Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Combinatorial In Silico Strategy towards Identifying Potential Hotspots during Inhibition of Structurally Identical HDAC1 and HDAC2 Enzymes for Effective Chemotherapy against Neurological Disorders

Histone deacetylases (HDACs) regulate epigenetic gene expression programs by modulating chromatin architecture and are required for neuronal development. Dysregulation of HDACs and aberrant chromatin acetylation homeostasis have been implicated in various diseases ranging from cancer to neurodegenerative disorders. Histone deacetylase inhibitors (HDACi), the small molecules interfering HDACs have shown enhanced acetylation of the genome and are gaining great attention as potent drugs for treating cancer and neurodegeneration. HDAC2 overexpression has implications in decreasing dendrite spine density, synaptic plasticity and in triggering neurodegenerative signaling. Pharmacological intervention against HDAC2 though promising also targets neuroprotective HDAC1 due to high sequence identity (94%) with former in catalytic domain, culminating in debilitating off-target effects and creating hindrance in the defined intervention. This emphasizes the need of designing HDAC2-selective inhibitors to overcome these vicious effects and for escalating the therapeutic efficacy. Here we report a top-down combinatorial in silico approach for identifying the structural variants that are substantial for interactions against HDAC1 and HDAC2 enzymes. We used extra-precision (XP)-molecular docking, Molecular Mechanics Generalized Born Surface Area (MMGBSA) for predicting affinity of inhibitors against the HDAC1 and HDAC2 enzymes. Importantly, we employed a novel in silico strategy of coupling the state-of-the-art molecular dynamics simulation (MDS) to energetically-optimized structure based pharmacophores (e-Pharmacophores) method via MDS trajectory clustering for hypothesizing the e-Pharmacophore models. Further, we performed e-Pharmacophores based virtual screening against phase database containing millions of compounds. We validated the data by performing the molecular docking and MM-GBSA studies for the selected hits among the retrieved ones. Our studies attributed inhibitor potency to the ability of forming multiple interactions and infirm potency to least interactions. Moreover, our studies delineated that a single HDAC inhibitor portrays differential features against HDAC1 and HDAC2 enzymes. The high affinity and selective HDAC2 inhibitors retrieved through e-Pharmacophores based virtual screening will play a critical role in ameliorating neurodegenerative signaling without hampering the neuroprotective isoform (HDAC1).

How Open Data Shapes In Silico Transporter Modeling

Chemical compound bioactivity and related data are nowadays easily available from open data sources and the open medicinal chemistry literature for many transmembrane proteins. Computational ligand-based modeling of transporters has therefore experienced a shift from local (quantitative) models to more global, qualitative, predictive models. As the size and heterogeneity of the data set rises, careful data curation becomes even more important. This includes, for example, not only a tailored cutoff setting for the generation of binary classes, but also the proper assessment of the applicability domain. Powerful machine learning algorithms (such as multi-label classification) now allow the simultaneous prediction of multiple related targets. However, the more complex, the less interpretable these models will get. We emphasize that transmembrane transporters are very peculiar, some of which act as off-targets rather than as real drug targets. Thus, careful selection of the right modeling technique is important, as well as cautious interpretation of results. We hope that, as more and more data will become available, we will be able to ameliorate and specify our models, coming closer towards function elucidation and the development of safer medicine.

Structural insights and functional implications of inter-individual variability in β2-adrenergic receptor

The human β₂-adrenergic receptor (β₂AR) belongs to the G protein-coupled receptor (GPCR) family and due to its central role in bronchodilation, is an important drug target. The inter-individual variability in β₂AR has been implicated in disease susceptibility and differential drug response. In this work, we identified nine potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) using a consensus approach. The deleterious nsSNPs were found to cluster near the ligand binding site and towards the G-protein binding site. To assess their molecular level effects, we built structural models of these receptors and performed atomistic molecular dynamics simulations. Most notably, in the Phe290Ser variant we observed the rotameric flip of Trp286^6.48, a putative activation switch that has not been reported in β₂AR thus far. In contrast, the variant Met82Lys was found to be the most detrimental to epinephrine binding. Additionally, a few of the nsSNPs were seen to cause perturbations to the lipid bilayer, while a few lead to differences at the G-protein coupling site. We are thus able to classify the variants as ranging from activating to damaging, prioritising them for experimental studies.

