Hao Sixi, Hu Xiuzhen, Feng Zhenxing, Sun Kai, You Xiaoxiao, Wang Ziyang, Yang Caiyun
College of Sciences, Inner Mongolia University of Technology, Hohhot, China.
Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Hohhot, China.
Front Genet. 2022 Aug 11;13:969412. doi: 10.3389/fgene.2022.969412. eCollection 2022.
Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn, Cu, Fe, Fe, Co, Mn, Ca and Mg metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.
蛋白质需要与不同的配体相互作用以发挥其功能。在这些配体中，金属离子是主要的配体。目前，预测蛋白质金属离子配体结合残基是一项挑战。在本研究中，我们从BioLip数据库中选择了锌、铜、铁、铁、钴、锰、钙和镁金属离子配体作为研究对象。基于氨基酸、理化性质和预测的结构信息，我们引入无序值作为特征参数。此外，基于组成信息、位置权重矩阵和信息熵，我们引入倾向因子作为预测参数。然后，我们使用深度神经网络算法进行预测。此外，我们对深度学习算法的超参数进行了优化，得到了比之前的IonSeq方法更好的结果。
