Homology Model-Based Virtual Screening for GPCR Ligands Using Docking and Target-Biased Scoring

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Understanding Membrane Protein Drug Targets in Computational Perspective

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

Ligand docking into homology models of G-protein-coupled receptors (GPCRs) is a widely used approach in computational compound screening. The generation of "double-hypothetical" models of ligand-target complexes has intrinsic accuracy limitations that further complicate compound ranking and selection compared to those of X-ray structures. Given these uncertainties, we have explored "fuzzy 3D similarity" between hypothetical binding modes of known ligands in homology models and docking poses of database compounds as an alternative to conventional scoring schemes. Therefore, GPCR homology models at varying accuracy levels were generated and used for docking. Increases in recall performance were observed for fuzzy 3D similarity ranking using single or multiple ligand poses compared to that of conventional scoring functions and interaction fingerprints. Fuzzy similarity ranking was also successfully applied to docking into an external model of a GPCR for which no experimental structure is currently available. Taken together, our results indicate that the use of putative ligand poses, albeit approximate at best, increases the odds of identifying active compounds in docking screens of GPCR homology models.

Computational Advances for the Development of Allosteric Modulators and Bitopic Ligands in G Protein-Coupled Receptors

Allosteric modulators of G protein-coupled receptors (GPCRs), which target at allosteric sites, have significant advantages against the corresponding orthosteric compounds including higher selectivity, improved chemical tractability or physicochemical properties, and reduced risk of receptor oversensitization. Bitopic ligands of GPCRs target both orthosteric and allosteric sites. Bitopic ligands can improve binding affinity, enhance subtype selectivity, stabilize receptors, and reduce side effects. Discovering allosteric modulators or bitopic ligands for GPCRs has become an emerging research area, in which the design of allosteric modulators is a key step in the detection of bitopic ligands. Radioligand binding and functional assays ([(35)S]GTPγS and ERK1/2 phosphorylation) are used to test the effects for potential modulators or bitopic ligands. High-throughput screening (HTS) in combination with disulfide trapping and fragment-based screening are used to aid the discovery of the allosteric modulators or bitopic ligands of GPCRs. When used alone, these methods are costly and can often result in too many potential drug targets, including false positives. Alternatively, low-cost and efficient computational approaches are useful in drug discovery of novel allosteric modulators and bitopic ligands to help refine the number of targets and reduce the false-positive rates. This review summarizes the state-of-the-art computational methods for the discovery of modulators and bitopic ligands. The challenges and opportunities for future drug discovery are also discussed.

Structure and putative signaling mechanism of Protease activated receptor 2 (PAR2) - a promising target for breast cancer

Experimental evidences have observed enhanced expression of protease activated receptor 2 (PAR2) in breast cancer consistently. However, it is not yet recognized as an important therapeutic target for breast cancer as the primary molecular mechanisms of its activation are not yet well-defined. Nevertheless, recent reports on the mechanism of GPCR activation and signaling have given new insights to GPCR functioning. In the light of these details, we attempted to understand PAR2 structure & function using molecular modeling techniques. In this work, we generated averaged representative stable models of PAR2, using protease activated receptor 1 (PAR1) as a template and selected conformation based on their binding affinity with PAR2 specific agonist, GB110. Further, the selected model was used for studying the binding affinity of putative ligands. The selected ligands were based on a recent publication on phylogenetic analysis of Class A rhodopsin family of GPCRs. This study reports putative ligands, their interacting residues, binding affinity and molecular dynamics simulation studies on PAR2-ligand complexes. The results reported from this study would be useful for researchers and academicians to investigate PAR2 function as its physiological role is still hypothetical. Further, this information may provide a novel therapeutic scheme to manage breast cancer.

