{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:31:24Z","timestamp":1776216684070,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Program for Guangdong Introducing Innovative and Entrepreneurial Teams","award":["2017ZT07S096"],"award-info":[{"award-number":["2017ZT07S096"]}]},{"DOI":"10.13039\/501100009334","name":"Pearl River S&T Nova Program of Guangzhou","doi-asserted-by":"publisher","award":["201906010088"],"award-info":[{"award-number":["201906010088"]}],"id":[{"id":"10.13039\/501100009334","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81972239"],"award-info":[{"award-number":["81972239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31601067"],"award-info":[{"award-number":["31601067"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81772635"],"award-info":[{"award-number":["81772635"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21672201"],"award-info":[{"award-number":["21672201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31301099"],"award-info":[{"award-number":["31301099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Cotton Biology Open Funds"},{"name":"Key program for Department of Science and Technology of Qinghai","award":["2017-ZJ-Y13"],"award-info":[{"award-number":["2017-ZJ-Y13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)\/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein\u2013protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http:\/\/deeppla.cancerbio.info.<\/jats:p>","DOI":"10.1093\/bib\/bbz107","type":"journal-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T11:33:29Z","timestamp":1565264009000},"page":"1798-1805","source":"Crossref","is-referenced-by-count":31,"title":["Deep learning based prediction of reversible HAT\/HDAC-specific lysine acetylation"],"prefix":"10.1093","volume":"21","author":[{"given":"Kai","family":"Yu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China"}]},{"given":"Qingfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China"}]},{"given":"Zekun","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China"}]},{"given":"Yimeng","family":"Du","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Zhengzhou University, Zhengzhou 450001, China"}]},{"given":"Xinjiao","family":"Gao","sequence":"additional","affiliation":[{"name":"Division of Molecular and Cell Biophysics, Hefei National Science Center for Physical Sciences at the Microscale, Anhui Key Laboratory of Cellular Dynamics and Chemical Biology, School of Life Sciences, University of Science and Technology of the China, Hefei 230027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8683-6145","authenticated-orcid":false,"given":"Qi","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oncology in South China, Collaborative 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