Discovery of novel chemotypes to a G-protein-coupled receptor through ligand-steered homology modeling and structure-based virtual screening

The GPCR crystallography boom: providing an invaluable source of structural information and expanding the scope of homology modeling

G protein-coupled receptors (GPCRs) are integral membrane proteins of high pharmaceutical interest. Until relatively recently, their structures have been particularly elusive, and rhodopsin has been for many years the only member of the superfamily with experimentally elucidated structures. However, a number of recent technical and scientific advancements made the determination of GPCR structures more feasible, thus leading to the solution of the structures of several receptors. Besides providing direct structural information, these experimental GPCR structures also provide templates for the construction of GPCR models. In depth studies have been performed to probe the accuracy of these models, in particular with respect to the interactions with their ligands, and to assess their applicability the rational discovery of GPCR modulators. Given the current state of the art and the pace of the field, the future of GPCR structural studies is likely to be characterized by a landscape populated by an increasingly higher number of experimental and theoretical structures.

Ligand pose and orientational sampling in molecular docking

Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×10¹⁰ to 4×10¹⁰ to 1×10¹¹ to 2×10¹¹ to 5×10¹¹ mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

Mastering tricyclic ring systems for desirable functional cannabinoid activity

There is growing interest in using cannabinoid receptor 2 (CB2) agonists for the treatment of neuropathic pain and other indications. In continuation of our ongoing program aiming for the development of new small molecule cannabinoid ligands, we have synthesized a novel series of carbazole and γ-carboline derivatives. The affinities of the newly synthesized compounds were determined by a competitive radioligand displacement assay for human CB2 cannabinoid receptor and rat CB1 cannabinoid receptor. Functional activity and selectivity at human CB1 and CB2 receptors were characterized using receptor internalization and [(35)S]GTP-γ-S assays. The structure-activity relationship and optimization studies of the carbazole series have led to the discovery of a non-selective CB1 and CB2 agonist, compound 4. Our subsequent research efforts to increase CB2 selectivity of this lead compound have led to the discovery of CB2 selective compound 64, which robustly internalized CB2 receptors. Compound 64 had potent inhibitory effects on pain hypersensitivity in a rat model of neuropathic pain. Other potent and CB2 receptor-selective compounds, including compounds 63 and 68, and a selective CB1 agonist, compound 74 were also discovered. In addition, we identified the CB2 ligand 35 which failed to promote CB2 receptor internalization and inhibited compound CP55,940-induced CB2 internalization despite a high CB2 receptor affinity. The present study provides novel tricyclic series as a starting point for further investigations of CB2 pharmacology and pain treatment.

Isoform selectivity of adenylyl cyclase inhibitors: characterization of known and novel compounds

Nine membrane-bound adenylyl cyclase (AC) isoforms catalyze the production of the second messenger cyclic AMP (cAMP) in response to various stimuli. Reduction of AC activity has well documented benefits, including benefits for heart disease and pain. These roles have inspired development of isoform-selective AC inhibitors, a lack of which currently limits exploration of functions and/or treatment of dysfunctions involving AC/cAMP signaling. However, inhibitors described as AC5- or AC1-selective have not been screened against the full panel of AC isoforms. We have measured pharmacological inhibitor profiles for all transmembrane AC isoforms. We found that 9-(tetrahydro-2-furanyl)-9H-purin-6-amine (SQ22,536), 2-amino-7-(furanyl)-7,8-dihydro-5(6H)-quinazolinone (NKY80), and adenine 9-β-d-arabinofuranoside (Ara-A), described as supposedly AC5-selective, do not discriminate between AC5 and AC6, whereas the putative AC1-selective inhibitor 5-[[2-(6-amino-9H-purin-9-yl)ethyl]amino]-1-pentanol (NB001) does not directly target AC1 to reduce cAMP levels. A structure-based virtual screen targeting the ATP binding site of AC was used to identify novel chemical structures that show some preference for AC1 or AC2. Mutation of the AC2 forskolin binding pocket does not interfere with inhibition by SQ22,536 or the novel AC2 inhibitor, suggesting binding to the catalytic site. Thus, we show that compounds lacking the adenine chemical signature and targeting the ATP binding site can potentially be used to develop AC isoform-specific inhibitors, and discuss the need to reinterpret literature using AC5/6-selective molecules SQ22,536, NKY80, and Ara-A.

