Kai Sun1,2, Xiuzhen Hu3,4, Zhenxing Feng1,2, Hongbin Wang5, Haotian Lv5, Ziyang Wang1,2, Gaimei Zhang6, Shuang Xu1,2, Xiaoxiao You1,2. 1. College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China. 2. Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China. 3. College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China. hxz@imut.edu.cn. 4. Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, People's Republic of China. hxz@imut.edu.cn. 5. College of Data Science and Application, Inner Mongolia University of Technology, Hohhot, 010051, People's Republic of China. 6. Hohhot First Hospital, Hohhot, 010051, People's Republic of China.
Abstract
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. RESULTS: In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. CONCLUSIONS: An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues. RESULTS: In this paper, Mg2+ and Ca2+ ligands are taken as the research objects. Based on the characteristic parameters of protein sequences, amino acids, physicochemical characteristics of amino acids and predicted structural information, deep neural network algorithm is used to predict the binding sites of proteins. By optimizing the hyper-parameters of the deep learning algorithm, the prediction results by the fivefold cross-validation are better than those of the Ionseq method. In addition, to further verify the performance of the proposed model, the undersampling data processing method is adopted, and the prediction results on independent test are better than those obtained by the support vector machine algorithm. CONCLUSIONS: An efficient method for predicting Mg2+ and Ca2+ ligand binding sites was presented.
Authors: Sebastian Gehrmann; Franck Dernoncourt; Yeran Li; Eric T Carlson; Joy T Wu; Jonathan Welt; John Foote; Edward T Moseley; David W Grant; Patrick D Tyler; Leo A Celi Journal: PLoS One Date: 2018-02-15 Impact factor: 3.240