Influenza virus neuraminidase inhibitors: generation and comparison of structure-based and common feature pharmacophore hypotheses and their application in virtual screening

X-ray crystallographic data of the influenza virus neuraminidase in complex with different inhibitors were used to generate chemical feature-based pharmacophore models of the binding site of this enzyme. The models were built using the software package Catalyst. Pharmacophore hypotheses derived from the 3-D structure of ligands cocrystallized with the enzyme were then compared with automatically generated common feature pharmacophore hypotheses for neuraminidase inhibitors. The latter models were found to contain fewer features and exhibited lower selectivity in virtual screening experiments. Some functions of the inhibitors obviously participate in more than one mode of interaction with the enzyme (charge-charge interaction and hydrogen bond) or form hydrogen bonds to several amino acids. Since such multiple interactions of one chemical function cannot be included into the Catalyst data format, strategies are presented to overcome these limitations. Finally, the results of 3-D database searching experiments using these hypotheses are described.     

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.     

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.     

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.     

Efficient 3D database screening for novel HIV-1 IN inhibitors

We describe the use of pharmacophore modeling as an efficient tool in the discovery of novel HIV-1 integrase (IN) inhibitors. A three-dimensional hypothetical model for the binding of diketo acid analogues to the enzyme was built by means of the Catalyst program. Using these models as a query for virtual screening, we found several compounds that contain the specified 3D patterns of chemical functions. Biological testing shows that our strategy was successful in searching for new structural leads as HIV-1 IN inhibitors.     

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.     

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.     

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.     

Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.     

5-羟色胺(5-HT)和去甲肾上腺素(NE)重摄取抑制剂药效团模型的建立及比较分析

近年来的研究表明, 去甲肾上腺素(NE)能系统和5-羟色胺(5-HT)能系统可能共同参与了抑郁症的发病机制. 采用Catalyst软件的Hypogen方法, 利用22个不同结构类型的5-HT重摄取抑制剂和19个不同结构类型和活性的NE重摄取抑制剂分别建立了5-HT药效团模型和NE药效团模型, 它们的相关系数分别为0.935, 0.844, 这表明所得到的模型能较好地表征重摄取抑制剂化合物的特征; 此外, 我们还选择了四种不同活性的预测集分别对所建立的药效团模型进行检验, 结果表明所建立的药效团模型具有较好的预测能力. 对这两个药效团模型进行了比较分析, 其结果可以为设计高活性的双重5-HT和NE重摄取抑制剂提供依据.     

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.     

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.     

CAESAR: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration

A highly efficient conformer search algorithm based on a divide-and-conquer and recursive conformer build-up approach is presented in this paper. This approach is combined with consideration of local rotational symmetry so that conformer duplicates due to topological symmetry in the systematic search can be efficiently eliminated. This new algorithm, termed CAESAR (Conformer Algorithm based on Energy Screening and Recursive Buildup), has been implemented in Discovery Studio 1.7 as part of the Catalyst Component Collection. CAESAR has been validated by comparing the conformer models generated by the new method and Catalyst/FAST. CAESAR is consistently 5-20 times faster than Catalyst/FAST for all data sets investigated. The speedup is even more dramatic for molecules with high topological symmetry or for molecules that require a large number of conformers to be sampled. The quality of the conformer models generated by CAESAR has been validated by assessing the ability to reproduce the receptor-bound X-ray conformation of ligands extracted for the Protein Data Bank (PDB) and assessing the ability to adequately cover the pharmacophore space. It is shown that CAESAR is able to reproduce the receptor-bound conformation slightly better than the Catalyst/FAST method for a data set of 918 ligands retrieved from the PDB. In addition, it is shown that CEASAR covers the pharmacophore space as well or better than Catalyst/FAST.     

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.     

A ligand-based molecular modeling study on some matrix metalloproteinase-1 inhibitors using several 3D QSAR techniques

Some three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) for a series of 84 proline-based plus 12 structurally more diversified nonproline matrix metalloproteinase inhibitors. The structures of these inhibitors were built from a structure template extracted from the crystal structure of stromelysin. The structures built were divided into the training and test sets for both the CoMFA and CoMSIA analyses for each being composed of 60 and 24 inhibitors, respectively. The structures in the training set were aligned using some alignment rules derived from the analysis of the Ligplot program on a recent crystal structure of ligand-collagenase-1 complex. Some stepwise CoMSIA's were performed on the aligned training set on which the best CoMFA result was obtained. The best CoMSIA model was identified from the stepwise results, and the corresponding pharmacophore features were used for the construction of a pharmacophore hypothesis by the Catalyst 4.9 program. The training set was extended to include 11 structurally more diversified and nonproline inhibitors. To construct a pharmacophore hypothesis, the conformation of 60 structurally aligned proline-based inhibitors was fixed, while that of the 11 structurally more diversified nonproline inhibitors was allowed to vary during the hypothesis construction process. It was found that the predicted activities by the top hypothesis constructed for both the training and test sets were as good in statistics as those predicted by the best CoMSIA model from which the hypothesis was derived. The top hypothesis was mapped onto the structures of several highly active inhibitors selected from both the training and test sets. The goodness of mapping on each inhibitor was found to be correlated well with the activity of each inhibitor.     

The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

A three-dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti-cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis （Hypo 1） was defined by four features： one hydrogen-bond donor and three hy- drophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross-validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.     

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。     

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.