ArticleProceedings of the National Academy of Sciences of the United States of America2024
Improved deep learning prediction of antigen-antibody interactions.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- ABAG-Rank: improving model selection of AlphaFold antibody-antigen complexes by learning to rank.Bioinformatics (Oxford, England) · 2026Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Predicting the evolutionary and functional landscapes of viruses with a unified nucleotide-protein language model: LucaVirus.National science review · 2026Article
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Biophysical trade-offs in antibody evolution are resolved by conformation-mediated epistasis.bioRxiv : the preprint server for biology · 2026Article
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Technological advancements in antibody-based therapeutics for treatment of diseases.Journal of biomedical science · 2025Review
- Predicting side chain conformations in folded proteins by AlphaFold: Perspective and challenges.Biophysical journal · 2025Article
- AF3Complex yields improved structural predictions of protein complexes.Bioinformatics (Oxford, England) · 2025Article
- AF3Complex Yields Improved Structural Predictions of Protein Complexes.bioRxiv : the preprint server for biology · 2025Article
- Bio-Inspired Mamba for Antibody-Antigen Interaction Prediction.Biomolecules · 2025Article
- Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins.bioRxiv : the preprint server for biology · 2025Article
- Investigating and evaluating potential antigen binding sites for monoclonal anti-HER2 antibodies: The LightDock approach.Computational and structural biotechnology journal · 2025Article
- Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning-based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen-antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen-antibody interactions.
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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 OpenQuestion graph.