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Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.<\/jats:p>","DOI":"10.1093\/bib\/bbad208","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T16:18:11Z","timestamp":1686327491000},"source":"Crossref","is-referenced-by-count":4,"title":["A Bayesian approach to estimate MHC-peptide binding threshold"],"prefix":"10.1093","volume":"24","author":[{"given":"Ran","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Hong Kong SAR , China"}]},{"given":"Ye-Fan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 3\/F, Laboratory Block, 21 Sassoon Road, Hong Kong SAR , China"},{"name":"Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 4\/F Professional Block, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong SAR , 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China"}]},{"given":"Xiaodan","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Statistics, The Chinese University of Hong Kong , Hong Kong SAR , China"}]}],"member":"286","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"2023072020062673000_ref1","doi-asserted-by":"crossref","first-page":"2680160","DOI":"10.1155\/2017\/2680160","article-title":"Fundamentals and methods for T- and B-cell epitope prediction","volume":"2017","author":"Sanchez-Trincado","year":"2017","journal-title":"J Immunol Res"},{"key":"2023072020062673000_ref2","doi-asserted-by":"crossref","DOI":"10.1201\/9781315533247","volume-title":"Janeway\u2019s Immunobiology","author":"Murphy","year":"2016"},{"key":"2023072020062673000_ref3","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3389\/fimmu.2017.00292","article-title":"Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen 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