α1A-肾上腺素受体的同源模建及其对接研究

以β2肾上腺素受体(β2-AR)为模板,采用同源模建和分子动力学模拟构建了人类α1A-肾上腺素受体(α1A-AR)的三维结构模型,并利用PROCHECK,PROSA和WHAT-IF评估了模型的合理性.所得的结构采用分子对接程序Flexidock与激动剂去甲肾上腺素和拮抗剂西罗多辛分别进行对接,结果表明,2种配基具有相似...     

膜蛋白选择性调节剂和具有抗癌活性天然产物的构效关系研究——常州大学陈新教授课题组研究方向简介

β2-肾上腺素受体(β2AR)是一种典型的G蛋白偶联受体(GPCR),长期作为研究GPCR调控机制的模型系统。β受体阻滞剂,即β2AR的正构拮抗剂,是用来治疗多种疾病的主干心血管药物。目前,所有已知的β2AR拮抗剂都是正构配体;别构拮抗剂有可能具有新颖的治疗特性。本课题组通过与美国杜克大学的结构生物学家合作,发现了第一个β2AR小分子别构拮抗剂CPD-15,并进行了后续的结构优化和作用机制研究。     

CCK1受体的同源模拟和分子对接研究

采用同源建模法对CCK1受体的三维结构进行了模拟,并采用分子动力学方法对模型进行修正和优化,再采用与训练集激动剂和拮抗剂分子对接的方法分别得到激动状态和拮抗状态CCK1受体的三维结构模型。得到的模型使用DOCK对接软件对训练集中的分子进行对接,所得结果与其实际活性拟合度较好,说明我们建立的激动和拮抗状态下的CCK1受体的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选。     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟,将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接,形成非特异性激动和特异性激动的受体-配体复合物.用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰且H碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns.将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分,以top5%富集因子(EF)作为评价依据,用占诺美林优化后的M1受体模型的EF为8.0,用乙酰胆碱优化后M1受体模型的EF为6.5,非复合物的EF为1.5.说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理,可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选,为找到新的选择性M1受体激动剂奠定了基础.     

毒蕈碱型M1受体的同源模建和分子对接

采用同源模建方法对M1受体的三维结构进行了模拟, 将得到的模型分别与M受体完全激动剂乙酰胆碱和M1受体选择性激动剂占诺美林进行分子对接, 形成非特异性激动和特异性激动的受体-配体复合物. 用分子动力学模拟方法分别将未与小分子对接的M1受体、M1受体-乙酰胆碱复合物、M1受体-占诺美林复合物置于磷脂双膜中模拟10 ns. 将模拟后的蛋白质结构与包含活性分子的测试库对接并将结果打分, 以top5%富集因子(EF)作为评价依据, 用占诺美林优化后的M1受体模型的EF为8.0, 用乙酰胆碱优化后M1受体模型的EF为6.5, 非复合物的EF为1.5. 说明M1受体选择性激动剂复合物进行分子动力学模拟后得到的三维结构模型比较合理, 可以作为化合物虚拟筛选的模型对新化合物进行虚拟筛选, 为找到新的选择性M1受体激动剂奠定了基础.     

大麻素受体CB1三维结构的同源模建及其对接研究

大麻素CB1受体属于G蛋白偶联受体. 以牛视紫红质的晶体结构为模板, 利用同源模建法对CB1受体的三维结构进行了模拟, 并采用分子动力学方法对模型进行了修正和优化. 在此基础上, 分析了活性位点的组成和结构, 研究了拮抗剂利莫那班与CB1受体的对接, 明确了CB1受体与利莫那班结合时起重要作用的氨基酸残基. 发现利莫那班与CB1受体残基Lys192形成氢键相互作用是CB1受体拮抗剂的重要分子作用基础.     

人类腺苷受体A3亚型拮抗剂的构效关系分析

构建人类腺苷受体A3亚型药效团模型和三维蛋白结构模型用于作用模式研究.以18个来源于文献具有腺苷受体A3亚型拮抗活性的化合物作为训练集,使用HypoGen方法构建药效团模型.通过同源模建和分子动力学模拟构建了人类腺苷受体A3亚型的三维蛋白模型,并利用PROCHECK方法评估该模型的合理性,对所得的结构使用分子对接程序进行作用模式分析,药效团模型和同源模建结果相互匹配较好.使用新药效团模型对MDL药物数据库(MDDR)中包含的约120000个化合物进行虚拟筛选,得到了8个候选化合物,用于进一步的生物学评价和活性测定.本工作对于人类腺苷受体A3亚型拮抗剂的设计和抗哮喘药物的研发具有一定的理论指导和应用价值.     

