Novel Scaffold Identification of mGlu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach

Metabotropic glutamate receptor 1 (mGluR1) is considered as an attractive drug target for neuropathic pain treatments. The hierarchical virtual screening approach for identifying novel scaffolds of mGluR1 allosteric modulators was performed using a homology model built with the dopamine D3 crystal structure as template. The mGluR1 mutagenesis data, conserved amino acid sequences across class A and class C GPCRs, and previously reported multiple sequence alignments of class C GPCRs to the rhodopsin template, were employed for the sequence alignment to overcome difficulties of model generation with low sequence identity of mGluR1 and dopamine D3. The structures refined by molecular dynamics simulations were employed for docking of Asinex commercial libraries after hierarchical virtual screening with pharmacophore and naïve Bayesian models. Five of 35 compounds experimentally evaluated using a calcium mobilization assay exhibited micromolar activities (IC50) with chemotype novelty that demonstrated the validity of our methods. A hierarchical structure and ligand‐based virtual screening approach with homology model of class C GPCR based on dopamine D3 class A GPCR structure was successfully performed and applied to discover novel negative mGluR1 allosteric modulators.     

Receptor-ligand docking simulation for membrane proteins

Protein structure-based molecular design using the computational techniques of protein structure prediction, ligand docking, and virtual screening is an integral part of drug discovery for limiting the application of the structure-based approach to target proteins such as G-protein-coupled receptors (GPCRs). GPCRs play an important role in living organisms and are of major interest to the pharmaceutical industry. However, structural data on ligand-binding forms for GPCRs from experiments to elucidate structural templates for docking simulations are lacking due to the difficulties associated with crystallization and crystallography. Therefore structural prediction of GPCRs in the ligand-bound state using computational methods has been introduced, but the prediction of ligand conformation onto target GPCRs is still constructed manually by human experts. We developed a molecular modeling technique for the prediction of ligand-receptor binding using comparative ligand-binding analysis (CoLBA) that not only considers interaction energy but also the similarity of interaction profiles among ligands. The advantage of CoLBA is that it can facilitate intuitive and flexible screening based on docking results when protein structures with low resolution (or theoretical models) are targeted. We applied CoLBA to ligand-binding prediction in several GPCRs. The predicted ligand-binding models were evaluated in site-directed mutagenesis experiments in collaborative research, and the enrichment rate of activated ligands was compared with random compounds in virtual screening simulations. We propose that CoLBA can be applied in large-scale modeling of ligand-receptor complexes and virtual screening for GPCRs.     

Modeling the 3D structure of GPCRs: advances and application to drug discovery

G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology. Assessment of recent rhodopsin-based homology modeling studies indicates that, despite significant progress, these models do not yield hit rates that are sufficiently high for in silico screening (10 to 40% when screening for known binders). In contrast, the PREDICT modeling algorithm, which is independent of the rhodopsin structure, has now been fully validated in the context of drug discovery. PREDICT models are successfully used for drug discovery, yielding excellent hit rates (85 to 100% when screening for known binders), leading to the discovery of nanomolar-range new chemical entities for a variety of GPCR targets. Thus, 3D models of GPCRs should now allow the use of productive structure-based approaches for drug discovery.     

Homology Model Versus X-ray Structure in Receptor-based Drug Design: A Retrospective Analysis with the Dopamine D3 Receptor

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Virtual screening for kinase targets

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.     

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.     

Molecular modeling of melanocortin receptors

Molecular modeling of G-protein coupled receptors (GPCRs) remains a challenge due to the limited availability of structural information for the receptors. Molecular modeling approaches for melanocortin receptors (MCRs) fall into three categories: structure-based, ligand-based, and proteochemometric. Homology modeling combined with the information obtained from site-directed mutagenesis of receptors, recombined chimeric mutations of receptors and the structures of melanocortin type 4 receptor (MC4R) peptide ligands, has provided insights on detailed ligand-receptor interactions. Still, homology models based on the structures of bacteriorhodopsin (bR) or bovine rhodopsin as templates have not reached atomic level accuracy, making them unsuitable for rational drug design. On the other hand, availability of a large number of potent ligands of MCRs, especially those for the therapeutically important MC4R, has fueled ligand-based approaches, including automated pharmacophore query optimization and pharmacophore-based virtual screening. Proteochemometrics, a novel technology for the analysis of intermolecular interactions between ligand and receptor, has also shown great value in obtaining detailed information on molecular recognition and providing guidance to ligand design. In this review, the strengths and limitations of homology modeling, pharmacophore modeling and proteochemometrics modeling of MCRs are evaluated.     

