Hu Xiuzhen, Feng Zhenxing, Zhang Xiaojin, Liu Liu, Wang Shan
College of Sciences, Inner Mongolla University of Technology, Hohhot, China.
Front Genet. 2020 Mar 19;11:214. doi: 10.3389/fgene.2020.00214. eCollection 2020.
Many proteins realize their special functions by binding with specific metal ion ligands during a cell's life cycle. The ability to correctly identify metal ion ligand-binding residues is valuable for the human health and the design of molecular drug. Precisely identifying these residues, however, remains challenging work. We have presented an improved computational approach for predicting the binding residues of 10 metal ion ligands (Zn Cu, Fe, Fe, Co, Ca, Mg, Mn, Na, and K) by adding reclassified relative solvent accessibility (RSA). The best accuracy of fivefold cross-validation was higher than 77.9%, which was about 16% higher than the previous result on the same dataset. It was found that different reclassification of the RSA information can make different contributions to the identification of specific ligand binding residues. Our study has provided an additional understanding of the effect of the RSA on the identification of metal ion ligand binding residues.
许多蛋白质在细胞生命周期中通过与特定金属离子配体结合来实现其特殊功能。正确识别金属离子配体结合残基的能力对人类健康和分子药物设计具有重要价值。然而，精确识别这些残基仍然是一项具有挑战性的工作。我们提出了一种改进的计算方法，通过添加重新分类的相对溶剂可及性（RSA）来预测10种金属离子配体（锌、铜、铁、钴、钙、镁、锰、钠和钾）的结合残基。五重交叉验证的最佳准确率高于77.9%，比同一数据集上之前的结果高出约16%。研究发现，RSA信息的不同重新分类对特定配体结合残基的识别有不同贡献。我们的研究为RSA对金属离子配体结合残基识别的影响提供了进一步的认识。
