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.     

4-苄基哌啶类拮抗剂选择性识别hCCR3的分子模拟

以牛视网膜紫质X射线衍射晶体结构为模板, 参考定位突变实验数据, 采用配体支持同源模建(Ligand-supported homology modeling)方法构建了拮抗剂键合(Antagonist-bound)的人类趋化因子受体hCCR3和hCCR1的三维结构模型. 将一系列1,4-二取代哌啶类拮抗剂分别对接进优化后的hCCR3和hCCR1模型中, 以配体在hCCR3中的结合自由能理论值对-lgIC₅₀值进行线性回归, 确定性系数r²为0.94. 分析对接结果发现, 配体主要通过疏水芳香作用与hCCR3结合, 而4-苄基哌啶类拮抗剂对hCCR3产生选择性的主要原因在于配体的哌啶环与hCCR3中Tyr255(TM7)的苯酚侧链产生面对面的范德华作用, 而且hCCR3中处于结合口袋的EL2区的疏水性也为拮抗剂对hCCR3的选择性做出了贡献.     

新型含吡啶酮（吡唑）结构的A2a/A2b双靶点腺苷受体拮抗剂的合成及生物活性研究

设计并合成了具有吡啶酮或吡唑结构的6个新型双靶点（A_2a和A_2b）腺苷受体拮抗剂。其结构经¹H NMR、¹³C NMR和HR-MS（ESI）表征。采用cAMP法评价了目标化合物（11a~11f）对A_2a和A_2b受体的抑制活性。活性测试结果表明：该系列化合物对A_2a和A_2b受体均有较好的抑制活性。其中化合物11e抑制活性最强,抑制A_2a和A_2b受体的IC₅₀值分别为8.188 nM和15.22 nM,11e对A_2bR受体的抑制活性优于阳性对照药AB928（IC₅₀=36.48 nM）。此外,利用分子对接研究了化合物11e与A_2a和A_2b靶点的结合情况,结果表明：化合物11e与A_2a和A_2b靶点具有较好的亲和作用。     

中药活性成分对血栓素A2受体抑制作用的分子模拟

中药中黄酮类化合物和白藜芦醇等活性成分对血栓素A2受体具有抑制作用,但具体机理不详.本研究通过同源模建方法,以墨鱼视紫红质蛋白为模板,构建血栓素A2受体的蛋白质结构模型.并使用分子对接方法研究中药活性成分白藜芦醇和芹菜苷元与血栓素A2受体模型的作用方式,据此建立药效团模型,筛选其他潜在的血栓素A2受体抑制剂.结果表明:白藜芦醇等中药活性成分能与血栓素A2受体活性口袋中的残基发生氢键作用,结合方式与血栓素相似.血栓素与Ser201、Leu198、Arg295和Thr298发生氢键作用,白藜芦醇等活性成分与Ser201、Leu198和Arg295发生氢键作用.建立的药效团模型由7个药效元素以及排斥性空间元素组成,经测试对高活性的血栓素A2受体抑制剂有比较好的选择性.使用该药效团模型对中药天然产物数据库进行筛选,命中了一批可能具有血栓素A2受体抑制作用的活性化合物.其中一些已经报道有抑制血小板凝聚活性.本研究表明血栓素A2受体可能是活血化瘀类中药的一个潜在的靶点.     

p53-MDM2结合抑制剂药效团模型的构建

采用Catalyst软件, 选择5类共24个p53-MDM2结合抑制剂作为训练集, 经计算机建模、构象优化, 由Catalyst系统构建出药效团模型, 并对药效团进行有效性分析, 结合已知的p53-MDM2结合抑制剂的结构信息, 筛选得到含有一个芳环中心、三个疏水中心和一个氢键受体的具有较好预测能力(Correl=0.941, Config=17.530, 吟cost=150.830)的药效团模型.     

