Author:
Sun Kai,Hu Xiuzhen,Feng Zhenxing,Wang Hongbin,Lv Haotian,Wang Ziyang,Zhang Gaimei,Xu Shuang,You Xiaoxiao
Abstract
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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Inner Mongolia
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology
Reference28 articles.
1. Brailoiu E, Shipsky MM, Yan G, et al. Mechanisms of modulation of brain microvascular endothelial cells function by thrombin. Brain Res. 2016;1657:167–75.
2. Touyz RM, Schiffrin EL. Signal transduction mechanisms mediating the physiological and pathophysiological actions of angiotensin II in vascular smooth muscle cells. Pharmacol Rev. 2000;52(4):639–72.
3. Lin CT, Lin KL, Yang CH, et al. Protein metal binding residue prediction based on neural networks. Int J Neural Syst. 2005;15(1–2):71–84.
4. Xiuzhen H, Qiwen D, Jianyi Y, et al. Recognizing metal and acid radical ion-binding sites by integrating, ab initio modeling with template-based transferals. Bioinformatics. 2016;32(23):3694–3694.
5. Jiang Z, Hu XZ, Geriletu G, et al. Identification of Ca2+-binding residues of a protein from its primary sequence. Genet Mol Res. 2016. https://doi.org/10.4238/gmr.15027618.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献