Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors

Abstract

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

Attention-based approach to predict drug-target interactions across seven target superfamilies

“…We based our value of n on literature [37] and set it to 20 to yield a 121-dimensional numerical vector. For our convenience, the Python code for calculating the descriptors was supplied by Di Rienzo et al [38], who used these successfully in their research.…”

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

“…In this protocol, we describe a portion of molecular surface as a 2D function and we expand it on the basis of such polynomials: the norms of the expansion coefficients are an ordered set of numerical descriptors, compactly summarizing the geometrical properties of a protein region. In the past years, such a description, independent of the orientation of the proteins in the space, has proven their efficacy in several molecular systems and applications, including the GPCR-ligand interaction [29] , [30] , [31] , [32] , [33] , [34] , [35] . Moreover, it is worth noting that the compactness of Zernike descriptors permits an easy evaluation of patch similarity, adopting a metric to calculate the dissimilarity between two sets of descriptors.…”

PGAR-Zernike: an ultra-fast, accurate and fully open-source structure retrieval toolkit for convenient structural database construction

“…Zernike descriptors have been widely used not only for retrieval problems but also in other problems of structural biology. For example, interface (binding site) prediction [20,21,22,23,24], embedding of polymers into EM maps [25], docking problems [26], and analysis of protein surfaces [27]. On the other hand, Zernike descriptors also show their advantages in image reconstruction [28] and 3D structure classification [29].…”