{"doi":"10.1126/sciadv.adz8759","title":"T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine","abstract":"<jats:p>T cell immunogenicity, the ability of peptide fragments to elicit T cell responses, is a critical determinant of the safety and efficacy of protein therapeutics and vaccines. While deep learning shows promise for in silico prediction, the scarcity of comprehensive immunogenicity data is a major challenge. We present T cell immunogenicity scoring via cross-domain aided predictive engine (T-SCAPE), a novel multidomain deep learning framework that leverages adversarial domain adaptation to integrate diverse immunologically relevant data sources, including major histocompatibility complex (MHC) presentation, peptide-MHC (pMHC) binding affinity, T cell receptor–pMHC interaction, source organism information, and T cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE demonstrates exceptional performance in predicting T cell activation for specific peptide-MHC pairs. It also accurately predicts the antidrug antibody–inducing potential of therapeutic antibodies without requiring MHC inputs. This success is attributed to T-SCAPE’s biologically grounded and data-driven multidomain pretraining. Its consistent and robust performance highlights its potential to advance the development of safer and more effective vaccines and protein therapeutics.</jats:p>","journal":"Science Advances","year":2025,"id":619558,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1598916,"name":"Nuri Jung","orcid":"0000-0002-4596-1257","position":1,"is_corresponding":false},{"id":1598917,"name":"Jayyoon Lee","orcid":"0000-0002-5802-7180","position":2,"is_corresponding":false},{"id":413897,"name":"Nam‐Hyuk Cho","orcid":"0000-0003-3673-6397","position":3,"is_corresponding":false},{"id":1598918,"name":"Jinsung Noh","orcid":"0000-0002-7167-8113","position":4,"is_corresponding":false},{"id":217344,"name":"Chaok Seok","orcid":"0000-0002-1419-9888","position":5,"is_corresponding":false},{"id":488040,"name":"Jeonghyeon Kim","orcid":"0000-0001-7808-2921","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine","abstract":"<jats:p>T cell immunogenicity, the ability of peptide fragments to elicit T cell responses, is a critical determinant of the safety and efficacy of protein therapeutics and vaccines. While deep learning shows promise for in silico prediction, the scarcity of comprehensive immunogenicity data is a major challenge. We present T cell immunogenicity scoring via cross-domain aided predictive engine (T-SCAPE), a novel multidomain deep learning framework that leverages adversarial domain adaptation to integrate diverse immunologically relevant data sources, including major histocompatibility complex (MHC) presentation, peptide-MHC (pMHC) binding affinity, T cell receptor–pMHC interaction, source organism information, and T cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE demonstrates exceptional performance in predicting T cell activation for specific peptide-MHC pairs. It also accurately predicts the antidrug antibody–inducing potential of therapeutic antibodies without requiring MHC inputs. This success is attributed to T-SCAPE’s biologically grounded and data-driven multidomain pretraining. Its consistent and robust performance highlights its potential to advance the development of safer and more effective vaccines and protein therapeutics.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"41348893","pmcid":"PMC12680054","openalex_id":null,"authors":[],"funders":[{"funder_name":"National Research Foundation of Korea","grant_id":"2020M3A9G7103933","title":null},{"funder_name":"Korea government","grant_id":"","title":null},{"funder_name":"Galux Inc.","grant_id":"","title":null}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":null,"license":null,"oa_locations":[{"url":"https://www.science.org/doi/pdf/10.1126/sciadv.adz8759","host_type":"publisher"},{"url":"https://europepmc.org/articles/PMC12680054","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC12680054?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":[],"mesh_terms":["T-Lymphocytes","Humans","Peptides","Receptors, Antigen, T-Cell","Lymphocyte Activation","Computational Biology","Major Histocompatibility Complex","Deep Learning"],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T07:36:33.564110Z","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":[]}