Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Modelling of ORL1 receptor-ligand interactions

An opioid receptor like (ORL1) receptor is one of a family of G-protein-coupled receptors (GPCR); it represents a new pharmaceutical target with extensive therapeutic potential for the regulation of important biological functions such as nociception, mood disorders, drug abuse, learning or cardiovascular control. Although the crystal structure of the inactive form of the ORL1 receptor has been determined, little is known about its activation. By using X-ray structures of the β2-adrenegic receptor in its inactive (2RH1) and active (3P0G) states as templates, inactive and active homology models of the ORL1 receptor were constructed. Structurally diverse sets of strongly binding antagonists and agonists were docked with both ORL1 receptor forms. The major receptor-ligand interactions responsible for antagonist and agonist binding were identified. Although both sets of ligands, agonists and antagonists, bind to the same region of the receptor, they occupy partially different binding pockets. Agonists bind to the inactive receptor in a slightly different manner than antagonists. This difference is more pronounced in binding to the active ORL1 receptor model and points to the amino acids at the extracellular end of TM6, suggesting that this region is important for receptor-activation.     

神经介素B受体与拮抗剂、激动剂的分子模拟研究

大鼠神经介素B受体(rat neuromedin B receptor, rNMBR)属于G蛋白偶联受体(G-protein coupled receptor, GPCR) A家族的成员. GPCR的结构特征和在信号传导中的重要作用决定了其可以作为很好的药物靶标. 关于rNMBR与内源性激动剂神经介素B (neuromedin B, NMB)以及与非肽类拮抗剂pd168368作用机制的研究对于合理设计受体药物分子有重要的指导意义. 在这一研究中, 我们使用同源模建, 构建受体的三维结构, 进行分子对接和分子动力学的计算. 基于受体三维结构, 通过10 ns的空载受体、激动剂-受体、拮抗剂-受体的分子动力学模拟, 探讨受体与激动剂与拮抗剂的作用机制. 研究表明rNMB-R中跨膜(transmembrane, TM)螺旋3, 5, 6, 7参与配体的结合. NMB与受体的结合, 使受体转变为活性构象, 而受体同拮抗剂pd168368恰好相反.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

Differential Epitope Mapping by STD NMR Spectroscopy To Reveal the Nature of Protein–Ligand Contacts

Saturation transfer difference (STD) NMR spectroscopy is extensively used to obtain epitope maps of ligands binding to protein receptors, thereby revealing structural details of the interaction, which is key to direct lead optimization efforts in drug discovery. However, it does not give information about the nature of the amino acids surrounding the ligand in the binding pocket. Herein, we report the development of the novel method differential epitope mapping by STD NMR (DEEP‐STD NMR) for identifying the type of protein residues contacting the ligand. The method produces differential epitope maps through 1) differential frequency STD NMR and/or 2) differential solvent (D₂O/H₂O) STD NMR experiments. The two approaches provide different complementary information on the binding pocket. We demonstrate that DEEP‐STD NMR can be used to readily obtain pharmacophore information on the protein. Furthermore, if the 3D structure of the protein is known, this information also helps in orienting the ligand in the binding pocket.     

基于结构和药效团特征的人类腺苷受体拮抗剂选择性比较

腺苷受体是重要的治疗靶标,选择性腺苷受体拮抗剂具有广泛的临床应用前景.本文通过同源模建构建了腺苷A₁、A_2B和A₃受体的结构,采用LigandScout 3.12软件分别构建了腺苷受体四种亚型的拮抗剂药效团模型.然后利用Schrödinger程序中的Induced Fit Docking模块完成受体-拮抗剂结合模式的预测,并与药效团结果进行比对.结果发现,由于结合口袋部位的残基在家族间高度保守,模建得到的各个亚型受体的初始结构活性口袋部位极为相似,无法用于亚型选择性拮抗剂的识别.而腺苷受体四种亚型拮抗剂药效团的药效特征与空间排布都不同,并与以前突变实验信息相吻合.研究结果说明,结合口袋部位的优化是模建中的关键步骤,基于配体的药效团模型所包含的一系列药效特征元素如氢键受体、氢键供体、疏水基团、芳环中心,可以很好地表征受体结合部位氢键、疏水空腔的位置及其方向.本文研究结果可以为进一步的优化同源模建结果,寻找新型的人类腺苷受体选择性拮抗剂提供理论依据.     

