{"entity": "researcher", "timestamp": "2026-11-06T18:29:39.295Z", "family": "Zhong", "given": "Yi", "initials": "Y", "orcid": "0000-0002-0929-8224", "affiliations": ["Department of Physiology and Pharmacology and Center for Molecular Medicine, Karolinska Institutet and University Hospital, Solnav\u00e4gen 9, Biomedicum, 17165 Stockholm, Sweden.", "Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Rd, 310058 Hangzhou, China."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5fdb847e9c944d5bba66333c80d40e6.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5fdb847e9c944d5bba66333c80d40e6"}}, "publications": [{"entity": "publication", "iuid": "ff620e29e0de4d02ac17b35a7cb3e51f", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ff620e29e0de4d02ac17b35a7cb3e51f.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ff620e29e0de4d02ac17b35a7cb3e51f"}}, "title": "Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.", "authors": [{"family": "Zhong", "given": "Yi", "initials": "Y", "orcid": "0000-0002-0929-8224", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f5fdb847e9c944d5bba66333c80d40e6.json"}}, {"family": "Lauschke", "given": "Volker M", "initials": "VM", "orcid": "0000-0002-1140-6204", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/82048d4da61547d08aa9d2bb3bae12dd.json"}}, {"family": "Wang", "given": "Yi", "initials": "Y", "orcid": "0000-0002-3676-9183", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a03091d5222a409ba70c5fe26ac5455f.json"}}, {"family": "Zhou", "given": "Yitian", "initials": "Y", "orcid": "0000-0003-3066-5444", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c4e92db79d554ad283927ad7e6e54e8d.json"}}], "type": "journal article", "published": "2026-07-03", "journal": {"title": "Brief. Bioinformatics", "issn": "1477-4054", "volume": "27", "issue": "4", "issn-l": "1467-5463"}, "abstract": "Human leukocyte antigen (HLA)-B*57:01 associated with abacavir-induced hypersensitivity syndrome (ABC-HSS) is one of the most extensively studied immune-mediated drug hypersensitivity reactions (DHRs). The high odds ratio and strong predictive values of HLA-B*57:01 for ABC-HSS have prompted the Food and Drug Administration and European Medicines Agency to require genetic testing before abacavir treatment. Abacavir binds to HLA-B*57:01 and alters the repertoire of presented peptides, resulting in the activation of autoimmunity. Previous studies employing computational approaches to investigate such DHRs have relied solely on a few crystallized tripartite structures, thus overlooking the full presented peptidome, leading to unsatisfactory predictive results. Here, we employed a state-of-the-art modeling approach to generate HLA structures complexed with over 13 000 presented peptides. We then established a novel computational modeling pipeline to simulate the binding of abacavir to these HLA-peptide complexes. Benchmarking against experimentally determined structures showed that this approach successfully recapitulated the crystalized tripartite structures with high accuracy (RMSD<2.2 \u00c5). We then profiled alterations of the peptide repertoire at key positions in the presence of abacavir and proposed a method that accurately predicts compounds known to trigger T-cell activation. Overall, these results show that comprehensive modeling of the HLA-bound peptidome using advanced structural approaches can enhance the prediction and mechanistic understanding of immune-mediated DHRs.", "doi": "10.1093/bib/bbag350", "pmid": "42398070", "labels": {"DDLS Fellow": null, "Yitian Zhou": null}, "xrefs": [{"db": "pmc", "key": "PMC13331351"}, {"db": "pii", "key": "8724529"}], "notes": [], "created": "2026-11-06T09:38:50.984Z", "modified": "2026-11-06T09:38:51.075Z"}]}