Discovery of GPCR ligands for probing signal transduction pathways

G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure–selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.

Graph analysis of β2 adrenergic receptor structures: a "social network" of GPCR residues

Purpose

G protein-coupled receptors (GPCRs) are a superfamily of membrane proteins of vast pharmaceutical interest. Here, we describe a graph theory-based analysis of the structure of the β₂ adrenergic receptor (β₂ AR), a prototypical GPCR. In particular, we illustrate the network of direct and indirect interactions that link each amino acid residue to any other residue of the receptor.

Methods

Networks of interconnected amino acid residues in proteins are analogous to social networks of interconnected people. Hence, they can be studied through the same analysis tools typically employed to analyze social networks – or networks in general – to reveal patterns of connectivity, influential members, and dynamicity. We focused on the analysis of closeness-centrality, which is a measure of the overall connectivity distance of the member of a network to all other members.

Results

The residues endowed with the highest closeness-centrality are located in the middle of the seven transmembrane domains (TMs). In particular, they are mostly located in the middle of TM2, TM3, TM6 or TM7, while fewer of them are located in the middle of TM1, TM4 or TM5. At the cytosolic end of TM6, the centrality detected for the active structure is markedly lower than that detected for the corresponding residues in the inactive structures. Moreover, several residues acquire centrality when the structures are analyzed in the presence of ligands. Strikingly, there is little overlap between the residues that acquire centrality in the presence of the ligand in the blocker-bound structures and the agonist-bound structures.

Conclusions

Our results reflect the fact that the receptor resembles a bow tie, with a rather tight knot of closely interconnected residues and two ends that fan out in two opposite directions: one toward the extracellular space, which hosts the ligand binding cavity, and one toward the cytosol, which hosts the G protein binding cavity. Moreover, they underscore how interaction network is by the conformational rearrangements concomitant with the activation of the receptor and by the presence of agonists or blockers.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-16) contains supplementary material, which is available to authorized users.

Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor

Prediction of 3D structures of membrane proteins, and of G-protein coupled receptors (GPCRs) in particular, is motivated by their importance in biological systems and the difficulties associated with experimental structure determination. In the present study, a novel method for the prediction of 3D structures of the membrane-embedded region of helical membrane proteins is presented. A large pool of candidate models are produced by repacking of the helices of a homology model using Monte Carlo sampling in torsion space, followed by ranking based on their geometric and ligand-binding properties. The trajectory is directed by weak initial restraints to orient helices towards the original model to improve computation efficiency, and by a ligand to guide the receptor towards a chosen conformational state. The method was validated by construction of the β₁ adrenergic receptor model in complex with (S)-cyanopindolol using bovine rhodopsin as template. In addition, models of the dopamine D₂ receptor were produced with the selective and rigid agonist (R)-N-propylapomorphine ((R)-NPA) present. A second quality assessment was implemented by evaluating the results from docking of a library of 29 ligands with known activity, which further discriminated between receptor models. Agonist binding and recognition by the dopamine D₂ receptor is interpreted using the 3D structure model resulting from the approach. This method has a potential for modeling of all types of helical transmembrane proteins for which a structural template with sequence homology sufficient for homology modeling is not available or is in an incorrect conformational state, but for which sufficient empirical information is accessible.