基于定点突变的抗克百威单链抗体的亲和力成熟

为获得亲和力更高的抗克百威(CBF)单链抗体(scFv), 从抗CBF scFv氨基酸序列出发, 通过同源模建获得抗体模型, 找出抗体中的活性口袋区域, 进而将小分子药物与抗体进行分子对接, 发现疏水作用和氢键对于抗体亲和力具有重要作用. 进一步对口袋内亲水氨基酸残基HArg40和LHis38进行模拟替换, 再进行分子对接分析, 发现当以亮氨酸为突变氨基酸时, 对接评分最高. 在此基础上, 通过构建突变scFv基因及可溶性表达, 采用ELISA法对进化后的单链抗体(evoscFv)进行了鉴定. 结果表明, evoscFv对CBF的IC₅₀值为18.11 μg/L, 低于野生型抗体的27.25 μg/L, 亲和解离常数K_d为4.06×10^-8 mol/L, 相对亲和力比野生型scFv提高了2.23倍, 说明通过分子对接分析及对抗体活性口袋中氨基酸残基进行替换, 获得了一个亲和力更高的突变体抗体.     

基于药效团模型的DHODH抑制剂构效关系研究

利用药效团模型研究二氢乳清酸脱氢酶(Dihydroorotate dehydrogenase,DHODH)抑制剂的构效关系,为DHODH抑制剂的虚拟筛选提供新的方法.以31个具有DHODH抑制活性的化合物为训练集化合物,半数抑制浓度(IC50)范围为7~63000 nmol/L,利用Catalyst/HypoGen算法构建DHODH抑制剂药效团模型,通过对训练集化合物多个构象进行叠合,提取药效团特征及三维空间限制构建药效团模型.利用基于CatScramble的交叉验证方法及评价模型对已知活性化合物的活性预测能力,确定较优药效团模型.模型包含1个氢键受体、3个疏水中心,表征了受体配体相互作用时可能发生的氢键相互作用、疏水相互作用和π-π相互作用,4个药效特征在三维空间的排列概括了DHODH抑制剂产生活性的结构特点.所得较优模型对训练集化合物及测试集化合物的计算活性值与实验活性值的相关系数分别为0.8405和0.8788.利用药效团模型对来源于微生物的系列化合物进行虚拟筛选,筛选出59个预测活性较好的化合物,可作为进一步药物研发的候选化合物.     

黄酮类醛糖还原酶抑制剂的抑制机理研究

为了研究黄酮类醛糖还原酶抑制剂的抑制机理, 选择了31个黄酮类化合物作为训练集, 使用Catalyst软件包构建了此类抑制剂的药效团模型. 并专门针对黄酮类化合物定制了氢键给体和受体模型, 效果优于使用Catalyst内预定义的模型. 最终的药效团模型由两个氢键给体和一个氢键受体组成, 对训练集具有较好预测能力(Correl=0.9013). 此外, 使用InsightII/Affinity对6个黄酮类化合物进行了分子对接研究. 综合药效团模型和分子对接研究的结果, 发现黄酮类化合物的抑制活性主要源于黄酮骨架上的C4’或C3’位的羟基与醛糖还原酶活性口袋中的TYR48、VAL47、GLN49和C7位的羟基与HIS110, TRP111所形成的两组氢键.     

表皮生长因子受体酪氨酸激酶抑制剂的药效团研究

根据一系列表皮生长因子受体酪氨酸激酶抑制剂的三维定量构效关系研究，得 到了该类抑制剂的药效团，研究结果与Novartis的药效团模型相当类似．药效团包 括一个氢键受体，一个氢键给体，一个疏水区和一个带有氯或溴原子药效团对于研 究表皮生长因子受体酪氨酸激酶抑制剂结构与活性的关系具有重要的意义．通过三 维数据库搜索可能会得到新的先导化合物．     

基于受体1-受体2型聚合物给体的高效有机太阳能电池

设计合成了2种宽带隙聚合物给体,分别命名为PDTz-BDD和PDTz-BDT.其中, PDTz-BDT是一种典型的给-受体(D-A)型共轭聚合物, PDTz-BDD是具有受体₁-受体₂(A₁-A₂)型结构的共轭聚合物.采用BTP-e C9作为受体,分别与PDTz-BDD和PDTz-BDT共混构建有机太阳能电池,系统研究了两种给体的光伏性能.研究结果表明,具有A₁-A₂型结构的PDTz-BDD表现出更强的光吸收能力、更明显的溶液聚集效应与更优良的器件形貌,相应的光伏电池可以实现更高的光电转换效率(10.36%).本文不仅设计合成了2种新型给体,而且为构建A₁-A₂型共聚物以开发高效聚合物给体提供了参考.     

