HMG-CoA还原酶抑制剂三维药效团的构建

以作用于鼠肝脏细胞的21个3-羟基-3-甲基戊二酰辅酶A(HMG-CoA)还原酶抑制剂(RI)为训练集, 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IC50范围在0.3-8000 nmol·L-1. 利用Catalyst 计算HMG-CoA还原酶抑制剂最优药效团由一个氢键受体, 一个氢键给体, 一个疏水基团和一个芳香环特征组成. 药效团模型Fixed cost值, Total cost值和Configuration cost值分别为88.75、111.5 和16.98. 训练集化合物活性计算值与实测值相关系数为0.8883, 偏差值为1.269, 交叉验证结果表明, 药效团模型具有较高的置信度, 对测试集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索发现新的具有该活性的化合物, 也可用于中药或天然产物药物的研究开发.     

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.     

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.     

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.     

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.     

α1A-亚型肾上腺素受体拮抗剂3D药效团模型的研究

运用Catalyst软件以34个α_1A-AR拮抗剂分子为训练集, 构建了包含一个氢键受体、一个正电中心和一个芳环中心的三元素药效团模型, 线性回归相关系数为0.89. 经13个分子组成的测试集验证该药效团模型具有较好的活性预测能力, 为寻找新的α_1A-AR拮抗剂分子提供了理论基础.     

基于药效团模型从中草药数据库中搜索gpIIb/IIIa受体抑制剂

选择20 个3,4-二氢-1(1H)-异喹啉酮类gpIIb/IIIa受体抑制剂作为训练集, 利用Catalyst软件包建立了gpIIb/IIIa受体抑制剂三维药效团模型. 探讨了药效团作用模式. 并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715), 从中草药数据库中虚拟筛选了gpIIb/IIIa受体抑制剂, 通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100 μmol·L-1的化合物, 进一步证明了所建药效团模型的有效性.     

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.     

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.     

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.     

Using pharmacophore models to gain insight into structural binding and virtual screening: an application study with CDK2 and human DHFR

This study provides results from two case studies involving the application of the HypoGenRefine algorithm within Catalyst for the automated generation of excluded volume from ligand information alone. A limitation of pharmacophore feature hypothesis alone is that activity prediction is based purely on the presence and arrangement of pharmacophoric features; steric effects remained unaccounted. Recently reported studies have illustrated the usefulness of combining excluded volumes to the pharmacophore models. In general, these excluded volumes attempt to penalize molecules occupying steric regions that are not occupied by active molecules. The HypoGenRefine algorithm in Catalyst accounts for steric effects on activity, based on the targeted addition of excluded volume features to the pharmacophores. The automated inclusion of excluded volumes to pharmacophore models has been applied to two systems: CDK2 and human DHFR. These studies are used as examples to illustrate how ligands could bind in the protein active site with respect to allowed and disallowed binding regions. Additionally, automated refinement of the pharmacophore with these excluded volume features provides a more selective model to reduce false positives and a better enrichment rate in virtual screening.     

Pharmacophore modeling as an efficient tool in the discovery of novel noncompetitive AMPA receptor antagonists

A three-dimensional pharmacophore model for the binding of noncompetitive AMPA receptor antagonists was developed in order to map common structural features of highly active compounds. This hypothesis, which consists of two hydrophobic regions, one hydrogen bond acceptor and one aromatic region, was successfully used as framework for the design of a new class of allosteric modulators containing a tetrahydroisoquinoline skeleton and for in silico screening. The promising biological results suggested that the identified molecules might be useful "lead compounds" for future drug development.     

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。     

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.     

Comparison of conformational analysis techniques to generate pharmacophore hypotheses using catalyst

Generation of reliable pharmacophore models is a key strategy in drug design. The quality of a pharmacophore model is known to depend on several factors, with the quality of the conformer sets used perhaps being one of the most important. The goal of this study was to compare different conformational analysis methods to determine if one was superior to the others for pharmacophore generation using Catalyst/HypoGen. The five methods selected were Catalyst/Fast, Catalyst/Best, Omega, Chem-X and MacroModel. Data sets for which Catalysts models had previously been published were selected using defined quality measures. Hypotheses were generated for each of the data sets and the performance of the different conformational analysis methods was compared using both quantitative (cost and correlation coefficients) and qualitative measures (by comparing the hypotheses in terms of the features present and their spatial relationships). Two main conclusions emerged from the study. First, it was not always possible to replicate the literature results. The reasons for these failures are explored in detail, and a template for use in publications that apply the Catalyst methodology is proposed. Second, the faster rule-based methods for conformational analysis give pharmacophore models that are just as good as, and in some cases better than, the models generated using the slower, more rigorous approaches.     

Influence of the conditions in pharmacophore generation, scoring, and 3D database search for chemical feature-based pharmacophore models: one application study of ETA- and ETB-selective antagonists

Using the commercial pharmacophore modeling suite Catalyst, we have studied the influence of the compare.scaledMultiBlobFeatureErrors . Catalyst parameter. The influence of this parameter has been studied in pharmacophore generation, hypothesis scoring, and database searching. This parameter, introduced in Catalyst 4.7, changed its default value in Catalyst 4.8, and it strongly influences the statistical quality of pharmacophore generation, scoring of the hypotheses, and database searching. Two different pharmacophore models have been constructed for the ETA and ETB receptor antagonists. Both models contain one positive ionizable, one negative ionizable, one hydrogen-bond acceptor, one hydrophobic aromatic, and one hydrophobic aliphatic feature. The models have been compared, and some differences in the position of the hydrogen-bond acceptor in the putative binding pocket have been highlighted.     

Toward the identification of a reliable 3D QSAR pharmacophore model for the CCK2 receptor antagonism

The present study describes application of computational approaches to identify a validated and reliable 3D QSAR pharmacophore model for the CCK-2R antagonism through integrated ligand and structure based studies using anthranilic sulfonamide and 1,3,4-benzotriazepine based CCK-2R antagonists. The best hypothesis consisted five features viz. two aliphatic hydrophobic, one aromatic hydrophobic, one H-bond acceptor, and one ring aromatic feature with an excellent correlation for 34 training set (r2(training) = 0.83) and 58 test set compounds (r2(test) = 0.74). This model was validated through F-test and docking studies at the active site of the plausible CCK-2R where the 99% significance and well corroboration with the pharmacophore model respectively describes the model's reliability. The model also predicts well to other known clinically effective CCK-2R antagonists. Therefore, the developed model may useful in finding new scaffolds that may aid in design and develop new chemical entities (NCEs) as potent CCK-2R antagonists before their synthesis.