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.     

Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

A marriage made in torsional space: using GALAHAD models to drive pharmacophore multiplet searches

Pharmacophore multiplets are useful tools for 3D database searching, with the queries used ordinarily being derived from ensembles of random conformations of active ligands. It seems reasonable to expect that their usefulness can be augmented by instead using queries derived from single ligand conformations obtained from aligned ligands. Comparisons of pharmacophore multiplet searching using random conformations with multiplet searching using single conformations derived from GALAHAD (a genetic algorithm with linear assignment for hypermolecular alignment of datasets) models do indeed show that, while query hypotheses based on random conformations are quite effective, hypotheses based on aligned conformations do a better job of discriminating between active and inactive compounds. In particular, the hypothesis created from a neuraminidase inhibitor model was more similar to half of 18 known actives than all but 0.2% of the compounds in a structurally diverse subset of the World Drug Index. Similarly, a model developed from five angiotensin II antagonists yielded hypotheses that placed 65 known antagonists within the top 0.1–1% of decoy databases. The differences in discriminating power ranged from 2 to 20-fold, depending on the protein target and the type of pharmacophore multiplet used.     

Identification of molecular fingerprints of phenylindole derivatives as cytotoxic agents: a multi-QSAR approach

Phenylindole is reported to be an interesting scaffold having promising cytotoxic activities and can overcome the cancer drug resistance possibly via binding to the colchicine binding site of tubulin. In order to find out the molecular fingerprints for the better cytotoxic activity of phenylindole derivatives, multiple validated chemometric modeling approaches namely hologram QSAR (HQSAR), Bayesian classification model, and pharmacophore mapping analyses were applied into a dataset of 102 phenylindole derivatives. The final HQSAR model shows good statistical significance (Q²?=?0.760; R²_Train?=?0.868; R²_Test?=?0.660), and the best pharmacophore hypothesis has the highest regression coefficient value (r?=?0.975) and the lowest RMS value of 0.679. Moreover, the Bayesian model is also statistically validated and robust to discriminate the cytotoxic and non-cytotoxic phenylindoles. These studies suggest that the amine group should be unsubstituted for retaining higher cytotoxicity. The pharmacophore mapping and Bayesian classification study suggest the importance of 2-phenyl group as a ring aromatic feature conducive to cytotoxicity. The steric and hydrophobic effect of long chain linear alkyl group has a positive influence on cytotoxicity as evidenced by the multi-QSAR study. Therefore, this multi-QSAR modeling reported here is beneficial in designing potential phenylindole cytotoxic agents in future.     

药效团检索设计新的HIV-1蛋白酶抑制剂

通过对自建的未开发化合物三维结构库进行药效团检索,得到了4个对HIV-1蛋白酶抑制活化的化合物,通过构象分析发现包含药效团的构象处于优势构象,而且4个结构都含有带两个邻位羟基的苯环和一个间位羰基的药效团以及公共子结构。通过计算发现它们的疏水参数都很小。在考虑满足包含药效团的结构特征和有适中的疏水参数两个因素的前提下,设计出了新的具有潜在抑制HIV-1蛋白酶活性的化合物。它们的结构都比检索得到的四个化合物更为简单,因此易于合成。     

A web-based 3D-database pharmacophore searching tool for drug discovery

Three-Dimensional (3D) structural database pharmacophore searching has become a very effective approach for discovery of novel lead compounds in drug discovery. Although several commercial programs are available, these commercial programs are primarily used as a stand alone and require a local database. In recent years, the Internet has become the main medium of choice for multiuser application program distribution. Herein, we describe our development of a Web-based 3D-database pharmacophore-searching tool based on the server-client Web architecture. Both rigid and conformationally flexible searching methods are implemented. Our results show that for a typical three-center rigid pharmacophore search, the run time for searching 50 000 compounds is less than three minutes, and for four-center pharmacophore searching, the run time is less than 10 minutes on a desktop computer. For a flexible 3D-pharmacophore search, the run time for searching 50 000 compounds generally takes between one and several hours. The search results are comparable to those obtained using a commercial program. We expect that this Web-based tool will be very useful for scientists who are interested in 3D-database pharmacophore searching via the Internet.     

