Homology modeling of GPCRs

Structural Investigation of the Dopamine-2 Receptor Agonist Bromocriptine Binding to Dimeric D2HighR and D2LowR States

The active (D2^HighR) and inactive (D2^LowR) states of dimeric dopamine D2 receptor (D2R) models were investigated to clarify the binding mechanisms of the dopamine agonist bromocriptine, using Molecular Dynamics (MD) simulation. The aim of this comprehensive study was to investigate the critical effects of bromocriptine binding on each distinct receptor conformation. The different binding modes of the bromocriptine ligand in the active and inactive states have a significant effect on the conformational changes of the receptor. Based on the MM/GBSA approach, the calculated binding enthalpies of bromocriptine demonstrated selectivity toward the D2^HighR active state. There is good agreement between the calculated and experimentally measured D2^HighR selectivity. In the ligand-binding site, the key amino acids identified for D2^HighR were Asp114(3.32) and Glu95(2.65), and for D2^LowR, it was Ser193(5.42). Moreover, analysis of replicate MD trajectories demonstrated that the bromocriptine structure was more rigid at the D2^HighR state and more flexible at the D2^LowR state. However, the side chains of the ligand-receptor complex of D2^HighR showed larger variations relative to the corresponding regions of D2^LowR. The present study is part of an ongoing research program to study D2R conformational changes during ligand activation and to evaluate the conformational state selectivity for ligand binding.

Using molecular dynamics for the refinement of atomistic models of GPCRs by homology modeling

Despite GPCRs sharing a common seven helix bundle, analysis of the diverse crystallographic structures available reveal specific features that might be relevant for ligand design. Despite the number of crystallographic structures of GPCRs steadily increasing, there are still challenges that hamper the availability of new structures. In the absence of a crystallographic structure, homology modeling remains one of the important techniques for constructing 3D models of proteins. In the present study we investigated the use of molecular dynamics simulations for the refinement of GPCRs models constructed by homology modeling. Specifically, we investigated the relevance of template selection, ligand inclusion as well as the length of the simulation on the quality of the GPCRs models constructed. For this purpose we chose the crystallographic structure of the rat muscarinic M3 receptor as reference and constructed diverse atomistic models by homology modeling, using different templates. Specifically, templates used in the present work include the human muscarinic M2; the more distant human histamine H1 and the even more distant bovine rhodopsin as shown in the GPCRs phylogenetic tree. We also investigated the use or not of a ligand in the refinement process. Hence, we conducted the refinement process of the M3 model using the M2 muscarinic as template with tiotropium or NMS docked in the orthosteric site and compared with the results obtained with a model refined without any ligand bound.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Molecular basis of agonist docking in a human GPR103 homology model by site-directed mutagenesis and structure-activity relationship studies

BACKGROUND AND PURPOSE

The neuropeptide 26RFa and its cognate receptor GPR103 are involved in the control of food intake and bone mineralization. Here, we have tested, experimentally, the predicted ligand-receptor interactions by site-directed mutagenesis of GPR103 and designed point-substituted 26RFa analogues.

EXPERIMENTAL APPROACH

Using the X-ray structure of the β2 -adrenoceptor, a 3-D molecular model of GPR103 has been built. The bioactive C-terminal octapeptide 26RFa(19-26) , KGGFSFRF-NH2 , was docked in this GPR103 model and the ligand-receptor complex was submitted to energy minimization.

KEY RESULTS

In the most stable complex, the Phe-Arg-Phe-NH2 part was oriented inside the receptor cavity, whereas the N-terminal Lys residue remained outside. A strong intermolecular interaction was predicted between the Arg(25) residue of 26RFa and the Gln(125) residue located in the third transmembrane helix of GPR103. To confirm this interaction experimentally, we tested the ability of 26RFa and Arg-modified 26RFa analogues to activate the wild-type and the Q125A mutant receptors transiently expressed in CHO cells. 26RFa (10(-6)  M) enhanced [Ca(2+) ]i in wild-type GPR103-transfected cells, but failed to increase [Ca(2+) ]i in Q125A mutant receptor-expressing cells. Moreover, asymmetric dimethylation of the side chain of arginine led to a 26RFa analogue, [ADMA(25) ]26RFa(20-26) , that was unable to activate the wild-type GPR103, but antagonized 26RFa-evoked [Ca(2+) ]i increase.

CONCLUSION AND IMPLICATIONS

Altogether, these data provide strong evidence for a functional interaction between the Arg(25) residue of 26RFa and the Gln(125) residue of GPR103 upon ligand-receptor activation, which can be exploited for the rational design of potent GPR103 agonists and antagonists.

Genome-wide Membrane Protein Structure Prediction

Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome.

Homology model-assisted elucidation of binding sites in GPCRs

G protein-coupled receptors (GPCRs) are important mediators of cell signaling and a major family of drug targets. Despite recent breakthroughs, experimental elucidation of GPCR structures remains a formidable challenge. Homology modeling of 3D structures of GPCRs provides a practical tool for elucidating the structural determinants governing the interactions of these important receptors with their ligands. The working model of the binding site can then be used for virtual screening of additional ligands that may fit this site, for determining and comparing specificity profiles of related receptors, and for structure-based design of agonists and antagonists. The current review presents the protocol and enumerates the steps for modeling and validating the residues involved in ligand binding. The main stages include (a) modeling the receptor structure using an automated fragment-based approach, (b) predicting potential binding pockets, (c) docking known binders, (d) analyzing predicted interactions and comparing with positions that have been shown to bind ligands in other receptors, (e) validating the structural model by mutagenesis.