Prediction of the 3D structure and dynamics of human DP G-protein coupled receptor bound to an agonist and an antagonist

Prostanoids play important physiological roles in the cardiovascular and immune systems and in pain sensation in peripheral systems through their interactions with eight G-protein coupled receptors. These receptors are important drug targets, but development of subtype specific agonists and antagonists has been hampered by the lack of 3D structures for these receptors. We report here the 3D structure for the human DP G-protein coupled receptor (GPCR) predicted by the MembStruk computational method. To validate this structure, we use the HierDock computational method to predict the binding mode for the endogenous agonist (PGD2) to DP. Based on our structure, we predicted the binding of different antagonists and optimized them. We find that PGD2 binds vertically to DP in the TM1237 region with the alpha chain toward the extracellular (EC) region and the omega chain toward the middle of the membrane. This structure explains the selectivity of the DP receptor and the residues involved in the predicted binding site correlate very well with available mutation experiments on DP, IP, TP, FP, and EP subtypes. We report molecular dynamics of DP in explicit lipid and water and find that the binding of the PGD2 agonist leads to correlated rotations of helices of TM3 and TM7, whereas binding of antagonist leads to no such rotations. Thus, these motions may be related to the mechanism of activation.     

High-throughput modeling of human G-protein coupled receptors: amino acid sequence alignment, three-dimensional model building, and receptor library screening

The current study describes the development of a computer package (GPCRmod) aimed at the high-throughput modeling of the therapeutically important family of human G-protein coupled receptors (GPCRs). GPCRmod first proposes a reliable alignment of the seven transmembrane domains (7 TMs) of most druggable human GPCRs based on pattern/motif recognition for each of the 7 TMs that are considered independently. It then converts the alignment into knowledge-based three-dimensional (3-D) models starting from a set of 3-D backbone templates and two separate rotamer libraries for side chain positioning. The 7 TMs of 277 human GPCRs have been accurately aligned, unambiguously clustered in three different classes (rhodopsin-like, secretin-like, metabotropic glutamate-like), and converted into high-quality 3-D models at a remarkable throughput (ca. 3s/model). A 3-D GPCR target library of 277 receptors has consequently been setup. Its utility for "in silico" inverse screening purpose has been demonstrated by recovering among top scorers the receptor of a selective GPCR antagonist as well as the receptors of a promiscuous antagonist. The current GPCR target library thus constitutes a 3-D database of choice to address as soon as possible the "virtual selectivity" profile of any GPCR antagonist or inverse agonist in an early hit optimization process.     

Matching cavities in g protein-coupled receptors to infer ligand-binding sites

To understand the activity and cross reactivity of ligands and G protein-coupled receptors, we take stock of relevant existing receptor mutation, sequence, and structural data to develop a statistically robust and transparent scoring system. Our method evaluates the viability of binding of any ligand for any GPCR sequence of amino acids. This enabled us to explore the binding repertoire of both receptors and ligands, relying solely on correlations between carefully identified receptor features and without requiring any chemical information about ligands. This study suggests that sequence similarity at specific binding pockets can predict relative affinity of ligands; enabling recovery of over 80% of known ligands for a withheld receptor and almost 80% of known receptors for a ligand. The method enables qualitative prediction of ligand binding for all nonredundant human G protein-coupled receptors.     

A pseudo-ligand approach to virtual screening

A virtual screening method is presented that is grounded on a receptor-derived pharmacophore model termed "virtual ligand" or "pseudo-ligand". The model represents an idealized constellation of potential ligand sites that interact with residues of the binding pocket. For rapid virtual screening of compound libraries the potential pharmacophore points of the virtual ligand are encoded as an alignment-free correlation vector, avoiding spatial alignment of pharmacophore features between the pharmacophore query (i.e., the virtual ligand) and the candidate molecule. The method was successfully applied to retrieving factor Xa inhibitors from a Ugi three-component combinatorial library, and yielded high enrichment of actives in a retrospective search for cyclooxygenase-2 (COX-2) inhibitors. The approach provides a concept for "de-orphanizing" potential drug targets and identifying ligands for hitherto unexplored or allosteric binding pockets.     