Prediction of substrates for glutathione transferases by covalent docking

Enzymes in the glutathione transferase (GST) superfamily catalyze the conjugation of glutathione (GSH) to electrophilic substrates. As a consequence they are involved in a number of key biological processes, including protection of cells against chemical damage, steroid and prostaglandin biosynthesis, tyrosine catabolism, and cell apoptosis. Although virtual screening has been used widely to discover substrates by docking potential noncovalent ligands into active site clefts of enzymes, docking has been rarely constrained by a covalent bond between the enzyme and ligand. In this study, we investigate the accuracy of docking poses and substrate discovery in the GST superfamily, by docking 6738 potential ligands from the KEGG and MetaCyc compound libraries into 14 representative GST enzymes with known structures and substrates using the PLOP program [ Jacobson Proteins 2004 , 55 , 351 ]. For X-ray structures as receptors, one of the top 3 ranked models is within 3 Å all-atom root mean square deviation (RMSD) of the native complex in 11 of the 14 cases; the enrichment LogAUC value is better than random in all cases, and better than 25 in 7 of 11 cases. For comparative models as receptors, near-native ligand-enzyme configurations are often sampled but difficult to rank highly. For models based on templates with the highest sequence identity, the enrichment LogAUC is better than 25 in 5 of 11 cases, not significantly different from the crystal structures. In conclusion, we show that covalent docking can be a useful tool for substrate discovery and point out specific challenges for future method improvement.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

FFA1-selective agonistic activity based on docking simulation using FFA1 and GPR120 homology models

BACKGROUND AND PURPOSE The free fatty acid FFA1 receptor and GPR120 are GPCRs whose endogenous ligands are medium- and long-chain FFAs, and they are important in regulating insulin and GLP-1 secretion respectively. Given that the ligands of FFA1 receptor and GPR120 have similar properties, selective pharmacological tools are required to study their functions further. EXPERIMENTAL APPROACH We used a docking simulation approach using homology models for each receptor. Biological activity was assessed by phosphorylation of ERK and elevation of intracellular calcium ([Ca(2+) ]i ) in cells transfected with FFA1 receptor or GPR120. Insulin secretion from murine pancreatic beta cells (MIN6) was also measured. KEY RESULTS Calculated hydrogen bonding energies between a series of synthetic carboxylic acid compounds and the homology models of the FFA1 receptor and GPR120, using docking simulations, correlated well with the effects of the compounds on ERK phosphorylation in transfected cells (R(2) = 0.65 for FFA1 receptor and 0.76 for GPR120). NCG75, the compound with the highest predicted selectivity for FFA1 receptors from this structure-activity relationship analysis, activated ERK and increased [Ca(2+) ]i as potently as the known FFA1 receptor-selective agonist, Compound 1. Site-directed mutagenesis analysis based on the docking simulation showed that different amino acid residues were important for the recognition and activation by FFA1 receptor agonists. Moreover, NCG75 strongly induced ERK and [Ca(2+) ]i responses, and promoted insulin secretion from MIN6 cells, which express endogenous FFA1 receptors. CONCLUSION AND IMPLICATIONS A docking simulation approach using FFA1 receptor and GPR120 homology models could be useful in predicting FFA1 receptor-selective agonists.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Modeling of mammalian olfactory receptors and docking of odorants

Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is over-expressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

FINDSITE(X): a structure-based, small molecule virtual screening approach with application to all identified human GPCRs

We have developed FINDSITE(X), an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER(VMT), TASSER(VMT)-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE(X) is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER(VMT)-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF(0.01)) is 22.7, whereas EF(0.01) by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF(0.01) reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html .

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.

Virtual ligand screening against comparative protein structure models

Virtual ligand screening uses computation to discover new ligands of a protein by screening one or more of its structural models against a database of potential ligands. Comparative protein structure modeling extends the applicability of virtual screening beyond the atomic structures determined by X-ray crystallography or NMR spectroscopy. Here, we describe an integrated modeling and docking protocol, combining comparative modeling by MODELLER and virtual ligand screening by DOCK.

The structure of active opsin as a basis for identification of GPCR agonists by dynamic homology modelling and virtual screening assays

Most of the currently available G protein-coupled receptor (GPCR) crystal structures represent an inactive receptor state, which has been considered to be suitable only for the discovery of antagonists and inverse agonists in structure-based computational ligand screening. Using the β(2)-adrenergic receptor (B2AR) as a model system, we show that a dynamic homology model based on an "active" opsin structure without further incorporation of experimental data performs better than the crystal structure of the inactive B2AR in finding agonists over antagonists/inverse agonists. Such "active-like state" dynamic homology models can therefore be used to selectively identify GPCR agonists in in silico ligand libraries.

