[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128559-en":3,"doc-seo-128559-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128559,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning","Accurate prediction of HLA class II antigen presentation is essential for developing immunotherapies and vaccines that activate CD4+ T cells. Existing approaches largely emphasize HLA-DR due to limited immunopeptidomics data for HLA-DQ and HLA-DP and their omission of alternative peptide binding modes. The work updates NetMHCIIpan by integrating large cross-locus immunopeptidomics datasets and applying targeted immunopeptidomics assays that expand HLA-DP coverage. NetMHCIIpan-4.3 delivers high accuracy and broad molecular coverage across HLA class II allotypes.","Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning  \nNilsson, J.B.; Kaabinejadian, S.; Yari, H.; Kester, M.G.D.; Balen, P. van; Hildebrand, W.H.; Nielsen, M.  \nCitation  \nNilsson, J. B., Kaabinejadian, S., Yari, H., Kester, M. G. D., Balen, P. van, Hildebrand, W. H., & Nielsen, M. (2023) . Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning. Science Advances, 9(47) .  \ndoi:10.1126/sciadv.adj6367  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY-NC 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/3721041](https://hdl.handle.net/1887/3721041)  \nNote: To cite this publication please use the final published version (if applicable) .  \nSCIENCE ADVANCES | RESEARCH ARTICLE  \nIMMUNOLOGY  \nAccurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning  \nJonas B. Nilsson1, Saghar Kaabinejadian2,3, Hooman Yari3, Michel G. D. Kester4, Peter van Balen4, William H. Hildebrand3, Morten Nielsen1*  \nAccurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.  \nCopyright © 2023 Authors, some  \nrights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC) .  \nINTRODUCTION  \nMajor histocompatibility complex (MHC) class II molecules, also known as human leukocyte antigen (HLA) class II in humans, are expressed on the surface of professional antigen-presenting cells and play a pivotal part in the function of the immune system by presenting antigenic peptides to CD4+ T cells (1, 2). Structurally, these molecules are heterodimers consisting of α and β chains encoded by three different loci (HLA-DR, HLA-DP, and HLA-DQ) that are among the most polymorphic genes in the human genome (3) . The majority of these polymorphisms are clustered around the peptide binding domain formed by the α and β chain, giving rise to a broad range of peptide binding specificities (4) .  \nWhile in HLA-DR, polymorphic variation is primarily defined by the β chain, in HLA-DP and HLA-DQ both α and β chains display polymorphism. Additional diversity can be provided by cis-and trans-dimerization, whereby distinct HLA-DP and HLADQ heterodimers are formed with α and β chains either encoded on the same chromosome (referred to as “cis”) or the opposite chromosomes (referred to as “trans”) . Although the expression of transencoded HLA class II molecules has been confirmed by previous studies (5), evidence suggests that not every α and β chain pairing forms a stable heterodimer (6, 7) .  \nAmong all HLA class II molecules, DRB1 molecules have been investigated most extensively because of their established association with different conditions such as autoimmune disorders in particular (2), as well as cancer (8–10) and infectious diseases ","cbCain7tTLF7Nvkb","https://ap.wps.com/l/cbCain7tTLF7Nvkb","pdf",2921992,2,1,20,"English","en",105,"# Introduction\n## HLA class II biology and polymorphism\n## Motivation: gaps in current prediction methods\n## Immunopeptidome complexity and dataset integration\n## Approach overview toward NetMHCIIpan-4.3","[{\"question\":\"Why is accurate prediction of HLA class II antigen presentation important?\",\"answer\":\"It supports rational development of immunotherapies and vaccines by enabling CD4+ T cell activation.\"},{\"question\":\"What limitation affects current HLA class II prediction methods?\",\"answer\":\"Most methods focus on HLA-DR because immunopeptidomics data for HLA-DQ and HLA-DP are limited and alternative peptide binding modes are not fully considered.\"},{\"question\":\"How does the proposed update improve NetMHCIIpan performance across HLA class II loci?\",\"answer\":\"It integrates large immunopeptidomics datasets across all loci with a refined machine learning framework and adds targeted immunopeptidomics assays to cover additional HLA-DP specificities.\"}]","Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning | 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is accurate prediction of HLA class II antigen presentation important?","Question",{"text":76,"@type":77},"It supports rational development of immunotherapies and vaccines by enabling CD4+ T cell activation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What limitation affects current HLA class II prediction methods?",{"text":81,"@type":77},"Most methods focus on HLA-DR because immunopeptidomics data for HLA-DQ and HLA-DP are limited and alternative peptide binding modes are not fully considered.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed update improve NetMHCIIpan performance across HLA class II loci?",{"text":85,"@type":77},"It integrates large immunopeptidomics datasets across all loci with a refined machine learning framework and adds targeted immunopeptidomics assays to cover additional HLA-DP 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