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.

我们开发了一个通用的AI驱动的工作流，该工作流利用异构的HPC资源来探索分子系统随时间变化的动力学。我们使用此工作流程来调查SARS-CoV-2峰值蛋白（主要的病毒感染机制）的感染性机制。我们的工作流程可在各种复杂环境中更有效地研究尖峰动力学，包括在完整的SARS-CoV-2病毒包膜模拟中进行，该仿真包含3.05亿个原子，并显示了使用NAMD在ORNL Summit上的强大缩放比例。我们提出了一些新颖的科学发现，包括阐明了穗状花序的完整聚糖屏蔽，穗状花序聚糖在调节病毒感染性中的作用以及穗状花序与人类ACE2受体之间柔性相互作用的表征。