Biophysical characterization of G-protein coupled receptor-peptide ligand binding

G-protein coupled receptors (GPCRs) are ubiquitous membrane proteins allowing intracellular responses to extracellular factors that range from photons of light to small molecules to proteins. Despite extensive exploitation of GPCRs as therapeutic targets, biophysical characterization of GPCR-ligand interactions remains challenging. In this minireview, we focus on techniques that have been successfully used for structural and biophysical characterization of peptide ligands binding to their cognate GPCRs. The techniques reviewed include solution-state nuclear magnetic resonance (NMR) spectroscopy, solid-state NMR, X-ray diffraction, fluorescence spectroscopy and single-molecule fluorescence methods, flow cytometry, surface plasmon resonance, isothermal titration calorimetry, and atomic force microscopy. The goal herein is to provide a cohesive starting point to allow selection of techniques appropriate to the elucidation of a given GPCR-peptide interaction.

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).

Structure-based systems biology for analyzing off-target binding

Here off-target binding implies the binding of a small molecule of therapeutic interest to a protein target other than the primary target for which it was intended. Increasingly such off-targeting appears to be the norm rather than the exception, rational drug design notwithstanding, and can lead to detrimental side-effects, or opportunities to reposition a therapeutic agent to treat a different condition. Not surprisingly, there is significant interest in determining a priori what off-targets exist on a proteome-wide scale. Beyond determining putative off-targets is the need to understand the impact of such binding on the complete biological system, with the ultimate goal of being able to predict the phenotypic outcome. While a very ambitious goal, some progress is being made.

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

The use of GPCR structures in drug design

Structure-based drug discovery is routinely applied to soluble targets such as proteases and kinases. It is only recently that multiple high-resolution X-ray structures of G protein-coupled receptors (GPCRs) have become available. Here we review the technology developments that have led to the recent plethora of GPCR structures. These include developments in protein expression and purification as well as techniques to stabilize receptors and crystallize them. We discuss the findings derived from the new structures with regard to understanding GPCR function and pharmacology. Finally, we examine the utility of structure-based drug discovery approaches including homology modeling, virtual screening, and fragment screening for GPCRs in the context of what has been learnt from other target classes.

Virtual screening-based discovery and mechanistic characterization of the acylthiourea MRT-10 family as smoothened antagonists

The seven-transmembrane receptor Smoothened (Smo) is the major component involved in signal transduction of the Hedgehog (Hh) morphogens. Smo inhibitors represent a promising alternative for the treatment of several types of cancers linked to abnormal Hh signaling. Here, on the basis of experimental data, we generated and validated a pharmacophoric model for Smo inhibitors constituted by three hydrogen bond acceptor groups and three hydrophobic regions. We used this model for the virtual screening of a library of commercially available compounds. Visual and structural criteria allowed the selection of 20 top scoring ligands, and an acylthiourea, N-(3-benzamidophenylcarbamothioyl)-3,4,5-trimethoxybenzamide (MRT-10), was identified and characterized as a Smo antagonist. The corresponding acylurea, N-(3-benzamidophenylcarbamoyl)-3,4,5-trimethoxybenzamide (MRT-14), was synthesized and shown to display, in various Hh assays, an inhibitory potency comparable to or greater than that of reference Smo antagonists cyclopamine and N-((3S,5S)-1-(benzo[d][1,3]dioxol-5-ylmethyl)-5-(piperazine-1-carbonyl)pyrrolidin-3-yl)-N-(3-methoxybenzyl)-3,3-dimethylbutanamide (Cur61414). Focused virtual screening of the same library further identified five additional related antagonists. MRT-10 and MRT-14 constitute the first members of novel families of Smo antagonists. The described virtual screening approach is aimed at identifying novel modulators of Smo and of other G-protein coupled receptors.