Optimized method of G-protein-coupled receptor homology modeling: its application to the discovery of novel CXCR7 ligands

Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening.

Fighting obesity with a sugar-based library: discovery of novel MCH-1R antagonists by a new computational-VAST approach for exploration of GPCR binding sites

Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural information, are very slow processes for membrane proteins and are not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of these methods to impact the drug discovery process for GPCR targets in "real-time", and hence, there is an urgent need for other practical and cost-efficient alternatives. We present here a conceptually pioneering approach that integrates GPCR modeling with design, synthesis, and screening of a diverse library of sugar-based compounds from the VAST technology (versatile assembly on stable templates) to provide structural insights on the MCH-1R binding site. This approach creates a cost-efficient new avenue for structure-based drug discovery (SBDD) against GPCR targets. In our work, a primary VAST hit was used to construct a high-quality MCH-1R model. Following model validation, a structure-based virtual screen yielded a 14% hit rate and 10 novel chemotypes of potent MCH-1R antagonists, including EOAI3367472 (IC50 = 131 nM) and EOAI3367474 (IC50 = 213 nM).

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.

High-throughput and in silico screenings in drug discovery

BACKGROUND

In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones.

OBJECTIVE

Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process.

CONCLUSION

Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.

Ligand-optimized homology models of D₁ and D₂ dopamine receptors: application for virtual screening

Recent breakthroughs in crystallographic studies of G protein-coupled receptors (GPCRs), together with continuous progress in molecular modeling methods, have opened new perspectives for structure-based drug discovery. A crucial enhancement in this area was development of induced fit docking procedures that allow optimization of binding pocket conformation guided by the features of its active ligands. In the course of our research program aimed at discovery of novel antipsychotic agents, our attention focused on dopaminergic D2 and D1 receptors (D2R and D1R). Thus, we decided to investigate whether the availability of a novel structure of the closely related D3 receptor and application of induced fit docking procedures for binding pocket refinement would permit the building of models of D2R and D1R that facilitate a successful virtual screening (VS). Here, we provide an in-depth description of the modeling procedure and the discussion of the results of a VS benchmark we performed to compare efficiency of the ligand-optimized receptors in comparison with the regular homology models. We observed that application of the ligand-optimized models significantly improved the VS performance both in terms of BEDROC (0.325 vs 0.182 for D1R and 0.383 vs 0.301 for D2R) as well as EF1% (20 vs 11 for D1R and 18 vs 10 for D2R). In contrast, no improvement was observed for the performance of a D2R model built on the D3R template, when compared with that derived from the structure of the previously published and more evolutionary distant β2 adrenergic receptor. The comparison of results for receptors built according to various protocols and templates revealed that the most significant factor for the receptor performance was a proper selection of "tool ligand" used in induced fit docking procedure. Taken together, our results suggest that the described homology modeling procedure could be a viable tool for structure-based GPCR ligand design, even for the targets for which only a relatively distant structural template is available.

Molecular docking methodologies

Molecular docking represents an important technology for structure-based drug design. Docking is a computational technique aimed at the prediction of the most favorable ligand-target spatial configuration and an estimate of the corresponding complex free energy, although as stated at the beginning accurate scoring methods remain still elusive. In this chapter, the state of art of molecular docking methodologies and their applications in drug discovery is summarized.

Homology modeling a fast tool for drug discovery: current perspectives

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

ALiBERO: evolving a team of complementary pocket conformations rather than a single leader

Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.

