Mechanisms of SARS-CoV-2 Evolution Revealing Vaccine-Resistant Mutations in Europe and America

Abstract

The importance of understanding SARS-CoV-2 evolution cannot be overlooked. Recent studies confirm that natural selection is the dominating mechanism of SARS-CoV-2 evolution, which favors mutations that strengthen viral infectivity. Here, we demonstrate that vaccine-breakthrough or antibody-resistant mutations provide a new mechanism of viral evolution. Specifically, vaccine-resistant mutation Y449S in the spike (S) protein receptor-binding domain, which occurred in co-mutations Y449S and N501Y, has reduced infectivity compared to that of the original SARS-CoV-2 but can disrupt existing antibodies that neutralize the virus. By tracking the evolutionary trajectories of vaccine-resistant mutations in more than 2.2 million SARS-CoV-2 genomes, we reveal that the occurrence and frequency of vaccine-resistant mutations correlate strongly with the vaccination rates in Europe and America. We anticipate that as a complementary transmission pathway, vaccine-breakthrough or antibody-resistant mutations, like those in Omicron, will become a dominating mechanism of SARS-CoV-2 evolution when most of the world’s population is either vaccinated or infected. Our study sheds light on SARS-CoV-2 evolution and transmission and enables the design of the next-generation mutation-proof vaccines and antibody drugs.

Supporting Information


The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpclett.1c03380.

  • Names of antibodies disrupted by mutations (S6.0.1: antibodies_disruptmutation.csv), PDB entries for all of the 130 SARS-CoV-2 antibodies (S6.0.2: antibodies.csv), all of the SNPs of RBD co-mutations by October 20, 2021 (S6.0.3: RBD_comutation_residue_10202021.csv), and all of the nondegenerate RBD co-mutations with their frequencies, antibody disruption counts, total BFE changes, and first detection dates and countries (S6.0.4: Track_Comutation_10202021.xlsx) (ZIP)
  • Data preprocessing and feature generation methods (section S1), machine learning methods (section S2), validations of our machine learning predictions with experimental data (section S3), the top 30 most observed S protein RBD mutations by October 20, 2021 (section S4), time evolution of the vaccination rate and the frequency of the Y449S mutation in CH and RO from December 26, 2020, to October 22, 2021 (section S5), and titles of tables of supplementary data (section S6) (PDF)

Original Article: https://pubs.acs.org/doi/10.1021/acs.jpclett.1c03380

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