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.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.     

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.     

A pharmacophore model for NK2 antagonist comprising compounds from several structurally diverse classes

A neurokinin 2 (NK2) antagonist pharmacophore model has been developed on the basis of five non-peptide antagonists from several structurally diverse classes. To evaluate the pharmacophore model, another 20 antagonists were fitted to the model. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 23 of the 25 antagonists were fitted to the model in a low energy conformation with a low RMS value. The pharmacophore model is described by four pharmacophore elements: Three hydrophobic groups and a hydrogen bond donor represented as a vector. The hydrophobic groups are generally aromatic rings, but this is not a requirement. The antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. The model was able to explain the enantioselectivity of SR48968 and GR159897.     

大麻素Ⅰ型受体拮抗剂的结构特征

以92个具有大麻素受体Ⅰ(CB1)拮抗活性的化合物为训练集, 39个化合物为测试集, 采用Discovery Studio V2.5(DS)软件中的3D构效关系药效团产生(QSAR Pharmacophore Generation)模块建立药效团模型. 获得的最佳药效团模型的构成为一个氢键受体(HBA)、 一个疏水基团(HY)和二个芳环中心(RA), 采用费用函数(Cost function)评价药效团模型, 该模型的Δcost为119.32, 相关性为0.921, 均方根偏差为0.730, Configuration cost为16.1229, 表明模型能较好地预测化合物的活性. 同时针对目前已知的近450个化合物的12种结构类型进行了探讨, 所得结果为进一步设计CB1拮抗剂提供了理论依据.     

氧肟酸类组蛋白去乙酰化酶抑制剂的药效团模型研究

基于24个目前已知的氧肟酸类组蛋白去乙酰化酶抑制剂,我们运用Catalyst软件建立了一个三维药效团模型。其中,最好的药效团模型1,包含了四个化学特征（一个氢键供体,一个芳环和两个疏水基）,相关系数达到0.946,并由另外20个化合物进行了测试验证。我们第一次特征性描述了组蛋白去乙酰化酶的帽子（CAP）部分。我们的研究结果对于设计全新组蛋白去乙酰化酶抑制剂具有很好的指导作用。     

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.     

β分泌酶抑制剂的药效团模型研究

选择活性跨越0.002至25 μmol•L－1的4类共25个β分泌酶抑制剂作为训练集, 使用Catalyst软件包构建出药效团模型, 并通过对药效团的有效性分析, 筛选得到的最佳模型(correlCorrel＝0.969, Config＝16.32, Δcost＝62.422)由一个环芳香性、一个疏水中心、一个正电荷中心和一个氢键供体组成. 并用其它209个抑制剂组成测试集对模型进行验证, 结果表明该模型显示出较强的预测能力, 能够为进一步的数据库搜索, 寻找新型的β分泌酶抑制剂先导物提供依据.     

γ-分泌酶抑制剂的药效团模型构建

利用Catalyst软件系统, 选择具有较高体外抑制活性的苯并二氮(艹卓)类化合物作为训练集, 经计算机建模, 构象优化, 由Catalyst系统构建出药效团模型. 并结合γ-分泌酶的作用机制等因素, 筛选出一个含有一个芳环中心, 一个疏水中心和两个氢键受体的具有较好预测能力(RMS=0.366343, Correl=0.95535, Weight=1.17389, Config=18.8671)的药效团模型. 该模型的建立有助于设计及合成新型结构的γ-分泌酶抑制剂.     

5-HT3受体拮抗剂药效团模型的构建

以31个来源于MDDR数据库中具有抑制鼠Bezold-Jarisch反射作用的5-HT3受体拮抗剂作为训练集化合物, 构建5-HT3受体拮抗剂药效团模型. 训练集化合物具备结构多样性, 来源于相同药理模型, 活性值ED50范围为0.05~320 μg/kg i.v.. 利用Catalyst计算5-HT3受体拮抗剂的最优药效团由一个氢键受体、一个疏水基团、一个正电离子化基团、一个芳香环特征和6个排除体积组成; Fixed cost值、Null cost 值、Δcost值和Configuration cost值分别为112.6, 172.0, 59.4和7.248. 训练集化合物活性的计算值与实测值相关系数为0.9031, 偏差值为0.8976, 基于Fischer的交叉验证结果表明药效团模型具有较高的置信度, 所得药效团对训练集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索指导发现新的具有该活性的先导化合物, 也可用于中药或天然产物药物研究开发.     

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.     