Triple-layered QSAR studies on substituted 1,2,4-trioxanes as potential antimalarial agents: superiority of the quantitative pharmacophore-based alignment over common substructure-based alignment

This study reports the utilization of three approaches – pharmacophore, CoMFA/CoMSIA and HQSAR studies – to identify the essential structural requirements in 3D chemical space for the modulation of the antimalarial activity of substituted 1,2,4-trioxanes. The superiority of quantitative pharmacophore-based alignment (QuantitativePBA) over global minima energy conformer-based alignment (GMCBA) has been reported in CoMFA and CoMSIA studies. The developed models showed good statistical significance in internal validation (q ², group cross-validation and bootstrapping) and performed very well in predicting the antimalarial activity of test set compounds. Structural features in terms of their steric, electrostatic and hydrophobic interactions in 3D space have been found to be important for the antimalarial activity of substituted 1,2,4-trioxanes. Further, the HQSAR studies based on the same training and test set acted as an additional tool to find the sub-structural fingerprints of substituted 1,2,4-trioxanes for their antimalarial activity. Together, these studies may facilitate the design and discovery of new substituted 1,2,4-trioxanes with potent antimalarial activity.     

Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points

Pharmacophore modeling can provide valuable insight into ligand-receptor interactions. It can also be used in 3D (dimensional) database searching for potentially finding biologically active compounds and providing new research ideas and directions for drug-discovery projects. To stimulate the structure-based drug design against SARS (severe acute respiratory syndrome), a pharmacophore search was conducted over 3.6 millions of compounds based on the atomic coordinates of the complex obtained by docking KZ7088 (a derivative of AG7088) to SARS CoV M(pro) (coronavirus main proteinase), as reportedly recently (Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Biochem. Biophys. Res. Commun. 2003, 308, 148-151). It has been found that, of the 3.6 millions of compounds screened, 0.07% are with the score satisfying five of the six pharmacophore points. Moreover, each of the hit compounds has been evaluated for druggability according to 13 metrics based on physical, chemical, and structural properties. Of the 0.07% compounds thus retrieved, 17% have a perfect score of 1.0; while 23% with one druggable rule violation, 13% two violations, and 47% more than two violations. If the criterion for druggability is set at a maximum allowance of two rule violations, we obtain that only about 0.03% of the compounds screened are worthy of further tests by experiments. These findings will significantly narrow down the search scope for potential compounds, saving substantial time and money. Finally, the featured templates derived from the current study will also be very useful for guiding the design and synthesis of effective drugs for SARS therapy.     

Pharmacophore based 3D-QSAR modeling,virtual screening and docking for identification of potential inhibitors of β-secretase

The enzyme β-secretase-1 is responsible for the cleavage of the amyloid precursor protein, a vital step in the process of the formation of amyloid-β peptides which are known to lead to neurodegeneration causing Alzheimer’s disease. Challenges associated with toxicity and blood brain permeation inability of potential inhibitors, continue to evade a successful therapy, thus demanding the search and development of highly active and effective inhibitors. Towards these efforts, we used a ligand based pharmacophore model generation from a dataset of known inhibitors whose activities against β-secretase hovered in the nano molar range. The identified 5 feature pharmacophore model, AHHPR, was validated via three dimensional quantitative structure activity relationship as indicated by r², q² and Pearson R values of 0.9013, 0.7726 and 0.9041 respectively. For a dataset of compounds with nano molar activity, the important pharmacophore features present in the current model appear to be similar with those observed in the models resulting from much wider activity range of inhibitors. Virtual screening of the ChemBridge CNS-Set™, a database having compounds with a better suitability for central nervous system based disorders followed by docking and analysis of the ligand protein interactions resulted in the identification of eight prospective compounds with considerable diversity. The current pharmacophore model can thus be useful for the identification, design and development of potent β-secretase inhibitors which by optimization can be potential therapeutics for Alzheimer’s disease.     

新型5-HT重摄取抑制剂的设计、合成及活性评价

在对已知各种结构类型的5-HT重摄取抑制剂分子结构全面分析的基础上, 建立了SSRIs药效团模型. 基于该模型应用UNITY程序对NCI-3D和Maybridge-3D数据库进行三维结构的限制性查询, 在获得的命中结构的信息指导下, 设计合成了3种全新结构类型的化合物, 并完成了初步的药理活性评价. 这些化合物均显示出不同程度的5-HT重摄取抑制活性, 其中5个化合物显示高抑制活性. 哌嗪取代的二苯脒类化合物的结构新颖, 较好地符合5-HT重摄取抑制剂药效团模型, 与SSRIs类化合物三维定量构效关系研究得到的CoMFA模型有较好的适配性.     

