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

  1. Anda Trifan
  2. Defne Gorgun
  3. Zongyi Li
  4. Alexander Brace
  5. Maxim Zvyagin
  6. Heng Ma
  7. Austin Clyde
  8. David Clark
  9. Michael Salim
  10. David J. Hardy
  11. Tom Burnley
  12. Lei Huang
  13. John McCalpin
  14. Murali Emani
  15. Hyenseung Yoo
  16. Junqi Yin
  17. Aristeidis Tsaris
  18. Vishal Subbiah
  19. Tanveer Raza
  20. Jessica Liu
  21. Noah Trebesch
  22. Geoffrey Wells
  23. Venkatesh Mysore
  24. Thomas Gibbs
  25. James Phillips
  26. S. Chakra Chennubhotla
  27. Ian Foster
  28. Rick Stevens
  29. Anima Anandkumar
  30. Venkatram Vishwanath
  31. John E. Stone
  32. Emad Tajkhorshid
  33. Sarah A. Harris
  34. Arvind Ramanathan
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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.

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