G-蛋白偶联受体GPR120分子模建研究

新的长链脂肪酸受体G-蛋白偶联受体120 (G-protein-coupled receptor120, GPR120)是2型糖尿病的潜在治疗靶标. 由于其晶体结构迄今尚未获得, 成为基于结构的新药设计的瓶颈. 首先, 以人体β2肾上腺能素受体(human β2 adrenergic receptor, β2AR)晶体结构为模板, 通过同源模建方法构建GPR120三维结构, 对整个体系进行包膜的分子动力学模拟. 然后采用分子对接技术模建了GPR120的小分子激动剂GW9508与GPR120的相互作用模型, 发现了受体分子识别的关键性残基, 为开展定点突变实验提供了指导意义. 所建模型为研究受体与配体作用提供了合理的初始结构, 此方法也适用于其他G蛋白偶联受体的分子模建.     

辛味中药与嗅觉受体相互作用的分子模拟

采用分子模拟的方法, 在Schrdinger软件平台上, 用同源模建的方法构建了嗅觉受体OR1D2, OR7D4和OR51E1的三维结构模型. 运用分子动力学模块Desmond将与激动剂以及抑制剂分别对接的嗅觉受体复合物置于磷脂双膜中进行模拟. 最后将辛味中药的小分子分别对接到嗅觉受体中, 并与苦味中药的对接结果相对照, 依据实验结果, 讨论辛味中药发挥作用的分子机制. 该研究着重于同源模建、分子动力学和分子对接技术的综合应用, 探讨辛味中药化学成分与嗅觉受体的相互作用及其分子机理, 为从分子层面揭示辛味中药的药效物质基础提供帮助, 也为中药药性的研究提供了新的思路和方法.     

μ阿片受体的三维结构预测及活性位点分析

通过对比多个与μ阿片受体同属G蛋白偶联受体的视紫质蛋白序列,选择以同源性最高的牛视紫质蛋白(PDB编号:1F88)为模板,采用同源模建的方法,基于Composer程序,构建了μ阿片受体的三维结构模型,并预测了其二级结构.模型的可靠性经Ramachandran图和ProTable验证,用Site ID确定了μ阿片受体的活性位点,得到了与文献报道相吻合的结果,并预测了新的可能的关键性氨基酸,为合理设计μ阿片受体的激动剂和拮抗剂提供重要依据,进一步指导药物分子设计.     

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.     

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.     

An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application

G protein-coupled receptors (GPCRs) share a common architecture consisting of seven transmembrane (TM) domains. Various lines of evidence suggest that this fold provides a generic binding pocket within the TM region for hosting agonists, antagonists, and allosteric modulators. Here, a comprehensive and automated method allowing fast analysis and comparison of these putative binding pockets across the entire GPCR family is presented. The method relies on a robust alignment algorithm based on conservation indices, focusing on pharmacophore-like relationships between amino acids. Analysis of conservation patterns across the GPCR family and alignment to the rhodopsin X-ray structure allows the extraction of the amino acids lining the TM binding pocket in a so-called ligand binding pocket vector (LPV). In a second step, LPVs are translated to simple 3D receptor pharmacophore models, where each amino acid is represented by a single spherical pharmacophore feature and all atomic detail is omitted. Applications of the method include the assessment of selectivity issues, support of mutagenesis studies, and the derivation of rules for focused screening to identify chemical starting points in early drug discovery projects. Because of the coarseness of this 3D receptor pharmacophore model, however, meaningful scoring and ranking procedures of large sets of molecules are not justified. The LPV analysis of the trace amine-associated receptor family and its experimental validation is discussed as an example. The value of the 3D receptor model is demonstrated for a class C GPCR family, the metabotropic glutamate receptors.     

