{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:40:10Z","timestamp":1772210410913,"version":"3.50.1"},"reference-count":52,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671361"],"award-info":[{"award-number":["31671361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Fund of Sichuan Provincial Education Department","award":["17ZA0052"],"award-info":[{"award-number":["17ZA0052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein\u2013protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein\u2013peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom\u2013residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised\u2013supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised\u2013supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson\u2019s correlation coefficients Rp\u00a0=\u20090.635 and 0.406, respectively.<\/jats:p>","DOI":"10.1093\/bib\/bbac097","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T12:09:10Z","timestamp":1645704550000},"source":"Crossref","is-referenced-by-count":64,"title":["Integrated unsupervised\u2013supervised modeling and prediction of protein\u2013peptide affinities at structural level"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5681-9937","authenticated-orcid":false,"given":"Peng","family":"Zhou","sequence":"first","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wen","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Lin","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-653X","authenticated-orcid":false,"given":"Li","family":"Mei","sequence":"additional","affiliation":[{"name":"Institute of Culinary, Sichuan Tourism University, Chengdu 610100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyong","family":"Shang","sequence":"additional","affiliation":[{"name":"of Ecological Environment Protection, Chengdu Normal University, Chengdu 611130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juelin","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Shu","sequence":"additional","affiliation":[{"name":"Center for Informational Biology, 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