As the effort to combat the SARS-CoV-2 virus began, among the most pressing questions were how to gauge its infectivity and get a better picture of those dynamics from the inside out. Answering them initially would bring together nearly 30 researchers, representing a dozen institutions and nearly as many disciplines, including scientists from the Computational Science Initiative (CSI) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory.
Matteo Turilli and Shantenu Jha, from CSI’s Computation and Data Driven Discovery (C3D) department, would provide new computational improvements that incorporated artificial intelligence (AI)-based methods as part of an integrated workflow used to generate simulated views of SARS-CoV-2 that otherwise could never be achieved solely by experiment. Their work with the multidisciplinary collective has yielded awards, accolades and, more importantly, progress both in mitigating the damage wrought by a roughly 0.1 micron-sized particle and in changing how scientists approach discovery.
While small in stature, SARS-CoV-2 decidedly has packed a big punch. This extended to its simulation. In 2020, the group, including Turilli and Jha, was seeking to generate simulations that would afford a more intricate understanding of the virus’ structure and dynamics to see how it moves, responds and infects a host. The work focused on modeling the spike protein, its main infection mechanism, using all-atom molecular dynamics (MD) simulations that can show biological systems at the atomic level and available (yet diverse) experimental data sets.