Molecular Basis of Inhibitory Mechanism of Naltrexone and Its Metabolites through Structural and Energetic Analyses

Naltrexone is a potent opioid antagonist with good blood–brain barrier permeability, targeting different endogenous opioid receptors, particularly the mu-opioid receptor (MOR). Therefore, it represents a promising candidate for drug development against drug addiction. However, the details of the molecular interactions of naltrexone and its derivatives with MOR are not fully understood, hindering ligand-based drug discovery. In the present study, taking advantage of the high-resolution X-ray crystal structure of the murine MOR (mMOR), we constructed a homology model of the human MOR (hMOR). A solvated phospholipid bilayer was built around the hMOR and submitted to microsecond (µs) molecular dynamics (MD) simulations to obtain an optimized hMOR model. Naltrexone and its derivatives were docked into the optimized hMOR model and submitted to µs MD simulations in an aqueous membrane system. The MD simulation results were submitted to the molecular mechanics–generalized Born surface area (MMGBSA) binding free energy calculations and principal component analysis. Our results revealed that naltrexone and its derivatives showed differences in protein–ligand interactions; however, they shared contacts with residues at TM2, TM3, H6, and TM7. The binding free energy and principal component analysis revealed the structural and energetic effects responsible for the higher potency of naltrexone compared to its derivatives.     

The Molecular Mechanism of P2Y1 Receptor Activation

Human purinergic G protein‐coupled receptor P2Y₁ (P2Y₁R) is activated by adenosine 5′‐diphosphate (ADP) to induce platelet activation and thereby serves as an important antithrombotic drug target. Crystal structures of P2Y₁R revealed that one ligand (MRS2500) binds to the extracellular vestibule of this GPCR, whereas another (BPTU) occupies the surface between transmembrane (TM) helices TM2 and TM3. We introduced a total of 20 μs all‐atom long‐timescale molecular dynamic (MD) simulations to inquire why two molecules in completely different locations both serve as antagonists while ADP activates the receptor. Our results indicate that BPTU acts as an antagonist by stabilizing extracellular helix bundles leading to an increase of the lipid order, whereas MRS2500 blocks signaling by occupying the ligand binding site. Both antagonists stabilize an ionic lock within the receptor. However, binding of ADP breaks this ionic lock, forming a continuous water channel that leads to P2Y₁R activation.     

Toward deciphering the code to aminergic G protein-coupled receptor drug design

The trace amine-associated receptor 1 (TAAR(1)) is a biogenic amine G protein-coupled receptor (GPCR) that is potently activated by 3-iodothyronamine (1, T(1)AM) in vitro. Compound 1 is an endogenous derivative of the thyroid hormone thyroxine which rapidly induces hypothermia, anergia, and bradycardia when administered to mice. To explore the role of TAAR(1) in mediating the effects of 1, we rationally designed and synthesized rat TAAR(1) superagonists and lead antagonists using the rotamer toggle switch model of aminergic GPCR activation. The functional activity of a ligand is proposed to be correlated to its probable interactions with the rotamer switch residues; agonists allow the rotamer switch residues to toggle to their active conformation, whereas antagonists interfere with this conformational transition. These agonist and antagonist design principles provide a conceptual model for understanding the relationship between the molecular structure of a drug and its pharmacological properties.     

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.     

Molecular modelling studies on the ORL1-receptor and ORL1-agonists

The ORLI (opioid receptor like 1)- receptor is a member of the family of rhodopsin-like G protein-coupled receptors (GPCR) and represents an interesting new therapeutical target since it is involved in a variety of biomedical important processes, such as anxiety, nociception, feeding, and memory. In order to shed light on the molecular basis of the interactions of the GPCR with its ligands, the receptor protein and a dataset of specific agonists were examined using molecular modelling methods. For that purpose, the conformational space of a very potent non-peptide ORL1-receptor agonist (Ro 64-6198) with a small number of rotatable bonds was analysed in order to derive a pharmacophoric arrangement. The conformational analyses yielded a conformation that served as template for the superposition of a set of related analogues. Structural superposition was achieved by employing the program FlexS. Using the experimental binding data and the superposition of the ligands, a 3D-QSAR analysis applying the GRID/GOLPE method was carried out. After the ligand-based modelling approach, a 3D model of the ORL1-receptor has been constructed using homology modelling methods based on the crystal structure of bovine rhodopsin. A representative structure of the model taken from molecular dynamics simulations was used for a manual docking procedure. Asp-130 and Thr-305 within the ORL1-receptor model served as important hydrophilic interaction partners. Furthermore, a hydrophobic cavity was identified stabilizing the agonists within their binding site. The manual docking results were supported using FlexX, which identified the same protein-ligand interaction points.     

