Recognition of Ion Ligand Binding Sites Based on Amino Acid Features with the Fusion of Energy, Physicochemical and Structural Features

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.