AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

Lorenzo Casalino, Abigail C. Dommer, Zied Gaieb, Emilia P. Barros, Terra Sztain, Surl Hee Ahn, Anda Trifan, Alexander Brace, Anthony T. Bogetti, Austin Clyde, Heng Ma, Hyungro Lee, Matteo Turilli, Syma Khalid, Lillian T. Chong, Carlos Simmerling, David J. Hardy, Julio D.C. Maia, James C. Phillips, Thorsten KurthAbraham C. Stern, Lei Huang, John D. McCalpin, Mahidhar Tatineni, Tom Gibbs, John E. Stone, Shantenu Jha, Arvind Ramanathan, Rommie E. Amaro

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

Original languageEnglish (US)
Pages (from-to)432-451
Number of pages20
JournalInternational Journal of High Performance Computing Applications
Issue number5
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture


  • AI
  • COVID19
  • GPU
  • HPC
  • Molecular dynamics
  • SARS-CoV-2
  • computational virology
  • deep learning
  • multiscale simulation
  • weighted ensemble


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