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Gram-negative bacteria are protected by a multicompartmental molecular architecture known as the cell envelope that contains two membranes and a thin cell wall. As the cell envelope controls influx and efflux of molecular species, in recent years both experimental and computational studies of such architectures have seen a resurgence due to the implications for antibiotic development. In this article we review recent progress in molecular simulations of bacterial membranes. We show that enormous progress has been made in terms of the lipidic and protein compositions of bacterial systems. The simulations have moved away from the traditional setup of one protein surrounded by a large patch of the same lipid type toward a more bio-logically representative viewpoint. Simulations with multiple cell envelope components are also emerging. We review some of the key method developments that have facilitated recent progress, discuss some current limitations, and offer a perspective on future directions. 

In the Big Data era, a change of paradigm in the use of molecular dynamics is required. Trajectories should be stored under FAIR (findable, accessible, interoperable and reusable) requirements to favor its reuse by the community under an open science paradigm. 

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.