Critical analysis of the successes and failures of homology models of G protein-coupled receptors

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Structure of the class D GPCR Ste2 dimer coupled to two G proteins

G-protein-coupled receptors (GPCRs) are divided phylogenetically into six classes^1,2, denoted A to F. More than 370 structures of vertebrate GPCRs (belonging to classes A, B, C and F) have been determined, leading to a substantial understanding of their function³. By contrast, there are no structures of class D GPCRs, which are found exclusively in fungi where they regulate survival and reproduction. Here we determine the structure of a class D GPCR, the Saccharomyces cerevisiae pheromone receptor Ste2, in an active state coupled to the heterotrimeric G protein Gpa1-Ste4-Ste18. Ste2 was purified as a homodimer coupled to two G proteins. The dimer interface of Ste2 is formed by the N terminus, the transmembrane helices H1, H2 and H7, and the first extracellular loop ECL1. We establish a class D1 generic residue numbering system (CD1) to enable comparisons with orthologues and with other GPCR classes. The structure of Ste2 bears similarities in overall topology to class A GPCRs, but the transmembrane helix H4 is shifted by more than 20 Å and the G-protein-binding site is a shallow groove rather than a cleft. The structure provides a template for the design of novel drugs to target fungal GPCRs, which could be used to treat numerous intractable fungal diseases⁴.

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study

How accurate do structures of the β₂ adrenergic receptor (β₂AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β₂AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the β₂AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (α = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/β₂AR sequence identity, models built on the basis of templates that show high sequence identity with the β₂AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

Aminergic GPCR-Ligand Interactions: A Chemical and Structural Map of Receptor Mutation Data

The aminergic family of G protein-coupled receptors (GPCRs) plays an important role in various diseases and represents a major drug discovery target class. Structure determination of all major aminergic subfamilies has enabled structure-based ligand design for these receptors. Site-directed mutagenesis data provides an invaluable complementary source of information for elucidating the structural determinants of binding of different ligand chemotypes. The current study provides a comparative analysis of 6692 mutation data points on 34 aminergic GPCR subtypes, covering the chemical space of 540 unique ligands from mutagenesis experiments and information from experimentally determined structures of 52 distinct aminergic receptor-ligand complexes. The integrated analysis enables detailed investigation of structural receptor-ligand interactions and assessment of the transferability of combined binding mode and mutation data across ligand chemotypes and receptor subtypes. An overview is provided of the possibilities and limitations of using mutation data to guide the design of novel aminergic receptor ligands.

Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment

GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications.

Toward high-resolution computational design of the structure and function of helical membrane proteins

The computational design of α-helical membrane proteins is still in its infancy but has already made great progress. De novo design allows stable, specific and active minimal oligomeric systems to be obtained. Computational reengineering can improve the stability and function of naturally occurring membrane proteins. Currently, the major hurdle for the field is the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress.

Structure and activation of the TSH receptor transmembrane domain

PURPOSE

The thyroid-stimulating hormone receptor (TSHR) is the target autoantigen for TSHR-stimulating autoantibodies in Graves' disease. The TSHR is composed of: a leucine-rich repeat domain (LRD), a hinge region or cleavage domain (CD) and a transmembrane domain (TMD). The binding arrangements between the TSHR LRD and the thyroid-stimulating autoantibody M22 or TSH have become available from the crystal structure of the TSHR LRD-M22 complex and a comparative model of the TSHR LRD in complex with TSH, respectively. However, the mechanism by which the TMD of the TSHR and the other glycoprotein hormone receptors (GPHRs) becomes activated is unknown.

METHODS

We have generated comparative models of the structures of the inactive (TMD_In) and active (TMD_Ac) conformations of the TSHR, follicle-stimulating hormone receptor (FSHR) and luteinizing hormone receptor (LHR) TMDs. The structures of TMD_Ac and TMD_In were obtained using class A GPCR crystal structures for which fully active and inactive conformations were available.

RESULTS

Most conserved motifs observed in GPCR TMDs are also observed in the amino acid sequences of GPHR TMDs. Furthermore, most GPCR TMD conserved helix distortions are observed in our models of the structures of GPHR TMDs. Analysis of these structures has allowed us to propose a mechanism for activation of GPHR TMDs.

