Forecasting the Evolutionary Pathways of the SARS-CoV-2 Spike Protein via the Calculation of Mutability Landscapes

Abstract

Viruses, known as infectious agents, cause diseases and deaths in humans. Although vaccines and drugs have been developed to prevent these effects, through mutations in the genetics of viruses, the virus acquires features such as escaping the immune system and binding better to the host cell, thus becoming resistant to the treatments offered. The global COVID-19 pandemic has highlighted the need for rapid, reliable, and efficient tracking and forecasting of the changes in genetic material as new SARS-CoV-2 variants arise. Developing appropriate drugs and vaccines for the new genetic content of the virus is costly and takes a long time. For this reason, it is of great importance to be able to predict the mutations that may occur in the genetics of SARS-CoV-2 and the effects of these mutations before they occur and take the necessary precautions. Here we studied the evolution process of SARS-CoV-2 and predicted the key mutations that may occur in the future. To this end, first, the gene sequences of the spike region are obtained from GISAID database, and then the sequences are aligned with various multiple alignment methods (Clustal Omega, MAFFT, TCoffee, etc.). Then these alignments are used to calculate the mutability scores for each amino acid using scoring functions (Karlin, Sander, Valder, etc.). By examining the correlation between the scores obtained, regions that are protected and prone to change have been obtained. Future mutations have been predicted by examining using the calculated mutability scores and the experimental mutation rate (8x10− 4 mutation/nucleotide/year) obtained, using random walk method.