{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T19:46:58Z","timestamp":1784317618744,"version":"3.55.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T00:00:00Z","timestamp":1684972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62173204"],"award-info":[{"award-number":["62173204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>CD8+ T cells can recognize peptides presented by class I human leukocyte antigen (HLA-I) of nucleated cells. Exploring this immune mechanism is essential for identifying T-cell vaccine targets in cancer immunotherapy. Over the past decade, the wealth of data generated by experiments has spawned many computational approaches for predicting HLA-I binding, antigen presentation and T-cell immune responses. Nevertheless, existing HLA-I binding and antigen presentation prediction approaches suffer from low precision due to the absence of T-cell receptor (TCR) recognition. Direct modeling of T-cell immune responses is less effective as TCR recognition\u2019s mechanism still remains underexplored. Therefore, directly applying these existing methods to screen cancer neoantigens is still challenging. Here, we propose a novel immune epitope prediction method termed IEPAPI by effectively incorporating antigen presentation and immunogenicity. First, IEPAPI employs a transformer-based feature extraction block to acquire representations of peptides and HLA-I proteins. Second, IEPAPI integrates the prediction of antigen presentation prediction into the input of immunogenicity prediction branch to simulate the connection between the biological processes in the T-cell immune response. Quantitative comparison results on an independent antigen presentation test dataset exhibit that IEPAPI outperformed the current state-of-the-art approaches NetMHCpan4.1 and mhcflurry2.0 on 100 (25\/25) and 76% (19\/25) of the HLA subtypes, respectively. Furthermore, IEPAPI demonstrates the best precision on two independent neoantigen datasets when compared with existing approaches, suggesting that IEPAPI provides a vital tool for T-cell vaccine design.<\/jats:p>","DOI":"10.1093\/bib\/bbad171","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T10:26:23Z","timestamp":1685096783000},"source":"Crossref","is-referenced-by-count":14,"title":["IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity"],"prefix":"10.1093","volume":"24","author":[{"given":"Juntao","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weibin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Guangdong Second Provincial General Hospital , Guangzhou 510317 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University , Beijing, 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhang","family":"Tian","sequence":"additional","affiliation":[{"name":"Guangdong Second Provincial General Hospital , Guangzhou 510317 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"2023072020020921900_ref1","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.it.2016.08.010","article-title":"Present yourself! 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