3D-pharmacophore models for selective A2A and A2B adenosine receptor antagonists

Three-dimensional pharmacophore models were generated for A2A and A2B adenosine receptors (ARs) based on highly selective A2A and A2B antagonists using the Catalyst program. The best pharmacophore model for selective A2A antagonists (Hypo-A2A) was obtained through a careful validation process. Four features contained in Hypo-A2A (one ring aromatic feature (R), one positively ionizable feature (P), one hydrogen bond acceptor lipid feature (L), and one hydrophobic feature (H)) seem to be essential for antagonists in terms of binding activity and A2A AR selectivity. The best pharmacophore model for selective A2B antagonists (Hypo-A2B) was elaborated by modifying the Catalyst common features (HipHop) hypotheses generated from the selective A2B antagonists training set. Hypo-A2B also consists of four features: one ring aromatic feature (R), one hydrophobic aliphatic feature (Z), and two hydrogen bond acceptor lipid features (L). All features play an important role in A2B AR binding affinity and are essential for A2B selectivity. Both A2A and A2B pharmacophore models have been validated toward a wide set of test molecules containing structurally diverse selective antagonists of all AR subtypes. They are capable of identifying correspondingly high potent antagonists and differentiating antagonists between subtypes. The results of our study will act as a valuable tool for retrieving structurally diverse compounds with desired biological activities and designing novel selective adenosine receptor ligands.     

Pharmacophore and receptor models for neurokinin receptors

Three neurokinin (NK) antagonist pharmacophore models (Models 1-3) accounting for hydrogen bonding groups in the 'head' and 'tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models I and 3 are not compatible with the receptor models.     

Comparative pharmacophore modeling of human adenosine receptor A1 and A3 antagonists

Adenosine receptors are promising therapeutic targets in drug discovery. In this study, three-dimensional pharmacophore models of human adenosine receptor A1 and A3 antagonists were developed based on 26 and 23 diverse compounds, respectively. The best A1 pharmacophore model (A 1 _Hopy1) consists of four features: one hydrogen bond donor, one hydrophobic point and two ring aromatics, while the best A 3 pharmacophore model (A3 _Hopy1) also has four features: one hydrogen bond acceptor, one hydrophobic point and two ring aromatics. The correlation coefficients were 0.840 for A 1 test set with 146 diverse compounds and 0.827 for A3 test set with 238 diverse compounds. In the simulated virtual screening experiments, high enrichment factors of 6.51 and 6.90 were obtained for A 1 _Hopy1 and A3 _Hopy1 models, respectively. Moreover, two models also showed high subtype-selectivity in the simulated virtual screening experiments. These results could be helpful for the discovery of novel potent and selective A 1 and A3 antagonists.     

Ligand-, structure- and pharmacophore-based molecular fingerprints: a case study on adenosine A1, A2A, A2B, and A3 receptor antagonists

FLAP fingerprints are applied in the ligand-, structure- and pharmacophore-based mode in a case study on antagonists of all four adenosine receptor (AR) subtypes. Structurally diverse antagonist collections with respect to the different ARs were constructed by including binding data to human species only. FLAP models well discriminate ??active?? (=highly potent) from ??inactive?? (=weakly potent) AR antagonists, as indicated by enrichment curves, numbers of false positives, and AUC values. For all FLAP modes, model predictivity slightly decreases as follows: A_2BR?>?A_2AR?>?A₃R?>?A₁R antagonists. General performance of FLAP modes in this study is: ligand-?>?structure-?>?pharmacophore- based mode. We also compared the FLAP performance with other common ligand- and structure-based fingerprints. Concerning the ligand-based mode, FLAP model performance is superior to ECFP4 and ROCS for all AR subtypes. Although focusing on the early first part of the A_2A, A_2B and A₃ enrichment curves, ECFP4 and ROCS still retain a satisfactory retrieval of actives. FLAP is also superior when comparing the structure-based mode with PLANTS and GOLD. In this study we applied for the first time the novel FLAPPharm tool for pharmacophore generation. Pharmacophore hypotheses, generated with this tool, convincingly match with formerly published data. Finally, we could demonstrate the capability of FLAP models to uncover selectivity aspects although single AR subtype models were not trained for this purpose.     