Molecular modeling of vasopressin receptor and in silico screening of V1b receptor antagonists

Introduction: G protein-coupled receptors (GPCRs) are integral membrane proteins which contain seven-transmembrane-spanning alpha-helices. GPCR-mediated signaling has been associated with various human diseases, positioning GPCRs as attractive targets in the drug discovery field. Recently, through advances in protein engineering and crystallography, the number of resolved GPCR structures has increased dramatically. This growing availability of GPCR structures has greatly accelerated structure-based drug design (SBDD) and in silico screening for GPCR-targeted drug discovery.

Areas covered: The authors introduce the current status of X-ray crystallography of GPCRs and what has been revealed from the resolved crystal structures. They also review the recent advances in SBDD and in silico screening for GPCR-targeted drug discovery and discuss a docking study, using homology modeling, with the discovery of potent antagonists of the vasopressin 1b receptor.

Expert opinion: Several innovative protein engineering techniques and crystallographic methods have greatly accelerated SBDD, not only for already-resolved GPCRs but also for those structures which remain unclear. These technological advances are expected to enable the determination of GPCR-fragment complexes, making it practical to perform fragment-based drug discovery. This paves the way for a new era of GPCR-targeted drug discovery.     

Structure-based discovery and binding site analysis of histamine receptor ligands

ABSTRACT

Introduction: The application of structure-based drug discovery in histamine receptor projects was previously hampered by the lack of experimental structures. The publication of the first X-ray structure of the histamine H1 receptor has been followed by several successful virtual screens and binding site analysis studies of H1-antihistamines. This structure together with several other recently solved aminergic G-protein coupled receptors (GPCRs) enabled the development of more realistic homology models for H2, H3 and H4 receptors.

Areas covered: In this paper, the authors review the development of histamine receptor models and their application in drug discovery.

Expert opinion: In the authors’ opinion, the application of atomistic histamine receptor models has played a significant role in understanding key ligand-receptor interactions as well as in the discovery of novel chemical starting points. The recently solved H1 receptor structure is a major milestone in structure-based drug discovery; however, our analysis also demonstrates that for building H3 and H4 receptor homology models, other GPCRs may be more suitable as templates. For these receptors, the authors envisage that the development of higher quality homology models will significantly contribute to the discovery and optimization of novel H3 and H4 ligands.     

Homology models in docking and high-throughput docking

The use of homology models in docking-based drug discovery is already established, and provides an effective and computationally affordable alternative whenever experimental structures are not available. Recent methodological studies have confirmed and benchmarked the feasibility of using structural models in docking. However, more accurate methods are expected to be developed in the near future, especially for the model refinement stage. In this review, the latest developments in homology modeling in the context of structure-based virtual screening are presented, together with the recent success stories of homology modeling in actual docking-based drug discovery endeavours.     

Structural models of class a G protein-coupled receptors as a tool for drug design: insights on transmembrane bundle plasticity

G protein-coupled receptors (GPCRs) interact with an extraordinary diversity of ligands by means of their extracellular domains and/or the extracellular part of the transmembrane (TM) segments. Each receptor subfamily has developed specific sequence motifs to adjust the structural characteristics of its cognate ligands to a common set of conformational rearrangements of the TM segments near the G protein binding domains during the activation process. Thus, GPCRs have fulfilled this adaptation during their evolution by customizing a preserved 7TM scaffold through conformational plasticity. We use this term to describe the structural differences near the binding site crevices among different receptor subfamilies, responsible for the selective recognition of diverse ligands among different receptor subfamilies. By comparing the sequence of rhodopsin at specific key regions of the TM bundle with the sequences of other GPCRs we have found that the extracellular region of TMs 2 and 3 provides a remarkable example of conformational plasticity within Class A GPCRs. Thus, rhodopsin-based molecular models need to include the plasticity of the binding sites among GPCR families, since the "quality" of these homology models is intimately linked with the success in the processes of rational drug-design or virtual screening of chemical databases.     

Spinal or systemic TY005, a peptidic opioid agonist/neurokinin 1 antagonist, attenuates pain with reduced tolerance

After many years of effort, recent technical breakthroughs have enabled the X-ray crystal structures of three G-protein-coupled receptors (GPCRs) (β1 and β2 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.This article is part of a themed section on Molecular Pharmacology of GPCR. To view the editorial for this themed section visit http://dx.doi.org/10.1111/j.1476-5381.2010.00695.x     

