Structure-based identification of binding sites,native ligands and potential inhibitors for G-protein coupled receptors

G-protein coupled receptors (GPCRs) are the largest family of cell-surface receptors involved in signal transmission. Drugs associated with GPCRs represent more than one fourth of the 100 top-selling drugs and are the targets of more than half of the current therapeutic agents on the market. Our methodology based on the internal coordinate mechanics (ICM) program can accurately identify the ligand-binding pocket in the currently available crystal structures of seven transmembrane (7TM) proteins [bacteriorhodopsin (BR) and bovine rhodopsin (bRho)]. The binding geometry of the ligand can be accurately predicted by ICM flexible docking with and without the loop regions, a useful finding for GPCR docking because the transmembrane regions are easier to model. We also demonstrate that the native ligand can be identified by flexible docking and scoring in 1.5% and 0.2% (for bRho and BR, respectively) of the best scoring compounds from two different types of compound database. The same procedure can be applied to the database of available chemicals to identify specific GPCR binders. Finally, we demonstrate that even if the sidechain positions in the bRho binding pocket are entirely wrong, their correct conformation can be fully restored with high accuracy (0.28 A) through the ICM global optimization with and without the ligand present. These binding site adjustments are critical for flexible docking of new ligands to known structures or for docking to GPCR homology models. The ICM docking method has the potential to be used to "de-orphanize" orphan GPCRs (oGPCRs) and to identify antagonists-agonists for GPCRs if an accurate model (experimentally and computationally validated) of the structure has been constructed or when future crystal structures are determined.     

Ligand supported homology modeling and docking evaluation of CCR2: docked pose selection by consensus scoring

Chemokine receptor 2 (CCR2) is a G-protein coupled receptor (GPCR) and a crucial target for various inflammatory and autoimmune diseases. The structure based antagonists design for many GPCRs, including CCR2, is restricted by the lack of an experimental three dimensional structure. Homology modeling is widely used for the study of GPCR-ligand binding. Since there is substantial diversity for the ligand binding pocket and binding modes among GPCRs, the receptor-ligand binding mode predictions should be derived from homology modeling with supported ligand information. Thus, we modeled the binding of our proprietary CCR2 antagonist using ligand supported homology modeling followed by consensus scoring the docking evaluation based on all modeled binding sites. The protein-ligand model was then validated by visual inspection of receptor-ligand interaction for consistency of published site-directed mutagenesis data and virtual screening a decoy compound database. This model was able to successfully identify active compounds within the decoy database. Finally, additional hit compounds were identified through a docking-based virtual screening of a commercial database, followed by a biological assay to validate CCR2 inhibitory activity. Thus, this procedure can be employed to screen a large database of compounds to identify new CCR2 antagonists.     

Limits of Ligand Selectivity from Docking to Models: In Silico Screening for A1 Adenosine Receptor Antagonists

G protein-coupled receptors (GPCRs) are attractive targets for pharmaceutical research. With the recent determination of several GPCR X-ray structures, the applicability of structure-based computational methods for ligand identification, such as docking, has increased. Yet, as only about 1% of GPCRs have a known structure, receptor homology modeling remains necessary. In order to investigate the usability of homology models and the inherent selectivity of a particular model in relation to close homologs, we constructed multiple homology models for the A₁ adenosine receptor (A₁AR) and docked ∼2.2 M lead-like compounds. High-ranking molecules were tested on the A₁AR as well as the close homologs A_2AAR and A₃AR. While the screen yielded numerous potent and novel ligands (hit rate 21% and highest affinity of 400 nM), it delivered few selective compounds. Moreover, most compounds appeared in the top ranks of only one model. These findings have implications for future screens.     

Roles for Ordered and Bulk Solvent in Ligand Recognition and Docking in Two Related Cavities

