Mutations strengthened SARS-CoV-2 infectivity

TL;DR

This study uses a novel algebraic topology-based machine learning approach to evaluate how mutations in SARS-CoV-2 affect its infectivity, revealing that most subtypes are becoming more infectious and predicting key residues likely to mutate further.

Contribution

The paper introduces an advanced topological machine learning method to systematically assess mutation impacts on SARS-CoV-2 infectivity, providing insights into future mutation risks.

Findings

Most SARS-CoV-2 subtypes are becoming more infectious.

SARS-CoV-2 is slightly more infectious than SARS-CoV.

Certain residues are highly likely to mutate into more infectious strains.

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 essentially impossible owing to its continuous evolution with over 13752 single nucleotide polymorphisms (SNP) variants in six different subtypes. We develop an advanced machine learning algorithm based on the algebraic topology to quantitatively evaluate the binding affinity changes of SARS-CoV-2 spike glycoprotein (S protein) and host angiotensin-converting enzyme 2 (ACE2) receptor following the mutations. Based on mutation-induced binding affinity changes, we reveal that five out of six SARS-CoV-2 subtypes have become either moderately or slightly more infectious, while one subtype has weakened its infectivity. We find that SARS-CoV-2 is slightly more infectious than SARS-CoV according to computed S protein-ACE2 binding affinity 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 affinity calculation, we predict that a few residues on the receptor-binding motif (RBM), i.e., 452, 489, 500, 501, and 505, have very high chances to mutate into significantly more infectious COVID-19 strains.