The identification of ligand features essential for PXR activation by pharmacophore modeling

Drug metabolizing enzymes and transporters are often involved in clinically relevant drug-drug interactions. These functional proteins can be induced by a wide range of xenobiotics. The induction is mediated by a group of receptors known as orphan nuclear receptors. The pregnane X receptor (PXR) is a member of this receptor family and regulates the expression of multiple Cytochrome P450 enzyme families (e.g. CYP 3A and 2B), phase II enzymes (e.g. UDP glucuronosyl transferases), and transporters (e.g. multidrug resistance protein 1). The software package Catalyst was employed to derive pharmacophore models for PXR activation. A structure based pharmacophore hypothesis and several ligand based ones were compared in order to identify ligand receptor interactions essential for receptor activation. The results suggest that hydrogen bonding to Gln285 is indispensable for PXR activation. Most ligands were found to form a second hydrogen bond to His407. Hydrophobic interactions are not essential for receptor activation but contribute to ligand affinity. Highly active compounds share up to five hydrophobic features that allow the ligand to occupy large areas of the predominantly hydrophobic binding pocket.     

EGFR酪氨酸激酶抑制剂的药效团模型构建

以80个作用方式相同, 分子结构特征不同的表皮生长因子受体酪氨酸激酶(EGFR TK)竞争性抑制剂为训练集, 利用计算机药物辅助软件Catalyst, 构建不同的药效团模型, 并结合酪氨酸激酶的作用位点等因素, 筛选出一个含有两个芳环中心, 一个疏水中心和一个阳离子基团的具有较好预测能力(RMS=0.438, Correl=0.908, Weight=1.52, Config=17.36)的药效团模型, 为设计和合成新型结构的EGFR TK抑制剂提供参考.     

Pharmacophore generation,atom-based 3D-QSAR and molecular dynamics simulation analyses of pyridine-3-carboxamide-6-yl-urea analogues as potential gyrase B inhibitors

DNA gyrase subunit B (GyrB) is an attractive drug target for the development of antibacterial agents with therapeutic potential. In the present study, computational studies based on pharmacophore modelling, atom-based QSAR, molecular docking, free binding energy calculation and dynamics simulation were performed on a series of pyridine-3-carboxamide-6-yl-urea derivatives. A pharmacophore model using 49 molecules revealed structural and chemical features necessary for these molecules to inhibit GyrB. The best fitted model AADDR.13 was generated with a coefficient of determination (r²) of 0.918. This model was validated using test set molecules and had a good r² of 0.78. 3D contour maps generated by the 3D atom-based QSAR revealed the key structural features responsible for the GyrB inhibitory activity. Extra precision molecular docking showed hydrogen bond interactions with key amino acid residues of ATP-binding pocket, important for inhibitor binding. Further, binding free energy was calculated by the MM-GBSA rescoring approach to validate the binding affinity. A 10 ns MD simulation of inhibitor #47 showed the stability of the predicted binding conformations. We identified 10 virtual hits by in silico high-throughput screening. A few new molecules were also designed as potent GyrB inhibitors. The information obtained from these methodologies may be helpful to design novel inhibitors of GyrB.     

2D conformationally sampled pharmacophore: a ligand-based pharmacophore to differentiate delta opioid agonists from antagonists

Pharmacophores are widely used for rational drug design and include those based on receptor binding sites or on known ligands. To date, ligand-based pharmacophores have typically used one or a small number of conformers of known receptor ligands. However, this method does not take into account the inherent dynamic nature of molecules, which sample a wide range of conformations, any of which could be the bound form. In the present study, molecular dynamics (MD) simulations were used as a means to sample the conformational space of ligands to include all accessible conformers at room temperature in the development of a pharmacophore. On the basis of these conformers, probability distributions of selected distances and angles in a series of delta specific opioid ligands were obtained and correlated with agonist versus antagonist activities. Individually, the distributions did not allow for unique agonist and antagonist pharmacophores to be identified. However, by extending the conformational analysis to two dimensions, a 2D conformationally sampled pharmacophore (CSP) for distinguishing delta receptor agonists and antagonists was developed. Application of this model to the compound DPI2505 suggests that it may have agonist activity. It is anticipated that the CSP method, which does not require alignment of compounds during pharmacophore development, will be a useful tool for obtaining structure-function relationships of ligands particularly in systems where the receptor 3D structure is not known.     