A key challenge in structure-based discovery is accounting for modulation of protein-ligand interactions by ordered and bulk solvent. To investigate this, we compared ligand binding to a buried cavity in Cytochrome c Peroxidase (CcP), where affinity is dominated by a single ionic interaction, versus a cavity variant partly opened to solvent by loop deletion. This opening had unexpected effects on ligand orientation, affinity, and ordered water structure. Some ligands lost over ten-fold in affinity and reoriented in the cavity, while others retained their geometries, formed new interactions with water networks, and improved affinity. To test our ability to discover new ligands against this opened site prospectively, a 534,000 fragment library was docked against the open cavity using two models of ligand solvation. Using an older solvation model that prioritized many neutral molecules, three such uncharged docking hits were tested, none of which was observed to bind; these molecules were not highly ranked by the new, context-dependent solvation score. Using this new method, another 15 highly-ranked molecules were tested for binding. In contrast to the previous result, 14 of these bound detectably, with affinities ranging from 8 µM to 2 mM. In crystal structures, four of these new ligands superposed well with the docking predictions but two did not, reflecting unanticipated interactions with newly ordered waters molecules. Comparing recognition between this open cavity and its buried analog begins to isolate the roles of ordered solvent in a system that lends itself readily to prospective testing and that may be broadly useful to the community.     

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.     

Homology modeling of dopamine d(2) and d(3) 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 D(3) (hD(3)) receptor has been recently solved. Based on the hD(3) receptor crystal structure we generated dopamine D(2) and D(3) 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 hD(3) and hD(2L) receptors was differentiated by means of MD simulations and D(3) selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K(i) was obtained for hD(3) 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.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Improving the apo-state detergent stability of NTS1 with CHESS for pharmacological and structural studies

The largest single class of drug targets is the G protein-coupled receptor (GPCR) family. Modern high-throughput methods for drug discovery require working with pure protein, but this has been a challenge for GPCRs, and thus the success of screening campaigns targeting soluble, catalytic protein domains has not yet been realized for GPCRs. Therefore, most GPCR drug screening has been cell-based, whereas the strategy of choice for drug discovery against soluble proteins is HTS using purified proteins coupled to structure-based drug design. While recent developments are increasing the chances of obtaining GPCR crystal structures, the feasibility of screening directly against purified GPCRs in the unbound state (apo-state) remains low. GPCRs exhibit low stability in detergent micelles, especially in the apo-state, over the time periods required for performing large screens. Recent methods for generating detergent-stable GPCRs, however, offer the potential for researchers to manipulate GPCRs almost like soluble enzymes, opening up new avenues for drug discovery. Here we apply cellular high-throughput encapsulation, solubilization and screening (CHESS) to the neurotensin receptor 1 (NTS₁) to generate a variant that is stable in the apo-state when solubilized in detergents. This high stability facilitated the crystal structure determination of this receptor and also allowed us to probe the pharmacology of detergent-solubilized, apo-state NTS₁ using robotic ligand binding assays. NTS₁ is a target for the development of novel antipsychotics, and thus CHESS-stabilized receptors represent exciting tools for drug discovery.     

Binding mode of ecdysone agonists to the receptor: comparative modeling and docking studies

Three-dimensional structure models of the ligand-binding domain of the ecdysone receptor of Heliothis virescens were built by the homology modeling technique from the crystal structures of nuclear receptors. Two models were created based both on known ligand-binding domain structures of the receptors with the highest sequence identity to the ecdysone receptor, and on those of steroid hormone receptors. The latter model, which was found to have better stereochemical quality and be in good agreement with the binding of the steroidal framework of the endogenous agonist 20-hydroxyecdysone, was used for docking studies. The docking of 20-hydroxyecdysone to the receptor model revealed that the ligand molecule can interact with the receptor in a similar manner to other steroid hormone-receptor complexes. The docking of a dibenzoylhydrazine agonist, chromafenozide, was performed based on the correspondences between the molecule and 20-dydroxyecdysone expected by molecular comparison. The interactions of the ligands with the receptor in the complexes modeled were investigated and found to be consistent with known structure-activity relationships.     

An evaluation of automated in silico ligand docking of amino acid ligands to Family C G-protein coupled receptors

Family C G-protein coupled receptors (GPCRs) consist of the metabotropic glutamate receptors (mGluRs), the calcium-sensing receptor (CaSR), the T1R taste receptors, the GABA(B) receptor, the V2R pheromone receptors, and several chemosensory receptors. A common feature of Family C receptors is the presence of an amino acid binding pocket. The objective of this study was to evaluate the ability of the automatic docking program FlexX to predict the favored amino acid ligand at several Family C GPCRs. The docking process was optimized using the crystal structure of mGluR1 and the 20 amino acids were docked into homology models of the CaSR, the 5.24 chemosensory receptor, and the GPRC6A amino acid receptor. Under optimized docking conditions, glutamate was docked in the binding pocket of mGluR1 with a root mean square deviation of 1.56 angstroms from the co-crystallized glutamate structure and was ranked as the best ligand with a significantly better FlexX score compared to all other amino acids. Ligand docking to a homology model of the 5.24 receptor gave generally correct predictions of the favored amino acids, while the results obtained with models of GPRC6A and the CaSR showed that some of the favored amino acids at these receptors were correctly predicted, while a few other top scoring amino acids appeared to be false positives. We conclude that with certain caveats, FlexX can be successfully used to predict preferred ligands at Family C GPCRs.     

