{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T13:21:37Z","timestamp":1778246497238,"version":"3.51.4"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"vor","delay-in-days":87,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12474192"],"award-info":[{"award-number":["12474192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ24A040002"],"award-info":[{"award-number":["LZ24A040002"]}]},{"name":"Beijing National Laboratory for Condensed Matter Physics","award":["2023BNLCMPKF009"],"award-info":[{"award-number":["2023BNLCMPKF009"]}]},{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials","award":["2025ZY01036"],"award-info":[{"award-number":["2025ZY01036"]}]},{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials","award":["2025E10072"],"award-info":[{"award-number":["2025E10072"]}]},{"DOI":"10.13039\/501100013910","name":"Wenzhou Institute, University of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["WIUCASQD2022019"],"award-info":[{"award-number":["WIUCASQD2022019"]}],"id":[{"id":"10.13039\/501100013910","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013910","name":"Wenzhou Institute, University of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["WIUCASQD2021010"],"award-info":[{"award-number":["WIUCASQD2021010"]}],"id":[{"id":"10.13039\/501100013910","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013910","name":"Wenzhou Institute, University of Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["WIUCASQD2023015"],"award-info":[{"award-number":["WIUCASQD2023015"]}],"id":[{"id":"10.13039\/501100013910","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>MHCII-peptide binding plays a vital role in immunology. MHCII molecules are primarily expressed on the surface of antigen presenting cells where they capture and present exogenous antigen peptides to helper T cells, thereby activating humoral and cellular immune responses. Designing artificial MHCII binding peptides mimicking native peptides is crucial for vaccine development. However, it is challenge to design artificial binding peptides with conventional structure-based methods since the structures of the peptides are highly flexible at unbound state. In this work, we trained Transformer neural network to design the artificial peptides with sequence-based evolutionary information including the frequency distribution of amino acids at each site and the joint frequency distribution between amino acid pairs extracted from multiple sequence alignment of native peptides. In light of accurate sequence-based scoring function and reliable AlphaFold3 for complex structure prediction, the designed artificial peptides were predicted to have comparable binding affinities as native peptides and high structural confidence (pLDDT\u2009&amp;gt;\u200990.0) when binding to MHCII. Our work establishes a paradigm for designing different kinds of functional peptides and will greatly provide significant assistance to biomedical researchers in the medical and industrial fields.<\/jats:p>","DOI":"10.1093\/bioadv\/vbag090","type":"journal-article","created":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T10:24:56Z","timestamp":1774779896000},"source":"Crossref","is-referenced-by-count":0,"title":["Computational design of class II MHC binding peptide with sequence-based evolution information"],"prefix":"10.1093","volume":"6","author":[{"given":"Ying","family":"Cao","sequence":"first","affiliation":[{"name":"Postgraduate Training Base Alliance, Wenzhou Medical University , Wenzhou, Zhejiang Province 325000,","place":["China"]},{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials, Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou, Zhejiang Province 325000,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials, Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou, Zhejiang Province 325000,","place":["China"]},{"name":"Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, National Engineering Research Center for Marine Aquaculture, Marine Science and Technology College , Zhoushan, Zhejiang Province 316004,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weitong","family":"Ren","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials, Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou, Zhejiang Province 325000,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-4075","authenticated-orcid":false,"given":"Wenfei","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials, Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou, Zhejiang Province 325000,","place":["China"]},{"name":"School of Physics, National Laboratory of Solid State Microstructure, Nanjing University , Nanjing, Jiangsu Province 