Since the outbreak of the novel coronavirus (SARS-CoV-2), detailed atomistic structures of the virus’s spike protein have become invaluable to research efforts seeking to engineer antibodies, peptides, and small molecule drugs to inhibit the membrane fusion process and thus prevent cell entry and proliferation of the virus. To this end, cryo-electron microscopy has produced a large number of static models aiding in the discovery of potential inhibitors to the membrane fusion process coordinated by spike proteins. However, the static models which have been produced are averaged representations of a diverse set of functionally relevant structures. Using manifold-based machine learning, we have conducted a search of the spike protein’s conformational space to uncover hidden structures in the “1 RBD up” state by reprocessing cryo-EM data to extract continuous conformational changes and augmenting the resulting maps with atomic models along the most probable pathway. We find that the global conformation of the RBD domain explores degrees of “openness” not seen in previous cryo-EM models, while local conformational changes of previously identified binding pockets show large variations in terms of their ligand accessibility, and thus provide new targets for inhibitors. Using a synergistic hybrid approach, called CryoFold, which integrates data derived from both cryo-EM and cryo-ET, we successfully capture and uncover the biophysical determinants of spike protein stalk flexibility, and resolve the energetic cost associated with bending and twisting. Finally, we identify key residues within the spike protein which are predicted T-cell targets, and show that patient populations with the enhanced ability to target these spike residues are characterized by lower overall population mortality.

Integrative models of SARS-CoV2 interactions correlate protein dynamics to patient mortality

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.