New insights for drug design from the X-ray crystallographic structures of G-protein-coupled receptors

Methodological advances in X-ray crystallography have made possible the recent solution of X-ray structures of pharmaceutically important G protein-coupled receptors (GPCRs), including receptors for biogenic amines, peptides, a nucleoside, and a sphingolipid. These high-resolution structures have greatly increased our understanding of ligand recognition and receptor activation. Conformational changes associated with activation common to several receptors entail outward movements of the intracellular side of transmembrane helix 6 (TM6) and movements of TM5 toward TM6. Movements associated with specific agonists or receptors have also been described [e.g., extracellular loop (EL) 3 in the A(2A) adenosine receptor]. The binding sites of different receptors partly overlap but differ significantly in ligand orientation, depth, and breadth of contact areas in TM regions and the involvement of the ELs. A current challenge is how to use this structural information for the rational design of novel potent and selective ligands. For example, new chemotypes were discovered as antagonists of various GPCRs by subjecting chemical libraries to in silico docking in the X-ray structures. The vast majority of GPCR structures and their ligand complexes are still unsolved, and no structures are known outside of family A GPCRs. Molecular modeling, informed by supporting information from site-directed mutagenesis and structure-activity relationships, has been validated as a useful tool to extend structural insights to related GPCRs and to analyze docking of other ligands in already crystallized GPCRs.

Drug-drug interaction through molecular structure similarity analysis

BACKGROUND

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

METHODS

We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.

RESULTS

The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.

CONCLUSION

The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

Predicting the biological activities through QSAR analysis and docking-based scoring

Numerous computational methodologies have been developed to facilitate the process of drug discovery. Broadly, they can be classified into ligand-based approaches, which are solely based on the calculation of the molecular properties of compounds, and structure-based approaches, which are based on the study of the interactions between compounds and their target proteins. This chapter deals with two major categories of ligand-based and structure-based methods for the prediction of biological activities of chemical compounds, namely quantitative structure-activity relationship (QSAR) analysis and docking-based scoring. QSAR methods are endowed with robustness and good ranking ability when applied to the prediction of the activity of closely related analogs; however, their great dependence on training sets significantly limits their applicability to the evaluation of diverse compounds. Instead, docking-based scoring, although not very effective in ranking active compounds on the basis of their affinities or potencies, offer the great advantage of not depending on training sets and have proven to be suitable tools for the distinction of active from inactive compounds, thus providing feasible platforms for virtual screening campaigns. Here, we describe the basic principles underlying the prediction of biological activities on the basis of QSAR and docking-based scoring, as well as a method to combine two or more individual predictions into a consensus model. Finally, we describe an example that illustrates the applicability of QSAR and molecular docking to G protein-coupled receptor (GPCR) projects.

Homology modeling of class a G protein-coupled receptors

G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines, and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the 3D models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments.

In silico screening for agonists and blockers of the β(2) adrenergic receptor: implications of inactive and activated state structures

Ten crystal structures of the β(2) adrenergic receptor have been published, reflecting different signaling states. Here, through controlled-docking experiments, we examined the implications of using inactive or activated structures on the in silico screening for agonists and blockers of the receptor. Specifically, we targeted the crystal structures solved in complex with carazolol (2RH1), the neutral antagonist alprenalol, the irreversible agonist FAUC50 (3PDS), and the full agonist BI-167017 (3P0G). Our results indicate that activated structures favor agonists over blockers, whereas inactive structures favor blockers over agonists. This tendency is more marked for activated than for inactive structures. Additionally, agonists tend to receive more favorable docking scores when docked at activated rather than inactive structures, while blockers do the opposite. Hence, the difference between the docking scores attained with an activated and an inactive structure is an excellent means for the classification of ligands into agonists and blockers as we determined through receiver operating characteristic curves and linear discriminant analysis. With respect to virtual screening, all structures prioritized well agonists and blockers over nonbinders. However, inactive structures worked better for blockers and activated structures worked better for agonists, respectively. Notably, the combination of individual docking experiments through receptor ensemble docking resulted in an excellent performance in the retrieval of both agonists and blockers. Finally, we demonstrated that the induced-fit docking of agonists is a viable way of modifying an inactive crystal structure and bias it toward the in silico recognition of agonists rather than blockers.

