Multiple-Docking and Affinity Fingerprint Methods for Protein Classification and Inhibitors Selection

摘要

The function-based protein classification holds tremendous promise for molecular recognition and the structure-based design process. We describe here a new strategy combined with multiple-docking tools and "affinity fingerprint" analysis technology to detect functional relationships among proteins based on the substrate binding features and protein-Iigand interaction matrix and applied it successfully for the family of Phospholipase A2 to investigate protein-ligand binding, function-based protein classification and Inhibitor selection, evaluation. Binding data and matrix was generated by multiple vs. multiple docking among 12 PLA2s and 84 PLA2 inhibitors. Three kinds of statistic techniques, Principal Component analysis, Multidimensional scaling and Cluster algorithms were chosen to distinguish the groups with similar binding characteristics. The 12 PLA2s were automatically categorized into reasonable subfamilies based on the protein-Iigand binding matrix and the classifying problem of cPLA2 (PDB ID: 1CJY) with relatively low homology is successfully dealt with. This approach was also used to identify and group out the selective inhibitors against human nonpancreatic sPLA2. A sound pharmacophore has been defined out from these selective inhibitors. We show that the method is quite robust against individual data deviation, especially false positive, which make it possible to be used in virtual screening with large enzyme families to generate selective inhibitors of targets base on limited structural /function information.