BACKGROUND Proteins perform their functions by interacting with acid radical ions. Recently, it was a challenging work to precisely predict the binding residues of acid radical ion ligands in the research field of molecular drug design. RESULTS In this study, we proposed an improved method to predict the acid radical ion binding residues by using K-nearest Neighbors classifier. Meanwhile, we constructed datasets of four acid radical ion ligand (NO2-, CO32-, SO42-, PO43-) binding residues from BioLip database. Then, based on the optimal window length for each acid radical ion ligand, we refined composition information and position conservative information and extracted them as feature parameters for K-nearest Neighbors classifier. In the results of 5-fold cross-validation, the Matthew's correlation coefficient was higher than 0.45, the values of accuracy, sensitivity and specificity were all higher than 69.2%, and the false positive rate was lower than 30.8%. Further, we also performed an independent test to test the practicability of the proposed method. In the obtained results, the sensitivity was higher than 40.9%, the values of accuracy and specificity were higher than 84.2%, the Matthew's correlation coefficient was higher than 0.116, and the false positive rate was lower than 15.4%. Finally, we identified binding residues of the six metal ion ligands. In the predicted results, the values of accuracy, sensitivity and specificity were all higher than 77.6%, the Matthew's correlation coefficient was higher than 0.6, and the false positive rate was lower than 19.6%. CONCLUSIONS Taken together, the good results of our prediction method added new insights in the prediction of the binding residues of acid radical ion ligands.

中文翻译：

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通过K近邻分类器预测酸性自由基离子结合残基。

背景技术蛋白质通过与酸根离子相互作用来执行其功能。最近，在分子药物设计研究领域中精确预测酸性自由基离子配体的结合残基是一项艰巨的工作。结果在这项研究中，我们提出了一种改进的方法，通过使用K近邻分类器来预测酸性自由基离子结合残基。同时，我们从BioLip数据库中构建了四个酸性自由基离子配体（NO2-，CO32-，SO42-，PO43-）结合残基的数据集。然后，基于每个酸性自由基离子配体的最佳窗口长度，我们完善了组成信息并定位了保守信息，并将其提取为K近邻分类器的特征参数。在5倍交叉验证的结果中，马修的相关系数高于0.45，准确性，敏感性和特异性均高于69.2％，假阳性率低于30.8％。此外，我们还进行了独立测试以测试所提出方法的实用性。在得到的结果中，灵敏度高于40.9％，准确度和特异性值高于84.2％，马修相关系数高于0.116，假阳性率低于15.4％。最后，我们确定了六个金属离子配体的结合残基。在预测结果中，准确性，敏感性和特异性均高于77.6％，马修相关系数高于0.6，假阳性率低于19.6％。结论总而言之，