A leishmaniasis study: Structure-based screening and molecular dynamics mechanistic analysis for discovering potent inhibitors of spermidine synthase

Protozoa Leishmania donovani (Ld) is the main cause of the endemic disease leishmaniasis. Spermidine synthase (SS), an important enzyme in the synthetic pathway of polyamines in Ld, is an essential element for the survival of this protozoan. Targeting SS may provide an important aid for the development of drugs against Ld. However, absence of tertiary structure of spermidine synthase of Leishmania donovani (LSS) limits the possibilities of structure based drug designing. Presence of the same enzyme in the host itself further challenges the drug development process. We modeled the tertiary structure of LSS using homology modeling approach making use of homologous X-ray crystallographic structure of spermidine synthase of Trypanosoma cruzi (TSS) (2.5? resolution). The modeled structure was stabilized using Molecular Dynamics simulations. Based on active site structural differences between LSS and human spermidine synthase (HSS), we screened a large dataset of compounds against modeled protein using Glide virtual screen docking and selected two best inhibitors based on their docking scores (-10.04 and -13.11 respectively) with LSS and having least/no binding with the human enzyme. Finally Molecular Dynamics simulations were used to assess the dynamic stability of the ligand bound structures and to elaborate on the binding modes. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Determination of key receptor–ligand interactions of dopaminergic arylpiperazines and the dopamine D2 receptor homology model

Interest in structure-based G-protein-coupled receptor (GPCR) ligand discovery is huge, given that almost 30 % of all approved drugs belong to this category of active compounds. The GPCR family includes the dopamine receptor subtype D2 (D2DR), but unfortunately—as is true of most GPCRs—no experimental structures are available for these receptors. In this publication, we present the molecular model of D2DR based on the previously published crystal structure of the dopamine D3 receptor (D3DR). A molecular modeling study using homology modeling and docking simulation provided a rational explanation for the behavior of the arylpiperazine ligand. The observed binding modes and receptor–ligand interactions provided us with fresh clues about how to optimize selectivity for D2DR receptors.

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

We present a critical assessment of the performance of our homology model refinement method for G protein‐coupled receptors (GPCRs), called LITICon that led to top ranking structures in a recent structure prediction assessment GPCRDOCK2010. GPCRs form the largest class of drug targets for which only a few crystal structures are currently available. Therefore, accurate homology models are essential for drug design in these receptors. We submitted five models each for human chemokine CXCR4 (bound to small molecule IT1t and peptide CVX15) and dopamine D3DR (bound to small molecule eticlopride) before the crystal structures were published. Our models in both CXCR4/IT1t and D3/eticlopride assessments were ranked first and second, respectively, by ligand RMSD to the crystal structures. For both receptors, we developed two types of protein models: homology models based on known GPCR crystal structures, and ab initio models based on the prediction method MembStruk. The homology‐based models compared better to the crystal structures than the ab initio models. However, a robust refinement procedure for obtaining high accuracy structures is needed. We demonstrate that optimization of the helical tilt, rotation, and translation is vital for GPCR homology model refinement. As a proof of concept, our in‐house refinement program LITiCon captured the distinct orientation of TM2 in CXCR4, which differs from that of adrenoreceptors. These findings would be critical for refining GPCR homology models in future. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Structure based inhibitor design targeting glycogen phosphorylase b. Virtual screening,synthesis, biochemical and biological assessment of novel N-acyl-β-d-glucopyranosylamines

Glycogen phosphorylase (GP) is a validated target for the development of new type 2 diabetes treatments. Exploiting the Zinc docking database, we report the in silico screening of 1888 N-acyl-β-d-glucopyranosylamines putative GP inhibitors differing only in their R groups. CombiGlide and GOLD docking programs with different scoring functions were employed with the best performing methods combined in a ‘consensus scoring’ approach to ranking of ligand binding affinities for the active site. Six selected candidates from the screening were then synthesized and their inhibitory potency was assessed both in vitro and ex vivo. Their inhibition constants’ values, in vitro, ranged from 5 to 377 μM while two of them were effective at causing inactivation of GP in rat hepatocytes at low μM concentrations. The crystal structures of GP in complex with the inhibitors were defined and provided the structural basis for their inhibitory potency and data for further structure based design of more potent inhibitors.     