CONCLUSIONS

Insight into the mechanism of activation of the TSHR by both TSH and TSHR autoantibodies is likely to be useful in the development of new treatments for Graves' disease.

Homology modeling of a Class A GPCR in the inactive conformation: A quantitative analysis of the correlation between model/template sequence identity and model accuracy

With the present work we quantitatively studied the modellability of the inactive state of Class A G protein-coupled receptors (GPCRs). Specifically, we constructed models of one of the Class A GPCRs for which structures solved in the inactive state are available, namely the β₂ AR, using as templates each of the other class members for which structures solved in the inactive state are also available. Our results showed a detectable linear correlation between model accuracy and model/template sequence identity. This suggests that the likely accuracy of the homology models that can be built for a given receptor can be generally forecasted on the basis of the available templates. We also probed whether sequence alignments that allow for the presence of gaps within the transmembrane domains to account for structural irregularities afford better models than the classical alignment procedures that do not allow for the presence of gaps within such domains. As our results indicated, although the overall differences are very subtle, the inclusion of internal gaps within the transmembrane domains has a noticeable a beneficial effect on the local structural accuracy of the domain in question.

A Molecular Pharmacologist's Guide to G Protein-Coupled Receptor Crystallography

G protein-coupled receptor (GPCR) structural biology has progressed dramatically in the last decade. There are now over 120 GPCR crystal structures deposited in the Protein Data Bank of 32 different receptors from families scattered across the phylogenetic tree, including class B, C, and Frizzled GPCRs. These structures have been obtained in combination with a wide variety of ligands and captured in a range of conformational states. This surge in structural knowledge has enlightened research into the molecular recognition of biologically active molecules, the mechanisms of receptor activation, the dynamics of functional selectivity, and fueled structure-based drug design efforts for GPCRs. Here we summarize the innovations in both protein engineering/molecular biology and crystallography techniques that have led to these advances in GPCR structural biology and discuss how they may influence the resulting structural models. We also provide a brief molecular pharmacologist's guide to GPCR X-ray crystallography, outlining some key aspects in the process of structure determination, with the goal to encourage noncrystallographers to interrogate structures at the molecular level. Finally, we show how chemogenomics approaches can be used to marry the wealth of existing receptor pharmacology data with the expanding repertoire of structures, providing a deeper understanding of the mechanistic details of GPCR function.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Improving homology modeling of G-protein coupled receptors through multiple-template derived conserved inter-residue interactions

Evidenced by the three-rounds of G-protein coupled receptors (GPCR) Dock competitions, improving homology modeling methods of helical transmembrane proteins including the GPCRs, based on templates of low sequence identity, remains an eminent challenge. Current approaches addressing this challenge adopt the philosophy of "modeling first, refinement next". In the present work, we developed an alternative modeling approach through the novel application of available multiple templates. First, conserved inter-residue interactions are derived from each additional template through conservation analysis of each template-target pairwise alignment. Then, these interactions are converted into distance restraints and incorporated in the homology modeling process. This approach was applied to modeling of the human β2 adrenergic receptor using the bovin rhodopsin and the human protease-activated receptor 1 as templates and improved model quality was demonstrated compared to the homology model generated by standard single-template and multiple-template methods. This method of "refined restraints first, modeling next", provides a fast and complementary way to the current modeling approaches. It allows rational identification and implementation of additional conserved distance restraints extracted from multiple templates and/or experimental data, and has the potential to be applicable to modeling of all helical transmembrane proteins.

