Prediction of infectivity of SARS-CoV2: Mathematical model with analysis of docking simulation for spike proteins and angiotensin-converting enzyme 2

SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches

The outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) has created a public health emergency globally. SARS-CoV-2 enters the human cell through the binding of the spike protein to human angiotensin converting enzyme 2 (ACE2) receptor. Significant changes have been reported in the mutational landscape of SARS-CoV-2 in the receptor binding domain (RBD) of S protein, subsequent to evolution of the pandemic. The present study examines the correlation between the binding affinity of mutated S-proteins and the rate of viral infectivity. For this, the binding affinity of SARS-CoV and variants of SARS-CoV-2 towards ACE2 was computationally determined. Subsequently, the RBD mutations were classified on the basis of the number of strains identified with respect to each mutation and the resulting variation in the binding affinity was computationally examined. The molecular docking studies indicated a significant correlation between the Z-Rank score of mutated S proteins and the rate of infectivity, suitable for predicting SARS-CoV-2 infectivity. Accordingly, a 30-mer peptide was designed and the inhibitory properties were computationally analyzed. Single amino acid-wise mutation was performed subsequently to identify the peptide with the highest binding affinity. Molecular dynamics and free energy calculations were then performed to examine the stability of the peptide-protein complexes. Additionally, selected peptides were synthesized and screened using a colorimetric assay. Together, this study developed a model to predict the rate of infectivity of SARS-CoV-2 variants and propose a potential peptide that can be used as an inhibitor for the viral entry to human.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 Omicron BA.2.75 Variant May Be Much More Infective than Preexisting Variants Based on In Silico Model

Previously, we developed a mathematical model via molecular simulation analysis to predict the infectivity of six SARS-CoV-2 variants. In this report, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 based on our previous research. We subjected Omicron BA.4/5 and BA.2.75 variants of SARS-CoV-2 to the analysis to determine the evolutionary distance of the spike protein gene (S gene) of the variants from the Wuhan variant so as to appreciate the changes in the spike protein. We performed molecular docking simulation analyses of the spike proteins with human angiotensin-converting enzyme 2 (ACE2) to understand the docking affinities of these variants. We then compared the evolutionary distances and the docking affinities of these variants with those of the variants that we had analyzed in our previous research. As a result, BA.2.75 has both the highest docking affinity (ratio per Wuhan variant) and the longest evolutionary distance of the S gene from the Wuhan variant. These results suggest that BA.2.75 infection can spread farther than can infections of preexisting variants.

Update on the omicron sub-variants BA.4 and BA.5

Several nations have recently begun to relax their public health protocols, particularly regarding the use of face masks when engaging in outdoor activities. This is because there has been a general trend towards fewer cases of coronavirus disease 2019 (COVID-19). However, new Omicron sub-variants (designated BA.4 and BA.5) have recently emerged. These two subvariants are thought to be the cause of an increase in COVID-19 cases in South Africa, the United States, and Europe. They have also begun to spread throughout Asia. They evolved from the Omicron lineage with characteristics that make them even more contagious and which allow them to circumvent immunity from a previous infection or vaccination. This article reviews a number of scientific considerations about these new variants, including their apparently reduced clinical severity.