tetano
Editor, Senior Moderator
J Mol Biol
. 2020 Jul 22;S0022-2836(20)30456-3.
doi: 10.1016/j.jmb.2020.07.009. Online ahead of print.
Mutations Strengthened SARS-CoV-2 Infectivity
Jiahui Chen[SUP] 1 [/SUP], Rui Wang[SUP] 1 [/SUP], Menglun Wang[SUP] 1 [/SUP], Guo-Wei Wei[SUP] 2 [/SUP]
Affiliations
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 ten thousand 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 (ACE2) receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 sub- types have become slightly more infectious, while 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-ACE2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain (RBD), 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 (RBM), i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.
Keywords: Binding free energy change; Deep learning; Mutation; Persistent homology; Protein–protein interaction; Viral infectivity.
. 2020 Jul 22;S0022-2836(20)30456-3.
doi: 10.1016/j.jmb.2020.07.009. Online ahead of print.
Mutations Strengthened SARS-CoV-2 Infectivity
Jiahui Chen[SUP] 1 [/SUP], Rui Wang[SUP] 1 [/SUP], Menglun Wang[SUP] 1 [/SUP], Guo-Wei Wei[SUP] 2 [/SUP]
Affiliations
- PMID: 32710986
- DOI: 10.1016/j.jmb.2020.07.009
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 ten thousand 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 (ACE2) receptor following mutations. We reveal that the SARS-CoV-2 virus becomes more infectious. Three out of six SARS-CoV-2 sub- types have become slightly more infectious, while 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-ACE2 binding free energy changes. Based on a systematic evaluation of all possible 3686 future mutations on the S protein receptor-binding domain (RBD), 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 (RBM), i.e., 452, 489, 500, 501, and 505, have high chances to mutate into significantly more infectious COVID-19 strains.
Keywords: Binding free energy change; Deep learning; Mutation; Persistent homology; Protein–protein interaction; Viral infectivity.