ArticleNature communications2025
A protein language model for exploring viral fitness landscapes.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- From sites to structure to serology: a roadmap for structure-aware molecular evolution of antigenically evolving viruses.Journal of virology · 2026Review
- EscaPRRS-ORF5: a structure-aware evolutionary framework for prioritizing immune escape-prone variants in porcine reproductive and respiratory syndrome virus.Bioinformatics (Oxford, England) · 2026Article
- Epistasis facilitates the long-term antigenic evolution of the influenza B virus hemagglutinin.bioRxiv : the preprint server for biology · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Forecasting virus evolution by integrating genotype-phenotype-epidemiology.Nature microbiology · 2026Article
- A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal resolution.Nature microbiology · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Predictive modeling of immune escape and antigenic grouping of SARS-CoV-2 variants.Journal of virology · 2026Article
- DeepTYLCV: An interpretable and experimentally validated AI model for predicting virulence of different tomato yellow leaf curl virus strains.Plant communications · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- Genomic-epidemiological analysis of 15 million SARS-CoV-2 genomes reveals accelerated fitness gain of JN.1 lineage.Virologica Sinica · 2026Article
- Inferring context-specific site variation with evotuned protein language models.NAR genomics and bioinformatics · 2026Article
- From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2.Nature communications · 2026Article
- Phylogenetic and Proteomic Analyses of Segment 2 Sequence Reveals the Presence of Two Variants of a Divergent Amnoonvirus (Family:Microorganisms · 2026Article
- Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics.Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Societe canadienne des sciences pharmaceutiques · 2026Review
- Protein Language Models in Virology: A Review of Advances and Applications.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Constrained Evolutionary Funnels Shape Viral Immune Escape.bioRxiv : the preprint server for biology · 2025Article
- Large language models for biological sequence analysis in infectious disease research.Biosafety and health · 2025Review
- SARITA: a large language model for generating the S1 subunit of the SARS-CoV-2 spike protein.Briefings in bioinformatics · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Successively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated fitness (i.e., relative effective reproduction number between variants). Modeling the genotype-fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we present CoVFit, a protein language model adapted from ESM-2, designed to predict variant fitness based solely on spike protein sequences. CoVFit was trained on genotype-fitness data derived from viral genome surveillance and functional mutation assays related to immune evasion. CoVFit successively ranked the fitness of unknown future variants harboring nearly 15 mutations with informative accuracy. CoVFit identified 959 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, we show that CoVFit is applicable for predicting viral evolution through single amino acid mutations. Our study gives insight into the SARS-CoV-2 fitness landscape and provides a tool for efficiently identifying SARS-CoV-2 variants with higher epidemic risk.
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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.