Allosteric modulation of metabotropic glutamate receptors: structural insights and therapeutic potential

Allosteric modulation of G protein-coupled receptors (GPCRs) represents a novel approach to the development of probes and therapeutics that is expected to enable subtype-specific regulation of central nervous system target receptors. The metabotropic glutamate receptors (mGlus) are class C GPCRs that play important neuromodulatory roles throughout the brain, as such they are attractive targets for therapeutic intervention for a number of psychiatric and neurological disorders including anxiety, depression, Fragile X Syndrome, Parkinson's disease and schizophrenia. Over the last fifteen years, selective allosteric modulators have been identified for many members of the mGlu family. The vast majority of these allosteric modulators are thought to bind within the transmembrane-spanning domains of the receptors to enhance or inhibit functional responses. A combination of mutagenesis-based studies and pharmacological approaches are beginning to provide a better understanding of mGlu allosteric sites. Collectively, when mapped onto a homology model of the different mGlu subtypes based on the β(2)-adrenergic receptor, the previous mutagenesis studies suggest commonalities in the location of allosteric sites across different members of the mGlu family. In addition, there is evidence for multiple allosteric binding pockets within the transmembrane region that can interact to modulate one another. In the absence of a class C GPCR crystal structure, this approach has shown promise with respect to the interpretation of mutagenesis data and understanding structure-activity relationships of allosteric modulator pharmacophores.

Homology modelling of the human adenosine A2B receptor based on X-ray structures of bovine rhodopsin, the beta2-adrenergic receptor and the human adenosine A2A receptor

A three-dimensional model of the human adenosine A2B receptor was generated by means of homology modelling, using the crystal structures of bovine rhodopsin, the beta2-adrenergic receptor, and the human adenosine A2A receptor as templates. In order to compare the three resulting models, the binding modes of the adenosine A2B receptor antagonists theophylline, ZM241385, MRS1706, and PSB601 were investigated. The A2A-based model was much better able to stabilize the ligands in the binding site than the other models reflecting the high degree of similarity between A2A and A2B receptors: while the A2B receptor shares about 21% of the residues with rhodopsin, and 31% with the beta2-adrenergic receptor, it is 56% identical to the adenosine A2A receptor. The A2A-based model was used for further studies. The model included the transmembrane domains, the extracellular and the intracellular hydrophilic loops as well as the terminal domains. In order to validate the usefulness of this model, a docking analysis of several selective and nonselective agonists and antagonists was carried out including a study of binding affinities and selectivities of these ligands with respect to the adenosine A2A and A2B receptors. A common binding site is proposed for antagonists and agonists based on homology modelling combined with site-directed mutagenesis and a comparison between experimental and calculated affinity data. The new, validated A2B receptor model may serve as a basis for developing more potent and selective drugs.

3D structural models of transmembrane proteins

Transmembrane proteins are macromolecules implicated in major biological processes and diseases. Because of their specific neighborhood, few transmembrane protein structures are currently available. The building of structural models of transmembrane proteins is a major research area. Because of the lack of available 3D structures, automatic homology modeling is not an efficient way of proposing pertinent structural models. Hence, most of the structural models of transmembrane proteins are developed through a more complex protocol that comprises the use of secondary structure prediction to complete the comparative modeling process. Then, refinement and assessment steps are performed go often to a novel comparative modeling process. Nowadays, it is also possible to take attention to the helix-helix and helix-lipid interactions, and even build quaternary structures. In all cases, the most important factor when proceeding to correct structural models is taking the experimental data into account.

The family of G protein-coupled receptors: an example of membrane proteins

The G protein coupled receptors belong to the largest group of membrane proteins that regulates many essential physiological properties and represents an important class of drug targets. In this chapter, we show how the synergy between a laboratory experiment and computational modeling leads to structural delineation of the ligand binding pocket and how the knowledge of ligand-protein interactions is used for rational local and global drug design in which the structural knowledge of a particular receptor and its ligands is used for drug design of this particular GPCR and others.

The impact of GPCR structures on pharmacology and structure-based drug design

After many years of effort, recent technical breakthroughs have enabled the X-ray crystal structures of three G-protein-coupled receptors (GPCRs) (beta1 and beta2 adrenergic and adenosine A(2a)) to be solved in addition to rhodopsin. GPCRs, like other membrane proteins, have lagged behind soluble drug targets such as kinases and proteases in the number of structures available and the level of understanding of these targets and their interaction with drugs. The availability of increasing numbers of structures of GPCRs is set to greatly increase our understanding of some of the key issues in GPCR biology. In particular, what constitutes the different receptor conformations that are involved in signalling and the molecular changes which occur upon receptor activation. How future GPCR structures might alter our views on areas such as agonist-directed signalling and allosteric regulation as well as dimerization is discussed. Knowledge of crystal structures in complex with small molecules will enable techniques in drug discovery and design, which have previously only been applied to soluble targets, to now be used for GPCR targets. These methods include structure-based drug design, virtual screening and fragment screening. This review considers how these methods have been used to address problems in drug discovery for kinase and protease targets and therefore how such methods are likely to impact GPCR drug discovery in the future.

