Abstract
Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein-ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein-ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site with ARP/wARP. The applicability of the method to the validation of protein-ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.
Highlights
The understanding of biochemical processes relies on the derived knowledge on how macromolecules, in their biological context, interact with a wide range of small molecules: the ligands
We propose a novel approach, LigEnergy, for the evaluation of protein–ligand binding in macromolecular X-ray crystallography (MX), which is based on estimation of the protein–ligand interaction energy (Pacholczyk & Kimmel, 2011)
We examined the use of the protein–ligand interaction energy as an additional parameter for the improvement of ligand-guessing protocols during the automated identification of ligands using sparse-density representations with ARP/ wARP (Carolan & Lamzin, 2014)
Summary
The understanding of biochemical processes relies on the derived knowledge on how macromolecules, in their biological context, interact with a wide range of small molecules: the ligands. The Privateer software (Agirre et al, 2015) has been designed for the modelling and validation of carbohydrates All of these packages apply different methods and approaches to accomplish the same task: to maximize the fit of the ligand to the experimentally derived electron density. The Coot package proceeds by identifying the density that fits predefined conformations of the ligand and adjusts the most suitable ligand model through its real-space fit to the density (Emsley & Cowtan, 2004) These methods provide tools for the identification of possible binding sites in cases where the search ligand is known but the corresponding density cluster is not. The method allows an improvement of the identification and fitting of ligands into specified electron density with ARP/wARP
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