Members of the Theoretical and Computational Biophysics Group were part of a multi-institutional interdisciplinary team awarded as finalists at Supercomputing 2021 with the internationally recognized ACM Gordon Bell Special Prize for COVID-19 Research, for 2021.

Announcement of the team's finalist paper at Supercomputing 2021.
The SARS-CoV-2 replication transcription complex (RTC) is responsible for replicating and transcribing the viral mRNA inside a human cell, and a key target for pharmaceutical treatments for COVID-19. We have developed an innovative multi-site scientific workflow that combines the best attributes of cryo-EM imaging, all-atom molecular dynamics, and fluctuating finite element analysis with AI methods, to elucidate the structure and dynamics for the RTC previously inaccessible by other techniques.

Team members from U. Illinois provided key methodological and scalability advances in the NAMD molecular dynamics simulation software, and technological improvements to VMD, a key molecular modeling tool used to prepare, visualize, and analyze FFEA and NAMD simulations of the RTC. The NAMD and VMD software advances provided by U. Illinois team members enabled the science campaign to more efficiently utilize state-of-the-art supercomputers at national computing centers, including NERSC Perlmutter, ALCF ThetaGPU, and TACC Frontera.

These achievements required the unique skills of a diverse multi-institutional team to overcome both technical challenges and an extremely compressed research timeline. Every team member played a vital role in achieving the final outcome.

Intelligent Resolution: Integrating Cryo-EM with AI-Driven Multi-Resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

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.

Team presentation at Supercomputing 2021:

Overview of the SARS-CoV-2 viral infection cycle

Overview of the structure and simulation data
used to refine the cryo-EM structure.

NAMD scaling on GPU-dense ALCF ThetaGPU DGX-A100
for the 1.1M- and 2.2M-atom RTC. The Black reference
line shows the performance of the 1.1M-atom RTC
on 128 nodes of TACC Frontera

Root-mean squared fluctuations of the SARS-CoV2 RTC,
provide insites that are helpful in determining the
intrinsic concerted motions in the RTC that are
implicitly encoded in the experimental data.

Research Team:

Map of research team members, their institutions,
and supercomputer centers used for the research.

    California Institute of Technology
  • Zongyi Li
    University of Leeds
  • Sarah A. Harris
    University College of London
  • Geoffrey Wells
    University of Pittsburgh
  • Chakra Chennubhotla
    Rutgers University and Brookhaven National Laboratory
  • Shantenu Jha
    Cerebras Corporation
  • Jessica Liu
  • Tavneer Raza
  • Vishal Subbiah
    NVIDIA Corporation
  • Anima Anandkumar
  • David Clark
  • Tom Gibbs
  • Venkatesh Mysore
    Oak Ridge National Laboratory
  • Aristeidis Tsaris
  • Junqi Yin
    Science and Technologies Facilities Council
  • Tom Burnley