ArticlePLoS computational biology2022
Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning.PLoS computational biology · 2026Article
- Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.Nature biomedical engineering · 2025Article
- Review
- Co-Mutations and Possible Variation Tendency of the Spike RBD and Membrane Protein in SARS-CoV-2 by Machine Learning.International journal of molecular sciences · 2024Article
- SARS-CoV-2 Omicron Subvariants Do Not Differ Much in Binding Affinity to Human ACE2: A Molecular Dynamics Study.The journal of physical chemistry. B · 2024Article
- Unraveling the Dynamics of SARS-CoV-2 Mutations: Insights from Surface Plasmon Resonance Biosensor Kinetics.Biosensors · 2024Review
- Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization.Frontiers in microbiology · 2024Article
- Genetic diversity and genomic epidemiology of SARS-CoV-2 during the first 3 years of the pandemic in Morocco: comprehensive sequence analysis, including the unique lineage B.1.528 in Morocco.Access microbiology · 2024Article
- Novel Entropy-Based Phylogenetic Algorithm: A New Approach for Classifying SARS-CoV-2 Variants.Entropy (Basel, Switzerland) · 2023Article
- Simplifying complex antibody engineering using machine learning.Cell systems · 2023Review
- Virological characteristics of the SARS-CoV-2 XBB variant derived from recombination of two Omicron subvariants.Nature communications · 2023Article
- Convergent evolution of SARS-CoV-2 Omicron subvariants leading to the emergence of BQ.1.1 variant.Nature communications · 2023Article
- Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.Journal of chemical information and modeling · 2023Review
- Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.BMJ health & care informatics · 2022Article
- Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics.
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Registered trials
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