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Accurate prediction of peptide-HLA-II binding serves as a cornerstone for deciphering cellular immune responses, and is essential for guiding the optimization of antibody therapeutics. Researchers have developed several computational approaches to identify peptide-HLA-II interaction and presentation. However, most computational approaches exhibit inconsistent predictive performance, poor generalization ability and limited biological interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this study, we present DSCA-HLAII, a novel predictive framework for peptide-HLA-II interactions and presentation based on a dual-stream cross-attention architecture. The framework proposes a dual-stream cross-attention (DSCA) mechanism to integrate pre-trained semantic embedding ESMC with sequence-level ONE-HOT features. The DSCA mechanism effectively models the interaction dynamics between peptides and HLA-II molecules, enabling the precise identification of key binding sites. Experimental results demonstrate that DSCA-HLAII consistently surpasses existing state-of-the-art approaches, demonstrating high accuracy and robustness in predicting peptide-HLA-II interactions and presentation. We further demonstrate the capability of DSCA-HLAII for predicting peptide binding cores and assessing antibody immunogenicity, which is expected to advance artificial intelligence-based peptide drug discovery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pcbi.1013836","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T18:23:45Z","timestamp":1767378225000},"page":"e1013836","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["DSCA-HLAII: A dual-stream cross-attention model for predicting peptide\u2013HLA class II interaction and presentation"],"prefix":"10.1371","volume":"22","author":[{"given":"Ke","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjun","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shutao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey 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Zou","year":"2019","journal-title":"RNA"},{"key":"pcbi.1013836.ref038","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2020.09.056","article-title":"A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome","volume":"422","author":"Z Lv","year":"2021","journal-title":"Neurocomputing"},{"key":"pcbi.1013836.ref039","unstructured":"Team E. ESM Cambrian: Revealing the mysteries of proteins with unsupervised learning. Evolutionary Scale. 2024."},{"issue":"8","key":"pcbi.1013836.ref040","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","article-title":"ProteinBERT: a universal deep-learning model of protein sequence and function","volume":"38","author":"N Brandes","year":"2022","journal-title":"Bioinformatics"},{"issue":"10","key":"pcbi.1013836.ref041","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: toward understanding the language of life through self-supervised learning","volume":"44","author":"A Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pcbi.1013836.ref042","volume-title":"Immunobiology: the immune system in health and disease","author":"CA Janeway","year":"2001"},{"issue":"20","key":"pcbi.1013836.ref043","doi-asserted-by":"crossref","first-page":"6097","DOI":"10.1093\/nar\/18.20.6097","article-title":"Sequence logos: a new way to display consensus sequences","volume":"18","author":"TD Schneider","year":"1990","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1013836.ref044","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1186\/1471-2105-8-238","article-title":"Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method","volume":"8","author":"M Nielsen","year":"2007","journal-title":"BMC Bioinformatics"},{"issue":"10","key":"pcbi.1013836.ref045","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1016\/S0969-2126(97)00288-8","article-title":"The class II MHC protein HLA-DR1 in complex with an endogenous peptide: implications for the structural basis of the specificity of peptide binding","volume":"5","author":"VL Murthy","year":"1997","journal-title":"Structure"},{"issue":"1","key":"pcbi.1013836.ref046","doi-asserted-by":"crossref","first-page":"4414","DOI":"10.1038\/s41467-020-18204-2","article-title":"Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction","volume":"11","author":"CG Rappazzo","year":"2020","journal-title":"Nat Commun"},{"key":"pcbi.1013836.ref047","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s002510050595","article-title":"SYFPEITHI: database for MHC ligands and peptide motifs","volume":"50","author":"H Rammensee","year":"1999","journal-title":"Immunogenetics"},{"key":"pcbi.1013836.ref048","first-page":"1135","volume-title":"\u201c Why should i trust you?\u201d Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"MT Ribeiro","year":"2016"},{"issue":"11","key":"pcbi.1013836.ref049","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1016\/S0149-2918(02)80075-3","article-title":"Immunogenicity of therapeutic proteins: clinical implications and future prospects","volume":"24","author":"H Schellekens","year":"2002","journal-title":"Clin Ther"},{"key":"pcbi.1013836.ref050","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.3389\/fimmu.2020.01301","article-title":"T-Cell Dependent Immunogenicity of Protein Therapeutics Pre-clinical Assessment and Mitigation-Updated Consensus and Review 2020","volume":"11","author":"V Jawa","year":"2020","journal-title":"Front Immunol"},{"issue":"7","key":"pcbi.1013836.ref051","article-title":"AI in drug development: advances in response, combination therapy, repositioning, and molecular design","volume":"68","author":"R