Structure prediction and molecular dynamics simulations of a G-protein coupled receptor: human CCR2 receptor

CC chemokine receptor type-2 (CCR2) is a member of G-protein coupled receptors superfamily, expressed on the cell surface of monocytes and macrophages. It binds to the monocyte chemoattractant protein-1, a CC chemokine, produced at the sites of inflammation and infection. A homology model of human CCR2 receptor based on the recently available C-X-C chemokine recepor-4 crystal structure has been reported. Ligand information was used as an essential element in the homology modeling process. Six known CCR2 antagonists were docked into the model using simple and induced fit docking procedure. Docked complexes were then subjected to visual inspection to check their suitability to explain the experimental data obtained from site directed mutagenesis and structure-activity relationship studies. The homology model was refined, validated, and assessed for its performance in docking-based virtual screening on a set of CCR2 antagonists and decoys. The docked complexes of CCR2 with the known antagonists, TAK779, a dual CCR2/CCR5 antagonist, and Teijin-comp1, a CCR2 specific antagonist were subjected to molecular dynamics (MD) simulations, which further validated the binding modes of these antagonists. B-factor analysis of 20 ns MD simulations demonstrated that Cys190 is helpful in providing structural rigidity to the extracellular loop (EL2). Residues important for CCR2 antagonism were recognized using free energy decomposition studies. The acidic residue Glu291 from TM7, a conserved residue in chemokine receptors, is favorable for the binding of Teijin-comp1 with CCR2 by ΔG of -11.4 kcal/mol. Its contribution arises more from the side chains than the backbone atoms. In addition, Tyr193 from EL2 contributes -0.9 kcal/mol towards the binding of the CCR2 specific antagonist with the receptor. Here, the homology modeling and subsequent molecular modeling studies proved successful in probing the structure of human CCR2 chemokine receptor for the structure-based virtual screening and predicting the binding modes of CCR2 antagonists.

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.

Ligand-guided receptor optimization

Receptor models generated by homology or even obtained by crystallography often have their binding pockets suboptimal for ligand docking and virtual screening applications due to insufficient accuracy or induced fit bias. Knowledge of previously discovered receptor ligands provides key information that can be used for improving docking and screening performance of the receptor. Here, we present a comprehensive ligand-guided receptor optimization (LiBERO) algorithm that exploits ligand information for selecting the best performing protein models from an ensemble. The energetically feasible protein conformers are generated through normal mode analysis and Monte Carlo conformational sampling. The algorithm allows iteration of the conformer generation and selection steps until convergence of a specially developed fitness function which quantifies the conformer's ability to select known ligands from decoys in a small-scale virtual screening test. Because of the requirement for a large number of computationally intensive docking calculations, the automated algorithm has been implemented to use Linux clusters allowing easy parallel scaling. Here, we will discuss the setup of LiBERO calculations, selection of parameters, and a range of possible uses of the algorithm which has already proven itself in several practical applications to binding pocket optimization and prospective virtual ligand screening.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC = 82.2 and ROC((2%)) = 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.

Structural modelling and dynamics of proteins for insights into drug interactions

Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.

Novel cGMP efflux inhibitors identified by virtual ligand screening (VLS) and confirmed by experimental studies

Elevated intracellular levels of cyclic guanosine monophosphate (cGMP) may induce apoptosis, and at least some cancer cells seem to escape this effect by increased efflux of cGMP, as clinical studies have shown that extracellular cGMP levels are elevated in various types of cancer. The human ATP binding cassette (ABC) transporter ABCC5 transports cGMP out of cells, and inhibition of ABCC5 may have cytotoxic effects. Sildenafil inhibits cGMP efflux by binding to ABCC5, and in order to search for potential novel ABCC5 inhibitors, we have identified sildenafil derivates using structural and computational guidance and tested them for the cGMP efflux effect. Eleven compounds from virtual ligand screening (VLS) were tested in vitro, using inside-out vesicles (IOV), for inhibition of cGMP efflux. Seven of 11 compounds predicted by VLS to bind to ABCC5 were more potent than sildenafil, and the two most potent showed K(i) of 50-100 nM.

Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4

G-protein-coupled receptors (GPCRs) are key signaling molecules and are intensely studied. Whereas GPCRs recognizing small-molecules have been successfully targeted for drug discovery, protein-recognizing GPCRs, such as the chemokine receptors, claim few drugs or even useful small molecule reagents. This reflects both the difficulties that attend protein-protein interface inhibitor discovery, and the lack of structures for these targets. Imminent structure determination of chemokine receptor CXCR4 motivated docking screens for new ligands against a homology model and subsequently the crystal structure. More than 3 million molecules were docked against the model and then against the crystal structure; 24 and 23 high-scoring compounds from the respective screens were tested experimentally. Docking against the model yielded only one antagonist, which resembled known ligands and lacked specificity, whereas the crystal structure docking yielded four that were dissimilar to previously known scaffolds and apparently specific. Intriguingly, several were potent and relatively small, with IC(50) values as low as 306 nM, ligand efficiencies as high as 0.36, and with efficacy in cellular chemotaxis. The potency and efficiency of these molecules has few precedents among protein-protein interface inhibitors, and supports structure-based efforts to discover leads for chemokine GPCRs.

Large multiple transmembrane domain fragments of a G protein-coupled receptor: biosynthesis, purification, and biophysical studies

To conduct biophysical analyses on large domains of GPCRs, multimilligram quantities of highly homogeneous proteins are necessary. This communication discusses the biosynthesis of four transmembrane and five transmembrane-containing fragments of Ste2p, a GPCR recognizing the Saccharomyces cerevisiae tridecapeptide pheromone α-factor. The target fragments contained the predicted four N-terminal Ste2p[G(31) -A(198) ] (4TMN), four C-terminal Ste2p[T(155) -L(340) ] (4TMC), or five C-terminal Ste2p[I(120) -L(340) ] (5TMC) transmembrane segments of Ste2p. 4TMN was expressed as a fusion protein using a modified pMMHa vector in L-arabinose-induced Escherichia coli BL21-AI, and cleaved with cyanogen bromide. 4TMC and 5TMC were obtained by direct expression using a pET21a vector in IPTG-induced E. coli BL21(DE3) cells. 4TMC and 5TMC were biosynthesized on a preparative scale, isolated in multimilligram amounts, characterized by MS and investigated by biophysical methods. CD spectroscopy indicated the expected highly α-helical content for 4TMC and 5TMC in membrane mimetic environments. Tryptophan fluorescence showed that 5TMC integrated into the nonpolar region of 1-stearoyl-2-hydroxy-sn-glycero-3-phospho-(1'-rac-glycerol) micelles. HSQC-TROSY investigations revealed that [(15) N]-labeled 5TMC in 50% trifluoroethanol-d(2) /H(2) O/0.05%-trifluoroacetic acid was stable enough to conduct long multidimensional NMR measurements. The entire Ste2p GPCR was not readily reconstituted from the first two and last five or first three and last four transmembrane domains.

Normal mode-based approaches in receptor ensemble docking

Explicitly accounting for target flexibility in docking still constitutes a difficult challenge due to the high dimensionality of the conformational space to be sampled. This especially applies to the high-throughput scenario, where the screening of hundreds of thousands compounds takes place. The use of multiple receptor conformations (MRCs) to perform ensemble docking in a sequential fashion is a simple but powerful approach that allows to incorporate binding site structural diversity in the docking process. Whenever enough experimental structures to build a diverse ensemble are not available, normal mode analysis provides an appealing and efficient approach to in silico generate MRCs by distortion along few low-frequency modes that represent collective mid- and large-scale displacements. In this way, the dimension of the conformational space to be sampled is heavily reduced. This methodology is especially suited to incorporate target flexibility at the backbone level. In this chapter, the main components of normal mode-based approaches in the context of ensemble docking are presented and explained, including the theoretical and practical considerations needed for the successful development and implementation of this methodology.

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.

Ligand and decoy sets for docking to G protein-coupled receptors

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe.

