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://22.214.171.124:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular