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.
ABSTRACT
Having claimed over 1 million lives worldwide to date, the ongoing COVID-19 pandemic has created one of the biggest challenges to develop an effective drug to treat infected patients. Among all the proteins expressed by the virus, RNA helicase is a fundamental protein for viral replication, and it is highly conserved among the coronaviridae family. To date, there is no high-resolution structure of helicase bound with ATP and RNA. We present here structural insights and molecular dynamics (MD) simulation results of the SARS-CoV-2 RNA helicase both in its apo form and in complex with its natural substrates. Our structural information of the catalytically competent helicase complex provides valuable insights for the mechanism and function of this enzyme at the atomic level, a key to develop specific inhibitors for this potential COVID-19 drug target.