Qi","year":"2025","journal-title":"Sci China Inf Sci"},{"issue":"1","key":"pcbi.1013836.ref052","doi-asserted-by":"crossref","first-page":"2020203","DOI":"10.1080\/19420862.2021.2020203","article-title":"BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning","volume":"14","author":"D Prihoda","year":"2022","journal-title":"MAbs"},{"issue":"9","key":"pcbi.1013836.ref053","doi-asserted-by":"crossref","first-page":"6189","DOI":"10.1109\/TSMC.2025.3578348","article-title":"FMvPCI: a multiview fusion neural network for identifying protein complex via fuzzy clustering","volume":"55","author":"Y Yang","year":"2025","journal-title":"IEEE Trans Syst Man Cybern, Syst"},{"issue":"8","key":"pcbi.1013836.ref054","doi-asserted-by":"crossref","first-page":"5730","DOI":"10.1109\/TSMC.2025.3572738","article-title":"Link-based attributed graph clustering via approximate generative Bayesian learning","volume":"55","author":"Y Yang","year":"2025","journal-title":"IEEE Trans Syst Man Cybern, Syst"},{"key":"pcbi.1013836.ref055","article-title":"The Immune Epitope Database (IEDB): 2024 update","volume":"53","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1013836.ref056","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gky1049","article-title":"UniProt: a worldwide hub of protein knowledge","volume":"47","author":"UniProt Consortium","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1013836.ref057","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkac1011","article-title":"The IPD-IMGT\/HLA Database","volume":"51","author":"DJ Barker","year":"2023","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"pcbi.1013836.ref058","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1186\/s12859-020-03546-x","article-title":"Amino acid encoding for deep learning applications","volume":"21","author":"H ElAbd","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"22","key":"pcbi.1013836.ref059","article-title":"BioSeq-BLM: a platform for analyzing DNA, RNA, and protein sequences based on biological language models","volume":"49","author":"H Li","year":"2021","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1013836.ref060","article-title":"Attention is all you need","volume":"30","author":"A Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"pcbi.1013836.ref061","first-page":"891","author":"CW Misner","year":"1973"},{"key":"pcbi.1013836.ref062","volume-title":"Deep learning","author":"I Goodfellow","year":"2016"},{"key":"pcbi.1013836.ref063","volume-title":"Adadelta: an adaptive learning rate method","author":"MD Zeiler","year":"2012"},{"issue":"1","key":"pcbi.1013836.ref064","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"N Srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"pcbi.1013836.ref065","first-page":"55","volume-title":"Early stopping-but when? In: Neural Networks: Tricks of the trade","author":"L Prechelt","year":"2002"},{"key":"pcbi.1013836.ref066","article-title":"A simple weight decay can improve generalization","volume":"4","author":"A Krogh","year":"1991","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"5","key":"pcbi.1013836.ref067","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-40072-y","article-title":"Computational approaches for predicting drug-disease associations: a comprehensive review","volume":"19","author":"Z Huang","year":"2024","journal-title":"Front Comput Sci"},{"issue":"1","key":"pcbi.1013836.ref068","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1186\/s12915-024-02085-8","article-title":"Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data","volume":"22","author":"Z Huang","year":"2024","journal-title":"BMC Biol"},{"issue":"7","key":"pcbi.1013836.ref069","article-title":"Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching","volume":"11","author":"X Guo","year":"2024","journal-title":"Adv Sci (Weinh)"},{"issue":"5","key":"pcbi.1013836.ref070","doi-asserted-by":"crossref","first-page":"8060","DOI":"10.1109\/TNNLS.2024.3419250","article-title":"ProFun-SOM: protein function prediction for specific ontology based on multiple sequence alignment reconstruction","volume":"36","author":"J Shao","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"pcbi.1013836.ref071","article-title":"sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure","volume":"39","author":"K Yan","year":"2023","journal-title":"Bioinformatics"},{"issue":"7","key":"pcbi.1013836.ref072","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"30","author":"AP Bradley","year":"1997","journal-title":"Pattern Recognition"},{"issue":"4","key":"pcbi.1013836.ref073","article-title":"scRiskCell: A single-cell framework for quantifying islet risk cells and their adaptive dynamics in type 2 diabetes","volume":"4","author":"X Xie","year":"2025","journal-title":"Imeta"},{"issue":"3","key":"pcbi.1013836.ref074","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"T Saito","year":"2015","journal-title":"PLoS One"},{"issue":"8","key":"pcbi.1013836.ref075","doi-asserted-by":"crossref","first-page":"15385","DOI":"10.1109\/TNNLS.2025.3540291","article-title":"Protein Language Pragmatic Analysis and Progressive Transfer Learning for Profiling Peptide-Protein Interactions","volume":"36","author":"S Chen","year":"2025","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013836","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T18:23:57Z","timestamp":1767378237000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013836"}},"subtitle":[],"editor":[{"given":"Lun","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,1,2]]},"references-count":75,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013836","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,2]]}}}