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.
Highlights
Understanding the biomolecular recognition of ligands and their interactions with macromolecular targets is of utmost importance for the successful discovery of novel biologically active compounds (Fenwick et al, 2011)
This paper presents a hierarchical graph representation of pharmacophore models “HGPM,” which aims at the easy to comprehend visualization of pharmacophore model related information and can greatly aid in the prioritization and selection of pharmacophore models for subsequent processing steps
Two crystallographic structures of the active conformation of GK with bound activators have been selected for this study (PDB codes: 1v4s and 4no7)
Summary
Understanding the biomolecular recognition of ligands and their interactions with macromolecular targets is of utmost importance for the successful discovery of novel biologically active compounds (Fenwick et al, 2011). Pharmacophore models are either derived from ligand-target complexes (structure-based) and/or a set of known active molecules (ligand-based) and can be used as queries for an in silico virtual screening (VS) to find compounds with similar stereoelectronic features (Langer, 2010; Leach et al, 2010; Schuster, 2010). One limitation of structure-based (SB) modeling is that all possible interactions between a target-ligand complex may not be captured since they are derived from static representations. Molecular dynamics (MD) simulations have recently been used to sample possible protein conformations (Durrant and McCammon, 2011; De Vivo et al, 2016; Liu et al, 2018) which were used to derive multiple pharmacophore models from an initially static crystallographic structure. In cases with new targets during early hit finding stages, this information may be yet not be available and prioritizing pharmacophore models for VS campaigns can be challenging
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