Anti-HIV small-molecule binding in the peptide subpocket of the CXCR4:CVX15 crystal structure

The CXC chemokine receptor 4 (CXCR4) is involved in chemotaxis and serves as a coreceptor for T-tropic HIV-1 viral entry, thus making this receptor an attractive drug target. Recently, crystal structures of CXCR4 were reported as complexes with the small molecule IT1t and the CVX15 peptide. Follow-up efforts to model different antagonists into the small molecule CXCR4:IT1t crystal structure did not generate poses consistent with either the X-ray crystal structure or site-directed mutagenesis (SDM). Here, we compare the binding pockets of the two CXCR4 crystal structures, revealing differences in helices IV, V, VI, and VII, with major differences for the His203 residue buried in the binding pocket. The small molecule antagonist AMD11070 was docked into both CXCR4 crystal structures. An AMD11070 pose identified from the CXCR4:CVX15 model presented interactions with Asp171, Glu288, Trp94, and Asp97, consistent with published SDM data, thus suggesting it is the bioactive pose. A CXCR4 receptor model was optimized around this pose of AMD11070, and the resulting model correlated HIV-1 inhibition with MM-GBSA docking scores for a congeneric AMD11070-like series. Subsequent NAMFIS NMR results successfully linked the proposed binding pose to an independent experimental structure. These results strongly suggest that not all small molecules will bind to CXCR4 in a similar manner as IT1t. Instead, the CXCR4:CVX15 crystal structure may provide a binding locus for small organic molecules that is more suitable than the secondary IT1t site. This work is expected to provide modeling insights useful for future CXCR4 antagonist and X4-tropic HIV-1 based drug design efforts.

High-resolution modeling of transmembrane helical protein structures from distant homologues

Eukaryotic transmembrane helical (TMH) proteins perform a wide diversity of critical cellular functions, but remain structurally largely uncharacterized and their high-resolution structure prediction is currently hindered by the lack of close structural homologues. To address this problem, we present a novel and generic method for accurately modeling large TMH protein structures from distant homologues exhibiting distinct loop and TMH conformations. Models of the adenosine A2AR and chemokine CXCR4 receptors were first ranked in GPCR-DOCK blind prediction contests in the receptor structure accuracy category. In a benchmark of 50 TMH protein homolog pairs of diverse topology (from 5 to 12 TMHs), size (from 183 to 420 residues) and sequence identity (from 15% to 70%), the method improves most starting templates, and achieves near-atomic accuracy prediction of membrane-embedded regions. Unlike starting templates, the models are of suitable quality for computer-based protein engineering: redesigned models and redesigned X-ray structures exhibit very similar native interactions. The method should prove useful for the atom-level modeling and design of a large fraction of structurally uncharacterized TMH proteins from a wide range of structural homologues.

Blocking metabotropic glutamate receptor subtype 7 (mGlu7) via the Venus flytrap domain (VFTD) inhibits amygdala plasticity, stress, and anxiety-related behavior

The metabotropic glutamate receptor subtype 7 (mGlu7) is an important presynaptic regulator of neurotransmission in the mammalian CNS. mGlu7 function has been linked to autism, drug abuse, anxiety, and depression. Despite this, it has been difficult to develop specific blockers of native mGlu7 signaling in relevant brain areas such as amygdala and limbic cortex. Here, we present the mGlu7-selective antagonist 7-hydroxy-3-(4-iodophenoxy)-4H-chromen-4-one (XAP044), which inhibits lateral amygdala long term potentiation (LTP) in brain slices from wild type mice with a half-maximal blockade at 88 nm. There was no effect of XAP044 on LTP of mGlu7-deficient mice, indicating that this pharmacological effect is mGlu7-dependent. Unexpectedly and in contrast to all previous mGlu7-selective drugs, XAP044 does not act via the seven-transmembrane region but rather via a binding pocket localized in mGlu7's extracellular Venus flytrap domain, a region generally known for orthosteric agonist binding. This was shown by chimeric receptor studies in recombinant cell line assays. XAP044 demonstrates good brain exposure and wide spectrum anti-stress and antidepressant- and anxiolytic-like efficacy in rodent behavioral paradigms. XAP044 reduces freezing during acquisition of Pavlovian fear and reduces innate anxiety, which is consistent with the phenotypes of mGlu7-deficient mice, the results of mGlu7 siRNA knockdown studies, and the inhibition of amygdala LTP by XAP044. Thus, we present an mGlu7 antagonist with a novel molecular mode of pharmacological action, providing significant application potential in psychiatry. Modeling the selective interaction between XAP044 and mGlu7's Venus flytrap domain, whose three-dimensional structure is already known, will facilitate future drug development supported by computer-assisted drug design.