Homology modeling of G-protein-coupled receptors and implications in drug design

G-protein-coupled receptors (GPCRs) are considered therapeutically important due to their involvement in a variety of processes governing several cellular functions, and their tractability as drug targets. A large percentage of drugs on the market, and in development stages, target the super family of the GPCRs. The enormous interest in GPCR drug design is, however, limited by the scarcity of structural information. The only GPCR for which a three dimensional (3D) structure is reported is bovine rhodopsin and it belongs to class A of the GPCR family. As a result, there has been considerable interest in alternative techniques, for example, homology modeling of GPCRs, in order to derive useful three dimensional models of other proteins for use in structure-based drug design. However, homology modeling of GPCRs is not straightforward, and encounters several problems, owing to the availability of a single structural template, as well as the low degree of sequence homology between the template and target sequences. There are several key issues which need to be considered during every stage of GPCR homology modeling, in order to derive reasonable 3D models. Homology modeling of GPCRs has been utilized increasingly in the past few years and has been successful, not only in furthering the understanding of ligand-protein interactions, but also in the identification of new and potent ligands. Thus, with the lessons learned from past experiences and new developments, homology modeling in case of GPCRs can be harnessed for developing more reliable three dimensional models. This, in turn, will provide better tools to use in structure-based drug design leading to the identification of novel and potent GPCR ligands for several therapeutic indications.     

Techniques: Recent developments in computer-aided engineering of GPCR ligands using the human adenosine A3 receptor as an example

G-protein-coupled receptors (GPCRs) represent the largest known family of signal-transducing molecules, and convey signals for light and many extracellular regulatory molecules. GPCRs are dysfunctional or dysregulated in several human diseases and are estimated to be the targets of >40% of the drugs used in clinical medicine today. The crystal structure of rhodopsin provides the first information on the three-dimensional structure of GPCRs, which now supports homology modeling studies and structure-based drug-design approaches. In this article, we review recent work on adenosine receptors, a family of GPCRs, and, in particular, on adenosine A(3) receptor antagonists. We focus on an iterative, bi-directional approach in which models are used to generate hypotheses that are tested by experimentation; the experimental findings are, in turn, used to refine the model. The success of this approach is due to the synergistic interaction between theory and experimentation.     

Optimization of adenosine 5'-carboxamide derivatives as adenosine receptor agonists using structure-based ligand design and fragment screening

Structures of G protein-coupled receptors (GPCRs) have a proven utility in the discovery of new antagonists and inverse agonists modulating signaling of this important family of clinical targets. Applicability of active-state GPCR structures to virtual screening and rational optimization of agonists, however, remains to be assessed. In this study of adenosine 5' derivatives, we evaluated the performance of an agonist-bound A(2A) adenosine receptor (AR) structure in retrieval of known agonists and then employed the structure to screen for new fragments optimally fitting the corresponding subpocket. Biochemical and functional assays demonstrate high affinity of new derivatives that include polar heterocycles. The binding models also explain modest selectivity gain for some substituents toward the closely related A(1)AR subtype and the modified agonist efficacy of some of these ligands. The study suggests further applicability of in silico fragment screening to rational lead optimization in GPCRs.     

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 .     

Homology Model and Docking-Based Virtual Screening for Ligands of the σ1 Receptor

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Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model

The neurokinin-1 (NK1) receptor belongs to the family of G-protein-coupled receptors (GPCRs), which represents one of the most relevant target families in small-molecule drug design. In this paper, we describe a homology modeling of the NK1 receptor based on the high-resolution X-ray structure of rhodopsin and the successful virtual screening based on this protein model. The NK1 receptor model has been generated using our new MOBILE (modeling binding sites including ligand information explicitly) approach. Starting with preliminary homology models, it generates improved models of the protein binding pocket together with bound ligands. Ligand information is used as an integral part in the homology modeling process. For the construction of the NK1 receptor, antagonist CP-96345 was used to restrain the modeling. The quality of the obtained model was validated by probing its ability to accommodate additional known NK1 antagonists from structurally diverse classes. On the basis of the generated model and on the analysis of known NK1 antagonists, a pharmacophore model was deduced, which subsequently guided the 2D and 3D database search with UNITY. As a following step, the remaining hits were docked into the modeled binding pocket of the NK1 receptor. Finally, seven compounds were selected for biochemical testing, from which one showed affinity in the submicromolar range. Our results suggest that ligand-supported homology models of GPCRs may be used as effective platforms for structure-based drug design.     

Automated protein structure homology modeling: a progress report

Understanding the molecular function of proteins is greatly enhanced by insights gained from their three-dimensional structures. Since experimental structures are only available for a small fraction of proteins, computational methods for protein structure modeling play an increasingly important role. Comparative protein structure modeling is currently the most accurate method, yielding models suitable for a wide spectrum of applications, such as structure-guided drug development or virtual screening. Stable and reliable automated prediction pipelines have been developed to apply large-scale comparative modeling to whole genomes or entire sequence databases. Model repositories give access to these annotated and evaluated models. In this review, we will discuss recent developments in automated comparative modeling and provide selected examples illustrating the use of homology models.