Trifan, Anda and Gorgun, Defne and Salim, Michael and Li, Zongyi and Brace, Alexander and Zvyagin, Maxim and Ma, Heng and Clyde, Austin and Clark, David and Hardy, David J. and Burnley, Tom and Huang, Lei and McCalpin, John and Emani, Murali and Yoo, Hyenseung and Yin, Junqi and Tsaris, Aristeidis and Subbiah, Vishal and Raza, Tanveer and Liu, Jessica and Trebesch, Noah and Wells, Geoffrey and Mysore, Venkatesh and Gibbs, Thomas and Phillips, James and Chennubhotla, S. Chakra and Foster, Ian and Stevens, Rick and Anandkumar, Anima and Vishwanath, Venkatram and Stone, John E. and Tajkhorshid, Emad and Harris, Sarah A. and Ramanathan, Arvind (2022) 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. International Journal of High Performance Computing Applications . ISSN 1094-3420. doi:10.1177/10943420221113513. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20221011-459145000.39
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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.
| Item Type: | Article | ||||||||||
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| DOI: | 10.1177/10943420221113513 | ||||||||||
| Record Number: | CaltechAUTHORS:20221011-459145000.39 | ||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20221011-459145000.39 | ||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||
| ID Code: | 117343 | ||||||||||
| Collection: | CaltechAUTHORS | ||||||||||
| Deposited By: | Donna Wrublewski | ||||||||||
| Deposited On: | 12 Oct 2022 22:43 | ||||||||||
| Last Modified: | 12 Oct 2022 22:43 |
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