{"doi":"10.1073/pnas.2216697120","title":"Peptide-binding specificity prediction using fine-tuned protein structure prediction networks","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.","journal":"Proceedings of the National Academy of Sciences","year":2023,"id":315996,"datarank":0.7218276533058626,"base_score":4.812184355372417,"endowment":4.812184355372417,"self_citation_contribution":0.7218276533058626,"citation_network_contribution":0.0,"self_endowment_contribution":0.7218276533058626,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":122,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9589,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":552413,"name":"Justas Dauparas","orcid":"0000-0002-0030-144X","position":1,"is_corresponding":false},{"id":614112,"name":"Minkyung Baek","orcid":"0000-0003-3414-9404","position":2,"is_corresponding":false},{"id":808806,"name":"Mohamad H. Abedi","orcid":"0000-0001-9717-6288","position":3,"is_corresponding":false},{"id":105706,"name":"David Baker","orcid":"0000-0001-7896-6217","position":4,"is_corresponding":false},{"id":114739,"name":"Philip Bradley","orcid":"0000-0002-0224-6464","position":5,"is_corresponding":false},{"id":1018572,"name":"Amir Motmaen","orcid":"0000-0003-4190-6215","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:06:32.274501Z","pmid":"36802421","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}