To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from the Department of Energy’s Oak Ridge National Laboratory developed a novel technique.

The team — including computational scientists Debsindhu Bhowmik, Serena Chen and John Gounley — ran molecular dynamics simulations of the novel virus that caused the COVID-19 disease pandemic on ORNL’s Summit supercomputer, an IBM AC922 system. The researchers then analyzed the output with a customized deep learning approach to produce a complete molecular picture of the “spike” protein on the virus’s surface.

This method enabled them to pinpoint specific flexible regions, which they studied in extreme detail to reveal promising therapeutic targets. Aiming for these targets could create more reliable treatment avenues that interrupt key structural transitions in the virus’s lifecycle while also supporting the body’s natural immune response.

“A better understanding of the spike protein could complement current COVID-19 vaccines by informing new treatments and providing insights into potential drug design,” Bhowmik said.

Using the Nanoscale Molecular Dynamics, or NAMD, code on Summit, the nation’s most powerful supercomputer, the researchers simulated the spike proteins’ molecular structures for SARS-CoV-2 and three other human coronaviruses: SARS-CoV-1, MERS-CoV and HCoV-HKU1. After completing this unique and comprehensive comparison of four different spike proteins, they compared the components and behavior of SARS-CoV-2 with thousands of sample structures from the other viruses using a deep learning architecture called a convolutional variational autoencoder, or CVAE.

These efforts revealed previously unexplored regions of the coronavirus’s spike protein in which targeted medical intervention might prevent SARS-CoV-2 from infecting healthy cells. The researchers presented their findings at the IEEE Big Data Workshop for COVID-19, and their paper is published in the proceedings of the IEEE International Conference on Big Data.