Combining self-organizing maps and hierarchical clustering for protein–ligand interaction analysis in post-fragment molecular orbital calculation

Abstract

Fragment molecular orbital (FMO) calculation is a useful ab initio method for analyzing protein–ligand interactions in the current structure-based drug design. When multiple ligands exist for one receptor, a post-FMO calculation tool is required because of large numbers of interaction energy decomposition terms calculated using this method. In this study, a method that combines self-organizing maps (SOM) and hierarchical clustering analysis (HCA) was proposed to analyze the results of the FMO energy components. This method could effectively compress the high-dimensional energy terms and is expected to be useful to analyze the interaction between protein and ligands. A case study of antitype 2 diabetes mellitus target DPP-IV and its inhibitors was analyzed to verify the feasibility of the proposed method. After performing dimensional compression using SOM and further grouping using HCA, we obtained superclasses of the inhibitors based on the dispersion energy (DI), which showed consistency with structural information, indicating that further analyses of detailed energies per superclass can be an effective approach for obtaining important ligand–protein interactions.