In silico identification of new ligands for GPR17: a promising therapeutic target for neurodegenerative diseases

GPR17, a previously orphan receptor responding to both uracil nucleotides and cysteinyl-leukotrienes, has been proposed as a novel promising target for human neurodegenerative diseases. Here, in order to specifically identify novel potent ligands of GPR17, we first modeled in silico the receptor by using a multiple template approach, in which extracellular loops of the receptor, quite complex to treat, were modeled making reference to the most similar parts of all the class-A GPCRs crystallized so far. A high-throughput virtual screening exploration of GPR17 binding site with more than 130,000 lead-like compounds was then applied, followed by the wet functional and pharmacological validation of the top-scoring chemical structures. This approach revealed successful for the proposed aim, and allowed us to identify five agonists or partial agonists with very diverse chemical structure. None of these compounds could have been expected 'a priori' to act on a GPCR, and all of them behaved as much more potent ligands than GPR17 endogenous activators.

In silico analysis of the binding of agonists and blockers to the β2-adrenergic receptor

Activation of G protein-coupled receptors (GPCRs) is a complex phenomenon. Here, we applied Induced Fit Docking (IFD) in tandem with linear discriminant analysis (LDA) to generate hypotheses on the conformational changes induced to the β(2)-adrenergic receptor by agonist binding, preliminary to the sequence of events that characterize activation of the receptor. This analysis, corroborated by a follow-up molecular dynamics study, suggested that agonists induce subtle movements to the fifth transmembrane domain (TM5) of the receptor. Furthermore, molecular dynamics also highlighted a correlation between movements of TM5 and the second extracellular loop (EL2), suggesting that freedom of motion of EL2 is required for the agonist-induced TM5 displacement. Importantly, we also showed that the IFD/LDA procedure can be used as a computational means to distinguish agonists from blockers on the basis of the differential conformational changes induced to the receptor. In particular, the two most predictive models obtained are based on the RMSD induced to Ser207 and on the counterclockwise rotation induced to TM5.

Predicting novel binding modes of agonists to β adrenergic receptors using all-atom molecular dynamics simulations

Understanding the binding mode of agonists to adrenergic receptors is crucial to enabling improved rational design of new therapeutic agents. However, so far the high conformational flexibility of G protein-coupled receptors has been an obstacle to obtaining structural information on agonist binding at atomic resolution. In this study, we report microsecond classical molecular dynamics simulations of β(1) and β(2) adrenergic receptors bound to the full agonist isoprenaline and in their unliganded form. These simulations show a novel agonist binding mode that differs from the one found for antagonists in the crystal structures and from the docking poses reported by in silico docking studies performed on rigid receptors. Internal water molecules contribute to the stabilization of novel interactions between ligand and receptor, both at the interface of helices V and VI with the catechol group of isoprenaline as well as at the interface of helices III and VII with the ethanolamine moiety of the ligand. Despite the fact that the characteristic N-C-C-OH motif is identical in the co-crystallized ligands and in the full agonist isoprenaline, the interaction network between this group and the anchor site formed by Asp(3.32) and Asn(7.39) is substantially different between agonists and inverse agonists/antagonists due to two water molecules that enter the cavity and contribute to the stabilization of a novel network of interactions. These new binding poses, together with observed conformational changes in the extracellular loops, suggest possible determinants of receptor specificity.

Conserved binding mode of human beta2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography

G protein-coupled receptors (GPCRs) represent a large fraction of current pharmaceutical targets, and of the GPCRs, the beta(2) adrenergic receptor (beta(2)AR) is one of the most extensively studied. Previously, the X-ray crystal structure of beta(2)AR has been determined in complex with two partial inverse agonists, but the global impact of additional ligands on the structure or local impacts on the binding site are not well-understood. To assess the extent of such ligand-induced conformational differences, we determined the crystal structures of a previously described engineered beta(2)AR construct in complex with two inverse agonists: ICI 118,551 (2.8 A), a recently described compound (2.8 A) (Kolb et al, 2009), and the antagonist alprenolol (3.1 A). The structures show the same overall fold observed for the previous beta(2)AR structures and demonstrate that the ligand binding site can accommodate compounds of different chemical and pharmacological properties with only minor local structural rearrangements. All three compounds contain a hydroxy-amine motif that establishes a conserved hydrogen bond network with the receptor and chemically diverse aromatic moieties that form distinct interactions with beta(2)AR. Furthermore, receptor ligand cross-docking experiments revealed that a single beta(2)AR complex can be suitable for docking of a range of antagonists and inverse agonists but also indicate that additional ligand-receptor structures may be useful to further improve performance for in-silico docking or lead-optimization in drug design.