210093,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3865-3229","authenticated-orcid":false,"given":"Zhiqiang","family":"Yan","sequence":"additional","affiliation":[{"name":"Postgraduate Training Base Alliance, Wenzhou Medical University , Wenzhou, Zhejiang Province 325000,","place":["China"]},{"name":"Zhejiang Key Laboratory of Soft Matter Biomedical Materials, Wenzhou Institute, University of Chinese Academy of Sciences , Wenzhou, Zhejiang Province 325000,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"2026050809063228000_vbag090-B1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2026050809063228000_vbag090-B2","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s41586-019-1671-8","article-title":"MHC-II neoantigens shape tumour immunity and response to immunotherapy","volume":"574","author":"Alspach","year":"2019","journal-title":"Nature"},{"key":"2026050809063228000_vbag090-B3","doi-asserted-by":"crossref","first-page":"e1700252","DOI":"10.1002\/pmic.201700252","article-title":"Computational tools for the identification and interpretation of sequence motifs in immunopeptidomes","volume":"18","author":"Alvarez","year":"2018","journal-title":"Proteomics"},{"key":"2026050809063228000_vbag090-B4","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"2026050809063228000_vbag090-B5","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1146\/annurev-immunol-032712-095910","article-title":"Pathways of antigen processing","volume":"31","author":"Blum","year":"2013","journal-title":"Annu Rev Immunol"},{"key":"2026050809063228000_vbag090-B6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/978-1-4939-6637-0_12","article-title":"An evolution-based approach to de novo protein design","volume":"1529","author":"Brender","year":"2017","journal-title":"Methods Mol Biol"},{"key":"2026050809063228000_vbag090-B7","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1016\/j.tcs.2010.10.040","article-title":"Co-evolution and information signals in biological sequences","volume":"412","author":"Carbone","year":"2011","journal-title":"Theor Comput Sci"},{"key":"2026050809063228000_vbag090-B8","volume-title":"Molecular Biology of the Cell","author":"Chaffey","year":"2003","edition":"4th edn"},{"key":"2026050809063228000_vbag090-B9","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1101\/gr.849004","article-title":"WebLogo: a sequence logo generator","volume":"14","author":"Crooks","year":"2004","journal-title":"Genome Res"},{"key":"2026050809063228000_vbag090-B10","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1126\/science.add2187","article-title":"Robust deep learning\u2013based protein sequence design using ProteinMPNN","volume":"378","author":"Dauparas","year":"2022","journal-title":"Science"},{"key":"2026050809063228000_vbag090-B11","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1084\/jem.20132000","article-title":"Lymph node stromal cells acquire peptide\u2013MHCII complexes from dendritic cells and induce antigen-specific CD4+ T cell tolerance","volume":"211","author":"Dubrot","year":"2014","journal-title":"J Exp Med"},{"key":"2026050809063228000_vbag090-B12","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1093\/molbev\/msy007","article-title":"How pairwise coevolutionary models capture the collective residue variability in proteins?","volume":"35","author":"Figliuzzi","year":"2018","journal-title":"Mol Biol Evol"},{"key":"2026050809063228000_vbag090-B13","doi-asserted-by":"crossref","first-page":"12186","DOI":"10.1073\/pnas.1607570113","article-title":"Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis","volume":"113","author":"Gueudr\u00e9","year":"2016","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026050809063228000_vbag090-B14","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv Neural Inform Process Syst"},{"key":"2026050809063228000_vbag090-B15","doi-asserted-by":"crossref","first-page":"btad469","DOI":"10.1093\/bioinformatics\/btad469","article-title":"Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling","volume":"39","author":"Hartout","year":"2023","journal-title":"Bioinformatics"},{"key":"2026050809063228000_vbag090-B16","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1038\/s41586-023-06728-8","article-title":"Illuminating protein space with a programmable generative model","volume":"623","author":"Ingraham","year":"2023","journal-title":"Nature"},{"key":"2026050809063228000_vbag090-B17","volume-title":"Immunobiology: The Immune System in Health and Disease","author":"Janeway","year":"2001"},{"key":"2026050809063228000_vbag090-B18","first-page":"157","volume-title":"Cancer Bioinformatics","author":"Jurtz","year":"2018"},{"key":"2026050809063228000_vbag090-B19","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/s41588-022-01273-y","article-title":"MHC