Journal of Molecular Biology
Volume 432, Issue 19, 4 September 2020, Pages 5212-5226
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Mutations Strengthened SARS-CoV-2 Infectivity

https://doi.org/10.1016/j.jmb.2020.07.009Get rights and content

Highlights

  • 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.

Abstract

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.

Graphical abstract

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.

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Keywords

COVID-19
viral infectivity
spike protein
mutation
protein-protein interaction

Abbreviations

COVID-19
coronavirus disease 2019
SARS-CoV-2
severe acute respiratory syndrome coronavirus 2
ACE2
angiotensin-converting enzyme 2
RBD
receptor-binding domain
PPI
protein–protein interaction
BFE
binding free energy
RBM
receptor-binding motif

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