leeds
  • Arvind Ramanathan; Argonne National Laboratory
Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-463779
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.
Texte intégral: Disponible Collection: Preprints Base de données: bioRxiv langue: Anglais Année: 2021 Type de document: Preprint

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Texte intégral: Disponible Collection: Preprints Base de données: bioRxiv langue: Anglais Année: 2021 Type de document: Preprint