Crystal structure-based virtual screening for fragment-like ligands of the human histamine H(1) receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.

Predicting protein ligand binding motions with the conformation explorer

BACKGROUND

Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods.

RESULTS

We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins.

CONCLUSIONS

We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Structure-based discovery of allosteric modulators of two related class B G-protein-coupled receptors

Despite the availability of X-ray crystal structure data for several members of the G-protein-coupled receptor (GPCR) superfamily, structure-based discovery of GPCR ligands has been exclusively restricted to class A (rhodopsin-like) receptors. Herein we report the identification, by a docking-based virtual screening approach, of noncompetitive ligands for two related class B (secretin-like) GPCRs: the glucagon receptor (GLR) and the glucagon-like peptide 1 receptor (GLP-1R). Starting from a knowledge-based three-dimensional model of the GLR, a database of 1.9 million commercially available drug-like compounds was screened for chemical similarity to existing GLR noncompetitive antagonists and docked to the transmembrane cavity of the GLR; 23 compounds were then selected based on protein-ligand interaction fingerprints, and were then purchased and evaluated for in vitro binding to GLR and modulation of glucagon-induced cAMP release. Two of the 23 compounds inhibited the effect of glucagon in a dose-dependent manner, with one inhibitor exhibiting the same potency as L-168 049, a reference noncompetitive GLR antagonist, in a whole-cell-based functional assay. Interestingly, one virtual hit that was inactive at the GLR was shown to bind to GLP-1R and potentiate the response to the endogenous GLP-1 ligand.

Ligand discovery from a dopamine D3 receptor homology model and crystal structure

G protein-coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery increased. Unfortunately, for most GPCRs no experimental structures are available. The determination of the D(3) receptor structure and the challenge to the community to predict it enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities ranging from 0.2 to 3.1 μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities ranging from 0.3 to 3.0 μM. One of the new ligands from the homology model screen was optimized for affinity to 81 nM. The feasibility of docking screens against modeled GPCRs more generally is considered.

Structure-based discovery of prescription drugs that interact with the norepinephrine transporter, NET

The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., β2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer's (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.

Understanding functional residues of the cannabinoid CB1

The brain cannabinoid (CB(1)) receptor that mediates numerous physiological processes in response to marijuana and other psychoactive compounds is a G protein coupled receptor (GPCR) and shares common structural features with many rhodopsin class GPCRs. For the rational development of therapeutic agents targeting the CB(1) receptor, understanding of the ligand-specific CB(1) receptor interactions responsible for unique G protein signals is crucial. For a more than a decade, a combination of mutagenesis and computational modeling approaches has been successfully employed to study the ligand-specific CB(1) receptor interactions. In this review, after a brief discussion about recent advances in understanding of some structural and functional features of GPCRs commonly applicable to the CB(1) receptor, the CB(1) receptor functional residues reported from mutational studies are divided into three different types, ligand binding (B), receptor stabilization (S) and receptor activation (A) residues, to delineate the nature of the binding pockets of anandamide, CP55940, WIN55212-2 and SR141716A and to describe the molecular events of the ligand-specific CB(1) receptor activation from ligand binding to G protein signaling. Taken these CB(1) receptor functional residues, some of which are unique to the CB(1) receptor, together with the biophysical knowledge accumulated for the GPCR active state, it is possible to propose the early stages of the CB(1) receptor activation process that not only provide some insights into understanding molecular mechanisms of receptor activation but also are applicable for identifying new therapeutic agents by applying the validated structure-based approaches, such as virtual high throughput screening (HTS) and fragment-based approach (FBA).