Molecular modeling of the human vasopressin V2 receptor/agonist complex

The V2 vasopressin renal receptor (V2R), which controls antidiuresis in mammals, is a member of the large family of heptahelical transmembrane (7TM) G protein-coupled receptors (GPCRs). Using the automated GPCR modeling facility available via Internet (http://expasy.hcuge.ch/swissmod/SWISS-MODEL.html) for construction of the 7TM domain in accord with the bovine rhodopsin (RD) footprint, and the SYBYL software for addition of the intra- and extracellular domains, the human V2R was modeled. The structure was further refined and its conformational variability tested by the use of a version of the Constrained Simulated Annealing (CSA) protocol developed in this laboratory. An inspection of the resulting structure reveals that the V2R (likewise any GPCR modeled this way) is much thicker and accordingly forms a more spacious TM cavity than most of the hitherto modeled GPCR constructs do, typically based on the structure of bacteriorhodopsin (BRD). Moreover, in this model the 7TM helices are arranged differently than they are in any BRD-based model. Thus, the topology and geometry of the TM cavity, potentially capable of receiving ligands, is in this model quite different than it is in the earlier models. In the subsequent step, two ligands, the native [arginine⁸]vasopressin (AVP) and the selective agonist [d-arginine⁸]vasopressin (DAVP) were inserted, each in two topologically non-equivalent ways, into the TM cavity and the resulting structures were equilibrated and their conformational variabilities tested using CSA as above. The best docking was selected and justified upon consideration of ligand-receptor interactions and structure-activity data. Finally, the amino acid residues were indicated, mainly in TM helices 3-7, as potentially important in both AVP and DAVP docking. Among those Cys¹¹², Val¹¹⁵-Lys¹¹⁶, Gln¹¹⁹, Met¹²³ in helix 3; Glu¹⁷⁴ in helix 4; Val²⁰⁶, Ala²¹⁰, Val²¹³-Phe²¹⁴ in helix 5; Trp²⁸⁴, Phe²⁸⁷-Phe²⁸⁸, Gln²⁹¹ in helix 6; and Phe³⁰⁷, Leu³¹⁰, Ala³¹⁴ and Asn³¹⁷ in helix 7 appeared to be the most important ones. Many of these residues are invariant for either the GPCR superfamily or the neurophyseal (vasopressin V2R, V1aR and V1bR and oxytocin OR) subfamily of receptors. Moreover, some of the equivalent residues in V1aR have already been found critical for the ligand affinity [Mouillac et al., J. Biol. Chem, 270 (1995) 25771].     

Computational Methods for Understanding the Selectivity and Signal Transduction Mechanism of Aminomethyl Tetrahydronaphthalene to Opioid Receptors

Opioid receptors are members of the group of G protein-couple receptors, which have been proven to be effective targets for treating severe pain. The interactions between the opioid receptors and corresponding ligands and the receptor’s activation by different agonists have been among the most important fields in opioid research. In this study, with compound M1, an active metabolite of tramadol, as the clue compound, several aminomethyl tetrahydronaphthalenes were designed, synthesized and assayed upon opioid receptors. With the resultant compounds FW-AII-OH-1 (Ki = 141.2 nM for the κ opioid receptor), FW-AII-OH-2 (Ki = 4.64 nM for the δ opioid receptor), FW-DI-OH-2 (Ki = 8.65 nM for the δ opioid receptor) and FW-DIII-OH-2 (Ki = 228.45 nM for the δ opioid receptor) as probe molecules, the structural determinants responsible for the subtype selectivity and activation mechanisms were further investigated by molecular modeling and molecular dynamics simulations. It was shown that Y^7.43 was a key residue in determining the selectivity of the three opioid receptors, and W^6.58 was essential for the selectivity of the δ opioid receptor. A detailed stepwise discovered agonist-induced signal transduction mechanism of three opioid receptors by aminomethyl tetrahydronaphthalene compounds was proposed: the 3–7 lock between TM3 and TM7, the DRG lock between TM3 and TM6 and rearrangement of I^3.40, P^5.50 and F^6.44, which resulted in the cooperative movement in 7 TMs. Then, the structural relaxation left room for the binding of the G protein at the intracellular site, and finally the opioid receptors were activated.