Temporal-Geographical Dispersion of SARS-CoV-2 Spike Glycoprotein Variant Lineages and Their Functional Prediction Using in Silico Approach.

ABSTRACT

SARS-CoV-2 is a positive-sense single-stranded RNA virus with emerging mutations, especially on the Spike glycoprotein (S protein). To delineate the genomic diversity in association with geographic dispersion of SARS-CoV-2 variant lineages, we collected 939,591 complete S protein sequences deposited in the Global Initiative on Sharing All Influenza Data (GISAID) from December 2019 to April 2021. An exponential emergence of S protein variants was observed since October 2020 when the four major variants of concern (VOCs), namely, alpha (α) (B.1.1.7), beta (ß) (B.1.351), gamma (γ) (P.1), and delta (δ) (B.1.617), started to circulate in various communities. We found that residues 452, 477, 484, and 501, the 4 key amino acids located in the hACE2 binding domain of S protein, were under positive selection. Through in silico protein structure prediction and immunoinformatics tools, we discovered D614G is the key determinant to S protein conformational change, while variations of N439K, T478I, E484K, and N501Y in S1-RBD also had an impact on S protein binding affinity to hACE2 and antigenicity. Finally, we predicted that the yet-to-be-identified hypothetical N439S, T478S, and N501K mutations could confer an even greater binding affinity to hACE2 and evade host immune surveillance more efficiently than the respective native variants. This study documented the evolution of SARS-CoV-2 S protein over the first 16 months of the pandemic and identified several key amino acid changes that are predicted to confer a substantial impact on transmission and immunological recognition. These findings convey crucial information to sequence-based surveillance programs and the design of next-generation vaccines. IMPORTANCE Our study showed the global distribution of SARS-CoV-2 S protein variants from January 2020 to the end of April 2021. We highlighted the key amino acids of S protein subjected to positive selection. Using computer-aided approaches, we predicted the impact of the amino acid variations in S protein on viral infectivity and antigenicity. We also predicted the potential amino acid mutations that could arise in favor of SARS-CoV-2 virulence. These findings are vital for vaccine designing and anti-SARS-CoV-2 drug discovery in an effort to combat COVID-19.