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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; Ma, Heng; ... Ramanathan, Arvind; + view all (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. The International Journal of High Performance Computing Applications 10.1177/10943420221113513. (In press). Green open access

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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.

Type: Article
Title: 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
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/10943420221113513
Publisher version: https://doi.org/10.1177/10943420221113513
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Multi-resolution simulations, SARS-CoV-2, COVID19, HPC, AI
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharma and Bio Chemistry
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
URI: https://discovery.ucl.ac.uk/id/eprint/10155798
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