Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Trifan, Anda; Gorgun, Defne; Salim, Michael; Li, Zongyi; Brace, Alexander; Zvyagin, Maxim Tsarkov; Ma, Heng; Clyde, Austin R; Clark, David; Hardy, David J; Burnley, Tom; Huang, Lei; McCalpin, John; Emani, Murali; Yoo, Hyunseung; Junqi, Yin; Tsaris, Aristedis; Foster, Ian; Stevens, Rick; Vishwanath, Venkatram; Ramanathan, Arvind

doi:10.1177/10943420221113513

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

Journal Article · 01 November 2022 · International Journal of High Performance Computing Applications

DOI:https://doi.org/10.1177/10943420221113513· OSTI ID:1960008

Trifan, Anda; Gorgun, Defne; Salim, Michael; Li, Zongyi; Brace, Alexander; Zvyagin, Maxim Tsarkov; Ma, Heng; Clyde, Austin R; Clark, David; Hardy, David J; Burnley, Tom; Huang, Lei; McCalpin, John; Emani, Murali; Yoo, Hyunseung; Junqi, Yin; Tsaris, Aristedis; Foster, Ian; Stevens, Rick; Vishwanath, Venkatram more »

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.

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Institutes of Health (NIH); USDOE Computational Science Graduate Fellowship (CSGF)

DOE Contract Number:: AC02-06CH11357

OSTI ID:: 1960008

Journal Information:: International Journal of High Performance Computing Applications, Vol. 36, Issue 5-6

Country of Publication:: United States

Language:: English

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