磷脂酶A2家族的功能性分类研究

采用“结合强度指纹图谱分析”方法, 通过对多重分子对接得到的作用强度数据进行聚类矩阵分析对蛋白质进行功能分类. 着重研究了磷脂酶A₂家族基于抑制剂作用强度的功能分类, 并且与基于序列的聚类结果进行比较, 成功地解决了序列比对方法不能处理的远源蛋白(cPLA₂)的分类问题.     

Three-dimensional quantitative structure-activity relationship of nucleosides acting at the A3 adenosine receptor: analysis of binding and relative efficacy

The binding affinity and relative maximal efficacy of human A3 adenosine receptor (AR) agonists were each subjected to ligand-based three-dimensional quantitative structure-activity relationship analysis. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) used as training sets a series of 91 structurally diverse adenosine analogues with modifications at the N6 and C2 positions of the adenine ring and at the 3', 4', and 5' positions of the ribose moiety. The CoMFA and CoMSIA models yielded significant cross-validated q2 values of 0.53 (r2 = 0.92) and 0.59 (r2 = 0.92), respectively, and were further validated by an external test set (25 adenosine derivatives), resulting in the best predictive r2 values of 0.84 and 0.70 in each model. Both the CoMFA and the CoMSIA maps for steric or hydrophobic, electrostatic, and hydrogen-bonding interactions well reflected the nature of the putative binding site previously obtained by molecular docking. A conformationally restricted bulky group at the N6 or C2 position of the adenine ring and a hydrophilic and/or H-bonding group at the 5' position were predicted to increase A3AR binding affinity. A small hydrophobic group at N6 promotes receptor activation. A hydrophilic and hydrogen-bonding moiety at the 5' position appears to contribute to the receptor activation process, associated with the conformational change of transmembrane domains 5, 6, and 7. The 3D-CoMFA/CoMSIA model correlates well with previous receptor-docking results, current data of A3AR agonists, and the successful conversion of the A3AR agonist into antagonists by substitution (at N6) or conformational constraint (at 5'-N-methyluronamide).     

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor

The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven homology modeling approach, we have built a model of the human adenosine A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine antagonists finding a good agreement between the theoretical and the experimental predictions.     

Homology modelling of the human adenosine A2B receptor based on X-ray structures of bovine rhodopsin, the β2-adrenergic receptor and the human adenosine A2A receptor

A three-dimensional model of the human adenosine A_2B receptor was generated by means of homology modelling, using the crystal structures of bovine rhodopsin, the β₂-adrenergic receptor, and the human adenosine A_2A receptor as templates. In order to compare the three resulting models, the binding modes of the adenosine A_2B receptor antagonists theophylline, ZM241385, MRS1706, and PSB601 were investigated. The A_2A-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 A_2A and A_2B receptors: while the A_2B receptor shares about 21% of the residues with rhodopsin, and 31% with the β₂-adrenergic receptor, it is 56% identical to the adenosine A_2A receptor. The A_2A-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 A_2A and A_2B 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 A_2B receptor model may serve as a basis for developing more potent and selective drugs.     

Substituted pyrazolo[3,4-b]pyridines as human A1 adenosine antagonists: developments in understanding the receptor stereoselectivity

A(1) adenosine receptor antagonists have been proposed to possess an interesting range of potential therapeutic applications. We have already reported the synthesis and the biological characterization of a family of pyrazolo[3,4-b]pyridine derivatives as A(1) adenosine ligands endowed with an antagonistic profile. In the present work, we report the LC separation of enantiomers of our most active A(1) antagonists together with the determination of their absolute configuration by means of X-ray crystal structure analysis. Biological assays confirmed a different activity for the two enantiomers, with the R one showing the higher human A(1)AR affinity. We also developed a homology model of this receptor subtype in order to suggest a binding disposition of the ligands into the hA(1)AR. All of the obtained data suggest that the compound's chirality plays a key role in A(1) affinity.