{"doi":"10.1101/2020.05.21.109967","title":"CD4+ T-cell epitope prediction by combined analysis of antigen conformational flexibility and peptide-MHCII binding affinity","abstract":"Abstract Antigen processing in the class II MHC pathway depends on conventional proteolytic enzymes, potentially acting on antigens in native-like conformational states. CD4+ epitope dominance arises from a competition between antigen folding, proteolysis, and MHCII binding. Protease-sensitive sites, linear antibody epitopes, and CD4+ T-cell epitopes were mapped in the plague vaccine candidate F1-V to evaluate the various contributions to CD4+ epitope dominance. Using X-ray crystal structures, antigen processing likelihood (APL) predicts CD4+ epitopes with significant accuracy without considering peptide-MHCII binding affinity. The profiles of conformational flexibility derived from the X-ray crystal structures of the F1-V proteins, Caf1 and LcrV, were similar to the biochemical profiles of linear antibody epitope reactivity and protease-sensitivity, suggesting that the role of structure in proteolysis was captured by the analysis of the crystal structures. The patterns of CD4+ T-cell epitope dominance in C57BL/6, CBA, and BALB/c mice were compared to epitope predictions based on APL, peptide binding to MHCII proteins, or both. For a sample of 13 diverse antigens larger than 200 residues, accuracy of epitope prediction by the combination of APL and I-A b -MHCII-peptide affinity approached 40%. When MHCII allele specificity is also diverse, such as in human immunity, prediction of dominant epitopes by APL alone approached 40%. Since dominant CD4+ epitopes tend to occur in conformationally stable antigen domains, crystal structures typically are available for analysis by APL; and thus, the requirement for a crystal structure is not a severe limitation.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":129693,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":581338,"name":"Daniel L. Moss","orcid":"0000-0001-7639-7276","position":1,"is_corresponding":false},{"id":581339,"name":"Pawan Bhat","orcid":"0000-0003-0520-4363","position":2,"is_corresponding":false},{"id":581961,"name":"Peyton W. Moore","orcid":null,"position":3,"is_corresponding":false},{"id":581340,"name":"Nicholas Kummer","orcid":"0000-0003-0334-4434","position":4,"is_corresponding":false},{"id":581341,"name":"Avik Bhattacharya","orcid":"0000-0003-4319-9235","position":5,"is_corresponding":false},{"id":581342,"name":"Ramgopal R. Mettu","orcid":"0000-0001-9479-9156","position":6,"is_corresponding":false},{"id":581343,"name":"Samuel J. Landry","orcid":"0000-0002-4082-0543","position":7,"is_corresponding":false},{"id":256756,"name":"Tysheena P. Charles","orcid":null,"position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:15:49.682497Z","pmid":null,"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":[]}