{"doi":"10.4049/jimmunol.2000224","title":"HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide–MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide–MHC-II binding data as well as improving MHC-II prediction tools.</jats:p>","journal":"The Journal of Immunology","year":2020,"id":599369,"datarank":0.41588830833596724,"base_score":2.772588722239781,"endowment":2.772588722239781,"self_citation_contribution":0.41588830833596724,"citation_network_contribution":0.0,"self_endowment_contribution":0.41588830833596724,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":15,"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":56546,"name":"Morten Nielsen","orcid":"0000-0001-7885-4311","position":1,"is_corresponding":false},{"id":1536100,"name":"Nadine L Dudek","orcid":null,"position":2,"is_corresponding":false},{"id":1536101,"name":"Sri H Ramarathinam","orcid":"0000-0002-2787-1282","position":3,"is_corresponding":false},{"id":287873,"name":"Anthony W. 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In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide–MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide–MHC-II binding data as well as improving MHC-II prediction tools.</jats:p>","is_dataset_classified":null,"base_score":2.772588722239781,"endowment":2.772588722239781,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"32482711","pmcid":"PMC7313418","openalex_id":"https://openalex.org/W3031013384","authors":[],"funders":[{"funder_name":"European Commission","grant_id":"278832","title":"High-Density Peptide MicroArrays and high-throughput, label-free detection of peptides, modifications and interactions"},{"funder_name":"Department of Health, National Health and Medical Research Council","grant_id":"1165490","title":null},{"funder_name":"Danmarks Vrie Forskningsfond","grant_id":"6110-00644","title":null},{"funder_name":"Scleroseforeningen","grant_id":"A31444","title":null},{"funder_name":"National Health and Medical Research Council (NHMRC)","grant_id":"1137739","title":"Understanding the complexity of antigen presentation"}],"total_grants":5,"fwci":0.9692,"citation_percentile":0.73288594,"influential_citations":0,"citation_trend":[{"year":2020,"count":3},{"year":2021,"count":4},{"year":2022,"count":3},{"year":2023,"count":3},{"year":2025,"count":2}],"oa_status":"bronze","license":"OUP Standard Publication Reuse","oa_locations":[{"url":"https://www.jimmunol.org/content/jimmunol/205/1/290.full.pdf","host_type":"journal"},{"url":"https://www.jimmunol.org/content/jimmunol/205/1/290.full.pdf","host_type":"publisher"},{"url":"https://academic.oup.com/jimmunol/article-pdf/205/1/290/61260899/ji2000224.pdf","host_type":"publisher"},{"url":"https://doi.org/10.4049/jimmunol.2000224","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/32482711","host_type":"repository"},{"url":"https://researchprofiles.ku.dk/da/publications/6e5f82d1-3f2f-41c3-a9f6-1b25fd21cdc9","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7313418","host_type":"repository"},{"url":"https://curis.ku.dk/ws/files/249251537/290.full.pdf","host_type":"repository"},{"url":"https://doi.org/10.1101/2020.02.28.969667","host_type":""},{"url":"https://dx.doi.org/10.1101/2020.02.28.969667","host_type":""},{"url":"https://dx.doi.org/10.4049/jimmunol.2000224","host_type":""},{"url":"https://backend.orbit.dtu.dk/ws/files/235391684/290.full.pdf","host_type":""},{"url":"https://orbit.dtu.dk/en/publications/772f8746-a951-401b-9274-f540aec25b6f","host_type":""},{"url":"http://hdl.handle.net/11336/112352","host_type":""},{"url":"http://dx.doi.org/10.1101/2020.02.28.969667","host_type":""},{"url":"https://doi.org/https://doi.org/10.4049/jimmunol.2000224","host_type":""}],"fields_of_study":["vaccines and immunoinformatics approaches","Immunotherapy and Immune Responses","Monoclonal and Polyclonal Antibodies Research","0301 basic medicine","03 medical and health sciences"],"mesh_terms":["B-Lymphocytes","Herpesvirus 4, Human","Feasibility Studies","HLA-DR Antigens","Humans","Peptides","Protein Binding","Epitope Mapping","Epitopes, T-Lymphocyte","Protein Array Analysis"],"keywords":["Human leukocyte antigen","Class (philosophy)","Computational biology","Peptide","Microarray","Biology","Genetics","Gene","Computer science","Antigen","Artificial intelligence","Gene expression","Biochemistry","Peptide microarray","B-Lymphocytes","Herpesvirus 4, Human","Protein Array Analysis","Epitopes, T-Lymphocyte","HLA-DR Antigens","https://purl.org/becyt/ford/3.3","MHC class II","High throughput","Feasibility Studies","Humans","https://purl.org/becyt/ford/3","/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being; name=SDG 3 - Good Health and Well-being","Peptides","Epitope Mapping","Protein Binding"],"sdg_mappings":[{"sdg_number":3,"sdg_label":"3. 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