Prediction of novel high-risk variants through co-occurrence analysis of mutation hotspots

Abstract

Various forms of S proteins of severe acute respiratory syndrome coronavirus 2 have given rise to high-risk variants capable of avoiding antibody immunity or increasing binding affinity with hACE2. We propose a statistical analysis method for predicting high-risk variants by analyzing co- occurrence of mutation hotspots using spike protein sequences. We identified S494P and V503I as high-risk variants. Interestingly, S494P was predicted to possess significantly increased binding affinity based on molecular docking and quantum mechanical energy calculations. In addition, we examined viral entry of high-risk variants using pseudotyped viruses (PV). Compared to PVs of spike Delta, PVs of spike Delta-S494P or spike Delta-V503I exhibited improved viral entrance in A549 cells. Our proposed analysis method can be used to predict novel high-risk variants and corresponding binding affinities.