Predictions of CCR1 Chemokine Receptor Structure and BX 471 Antagonist Binding Followed by Experimental Validation

Abstract

A major challenge in the application of structure-based drug design methods to proteins belonging to the superfamily of G protein-coupled receptors (GPCRs) is the paucity of structural information (1). The 19 chemokine receptors, belonging to the Class A family of GPCRs, are important drug targets not only for autoimmune diseases like multiple sclerosis but also for the blockade of human immunodeficiency virus type 1 entry (2). Using the MembStruk computational method (3), we predicted the three-dimensional structure of the human CCR1 receptor. In addition, we predicted the binding site of the small molecule CCR1 antagonist BX 471, which is currently in Phase II clinical trials (4). Based on the predicted antagonist binding site we designed 17 point mutants of CCR1 to validate the predictions. Subsequent competitive ligand binding and chemotaxis experiments with these mutants gave an excellent correlation to these predictions. In particular, we find that Tyr-113 and Tyr-114 on transmembrane domain 3 and Ile-259 on transmembrane 6 contribute significantly to the binding of BX 471. Finally, we used the predicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base, seeded with selective CCR1 antagonists. The screen identified 63% of CCR1 antagonists in the top 5% of the hits. Our results indicate that rational drug design for GPCR targets is a feasible approach.Chemokines belong to a large family of small, chemotactic cytokines that regulate the trafficking of immune cells (5) by binding to cell surface receptors belonging to the GPCR 3 superfamily (5). CCR1, the first CC chemokine receptor to be identified, responds to a number of ligands, including MIP-1␣ (CCL3) and RANTES (regulated on activation normal T cell expressed and secreted) (CCL5) (6, 7). The strong association with a wide variety of autoimmune and pro-inflammatory diseases has made the CCR1 protein an attractive therapeutic target, and Berlex has developed a potent, specific, orally available antagonist, BX 471, currently in a Phase II clinical trial (8).The CCR1 antagonist program that yielded the clinical compound BX 471 followed a traditional drug discovery approach starting with high throughput screening of large compound libraries (9). Although high throughput screening is a main pillar of drug-finding programs in the pharmaceutical industry, it has recently been supplemented by in silico methods to maximize the probability of finding attractive novel leads. Structurebased in silico approaches have been challenging for GPCRs, because only one experimental GPCR structure, that of bovine rhodopsin, with only ϳ20% sequence identity to CCR1 (10), has been reported. Recent developments in GPCR structure prediction methods show great potential for structure-based drug design and identifying novel hits from virtual screens (11)(12)(13)(14)(15).In this communication we report a significant test of the computational method MembStruk by predicting the structure of CCR1. Further, we scanned the entire predicted struc...

Molecular Interaction of a Potent Nonpeptide Agonist with the Chemokine Receptor CCR8

“…But the almost 20-fold gain of function with respect to potency for LMD-009 observed upon Ala substitution of PheVI:16 indicates a close proximity of part of the nonpeptide to this (Tyr 172 ). A Tyr in position III:09 is also found in CCR1, where it was recently shown to be important for the binding of the nonpeptide antagonist BX471 (Vaidehi et al, 2006). However, we observed only a minor decrease in the potency of LMD-009 (3.9-fold) for the TyrIII:09 to Ala substitution, whereas the potency of CCL1 was decreased 15-fold (Table 1).…”

“…This amine has been shown to interact with a highly conserved Glu in the extracellular end of TM-VII (in position VII:06), whereas the flanking groups have been shown to interact with conserved aromatic residues, as well as other residues specific for each chemokine receptor (Mirzadegan et al, 2000;Castonguay et al, 2003;Tsamis et al, 2003;de Mendonça et al, 2005;Maeda et al, 2006;Seibert et al, 2006). Thus, all CC-chemokine receptor nonpeptide antagonists with characterized binding modes interact with GluVII:06 [except for the recently characterized binding pocket of BX471 in CCR1 (Vaidehi et al, 2006)] in addition to aromatic residues in the major binding pocket (composed of TM-III, TM-IV, TM-V, TM-VI, and TM-VII) and in the minor binding pocket (composed of TM-I, TM-II, TM-III, and TM-VII) ( Fig. 1) (Mirzadegan et al, 2000;Castonguay et al, 2003;Tsamis et al, 2003;de Mendonça et al, 2005;Maeda et al, 2006;Seibert et al, 2006).…”

“…Nonpeptide antagonists following this simplified "pharmacophore model" have almost all been shown to interact with the GluVII:06 via the positively charged nitrogen . However, an elegant study in CCR1 showed that the nonpeptide antagonist, BX471, was not dependent upon GluVII:06 but instead interacted with aromatic residues on each side of it, TyrI:07, TyrII:20 and TyrIII:09 (Vaidehi et al, 2006). GluVII:06 is conserved among chemokine receptors (found in 16 of 21 receptors) and occurs very rarely in nonchemokine receptors .…”

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

“…The results reported here are comparable to those from other similar works [19][20][21][22] which showed that GPCR modeling [23][24][25][26][27][28][29][30][31] in the absence of a crystal structure can be a valid replacement [32][33][34][35][36][37][38][39] for structural and functional exploration of GPCR receptors, and for the discovery [21,[40][41][42][43], VS [44][45][46][47][48][49][50][51][52] and optimisation [23,53] of their ligands.…”

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

“…In 2003, Cavasotto and coworkers demonstrated the applicability of this scoring function to the identification of the binding site of retinal in bovine rhodopsin and to the discrimination between binders and nonbinders of rhodopsin [67]. Similarly, using molecular databases spiked with known binders, a number of studies based on combinations of different scoring functions (such as Gold [68], Dock [69], CScore (Tripos), Fresno [70], Score [71], FlexX [72], and PMF [66]) have demonstrated the applicability of molecular docking at GPCR models to virtual screening [9,10,73]. In agreement with the expectations set by these studies, a variety of docking programs and scoring functions have been successfully applied to the discovery of novel ligands for a plethora of GPCRs, including neurorokinin [74,75], adrenergic [76], chemokine [75,77], dopamine [75], serotonin [75,78], cannabinoid [79], and free fatty acid receptors [80], among others.…”