The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Anda Trifan and Defne Gorgun (University of Illinois), Zongyi Li (California Institute of Technology), Alexander Brace (University of Chicago), Maxim Zvyagin and Heng Ma (Argonne National Laboratory (ANL)), Anima Anandkumar (California Institute of Technology), Venkatram Vishwanath (Argonne National Laboratory (ANL)), John E. Stone (University of Illinois), Sarah A. Harris (University of Leeds), and Arvind Ramanathan and Team Intelligent Resolution (Argonne National Laboratory (ANL))
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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