II immunogenicity shapes the neoepitope landscape in human tumors","volume":"55","author":"Kim","year":"2023","journal-title":"Nat Genet"},{"key":"2026050809063228000_vbag090-B20","doi-asserted-by":"crossref","first-page":"3600","DOI":"10.1093\/bioinformatics\/btv371","article-title":"High-order neural networks and kernel methods for peptide-MHC binding prediction","volume":"31","author":"Kuksa","year":"2015","journal-title":"Bioinformatics"},{"key":"2026050809063228000_vbag090-B21","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S1074-7613(01)00092-9","article-title":"Crystal structure of a superantigen bound to the high-affinity, zinc-dependent site on MHC class II","volume":"14","author":"Li","year":"2001","journal-title":"Immunity"},{"key":"2026050809063228000_vbag090-B22","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1038\/s41587-022-01618-2","article-title":"Large language models generate functional protein sequences across diverse families","volume":"41","author":"Madani","year":"2023","journal-title":"Nat. Biotechnol"},{"key":"2026050809063228000_vbag090-B23","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1038\/nbt.2419","article-title":"Protein structure prediction from sequence variation","volume":"30","author":"Marks","year":"2012","journal-title":"Nat Biotechnol"},{"key":"2026050809063228000_vbag090-B24","doi-asserted-by":"crossref","first-page":"E1293","DOI":"10.1073\/pnas.1111471108","article-title":"Direct-coupling analysis of residue coevolution captures native contacts across many protein families","volume":"108","author":"Morcos","year":"2011","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2026050809063228000_vbag090-B25","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1038\/s41592-023-01891-2","article-title":"Machine learning for MHC II specificities","volume":"20","author":"Mukhopadhyay","year":"2023","journal-title":"Nat Methods"},{"key":"2026050809063228000_vbag090-B26","doi-asserted-by":"crossref","first-page":"eadj6367","DOI":"10.1126\/sciadv.adj6367","article-title":"Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning","volume":"9","author":"Nilsson","year":"2023","journal-title":"Sci Adv"},{"key":"2026050809063228000_vbag090-B27","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.cbpa.2021.08.004","article-title":"Structure-based protein design with deep learning","volume":"65","author":"Ovchinnikov","year":"2021","journal-title":"Curr Opin Chem Biol"},{"key":"2026050809063228000_vbag090-B28","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1126\/science.aah4043","article-title":"Protein structure determination using metagenome sequence data","volume":"355","author":"Ovchinnikov","year":"2017","journal-title":"Science"},{"key":"2026050809063228000_vbag090-B29","volume-title":"Major Histocompatibility Complex: Antigen Processing and Presentation","author":"Paola Cruz-Tapias","year":"2013"},{"key":"2026050809063228000_vbag090-B30","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1002\/prot.26171","article-title":"High-accuracy protein structure prediction in CASP14","volume":"89","author":"Pereira","year":"2021","journal-title":"Proteins: Structure, Function, and Bioinformatics"},{"key":"2026050809063228000_vbag090-B31","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.2174\/1385272824999200910094034","article-title":"Designing short peptides: a sisyphean task?","volume":"24","author":"Pineda-Casta\u00f1eda","year":"2020","journal-title":"COC"},{"key":"2026050809063228000_vbag090-B32","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1016\/j.immuni.2023.03.009","article-title":"Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes","volume":"56","author":"Racle","year":"2023","journal-title":"Immunity"},{"key":"2026050809063228000_vbag090-B33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s002510050595","article-title":"SYFPEITHI: database for MHC ligands and peptide motifs","volume":"50","author":"Rammensee","year":"1999","journal-title":"Immunogenetics"},{"key":"2026050809063228000_vbag090-B34","doi-asserted-by":"crossref","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","article-title":"NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data","volume":"48","author":"Reynisson","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2026050809063228000_vbag090-B35","doi-asserted-by":"crossref","first-page":"e1004817","DOI":"10.1371\/journal.pcbi.1004817","article-title":"Evolution-based functional decomposition of proteins","volume":"12","author":"Rivoire","year":"2016","journal-title":"PLoS Comput