P2Y12受体拮抗剂定量构效关系的研究

P2Y12受体拮抗剂是一类重要的抗血小板药物,研究分子活性与其结构参数的关系,对于合成新的P2Y12受体拮抗剂具有一定指导作用.选用178个结构多样的P2Y12受体拮抗剂分子作为数据集,随机选取了143个P2Y12受体拮抗剂作为训练集,剩余分子作为检验集.采用多元线性回归(MLR)方法和主成分回归分析(PCA)方法对每个分子的636个分子参数进行线性回归分析.MLR所建模型的结果为:训练集R2=0.800,检验集R2=0.834;PCA模型结果为:训练集R2=0.545,检验集R2=0.665.相比之下MLR法所建模型具有良好的预测性和可靠性.通过模型分析,确定了影响分子活性的关键因素.以上模型对筛选和合成新型高效P2Y12受体拮抗剂提供了一定理论指导.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

Pharmacophore modelling,virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure–activity relationship model with r² = 0.97 (internal training set), r² = 0.71 (internal test set) and Q² = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r² = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.     

Exploring the structural requirements in multiple chemical scaffolds for the selective inhibition of Plasmodium falciparum calcium-dependent protein kinase-1 (PfCDPK-1) by 3D-pharmacophore modelling,and docking studies

Current research on antimalarial protein kinases has provided an opportunity to design kinase-based antimalarial drugs. We have developed a common feature-based pharmacophore model from a set of multiple chemical scaffolds including derivatives of 3,6-imidazopyridazines, pyrazolo[2,3-d]pyrimidines and imidazo[1,5-a]pyrazines, in order to incorporate the maximum structural diversity information in the model for the Plasmodium falciparum calcium-dependent protein kinase-1 (PfCDPK-1) target. The best pharmacophore model (Hypo-1) with the essential features of two hydrogen bond donors (HBD), one hydrophobic aromatic (HYAr) and one ring aromatic (RA) showed the classification accuracies of 86.27%, 78.43% and 100.00% in labelling the training and test set (test set-1 and test set-2) compounds into more active and less active classes. In order to identify the crucial interaction between multiple scaffold ligands and the target protein, we first developed the homology model using a template structure of P. bergheii (PbCDPK1; PDB ID: 3Q5I), and thereafter performed the docking studies. The residues such as Lys85, Phe147, Tyr148, Leu198, Val211, and Asp212 were found to be the most important interacting residues for possessing PfCDPK-1 inhibitory activity.     

Pharmacophore Model Generation Based on Pyrrolidine- and Butane-derived CCR5 Antagonists

A three-dimensional pharmacophore model was developed for a considerable number of pyrrolidine-based and butane-based chemokine (C-C motif) receptor 5 (CCR5) antagonists, which can block the entry of human immunodeficiency virus type 1 (HIV-1) by inhibiting the interaction of HIV-1 envelope protein and CCR5. The pharmacophore model was generated using a training set consisting of 25 carefully selected antagonists with the diverse molecular architecture and bioactivity, as required by the Catalyst/HypoGen program. The activity of the training set molecules expressed in IC₅₀ (half-inhibitory concentration) covered from 0.06 to 10000 nmol·L^–1. The most predictive pharmacophore model (Hypo 1), consisting of two positive ionizable points and three hydrophobic groups, had a correlation of 0.924 and a root mean square of 1.068, and a cost difference of 63.67 bits between the null cost and the total cost. The model was applied in predicting the activity of 74 compounds as a test set. The results indicated that the model was able to provide clear guidelines and accurate activity prediction for novel antagonist design.     

基于吡咯烷与正丁烷类衍生物CCR5拮抗剂的药效团模型构建

吡咯烷与正丁烷类CCR5(化学趋化因子受体5)拮抗剂可通过抑制人类免疫缺陷病毒(HIV-1)包膜蛋白与CCR5的相互作用而阻断病毒进入细胞. 本文使用已知拮抗剂结构和活性信息构建了一个三维药效团模型. 按照Catalyst/HypoGen模块的要求, 选择了25个结构和活性均具备差异性的分子作为药效团产生的训练集. 其中训练集分子以IC50值表示的生物活性值跨度为0.06到10000 nmol·L-1. 最好的药效团模型(Hypo 1)由两个正离子化特征以及三个疏水特征组成, 训练集预测相关系数为0.924, 均方根偏差为1.068. 模型用于预测由74个分子组成的测试集化合物活性, 结果表明模型可以提供较好的活性预测结果并用于新的拮抗剂的设计.     

Hierarchical virtual screening: identification of potential high-affinity and selective β(3)-adrenergic receptor agonists

The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.     

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.