ArticleProceedings of the National Academy of Sciences of the United States of America2024
Biophysical principles predict fitness of SARS-CoV-2 variants.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. 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.
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Forecasting virus evolution by integrating genotype-phenotype-epidemiology.Nature microbiology · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- The role of receptor binding and immunity in SARS-CoV-2 fitness landscape: A modeling study.iScience · 2026Article
- Phase diagrams for biophysical fitness landscape design.bioRxiv : the preprint server for biology · 2026Article
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Mechanistic modeling or machine learning for detecting variants of concern: Why not both?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predicting high-fitness viral protein variants with Bayesian active learning and biophysics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.Journal of the Royal Society, Interface · 2025Article
- Biophysics of SARS-CoV-2 spike protein's receptor-binding domain interaction with ACE2 and neutralizing antibodies: from computation to functional insights.Biophysical reviews · 2025Review
- Few-Shot Viral Variant Detection via Bayesian Active Learning and Biophysics.bioRxiv : the preprint server for biology · 2025Article
- Without safeguards, AI-Biology integration risks accelerating future pandemics.Frontiers in microbiology · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- Structure and dynamics of the interaction of Delta and Omicron BA.1 SARS-CoV-2 variants with REGN10987 Fab reveal mechanism of antibody action.Communications biology · 2024Article
- The biophysical landscape of viral evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
4 authors.
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Abstract
SARS-CoV-2 employs its spike protein's receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBD's biophysical properties contribute to SARS-CoV-2's epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the identification of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by dissociation constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto an epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low-frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.