Accurate identification of ligand-binding sites and discovering the protein-ligand interaction mechanism are important for understanding proteins' functions and designing new drugs. Meanwhile, accurate computational prediction and mechanism research are two grand challenges in proteomics. In this article, ligand-binding residues of five ligands (ATP, ADP, GTP, GDP, and NAD) are predicted as a group, due to their similar chemical structures and close biological function relations. The data set of binding sites by five ligands (ATP, ADP, GTP, GDP, and NAD) are collated from Biolip database. Then, five features, containing increment of diversity value, matrix scoring value, auto-covariance, secondary structure information, and surface accessibility information are used in binding site predictions. The support vector machine (SVM) model is used with the five features to predict ligand-binding sites. Finally, prediction results are tested by fivefold cross validation. Accuracy (Acc) of five ligands (ATP, ADP, GTP, GDP, and NAD) achieves 77.4%, 71.2%, 82.1%, 82.9%, and 85.3%, respectively; and Matthew correlation coefficient (MCC) of the above five ligands achieves 0.549, 0.424, 0.643, 0.659, and 0.702, respectively. The research result shows that for ligands with similar chemical structures, microenvironment of their binding sites and their sensitivities to features are similar, while, differences of their ligand-binding properties exist at the same time. © 2019 Wiley Periodicals, Inc.
Keywords: increment of diversity algorithm; matrix scoring algorithm; physical and chemical characteristics; secondary structure information; support vector machine.
© 2019 Wiley Periodicals, Inc.