ArticleBioinformatics (Oxford, England)2020
Learning context-aware structural representations to predict antigen and antibody binding interfaces.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- Computational design and translational development of TCR-mimic scFv antibodies for peptide-HLA neoantigen targeting.npj drug discovery · 2026Review
- ParaEM: antigen-aware sequence-based paratope prediction with expectation-maximization.Bioinformatics (Oxford, England) · 2026Article
- Scaling antibody language models improves structure aware representation for antibody engineering.Communications biology · 2026Article
- The sequence alignment problem: boundary conditions as the unifying principle.Briefings in bioinformatics · 2026Review
- Interpretable Antibody-Antigen Structural Interface Prediction via Adaptive Graph Learning and Cyclic Transfer.bioRxiv : the preprint server for biology · 2026Article
- MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C.PLoS computational biology · 2026Article
- Antibody-antigen neutralization prediction by integrating structural information distillation and physicochemical constraints.Briefings in bioinformatics · 2026Article
- Disulphide and sequence-encoded conformational priors guide nanobody structure prediction.bioRxiv : the preprint server for biology · 2026Article
- Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics.PLoS computational biology · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Review
- Improving B-cell epitope prediction.Drug discovery today · 2025Review
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Enhancing antibody-antigen interaction prediction with atomic flexibility.PLoS computational biology · 2025Article
- A deep learning method for predicting interactions for intrinsically disordered regions of proteins.bioRxiv : the preprint server for biology · 2025Article
- Bio-Inspired Mamba for Antibody-Antigen Interaction Prediction.Biomolecules · 2025Article
- A comprehensive antigen-antibody complex database unlocking insights into interaction interface.eLife · 2025Article
- Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.Journal of biomedical science · 2025Review
18 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
motivationUnderstanding how antibodies specifically interact with their antigens can enable better drug and vaccine design, as well as provide insights into natural immunity. Experimental structural characterization can detail the 'ground truth' of antibody-antigen interactions, but computational methods are required to efficiently scale to large-scale studies. To increase prediction accuracy as well as to provide a means to gain new biological insights into these interactions, we have developed a unified deep learning-based framework to predict binding interfaces on both antibodies and antigens.
resultsOur framework leverages three key aspects of antibody-antigen interactions to learn predictive structural representations: (i) since interfaces are formed from multiple residues in spatial proximity, we employ graph convolutions to aggregate properties across local regions in a protein; (ii) since interactions are specific between antibody-antigen pairs, we employ an attention layer to explicitly encode the context of the partner; (iii) since more data are available for general protein-protein interactions, we employ transfer learning to leverage this data as a prior for the specific case of antibody-antigen interactions. We show that this single framework achieves state-of-the-art performance at predicting binding interfaces on both antibodies and antigens, and that each of its three aspects drives additional improvement in the performance. We further show that the attention layer not only improves performance, but also provides a biologically interpretable perspective into the mode of interaction. AVAILABILITY AND IMPLEMENTATION: The source code is freely available on github at https://github.com/vamships/PECAN.git.
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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.