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人工智能驱动的工作流程，利用异构 HPC 资源来探索分子系统的时间依赖性动态。我们使用此工作流程来研究 SARS-CoV-2 刺突蛋白（主要病毒感染机制）的感染机制。我们的工作流程能够更有效地研究各种复杂环境中的尖峰动态，包括在完整的 SARS-CoV-2 病毒包膜模拟中，该模拟包含 3.05 亿个原子，并在使用 NAMD 的 ORNL Summit 上显示出强大的扩展性。我们提出了几项新颖的科学发现，包括阐明刺突的完整聚糖屏蔽、刺突聚糖在调节病毒感染性中的作用，以及刺突和人类 ACE2 受体之间灵活相互作用的表征。我们还展示了人工智能如何加速跨不同系统的构象采样，并为未来将此类方法应用于 SARS-CoV-2 和其他分子系统的其他研究铺平道路。