ArticleImmunity2024
An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies.
Article in Immunity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Prediction of antibody non-specificity using protein language models and biophysical parameters.mAbs · 2026Article
- Epistasis and pleiotropy constrain the evolution of cross-reactive breadth in a broadly neutralizing influenza antibody.PLoS biology · 2026Article
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- Charge-driven antiviral responses enhance autoreactivity in severe COVID-19.Research square · 2026Article
- Predicting the evolutionary and functional landscapes of viruses with a unified nucleotide-protein language model: LucaVirus.National science review · 2026Article
- Structural basis for distinct protective mechanisms of IGHV3-23 antibodies targeting influenza hemagglutinin stem.Nature communications · 2026Article
- Evaluating Expert Specialization in Mixture-of-Experts Antibody Language Models.bioRxiv : the preprint server for biology · 2026Article
- Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- Structural insights into antibody responses against influenza A virus in its natural reservoir.bioRxiv : the preprint server for biology · 2026Article
- Affinity maturation and light-chain-mediated paratope diversification anticipates viral evolution.Cell reports · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- ProTCR: a protein language model-driven framework for decoding TCR-antigen recognition toward precision immunotherapies.Briefings in bioinformatics · 2026Article
- Confronting the known unknown: historical lessons and future strategies for Disease X.Frontiers in public health · 2026Review
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Antibody-drug conjugates for infectious and neglected tropical diseases: chemical design principles, target biology, and translational challenges.Frontiers in chemistry · 2026Review
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- Characterization of two non-competing antibodies to influenza H3N2 hemagglutinin stem reveals its evolving antigenicity.Nature communications · 2025Article
- Data-optimal scaling of paired antibody language models.bioRxiv : the preprint server for biology · 2025Article
- Vulnerability in the breadth evolution of an influenza broadly neutralizing antibody.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and the inaccessibility of datasets for model training. In this study, we curated >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM could identify key sequence features of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of the antibody response to the influenza virus but also provides a valuable resource for applying deep learning to antibody research.
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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 OpenQuestion graph.