Structure and dynamics of G-protein coupled receptors

G-protein coupled receptors (GPCRs) are seven helical transmembrane proteins that mediate cell-to-cell communication. They also form the largest superfamily of drug targets. Hence detailed studies of the three dimensional structure and dynamics are critical to understanding the functional role of GPCRs in signal transduction pathways, and for drug design. In this chapter we compare the features of the crystal structures of various biogenic amine receptors, such as β1 and β2 adrenergic receptors, dopamine D3 receptor, M2 and M3 muscarinic acetylcholine receptors. This analysis revealed that conserved residues are located facing the inside of the transmembrane domain in these GPCRs improving the efficiency of packing of these structures. The NMR structure of the chemokine receptor CXCR1 without any ligand bound, shows significant dynamics of the transmembrane domain, especially the helical kink angle on the transmembrane helix6. The activation mechanism of the β2-adrenergic receptor has been studied using multiscale computational methods. The results of these studies showed that the receptor without any ligand bound, samples conformations that resemble some of the structural characteristics of the active state of the receptor. Ligand binding stabilizes some of the conformations already sampled by the apo receptor. This was later observed in the NMR study of the dynamics of human β2-adrenergic receptor. The dynamic nature of GPCRs leads to a challenge in obtaining purified receptors for biophysical studies. Deriving thermostable mutants of GPCRs has been a successful strategy to reduce the conformational heterogeneity and stabilize the receptors. This has lead to several crystal structures of GPCRs. However, the cause of how these mutations lead to thermostability is not clear. Computational studies are beginning to shed some insight into the possible structural basis for the thermostability. Molecular Dynamics simulations studying the conformational ensemble of thermostable mutants have shown that the stability could arise from both enthalpic and entropic factors. There are regions of high stress in the wild type GPCR that gets relieved upon mutation conferring thermostability.

SuperBiHelix method for predicting the pleiotropic ensemble of G-protein-coupled receptor conformations

There is overwhelming evidence that G-protein-coupled receptors (GPCRs) exhibit several distinct low-energy conformations, each of which might favor binding to different ligands and/or lead to different downstream functions. Understanding the function of such proteins requires knowledge of the ensemble of low-energy configurations that might play a role in this pleiotropic functionality. We earlier reported the BiHelix method for efficiently sampling the (12)(7) = 35 million conformations resulting from 30° rotations about the axis (η) of all seven transmembrane helices (TMHs), showing that the experimental structure is reliably selected as the best conformation from this ensemble. However, various GPCRs differ sufficiently in the tilts of the TMHs that this method need not predict the optimum conformation starting from any other template. In this paper, we introduce the SuperBiHelix method in which the tilt angles (θ, ϕ) are optimized simultaneously with rotations (η) efficiently enough that it is practical and sufficient to sample (5 × 3 × 5)(7) = 13 trillion configurations. This method can correctly identify the optimum structure of a GPCR starting with the template from a different GPCR. We have validated this method by predicting known crystal structure conformations starting from the template of a different protein structure. We find that the SuperBiHelix conformational ensemble includes the higher energy conformations associated with the active protein in addition to those associated with the more stable inactive protein. This methodology was then applied to design and experimentally confirm structures of three mutants of the CB1 cannabinoid receptor associated with different functions.

Discovery and computer aided potency optimization of a novel class of small molecule CXCR4 antagonists

Amongst the chemokine signalling axes involved in cancer, chemokine CXCL12 acting on chemokine receptor CXCR4 is particularly significant since it orchestrates migration of cancer cells in a tissue-specific metastatic process. High CXCR4 tumour expression is associated with poor prognosis of lung, brain, CNS, blood and breast cancers. We have identified a new class of small molecule CXCR4 antagonists based on the use of computational modelling studies in concert with experimental determination of in vitro activity against CXCL12-induced intracellular calcium mobilisation, proliferation and chemotaxis. Molecular modelling proved to be a useful tool in rationalising our observed potencies, as well as informing the direction of the synthetic efforts aimed at producing more potent compounds.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes.