ArticleProceedings of the National Academy of Sciences of the United States of America2023
Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- PEARL: novel protein targets recommendation model via heterogeneous graph representation learning.Briefings in bioinformatics · 2026Article
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.BMC medical genomics · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- New developments and applications of human organoids.Nature reviews. Molecular cell biology · 2026Review
- A generalizable system for antigenic peptide targeting across HLA-I allotypes.bioRxiv : the preprint server for biology · 2026Article
- A high-speed attention network for MHC-bound peptide identification and 3D modeling.Cell reports methods · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Review
- Decoding TCR recognition via geometric deep learning of immunological fingerprints.Briefings in bioinformatics · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- STRUMP-I: Structure-Based Machine Learning Approach to pMHC-I Binding Prediction Using Force Field Energy Features.Computational and structural biotechnology journal · 2026Article
- A protein-fragment complementation assay to quantify synthetic protein scaffold efficiency.Synthetic biology (Oxford, England) · 2026Article
- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Targeting peptide-MHC complexes with designed T cell receptors and antibodies.bioRxiv : the preprint server for biology · 2025Article
- Predicted peptide scaffolds for drug screening in endometrial cancer organoids.Scientific reports · 2025Article
- STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features.bioRxiv : the preprint server for biology · 2025Article
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
13 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in part because it has been a challenge to model the subtle structural changes accompanying sequence substitutions. Protein structure prediction networks such as AlphaFold model sequence-structure relationships very accurately, and we reasoned that if it were possible to specifically train such networks on binding data, more generalizable models could be created. We show that placing a classifier on top of the AlphaFold network and fine-tuning the combined network parameters for both classification and structure prediction accuracy leads to a model with strong generalizable performance on a wide range of Class I and Class II peptide-MHC interactions that approaches the overall performance of the state-of-the-art NetMHCpan sequence-based method. The peptide-MHC optimized model shows excellent performance in distinguishing binding and non-binding peptides to SH3 and PDZ domains. This ability to generalize well beyond the training set far exceeds that of sequence-only models and should be particularly powerful for systems where less experimental data are available.
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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.