Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action
Year: 2021
Type: preprint
Abstract: 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.
Authors: Anda Trifan, Defne Gorgun, Zongyi Li, Alexander Brace, Maxim Zvyagin, Heng Ma, Austin Clyde, David Clark, Michael Salim, David J. Hardy, Tom Burnley, Lei Huang, John McCalpin, Murali Emani, Hyenseung Yoo, Junqi Yin, Aristeidis Tsaris, Vishal Subbiah, Tanveer Raza, Jessica Liu, Noah Trebesch, Geoffrey Wells, Venkatesh Mysore, Thomas Gibbs, James Phillips, S. Chakra Chennubhotla, Ian Foster, Rick Stevens, Anima Anandkumar, Venkatram Vishwanath, John E. Stone, Emad Tajkhorshid, Sarah A. Harris, Arvind Ramanathan
Institutions: Argonne National Laboratory, University of Illinois Urbana-Champaign, California Institute of Technology, University of Chicago, Nvidia (United Kingdom), Nvidia (United States), Science and Technology Facilities Council, Texas Advanced Computing Center, Oak Ridge National Laboratory, Cerebras Systems (United States), University College London, University of Pittsburgh, University of Leeds
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, National Institutes of Health, National Nuclear Security Administration, National Institute of General Medical Sciences, Division of Molecular and Cellular Biosciences, Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory
Awards: G102163711, G1071216263, G107546177, G1079818582, G1104325616, G1143651532, G1193633358, G1286236842, G1313983767, G1645119126, G1664757510, G1796307089, G1816284028, G1863003419, G203625172, G2366088775, G2411683797, G2584197162, G262962599, G2638771445, G2857863752, G2900453181, G2988778103, G3075337988, G3083819904, G3318903820, Center for Macromolecular Modeling and Bioinformatics, G365575497, G382641700, G3945850812, G3976335900, G4158950854, G42916606, G4423657506, G513797252, G5514976981, G5607048534, G6144542739, G654302390, G660579501, G675320460, G6848031779, DOE COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP (CSGF) GRANT 2019 COHORT, G7186599013, G7354628648, G816254390, G8234470690, G8286507625, G8757742288, G8906985441, G9000013378, G9000133700, G969889393
OpenAlex ID: W3205636900