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.     

Epitopes based drug design for dengue virus envelope protein: A computational approach

Dengue virus (DENV) has emerged as a rapidly spreading epidemic throughout the tropical and subtropical regions around the globe. No suitable drug has been designed yet to fight against DENV, therefore, the need for safe and effective antiviral drug has become imperative. The envelope protein of DENV is responsible for mediating the fusion process between viral and host membranes. This work reports an in silico approach to target B and T cell epitopes for dengue envelope protein inhibition. A conserved region “QHGTI” in B and T cell epitopes of dengue envelope glycoprotein was confirmed to be valid for targeting by visualizing its interactions with the host cell membrane TIM-1 protein which acts as a receptor for serotype 2 and 3. A reverse pharmacophore mapping approach was used to generate a seven featured pharmacophore model on the basis of predicted epitope. This pharmacophore model as a 3D query was used to virtually screen a chemical compounds dataset “Chembridge”. A total of 1010 compounds mapped on the developed pharmacophore model. These retrieved hits were subjected to filtering via Lipinski’s rule of five, as a result 442 molecules were shortlisted for further assessment using molecular docking. Finally, 14 hits of different structural properties having interactions with the active site residues of dengue envelope glycoprotein were selected as lead candidates. These structurally diverse lead candidates have strong likelihood to act as further starting structures in the development of novel and potential drugs for the treatment of dengue fever.     

基于药效团的三维数据库搜索

用表皮生长因子受体酪氨酸激酶抑制剂的药效团作为提问结构在三维数据库中进行了搜索.从得到的命中结构中挑选了12个化合物用柔性受体模型方法对其活性进行了预测, 发现有2个化合物具有一定的预测活性.这2个化合物可能具有酪氨酸激酶抑制剂的活性, 可能作为先导化合物进行结构优化.     

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.     

Pharmacophore-guided design, synthesis and evaluation of quinazoline-arylpiperazines as new α1-adrenoceptor antagonists

A series of arylpiperazinesquinazoline-2,4-diamine compounds were designed and synthesized based on pharmacophore for uro-selectiveα_1-adrenoceptor antagonists and 3D chemical database searching.The in vitro functional analysis showed that compounds 9 and 14 showed better and similarα_1-AR antagonistic activity compared with prazosin.     

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.     

From pharmacophore to leads: Bioactive compound discovery via computer-aided techniques

Based on the concept of Green Chemistry, a new procedure of finding bioactive compounds and their synthetic routes by computer-aided techniques was proposed. The procedure consists of pharmacophore search against a 3D structural database of natural products for lead discovery and computer-aided synthesis design for avoiding unuseful synthetic experiments. This work demonstrated that computer aided drug design and synthesis design would help us to make the consideration of environmental concerns systematically, rather than having to deal later with the unnecessary waste chemicals. Thus, it is shown that chemical computer-aided design (CAD) is an indispensable part of Green Chemistry.     

A virtual screening approach for thymidine monophosphate kinase inhibitors as antitubercular agents based on docking and pharmacophore models

Docking and pharmacophore screening tools were used to examine the binding of ligands in the active site of thymidine monophosphate kinase of Mycobacterium tuberculosis. Docking analysis of deoxythymidine monophosphate (dTMP) analogues suggests the role of hydrogen bonding and other weak interactions in enzyme selectivity. Water-mediated hydrogen-bond networks and a halogen-bond interaction seem to stabilize the molecular recognition. A pharmacophore model was developed using 20 dTMP analogues. The pharmacophoric features were complementary to the active site residues involved in the ligand recognition. On the basis of these studies, a composite screening model that combines the features from both the docking analysis and the pharmacophore model was developed. The composite model was validated by screening a database spiked with 47 known inhibitors. The model picked up 42 of these, giving an enrichment factor of 17. The validated model was used to successfully screen an in-house database of about 500,000 compounds. Subsequent screening with other filters gave 186 hit molecules.