Three-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structure

Stanisław Jastrzębski,Igor Sieradzki,Andrzej J Bojarski,Jacek Tabor,Sabina Podlewska,Damian Leśniak

doi:10.1007/s11030-018-9894-4

Abstract

Three-dimensional descriptors are often used to search for new biologically active compounds, in both ligand- and structure-based approaches, capturing the spatial orientation of molecules. They frequently constitute an input for machine learning-based predictions of compound activity or quantitative structure–activity relationship modeling; however, the distribution of their values and the accuracy of depicting compound orientations might have an impact on the power of the obtained predictive models. In this study, we analyzed the distribution of three-dimensional descriptors calculated for docking poses of active and inactive compounds for all aminergic G protein-coupled receptors with available crystal structures, focusing on the variation in conformations for different receptors and crystals. We demonstrated that the consistency in compound orientation in the binding site is rather not correlated with the affinity itself, but is more influenced by other factors, such as the number of rotatable bonds and crystal structure used for docking studies. The visualizations of the descriptors distributions were prepared and made available online at http://chem.gmum.net/vischem_stability, which enables the investigation of chemical structures referring to particular data points depicted in the figures. Moreover, the performed analysis can assist in choosing crystal structure for docking studies, helping in selection of conditions providing the best discrimination between active and inactive compounds in machine learning-based experiments.Graphical abstract

Highlights

Computational methods are an indispensable part of the drug design process, supporting the search for new compounds with desired biological activity
The averaged std values of generated descriptors between various compound conformations, depending on the number of compound orientations taken into account, are gathered in Table 3 with the stds of atom positions for docking poses obtained for a particular compound
The analysis clearly shows that the variation of compound orientations in the binding site depends on the crystal structure used for docking rather than on the compound affinity for the receptor

Summary

Introduction

Computational methods are an indispensable part of the drug design process, supporting the search for new compounds with desired biological activity. Fingerprints are used for chemical structure characterization, but they are applied for description of ligand–receptor complexes obtained in docking They provide information about the interaction of a ligand with particular amino acids of a protein, as in the case of interaction fingerprints (IFts) [12] and structural interaction fingerprints (SIFts) [13]. The most important advantages of fingerprints are the relative simplicity, low computational costs connected with their generation, and simplicity of making comparisons between two 0–1 strings. The latter procedure can be carried out with the use of various similarity coefficients or by application of machine learning approaches [14, 15]

Methods

Results

Conclusion