ArticleProceedings of the National Academy of Sciences of the United States of America2025
Learning the language of antibody hypervariability.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Prediction of antibody non-specificity using protein language models and biophysical parameters.mAbs · 2026Article
- Charge-driven antiviral responses enhance autoreactivity in severe COVID-19.Research square · 2026Article
- Language Model Embedding Classifiers Enable Identification of Multiple Sclerosis-Associated BCRs and Repertoires.bioRxiv : the preprint server for biology · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- Article
- Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026Article
- Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- IFNg_DeepKG: A Novel Model for Identifying Interferon-Gamma-Inducing Epitopes Using Knowledge Graph RAG in Biomedical Applications.Journal of chemical information and modeling · 2026Article
- Learning the language of protein-protein interactions.Nature communications · 2026Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.PLoS computational biology · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- Iterative immunogen optimization to focus immune responses on a conserved, subdominant viral epitope.bioRxiv : the preprint server for biology · 2025Article
- Delineating inter- and intra-antibody repertoire evolution with AntibodyForests.Bioinformatics (Oxford, England) · 2025Article
- Large language models for biological sequence analysis in infectious disease research.Biosafety and health · 2025Review
- Protein language model pseudolikelihoods capture features of in vivo B cell selection and evolution.Briefings in bioinformatics · 2025Article
- Learning the language of protein-protein interactions.bioRxiv : the preprint server for biology · 2025Article
- Aggregating residue-level protein language model embeddings with optimal transport.Bioinformatics advances · 2025Article
- SResearch (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP's transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.
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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.