Improving the quality of 3D-QSAR by using flexible-ligand receptor models

To address the problems associated with molecular conformations and alignments in the 3D-QSAR studies, we have developed the Flexible Ligand - Atomic Receptor Model (FLARM) 2.0 method. The FLARM 2.0 method has three unique features as compared to other pseudoreceptor model methods: (1) the training ligands are flexibly optimized inside the receptors to achieve minimal docking energies; (2) the receptor atoms are spatially moveable in the process of genetic evolving in order to avoid improper initial receptor shapes; and (3) void receptor sites are specially favored in order to obtain open receptor models that allow large gaps. Advantages of an open model include less noise information, a smaller risk of overfitting, and ease of locating the key interaction sites. The latter two features, inherited from the previous FLARM 1.0 method, can improve the predictive ability of the 3D-QSAR models, while the first feature is newly implemented to relieve the uncertainty caused by improper conformation and alignment. Three FLARM 2.0 case studies were performed, and the results show that FLARM 2.0 models are highly predictive and robust. FLARM 2.0 pseudoreceptor models can correspond well with the pharmacophore models and/or the binding sites of the real protein receptors.     

Genetically encoded photocrosslinkers as molecular probes to study G-protein-coupled receptors (GPCRs)

The genetic code was expanded with orthogonal pairs to introduce photoactivatable amino acids into G-protein-coupled receptors (GPCRs) in a noninvasive manner. In this way the receptor surface could be mapped by searching for specific ligand interaction sites and the complex dynamics could be studied. This method is also useful for probing the structure of GPCR complexes in living cells.     

Molecular dynamics simulation of the human adenosine A3 receptor: agonist induced conformational changes of Trp243

The adenosine A(3) receptor together with rhodopsin belongs to Class A of the G-protein coupled receptors. As the crystal structure of bovine rhodopsin represents the dark (inactive) state of the receptor, the details of GPCR activation are still unknown. In this molecular dynamics study we investigate how the homology model of the human adenosine A(3) receptor responds to ligand exposure. To this end we placed the homology model in a POPC membrane model. After equilibrating for 13 ns an agonist (Cl-IB-MECA) and an inverse agonist (PSB-10) were placed inside the putative binding pocket. In the following 10 ns molecular dynamics simulation we observed a different behaviour of the side-chain torsions of Trp243(6.48), depending on the presence or absence of the agonist or inverse agonist. This conformational change of Trp243 correlates with the assumed influence of ligands on receptor activation. Other predicted conformational changes of the receptor could not be observed yet. So Trp243 may represent the first switch in receptor activation.     

Three-dimensional pharmacophore modeling of liver-X receptor agonists

High cholesterol levels contribute to hyperlipidemia. Liver X receptors (LXRs) are the drug targets. LXRs regulate the cholesterol absorption, biosynthesis, transportation, and metabolism. Novel agonists of LXR, especially LXRβ, are attractive solutions for treating hyperlipidemia. In order to discover novel LXRβ agonists, a three-dimensional pharmacophore model was built based upon known LXRβ agonists. The model was validated with a test set, a virtual screening experiment, and the FlexX docking approach. Results show that the model is capable of predicting a LXRβ agonist activity. Ligand-based virtual screening results can be refined by cross-linking by structure-based approaches. This is because two ligands that are mapped in the same way to the same pharmacophore model may have significantly different binding behaviors in the receptor's binding pocket. This paper reports our approach to identify reliable pharmacophore models through combining both ligand- and structure-based approaches.     

A cation-pi binding interaction with a tyrosine in the binding site of the GABAC receptor

GABA(C) (rho) receptors are members of the Cys-loop superfamily of neurotransmitter receptors, which includes nicotinic acetylcholine (nACh), 5-HT(3), and glycine receptors. As in other members of this family, the agonist binding site of GABA(C) receptors is rich in aromatic amino acids, but while other receptors bind agonist through a cation-pi interaction to a tryptophan, the GABA(C) binding site has tyrosine at the aligning positions. Incorporating a series of tyrosine derivatives at position 198 using unnatural amino acid mutagenesis reveals a clear correlation between the cation-pi binding ability of the side chain and EC(50) for receptor activation, thus demonstrating a cation-pi interaction between a tyrosine side chain and a neurotransmitter. Comparisons among four homologous receptors show variations in cation-pi binding energies that reflect the nature of the cationic center of the agonist.