Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation
Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation
复制标题
基于深度学习的可逆 HAT/HDAC 特异性赖氨酸乙酰化预测
DOI：
10.1093/bib/bbz107
复制
发表时间：
2019-11
影响因子：
9.5
通讯作者：
Liu Ze-Xian
中科院分区：
文献类型：
--
作者：
Yu Kai;Zhang Qingfeng;Liu Zekun;Du Yimeng;Gao Xinjiao;Zhao Qi;Cheng Han;Li Xiaoxing;Liu Ze-Xian
蛋白质赖氨酸乙酰化调控是调节细胞过程的一种重要分子机制，在癌症及其他疾病中发挥着关键的生理和病理作用。尽管已通过……鉴定出大量乙酰化位点 （注：原文“through ex”表述不完整，推测可能是through后面接了某个技术或方法，但此处缺失关键信息）
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-protein 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.
登录
查看更多内容
影响因子：
56.9
作者：
Choudhary, Chunaram;Kumar, Chanchal;Mann, Matthias
通讯作者：
Mann, Matthias
影响因子：
3.7
作者：
Wuyun Q;Zheng W;Zhang Y;Ruan J;Hu G
通讯作者：
Hu G
影响因子：
8
作者：
L Wuchao Yuan;A. Giordano
通讯作者：
L Wuchao Yuan;A. Giordano
通过整合蛋白质序列衍生和功能特征，在计算机上准确识别物种特异性乙酰化位点
影响因子：
4.6
作者：
Li Y;Wang M;Wang H;Tan H;Zhang Z;Webb GI;Song J
通讯作者：
Song J
GPS-ARM：通过预测 D-Box 和 KEN-Box 对 APC/C 识别基序进行计算分析
影响因子：
3.7
作者：
Liu Z;Yuan F;Ren J;Cao J;Zhou Y;Yang Q;Xue Y
通讯作者：
Xue Y