Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

作者：Kai Yu, Qing‐Feng Zhang, Zekun Liu, Zekun Liu, Yimeng Du, Xinjiao Gao, Qi Zhao, Han Cheng, Zexian Liu, Zexian Liu, Zexian Liu · 发表于：Briefings in Bioinformatics · 年份：2019 · DOI：10.1093/bib/bbz107 · 被引用次数：41 · 研究领域：Histone Deacetylase Inhibitors Research、Peptidase Inhibition and Analysis、Machine Learning in Bioinformatics

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 provi...