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

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.

Automated summary

The study introduced a generalizable AI-driven workflow that allows for more efficient exploration of the dynamics of molecular systems, particularly focusing on the mechanisms of infectivity of the SARS-CoV-2 virus. Several novel scientific discoveries were made, including the elucidation of the spike protein's full glycan shield, the role of spike glycans in modulating virus infectivity, and the characterization of flexible interactions between the spike protein and the human ACE2 receptor. The research also demonstrated the potential of AI in accelerating conformational sampling across different systems for future applications in SARS-CoV-2 and other molecular systems.