Progress in the structural prediction of G protein-coupled receptors: D3 receptor in complex with eticlopride

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.

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.

A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors

The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target-template sequence similarity is relatively high. On the other hand, the observation of a low target-template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT1B and 5-HT2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures.

Molecular modelling studies on 2-substituted octahydropyrazinopyridoindoles for histamine H2 receptor antagonism

The human histamine H2 receptor (hH2HR) is a G-protein coupled receptor protein with seven transmembrane (TM)-spanning helices primarily involved in regulation of gastric acid secretion. Antagonists targeting hH2HR are useful in the treatment of hyperacidic conditions such as peptic ulcers, gastresophageal reflux disease and gastrointestinal bleeding. We have previously reported the antagonism of 2-substituted pyrazinopyridoindoles at the human histamine H1 receptor and mode of binding of these compounds at the hH1HR using in silico methods. Interestingly, some of the compounds in the series also showed promising activity towards hH2HR that prompted us to investigate the mode of binding of these compounds at hH2HR. In the absence of the crystal structure of hH2HR a homology model has been constructed using multiple sequence alignment, using the X-ray crystal structures of Turkey β1-adrenergic receptor (tβ1AR), Human histamine H1 receptor (hH1HR), Human β2-adrenergic receptor (hβ2AR) and Human D3 dopamine receptor (hD3R). The important residues for binding were depicted in TMIII, TMV, TMVI and TMVII by the homology modelled hH2HR for 2-substituted pyrazinopyridoindoles. A comparative study for deducing the selectivity regarding the binding towards hH1HR and hH2HR has been carried out, which may be useful in designing of selective hH1HR/hH2HR antagonists in these classes of compounds.

Towards predictive docking at aminergic G-protein coupled receptors

G protein-coupled receptors (GPCRs) are hard to crystallize. However, attempts to predict their structure have boomed as a result of advancements in crystallographic techniques. This trend has allowed computer-aided molecular modeling of GPCRs. We analyzed the performance of four molecular modeling programs in pose evaluation of re-docked antagonists / inverse agonists to 11 original crystal structures of aminergic GPCRs using an induced fit-docking procedure. AutoDock and Glide were used for docking. AutoDock binding energy function, GlideXP, Prime MM-GB/SA, and YASARA binding function were used for pose scoring. Root mean square deviation (RMSD) of the best pose ranged from 0.09 to 1.58 Å, and median RMSD of the top 60 poses ranged from 1.47 to 3.83 Å. However, RMSD of the top pose ranged from 0.13 to 7.33 Å and ranking of the best pose ranged from the 1st to 60th out of 60 poses. Moreover, analysis of ligand-receptor interactions of top poses revealed substantial differences from interactions found in crystallographic structures. Bad ranking of top poses and discrepancies between top docked poses and crystal structures render current simple docking methods unsuitable for predictive modeling of receptor-ligand interactions. Prime MM-GB/SA optimized for 3NY9 by multiple linear regression did not work well at 3NY8 and 3NYA, structures of the same receptor with different ligands. However, 9 of 11 trajectories of molecular dynamics simulations by Desmond of top poses converged with trajectories of crystal structures. Key interactions were properly detected for all structures. This procedure also worked well for cross-docking of tested β2-adrenergic antagonists. Thus, this procedure represents a possible way to predict interactions of antagonists with aminergic GPCRs.

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.

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.

Strategies for improved modeling of GPCR-drug complexes: blind predictions of serotonin receptors bound to ergotamine

The recent increase in the number of atomic-resolution structures of G protein-coupled receptors (GPCRs) has contributed to a deeper understanding of ligand binding to several important drug targets. However, reliable modeling of GPCR-ligand complexes for the vast majority of receptors with unknown structure remains to be one of the most challenging goals for computer-aided drug design. The GPCR Dock 2013 assessment, in which researchers were challenged to predict the crystallographic structures of serotonin 5-HT(1B) and 5-HT(2B) receptors bound to ergotamine, provided an excellent opportunity to benchmark the current state of this field. Our contributions to GPCR Dock 2013 accurately predicted the binding mode of ergotamine with RMSDs below 1.8 Å for both receptors, which included the best submissions for the 5-HT(1B) complex. Our models also had the most accurate description of the binding sites and receptor-ligand contacts. These results were obtained using a ligand-guided homology modeling approach, which combines extensive molecular docking screening with incorporation of information from multiple crystal structures and experimentally derived restraints. In this work, we retrospectively analyzed thousands of structures that were generated during the assessment to evaluate our modeling strategies. Major contributors to accuracy were found to be improved modeling of extracellular loop two in combination with the use of molecular docking to optimize the binding site for ligand recognition. Our results suggest that modeling of GPCR-drug complexes has reached a level of accuracy at which structure-based drug design could be applied to a large number of pharmaceutically relevant targets.

