AI-Enabled Multi Resolution Simulations To Uncover Mechanisms Of SARS-CoV-2 virus
Anda Trifan, University of Illinois at Urbana-Champaign
Respiratory pathogens, such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and influenza, are the cause of significant morbidity and mortality worldwide. Using an AI-driven workflow, we investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery, elucidating the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems. The 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.
Abstract Author(s): Anda Trifan, Defne Gorgun, Michael Salim, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Abigail Dommer, Zied Gaieb, Rommie Amaro, Emad Tajkhorshid, Arvind Ramanathan