Hierarchical Graph Representation of Pharmacophore Models

Abstract

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

A Guide to In Silico Drug Design

“…The six classical pharmacophore features classified are H-bond donors, H-bond acceptors, negative ionic, positive ionic, hydrophobic regions, and aromatic regions ( Figure 8 ). On top of that, less common features can also better characterise the chemical functionalities, such various metal binding locations are supported by LigandScout [ 243 , 244 , 245 , 246 ]. Constraints and restrictions can also be applied by introducing excluded volumes to the model to prevent ligands from occupying certain spaces (ligand-inaccessible) [ 247 ].…”