Insights into the structural biology of G-protein coupled receptors impacts drug design for central nervous system neurodegenerative processes

In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selectivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and orthosteric binding sites on dopamine receptors for the treatment of Parkinson's disease, and on muscarinic receptors for Alzheimer's disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopamine receptor holds promise as a relevant therapeutic strategy for Parkinson's disease. Regarding the treatment of Alzheimer's disease, the design of dualsteric ligands for mono-oligomeric rinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.

Molecular dynamics simulations of the adenosine A2a receptor: structural stability, sampling, and convergence

Molecular dynamics (MD) simulations of membrane-embedded G-protein coupled receptors (GPCRs) have rapidly gained popularity among the molecular simulation community in recent years, a trend which has an obvious link to the tremendous pharmaceutical importance of this group of receptors and the increasing availability of crystal structures. In view of the widespread use of this technique, it is of fundamental importance to ensure the reliability and robustness of the methodologies so they yield valid results and enable sufficiently accurate predictions to be made. In this work, 200 ns simulations of the A2a adenosine receptor (A2a AR) have been produced and evaluated in the light of these requirements. The conformational dynamics of the target protein, as obtained from replicate simulations in both the presence and absence of an inverse agonist ligand (ZM241385), have been investigated and compared using principal component analysis (PCA). Results show that, on this time scale, convergence of the replicates is not readily evident and dependent on the types of the protein motions considered. Thus rates of inter- as opposed to intrahelical relaxation and sampling can be different. When studied individually, we find that helices III and IV have noticeably greater stability than helices I, II, V, VI, and VII in the apo form. The addition of the inverse agonist ligand greatly improves the stability of all helices.

The role of dopamine D(3) receptors in antipsychotic activity and cognitive functions

Dopamine D(3) receptors have a pre- and postsynaptic localization in brain stem nuclei, limbic parts of the striatum, and cortex. Their widespread influence on dopamine release, on dopaminergic function, and on several other neurotransmitters makes them attractive targets for therapeutic intervention. The signaling pathways of D(3) receptors are distinct from those of other members of the D(2)-like receptor family. There is increasing evidence that D(3) receptors can form heteromers with dopamine D(1), D(2), and probably other G-protein-coupled receptors. The functional consequences remain to be characterized in more detail but might open new interesting pharmacological insight and opportunities. In terms of behavioral function, D(3) receptors are involved in cognitive, social, and motor functions, as well as in filtering and sensitization processes. Although the role of D(3) receptor blockade for alleviating positive symptoms is still unsettled, selective D(3) receptor antagonism has therapeutic features for schizophrenia and beyond as demonstrated by several animal models: improved cognitive function, emotional processing, executive function, flexibility, and social behavior. D(3) receptor antagonism seems to contribute to atypicality of clinically used antipsychotics by reducing extrapyramidal motor symptoms; has no direct influence on prolactin release; and does not cause anhedonia, weight gain, or metabolic dysfunctions. Unfortunately, clinical data with new, selective D(3) antagonists are still incomplete; their cognitive effects have only been communicated in part. In vitro, virtually all clinically used antipsychotics are not D(2)-selective but also have affinity for D(3) receptors. The exact D(3) receptor occupancies achieved in patients, particularly in cortical areas, are largely unknown, mainly because only nonselective or agonist PET tracers are currently available. It is unlikely that a degree of D(3) receptor antagonism optimal for antipsychotic and cognitive function can be achieved with existing antipsychotics. Therefore, selective D(3) antagonism represents a promising mechanism still to be fully exploited for the treatment of schizophrenia, cognitive deficits in schizophrenia, and comorbid conditions such as substance abuse.

Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking evaluation

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D₃ (hD₃) receptor has been recently solved. Based on the hD₃ receptor crystal structure we generated dopamine D₂ and D₃ receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD₃ and hD_2L receptors was differentiated by means of MD simulations and D₃ selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K_i was obtained for hD₃ and hD_2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands.

Membrane-sensitive conformational states of helix 8 in the metabotropic Glu2 receptor, a class C GPCR

The recent elucidation of the X-ray structure of several class A GPCRs clearly indicates that the amphipathic helix 8 (H8) is a conserved structural domain in most crystallized GPCRs. Very little is known about the presence and the possible role of an analogous H8 domain in the distantly related class C GPCRs. In this study, we investigated the structural properties for the H8 domain of the mGluR2 receptor, a class C GPCR, by applying extended molecular dynamics simulations. Our study indicates that the amphipathic H8 adopts membrane-sensitive conformational states, which depend on the membrane composition. Cholesterol-rich membranes stabilize the helical structure of H8 whereas cholesterol-depleted membranes induce a disruption of H8. The observed link between membrane cholesterol levels and H8 conformational states suggests that H8 behaves as a sensor of cholesterol concentration.