Docking-based virtual screening for ligands of G protein-coupled receptors: not only crystal structures but also in silico models

G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs.

Beyond rhodopsin: G protein-coupled receptor structure and modeling incorporating the beta2-adrenergic and adenosine A(2A) crystal structures

For quite some time, the majority of GPCR models have been based on a single template structure: dark-adapted bovine rhodopsin. The recent solution of β2AR, β1AR and adenosine A(2A) receptor crystal structures has dramatically expanded the GPCR structural landscape and provided many new insights into receptor conformation and ligand binding. They will serve as templates for the next generation of GPCR models, but also allow direct validation of previous models and computational techniques. This review summarizes key findings from the new structures, comparison of existing models to these structures and highlights new models constructed from these templates.

Structural basis of the selectivity of the beta(2)-adrenergic receptor for fluorinated catecholamines

The important and diverse biological functions of adrenergic receptors, a subclass of G protein-coupled receptors (GPCRs), have made the search for compounds that selectively stimulate or inhibit the activity of different adrenergic receptor subtypes an important area of medicinal chemistry. We previously synthesized 2-, 5-, and 6-fluoronorepinehprine (FNE) and 2-, 5-, and 6-fluoroepinephrine (FEPI) and found that 2FNE and 2FEPI were selective beta-adrenergic agonists and that 6FNE and 6FEPI were selective alpha-adrenergic agonists, while 5FNE and 5FEPI were unselective. Agonist potencies correlated well with receptor binding affinities. Here, through a combination of molecular modeling and site-directed mutagenesis, we have identified N293 in the beta(2)-adrenergic receptor as a crucial residue for the selectivity of the receptor for catecholamines fluorinated at different positions.

Comparative sequence and structural analyses of G-protein-coupled receptor crystal structures and implications for molecular models

BACKGROUND

Up until recently the only available experimental (high resolution) structure of a G-protein-coupled receptor (GPCR) was that of bovine rhodopsin. In the past few years the determination of GPCR structures has accelerated with three new receptors, as well as squid rhodopsin, being successfully crystallized. All share a common molecular architecture of seven transmembrane helices and can therefore serve as templates for building molecular models of homologous GPCRs. However, despite the common general architecture of these structures key differences do exist between them. The choice of which experimental GPCR structure(s) to use for building a comparative model of a particular GPCR is unclear and without detailed structural and sequence analyses, could be arbitrary. The aim of this study is therefore to perform a systematic and detailed analysis of sequence-structure relationships of known GPCR structures.

METHODOLOGY

We analyzed in detail conserved and unique sequence motifs and structural features in experimentally-determined GPCR structures. Deeper insight into specific and important structural features of GPCRs as well as valuable information for template selection has been gained. Using key features a workflow has been formulated for identifying the most appropriate template(s) for building homology models of GPCRs of unknown structure. This workflow was applied to a set of 14 human family A GPCRs suggesting for each the most appropriate template(s) for building a comparative molecular model.

CONCLUSIONS

The available crystal structures represent only a subset of all possible structural variation in family A GPCRs. Some GPCRs have structural features that are distributed over different crystal structures or which are not present in the templates suggesting that homology models should be built using multiple templates. This study provides a systematic analysis of GPCR crystal structures and a consistent method for identifying suitable templates for GPCR homology modelling that will help to produce more reliable three-dimensional models.