New insights into the binding mode of melanin concentrating hormone receptor-1 antagonists: homology modeling and explicit membrane molecular dynamics simulation study

Melanin concentrating hormone (MCH) is a cyclic 19-amino-acid peptide expressed mainly in the hypothalamus. It is involved in the control of feeding behavior, energy homeostasis, and body weight. Administration of MCH-R1 antagonists has been proved to reduce food intake and cause weight loss in animal models. In the present study, a homology model of the human MCH-R1 was constructed using the crystal structure of bovine rhodopsin (PDB: 1u19) as a template. Based on the observation that MCH-R1 can bind ligands of high chemical diversity, the initial model was subjected to an extensive ligand-supported refinement using antagonists of different chemotypes. The refinement process involved stepwise energy minimizations and molecular dynamics simulations. The refined model was inserted into a pre-equilibrated DPPC/TIP3P membrane system and then simulated for 20 ns in complex with structurally diverse antagonists. This protocol was able to explain the SAR of MCH-R1 antagonists with diverse chemical structures. Moreover, it reveals new insights into the critical recognition sites within the receptor. This work represents the first detailed study of molecular dynamics of MCH-R1 inserted into a membrane-aqueous environment.

A reliable docking/scoring scheme based on the semiempirical quantum mechanical PM6-DH2 method accurately covering dispersion and H-bonding: HIV-1 protease with 22 ligands

In this study, we introduce a fast and reliable rescoring scheme for docked complexes based on a semiempirical quantum mechanical PM6-DH2 method. The method utilizes a PM6-based Hamiltonian with corrections for dispersion energy and hydrogen bonds. The total score is constructed as the sum of the PM6-DH2 interaction enthalpy, the empirical force field (AMBER) interaction entropy, and the sum of the deformation (PM6-DH2, SMD) and the desolvation (SMD) energies of the ligand. The main advantage of the procedure is the fact that we do not add any empirical parameter for either an individual component of the total score or an individual protein-ligand complex. This rescoring method is applied to a very challenging system, namely, the HIV-1 protease with a set of ligands. As opposed to the conventional DOCK procedure, the PM6-DH2 rescoring based on all of the terms distinguishes between binders and nonbinders and provides a reliable correlation of the theoretical and experimental binding free energies. Such a dramatic improvement, resulting from the PM6-DH2 rescoring of all the complexes, provides a valuable yet inexpensive tool for rational drug discovery and de novo ligand design.

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.

Docking methods for structure-based library design

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Preparation and refinement of model protein-ligand complexes

The formation of ligand-protein complexes are critical for the correct functioning of a cell. The prediction of these interactions is important for our understanding of how the cell works and for the development of new drug molecules. Homology modeling is a method for predicting the structure of a protein based on a crystal structure template. Once a model of the protein is complete, a ligand-docking algorithm predicts the ligand-protein model interaction by searching for the best steric and energetically favorable fit. A refinement of the ligand-binding pocket improves the predicted interactions by considering the flexible nature of the ligand-binding pocket. In this chapter, we describe, from first principles, methods to identify and prepare the ligand-binding pocket in a protein model, to dock the ligand, and refine the resulting complex.

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.

Cellular models for the study of the pharmacology and signaling of melanin-concentrating hormone receptors

Cellular models for the study of the neuropeptide melanin-concentrating hormone (MCH) have become indispensable tools for pharmacological profiling and signaling analysis of MCH and its synthetic analogues. Although expression of MCH receptors is most abundant in the brain, MCH-R(1) is also found in different peripheral tissues. Therefore, not only cell lines derived from nervous tissue but also from peripheral tissues that naturally express MCH receptors have been used to study receptor signaling and regulation. For screening of novel compounds, however, heterologous expression of MCH-R(1) or MCH-R(2) genes in HEK293, Chinese hamster ovary, COS-7, or 3T3-L1 cells, or amplified MCH-R(1) expression/signaling in IRM23 cells transfected with the G(q) protein gene are the preferred tools because of more distinct pharmacological effects induced by MCH, which include inhibition of cAMP formation, stimulation of inositol triphosphate production, increase in intracellular free Ca(2+) and/or activation of mitogen-activated protein kinases. Most of the published data originate from this type of model system, whereas data based on studies with cell lines endogenously expressing MCH receptors are more limited. This review presents an update on the different cellular models currently used for the analysis of MCH receptor interaction and signaling.