Graph-based coevolutionary approach on SARS-CoV-2 spike protein

Abstract

Amino acids that coevolve can be indicative of functionality, so coevolution-based methods can be used to detect important amino acids in proteins, known as hotspots. Here, we apply a recently published method based on network metrics and coevolutionary information to detect functional hotspots in the SARS-CoV-2 spike protein. We found 275 potential hotspots withavailable experimental information in the literature for 4 of them, for example, position 614 (ASP), known to increment the infectivity of SARS-CoV-2 towards human host cells. In addition, a hotspot enrichment analysis was performed, as well as a study of the relative solvent accessibility of hotspot versus non-hotspot positions for the receptor binding domain. The hotspots showed less surface area available when bound to the human receptor compared to when not bound, which does not occur for non-hotspot positions, indicating that the hotspots obtained may be important for the binding of the spike protein to the receptor of the host cell