Biol"},{"key":"2026050809063228000_vbag090-B36","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1126\/science.aba3304","article-title":"An evolution-based model for designing chorismate mutase enzymes","volume":"369","author":"Russ","year":"2020","journal-title":"Science"},{"key":"2026050809063228000_vbag090-B37","author":"Shah","year":"2024"},{"key":"2026050809063228000_vbag090-B38","doi-asserted-by":"crossref","first-page":"855","DOI":"10.2174\/1568026620666200226105312","article-title":"Decoding protein-protein interactions: an overview","volume":"20","author":"Slater","year":"2020","journal-title":"Curr Top Med Chem"},{"key":"2026050809063228000_vbag090-B39","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1038\/nature03991","article-title":"Evolutionary information for specifying a protein fold","volume":"437","author":"Socolich","year":"2005","journal-title":"Nature"},{"key":"2026050809063228000_vbag090-B40","doi-asserted-by":"crossref","first-page":"11804","DOI":"10.1038\/ncomms11804","article-title":"Efficient generation of monoclonal antibodies against peptide in the context of MHCII using magnetic enrichment","volume":"7","author":"Spanier","year":"2016","journal-title":"Nat Commun"},{"key":"2026050809063228000_vbag090-B41","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1744-313X.2008.00765.x","article-title":"Human MHC architecture and evolution: implications for disease association studies","volume":"35","author":"Traherne","year":"2008","journal-title":"Int J Immunogenet"},{"key":"2026050809063228000_vbag090-B42","doi-asserted-by":"crossref","first-page":"i399","DOI":"10.1093\/bioinformatics\/btaa479","article-title":"MHCAttnNet: predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model","volume":"36","author":"Venkatesh","year":"2020","journal-title":"Bioinformatics"},{"key":"2026050809063228000_vbag090-B43","doi-asserted-by":"crossref","first-page":"D854","DOI":"10.1093\/nar\/gkp1004","article-title":"The immune epitope database 2.0","volume":"38","author":"Vita","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2026050809063228000_vbag090-B44","doi-asserted-by":"crossref","first-page":"27218","DOI":"10.1073\/pnas.2013822117","article-title":"Funneled energy landscape unifies principles of protein binding and evolution","volume":"117","author":"Yan","year":"2020","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2026050809063228000_vbag090-B45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1038\/s42004-024-01098-2","article-title":"Evolution shapes interaction patterns for epistasis and specific protein binding in a two-component signaling system","volume":"7","author":"Yan","year":"2024","journal-title":"Commun Chem"},{"key":"2026050809063228000_vbag090-B46","doi-asserted-by":"crossref","first-page":"1293706","DOI":"10.3389\/fimmu.2024.1293706","article-title":"MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods","volume":"15","author":"Yang","year":"2024","journal-title":"Front Immunol"},{"key":"2026050809063228000_vbag090-B47","doi-asserted-by":"crossref","first-page":"i220","DOI":"10.1093\/bioinformatics\/btac225","article-title":"DeepMHCII: a novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction","volume":"38","author":"You","year":"2022","journal-title":"Bioinformatics"},{"key":"2026050809063228000_vbag090-B48","doi-asserted-by":"crossref","first-page":"016108","DOI":"10.1063\/1.5133026","article-title":"Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling","volume":"4","author":"Yu","year":"2020","journal-title":"APL Bioeng"},{"key":"2026050809063228000_vbag090-B49","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1093\/bib\/bbr060","article-title":"Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools","volume":"13","author":"Zhang","year":"2012","journal-title":"Brief Bioinform"}],"container-title":["Bioinformatics Advances"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/advance-article-pdf\/doi\/10.1093\/bioadv\/vbag090\/67637130\/vbag090.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article-pdf\/6\/1\/vbag090\/67637130\/vbag090.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article-pdf\/6\/1\/vbag090\/67637130\/vbag090.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T13:06:44Z","timestamp":1778245604000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformaticsadvances\/article\/doi\/10.1093\/bioadv\/vbag090\/8555605"}},"subtitle":[],"editor":[{"given":"Michael","family":"DeGiorgio","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,7]]}},"URL":"https:\/\/doi.org\/10.1093\/bioadv\/vbag090","relation":{},"ISSN":["2635-0041"],"issn-type":[{"value":"2635-0041","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026]]},"published":{"date-parts":[[2026]]},"article-number":"vbag090"}}