Yang Caiyun, Hu Xiuzhen, Feng Zhenxing, Hao Sixi, Zhang Gaimei, Chen Shaohua, Guo Guodong
College of Sciences, Inner Mongolia University of Technology, Hohhot, China.
Hohhot First Hospital, Hohhot, China.
IET Syst Biol. 2025 Jan-Dec;19(1):e70001. doi: 10.1049/syb2.70001.
Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein-metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep learning algorithm was incorporated in the prediction, resulting in improved results for the sets of Ca and Mg compared to previous studies. The validation matrix provided the optimal prediction model for each ionic ligand binding residue, exhibiting the capability of effectively predicting the binding sites of metal ion ligands for real protein chains.
金属离子是与蛋白质结合的重要配体，在细胞代谢、物质运输和信号转导中发挥着关键作用。在理论计算中，准确预测蛋白质-金属离子配体结合残基（PMILBRs）是一项具有挑战性的任务。在本研究中，作者采用融合氨基酸及其衍生信息作为特征参数，使用三种经典机器学习算法预测PMILBRs，取得了良好的预测结果。随后，将深度学习算法纳入预测，与先前的研究相比，钙和镁离子集的预测结果得到了改善。验证矩阵为每个离子配体结合残基提供了最佳预测模型，展示了有效预测真实蛋白质链上金属离子配体结合位点的能力。
