Shan Wang, Xiuzhen Hu*, Zhenxing Feng, Liu Liu, Kai Sun and Shuang Xu Pages 1093 - 1102 ( 10 )
Background: Rational drug molecular design based on virtual screening requires the ligand binding site to be known. Recently, the recognition of ion ligand binding site has become an important research direction in pharmacology.
Methods: In this work, we selected the binding residues of 4 acid radical ion ligands (NO2 -, CO3 2-, SO4 2- and PO4 3-) and 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical, and structure features. Then, we incorporated the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms.
Results: In the independent test, the best accuracy was higher than 92.5%, which was better than the previous results on the same dataset. In addition, we found that energy information is an important factor affecting the prediction results.
Conclusion: Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.
Energy feature, binding site, ion ligand, information entropy, protein-ion ligand binding, rational drug molecular design.
College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051