Mutations Strengthened SARS-CoV-2 Infectivity

• Published in: Journal of molecular biology Vol. 432; no. 19; pp. 5212 - 5226
• Main Authors: Chen, Jiahui, Wang, Rui, Wang, Menglun, Wei, Guo-Wei
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 04.09.2020
• Subjects: Amino Acid Sequence; Angiotensin-Converting Enzyme 2; Betacoronavirus - classification; Betacoronavirus - genetics; Betacoronavirus - pathogenicity; Cluster Analysis; Coronavirus Infections - virology; COVID-19; COVID-19 infection; DNA Mutational Analysis; evolution; Evolution, Molecular; Genotype; Geographic Mapping; Gibbs free energy; glycoproteins; Humans; Machine Learning; Models, Molecular; molecular biology; Mutation; Pandemics; pathogenicity; peptidyl-dipeptidase A; Peptidyl-Dipeptidase A - metabolism; Pneumonia, Viral - virology; Polymorphism, Single Nucleotide - genetics; Probability; probability analysis; Protein Binding - genetics; protein-protein interaction; Receptors, Virus - metabolism; SARS-CoV-2; Sequence Alignment; Severe acute respiratory syndrome coronavirus 2; Severe acute respiratory syndrome-related coronavirus; Severe acute respiratory syndrome-related coronavirus - chemistry; Severe acute respiratory syndrome-related coronavirus - genetics; Severe acute respiratory syndrome-related coronavirus - metabolism; Severe acute respiratory syndrome-related coronavirus - pathogenicity; Spike Glycoprotein, Coronavirus - chemistry; Spike Glycoprotein, Coronavirus - genetics; Spike Glycoprotein, Coronavirus - metabolism; spike protein; Thermodynamics; viral infectivity; viruses; COVID-19; BFE; mutation; ACE2; SARS-CoV-2; RBD; spike protein; PPI; protein-protein interaction; RBM; viral infectivity
• ISSN: 0022-2836; 1089-8638; 1089-8638
• DOI: 10.1016/j.jmb.2020.07.009

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is a major concern in coronavirus disease 2019 (COVID-19) prevention and economic reopening. However, rigorous determination of SARS-CoV-2 infectivity is very difficult owing to its continuous evolution with over 10,000 single nucleotide polymorphisms (SNP) variants in many subtypes. We employ an algebraic topology-based machine learning model to quantitatively evaluate the binding free energy changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 subtypes have become slightly more infectious, while the other three subtypes have significantly strengthened their infectivity. We also find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-angiotensin-converting enzyme 2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain, we show that most likely future mutations will make SARS-CoV-2 more infectious. Combining sequence alignment, probability analysis, and binding free energy calculation, we predict that a few residues on the receptor-binding motif, i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains. More than 8000 observed single mutations in the SARS-CoV-2 genomes have raised serious concerns about changes in infectivity. Qualitatively, such infectivity is proportional to the binding affinity between SARS-CoV-2 spike glycoprotein (S protein) and host ACE2 receptor. This work proposes a machine learning model to evaluate the relative infectivity following the mutations. We show that five out of six SARS-CoV-2 substrains have become more infectious, while the other one becomes less infectious. We found that a few potential future mutations on the S protein could lead to more dangerous new viruses. [Display omitted] •SARS-CoV-2 has had many mutations and evolved into six subtypes.•Three SARS-CoV-2 subtypes have significantly strengthened their infectivity.•A few future mutations have high chances to produce more contagious viruses.