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DTSTAMP:20211207T054724Z
LOCATION:240-241-242
DTSTART;TZID=America/Chicago:20211117T143000
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UID:submissions.supercomputing.org_SC21_sess249_gbv106@linklings.com
SUMMARY:Intelligent Resolution: Integrating Cryo-EM with AI-Driven Multi-R
 esolution Simulations to Observe the SARS-CoV-2 Replication-Transcription 
 Machinery in Action
DESCRIPTION:ACM Gordon Bell Finalist, Awards Presentation\n\nIntelligent R
 esolution: Integrating Cryo-EM with AI-Driven Multi-Resolution Simulations
  to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action\n
 \nTrifan, Gorgun, Li, Brace, Zvyagin...\n\nThe severe acute respiratory sy
 ndrome 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 pharmaceutica
 l compounds is a pathway to treating COVID-19. Conventional tools, e.g., c
 ryo-electron microscopy and all-atom molecular dynamics (AAMD), do not pro
 vide sufficiently high resolution or timescale to capture important dynami
 cs of this molecular machine. Consequently, we develop an innovative workf
 low that bridges the gap between these resolutions, using mesoscale fluctu
 ating "finite element analysis (FFEA) continuum simulations and a hierarch
 y of AI-methods that continually learn and infer features for maintaining 
 consistency between AAMD and FFEA simulations. We leverage a multi-site di
 stributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providi
 ng optimal resource utilization across HPC centers. Our study provides unp
 recedented access to study the SARS-CoV-2 RTC machinery, while providing g
 eneral capability for AI-enabled multi-resolution simulations at scale.\n\
 nTag: AI-HPC Convergence, Computational Science, Extreme Scale Computing, 
 Performance, Scientific Computing\n\nRegistration Category: Tech Program R
 eg Pass
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