An evaluation of automated in silico ligand docking of amino acid ligands to Family C G-protein coupled receptors

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Abstract

Family C G-protein coupled receptors (GPCRs) consist of the metabotropic glutamate receptors (mGluRs), the calcium-sensing receptor (CaSR), the T1R taste receptors, the GABAB receptor, the V2R pheromone receptors, and several chemosensory receptors. A common feature of Family C receptors is the presence of an amino acid binding pocket. The objective of this study was to evaluate the ability of the automatic docking program FlexX to predict the favored amino acid ligand at several Family C GPCRs. The docking process was optimized using the crystal structure of mGluR1 and the 20 amino acids were docked into homology models of the CaSR, the 5.24 chemosensory receptor, and the GPRC6A amino acid receptor. Under optimized docking conditions, glutamate was docked in the binding pocket of mGluR1 with a root mean square deviation of 1.56 angstroms from the co-crystallized glutamate structure and was ranked as the best ligand with a significantly better FlexX score compared to all other amino acids. Ligand docking to a homology model of the 5.24 receptor gave generally correct predictions of the favored amino acids, while the results obtained with models of GPRC6A and the CaSR showed that some of the favored amino acids at these receptors were correctly predicted, while a few other top scoring amino acids appeared to be false positives. We conclude that with certain caveats, FlexX can be successfully used to predict preferred ligands at Family C GPCRs.

Graphical abstract

Amino acids docked into the binding pocket of mGluR1 and scored by FlexX.

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Introduction

Family C G-protein coupled receptors (GPCRs) possess large extracellular ligand binding domains which are homologous to the bacteria periplasmic binding proteins.1 Most Family C receptors possess a conserved binding motif recognizing l-amino acids. Family C receptors include the metabotropic glutamate receptors (mGluRs) which regulate neurotransmission and are therapeutic targets for a number of neurological and psychiatric diseases.1 Another member of this receptor family, the calcium-sensing receptor (CaSR), plays a critical role in regulating calcium homeostasis in the body. Inactivating and activating mutations in the CaSR have been identified in inherited human hypercalcemic and hypocalcemic disorders, respectively.2 Aromatic amino acids can enhance the response of the CaSR to calcium,3 presumably through an amino acid binding pocket analogous to that of the glutamate binding pocket in the mGluRs.4, 5 Other Family C receptors such as the T1R1/T1R3 heteromeric taste receptor can sense most amino acids,6 while arginine and lysine are the most potent amino acid ligands for the fish 5.24 chemosensory receptor.7 A presumed mammalian homolog of the fish 5.24 receptor, GPRC6A, has been cloned and also shown to be activated by amino acids with preference for positively charged amino acids.8, 9

The availability of crystal structure of the ligand binding domain of mGluR110 provides an excellent template to study the molecular determinants of ligand and drug binding to other Family C GPCRs. Based on extensive medicinal chemistry and pharmacophore studies, and molecular modeling and mutagenesis studies, the molecular determinants for glutamate and drug binding to mGluRs have been identified.11, 12, 13, 14 Although the mGluRs share several highly conserved residues in the binding pocket, subtle differences at other sites within pocket confer ligand and drug selectivity. The mode of glutamate binding in the mGluRs has shed light on ligand docking at other Family C GPCRs. For example, the 5.24 receptor was found to bind lysine and arginine in a manner similar to the mode of glutamate binding in the mGluRs.15, 16

Structure-based drug design has shown promise in reducing the time and money spent in the drug discovery process.17, 18 Automatic docking using computer programs to identify small molecule receptor ligands plays an increasingly important role in the drug discovery process.19 Although homology models are not as good as experimentally determined structures for docking applications, the use of homology models in ligand screening is gaining momentum.20 The effective use of homology models in drug screening usually requires greater than 50% sequence identity between the target and the template.21 Therefore, the lower sequence identities among members of the Family C GPCRs to the only available template mGluR1 (36–42% for mGluRs, except for 69% for mGluR5, and 20–30% for non-mGluR Family C receptors) have resulted in the use of primarily manual docking of ligands and drugs.12, 13, 15, 16 Recently, however, several studies employing automated ligand docking and screening using homology models of mGluRs have been reported.22, 23, 24

Here we report our experience in using FlexX25 to dock amino acids in homology models of the CaSR, the 5.24 receptor, and GPRC6A. We evaluated the ability of this program to correctly predict the rank order of amino acids at each of these receptors. By comparing the in silico docking score with experimental data from the literature, we found that the FlexX scoring functions can be used to discriminate and predict ligand selectivity.

Section snippets

Homology modeling

Homology models of the CaSR, GPRC6A, and the 5.24 receptor were constructed and refined in parallel. The amino acids aligned in the binding pockets are shown in Table 1. The residues in the binding pocket that interact with the glycine moiety (the α-carboxyl group and α-amino group of the bound amino acid ligand) are highly conserved in Family C receptors.1, 16 In contrast, the amino acids that interact with the side chains of the bound amino acid ligands are not conserved and are responsible

Optimization of the docking parameters

Among the ligand–receptor docking programs available, FlexX,25 Dock,26 Gold,31 and Glide32 are the most widely used. The performances of these programs have varied depending on the system investigated, and the intrinsic physicochemical properties of the ligand–protein interactions involved.32, 33, 34 Thus, optimization of the individual docking system and its parameters is important in drug and ligand docking studies. In the present study, the optimization of FlexX in Sybyl was performed by

Molecular modeling

Homology models of the fish 5.24 receptor, the mouse CaSR, and mouse GPRC6A were generated using the X-ray crystal structure of the extracellular domain of rat mGluR1 as the template (PDB coordinates, 1EWK10) and version 6.0 of the MODELER program38 to generate the models. The sequences of the receptors were aligned using ClustalX and Sybyl 6.9 (Tripos Inc., St. Louis, MO) was used to view, analyze, and manipulate the structure. The preferred highest affinity ligands (arginine for fish 5.24

Acknowledgments

We thank Dr. L. P. Kotra for insightful comments on the manuscript. This work was supported by grants from the Canadian Institutes for Health Research, and the Natural Sciences and Engineering Research Council of Canada, and by the resources of the Molecular Design and Information Technology Centre in the Faculty of Pharmacy, University of Toronto.

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