Do crystal structures obviate the need for theoretical models of GPCRs for structure‐based virtual screening?

Recent highly expected structural characterizations of agonist-bound and antagonist-bound beta-2 adrenoreceptor (β2AR) by X-ray crystallography have been widely regarded as critical advances to enable more effective structure-based discovery of GPCRs ligands. It appears that this very important development may have undermined many previous efforts to develop 3D theoretical models of GPCRs. To address this question directly, we have compared several historical β2AR models versus the inactive state and nanobody-stabilized active state of β2AR crystal structures in terms of their structural similarity and effectiveness of use in virtual screening for β2AR specific agonists and antagonists. Theoretical models, incluing both homology and de novo types, were collected from five different groups who have published extensively in the field of GPCRs modeling. All models were built before X-ray structures became available. In general, β2AR theoretical models differ significantly from the crystal structure in terms of TMH definition and the global packing. Nevertheless, surprisingly, several models afforded hit rates resulting from virtual screening of large chemical library enriched by known β2AR ligands that exceeded those using X-ray structures. The hit rates were particularly higher for agonists. Furthemore, the screening performance of models is associated with local structural quality, such as the RMSDs for binding pocket residues and the ability to capture accurately, most if not all critical protein/ligand interactions. These results suggest that carefully built models of GPCRs could capture critical chemical and structural features of the binding pocket, and thus may be even more useful for practical structure-based drug discovery than X-ray structures.     

Ligand-induced conformational changes: improved predictions of ligand binding conformations and affinities

A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.     

Simulations of a G protein-coupled receptor homology model predict dynamic features and a ligand binding site

A computational approach to predict structures of rhodopsin-like G protein-coupled receptors (GPCRs) is presented and evaluated by comparison to the X-ray structural models. By combining sequence alignment, the rhodopsin crystal structure, and point mutation data on the beta2 adrenoreceptor (b2ar), we predict a (-)-epinephrine-bound computational model of the beta2 adrenoreceptor. The model is evaluated by molecular dynamics simulations and by comparison with the recent X-ray structures of b2ar. The overall correspondence between the predicted and the X-ray structural model is high. Especially the prediction of the ligand binding site is accurate. This shows that the proposed dynamic homology modelling approach can be used to create reasonable models for the understanding of structure and dynamics of other rhodopsin-like GPCRs.     

Modeling GPCR active state conformations: the β(2)-adrenergic receptor

The recent publication of several G protein-coupled receptor (GPCR) structures has increased the information available for homology modeling inactive class A GPCRs. Moreover, the opsin crystal structure shows some active features. We have therefore combined information from these two sources to generate an extensively validated model of the active conformation of the β(2)-adrenergic receptor. Experimental information on fully active GPCRs from zinc binding studies, site-directed spin labeling, and other spectroscopic techniques has been used in molecular dynamics simulations. The observed conformational changes reside mainly in transmembrane helix 6 (TM6), with additional small but significant changes in TM5 and TM7. The active model has been validated by manual docking and is in agreement with a large amount of experimental work, including site-directed mutagenesis information. Virtual screening experiments show that the models are selective for β-adrenergic agonists over other GPCR ligands, for (R)- over (S)-β-hydroxy agonists and for β(2)-selective agonists over β(1)-selective agonists. The virtual screens reproduce interactions similar to those generated by manual docking. The C-terminal peptide from a model of the stimulatory G protein, readily docks into the active model in a similar manner to which the C-terminal peptide from transducin, docks into opsin, as shown in a recent opsin crystal structure. This GPCR-G protein model has been used to explain site-directed mutagenesis data on activation. The agreement with experiment suggests a robust model of an active state of the β(2)-adrenergic receptor has been produced. The methodology used here should be transferable to modeling the active state of other GPCRs.