Molecular docking screens using comparative models of proteins

Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.

Synergism of virtual screening and medicinal chemistry: identification and optimization of allosteric antagonists of metabotropic glutamate receptor 1

We report the identification of novel potent and selective metabotropic glutamate receptor 1 (mGluR1) antagonists by virtual screening and subsequent hit optimization. For ligand-based virtual screening, molecules were represented by a topological pharmacophore descriptor (CATS-2D) and clustered by a self-organizing map (SOM). The most promising compounds were tested in mGluR1 functional and binding assays. We identified a potent chemotype exhibiting selective antagonistic activity at mGluR1 (functional IC(50)=0.74+/-0.29 microM). Hit optimization yielded lead structure 16 with an affinity of K(i)=0.024+/-0.001 microM and greater than 1000-fold selectivity for mGluR1 versus mGluR5. Homology-based receptor modelling suggests a binding site compatible with previously reported mutation studies. Our study demonstrates the usefulness of ligand-based virtual screening for scaffold-hopping and rapid lead structure identification in early drug discovery projects.

Closing the side-chain gap in protein loop modeling

The success of structure-based drug design relies on accurate protein modeling where one of the key issues is the modeling and refinement of loops. This study takes a critical look at modeled loops, determining the effect of re-sampling side-chains after the loop conformation has been generated. The results are evaluated in terms of backbone and side-chain conformations with respect to the native loop. While models can contain loops with high quality backbone conformations, the side-chain orientations could be poor, and therefore unsuitable for ligand docking and structure-based design. In this study, we report on the ability to model loop side-chains accurately using a variety of commercially available algorithms that include rotamer libraries, systematic torsion scans and knowledge-based methods.

Homology modeling in drug discovery: current trends and applications

As structural genomics (SG) projects continue to deposit representative 3D structures of proteins, homology modeling methods will play an increasing role in structure-based drug discovery. Although computational structure prediction methods provide a cost-effective alternative in the absence of experimental structures, developing accurate enough models still remains a big challenge. In this contribution, we report the current developments in this field, discuss in silico modeling limitations, and review the successful application of this technique to different stages of the drug discovery process.

Targeted scoring functions for virtual screening

The benefit offered by virtual screening methods during the early drug discovery process is directly related to the predictivity of scoring functions that assess protein-ligand binding affinity. The scoring of protein-ligand complexes, however, is still a challenge: despite great efforts, a universal and accurate scoring method has not been developed up to now. Targeted scoring functions, in contrast, enhance virtual screening performance significantly. This review analyzes recent developments and future directions in the area of targeted scoring functions.

Recent advances in structure-based virtual screening of G-protein coupled receptors

In addition to the rhodopsin crystal structure, high-resolution crystal structures of ligand-mediated G-protein-coupled receptors (GPCRs) have recently become available, and these have become attractive templates for developing homology models of several GPCRs of therapeutic interest. These crystal structures and the homology models derived from them have provided significant insights into ligand-receptor interactions. Moreover, several studies have demonstrated that the structural models are indeed suitable for virtual screening of compound databases to identify new ligands for various GPCRs. Recent examples of such virtual screening against GPCRs are discussed in this review.

Organizing bioactive compound discovery in target families

The sequencing of genomes gave access to the complete set of building blocks for organisms of various species. A plethora of "-omics"-technologies has been developed to investigate the dynamic interactions of the building blocks in order to understand the functioning of living organisms. This has given rise to the clustering of proteins into target families based on the phylogenetic and structural similarities. In this chapter we will discuss how the concept of target families enables to investigate and modulate biochemical function in the quest to chart Chemical and Biological Spaces.