Pharmacophore modeling for protein tyrosine phosphatase 1B inhibitors

A three dimensional chemical feature based pharmacophore model was developed for the inhibitors of protein tyrosine phosphatase 1B (PTP1B) using the CATALYST software, which would provide useful knowledge for performing virtual screening to identify new inhibitors targeted toward type II diabetes and obesity. A dataset of 27 inhibitors, with diverse structural properties, and activities ranging from 0.026 to 600 microM, was selected as a training set. Hypol, the most reliable quantitative four featured pharmacophore hypothesis, was generated from a training set composed of compounds with two H-bond acceptors, one hydrophobic aromatic and one ring aromatic features. It has a correlation coefficient, RMSD and cost difference (null cost-total cost) of 0.946, 0.840 and 65.731, respectively. The best hypothesis (Hypol) was validated using four different methods. Firstly, a cross validation was performed by randomizing the data using the Cat-Scramble technique. The results confirmed that the pharmacophore models generated from the training set were valid. Secondly, a test set of 281 molecules was scored, with a correlation of 0.882 obtained between the experimental and predicted activities. Hypol performed well in correctly discriminating the active and inactive molecules. Thirdly, the model was investigated by mapping on two PTP1B inhibitors identified by different pharmaceutical companies. The Hypol model correctly predicted these compounds as being highly active. Finally, docking simulations were performed on few compounds to substantiate the role of the pharmacophore features at the binding site of the protein by analyzing their binding conformations. These multiple validation approaches provided confidence in the utility of this pharmacophore model as a 3D query for virtual screening to retrieve new chemical entities showing potential as potent PTP1B inhibitors.     

Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors

Aim:

To identify the critical chemical features, with reliable geometric constraints, that contributes to the inhibition of butyrylcholinesterase (BChE) function.

Methods:

Ligand-based pharmacophore modeling was used to identify the critical chemical features of BChE inhibitors. The generated pharmacophore model was validated using various techniques, such as Fischer''s randomization method, test set, and decoy set. The best pharmacophore model was used as a query in virtual screening to identify novel scaffolds that inhibit BChE. Compounds selected by the best hypothesis in the virtual screening were tested for drug-like properties, and molecular docking study was applied to determine the optimal orientation of the hit compounds in the BChE active site. To find the reactivity of the hit compounds, frontier orbital analysis was carried out using density functional theory.

Results:

Based on its correlation coefficient (0.96), root mean square (RMS) deviation (1.01), and total cost (105.72), the quantitative hypothesis Hypo1 consisting of 2 HBA, 1 Hy-Ali, and 1 Hy-Ar was selected as the best hypothesis. Thus, Hypo1 was used as a 3D query in virtual screening of the Maybridge and Chembridge databases. The hit compounds were filtered using ADMET, Lipinski''s Rule of Five, and molecular docking to reduce the number of false positive results. Finally, 33 compounds were selected based on their critical interactions with the significant amino acids in BChE''s active site. To confirm the inhibitors'' potencies, the orbital energies, such as HOMO and LUMO, of the hit compounds and 7 training set compounds were calculated. Among the 33 hit compounds, 10 compounds with the highest HOMO values were selected, and this set was further culled to 5 compounds based on their energy gaps important for stability and energy transfer. From the overall results, 5 hit compounds were confirmed to be potential BChE inhibitors that satisfied all the pharmacophoric features in Hypo1.

Conclusion:

This study pinpoints important chemical features with geometric constraints that contribute to the inhibition of BChE activity. Five compounds are selected as the best hit BchE-inhibitory compounds.     

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

Aim:

Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches.

Methods:

The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations.

Results:

The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer''s randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10.

Conclusion:

Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors.     

乙酰胆碱酯酶抑制剂药效团模型的构建

利用CATALYST系统，由具有相同作用机制，不同结构特征的93个已知的乙酰胆碱酯酶抑制剂(AChEIs)构建出了含有3个疏水单元，1个环芳香性单元和1个氢键受体单元的药效团模型，优化的模型(RMS=0.53, correl=0.93, weight=3.29, config=19.05，total cost-null cost=62.75)可分别作用于乙酰胆碱酯酶的双活性作用部位，并能准确预测用于临床的阿尔茨海默病(AD)治疗的乙酰胆碱酶抑制剂的活性，有利于设计和改造具有新结构的用于AD治疗的乙酰胆碱酶抑制剂。     

Fatty acid amide hydrolase inhibitors from virtual screening of the endocannabinoid system

The endocannabinoid system consists of two cannabinoid receptors (CB1 and CB2), endogenous ligands (endocannabinoids), and the enzymes involved in the metabolism of the endocannabinoids, including fatty acid amide hydrolase (FAAH) and monoglyceride lipase (MGL). In the present study, virtual screening of MGL inhibitors was performed by utilizing a comparative model of the human MGL enzyme. All hit molecules were tested for their potential MGL inhibitory activity, but no compounds were found capable of inhibiting MGL-like enzymatic activity in rat cerebellar membranes. However, these compounds were also tested for their potential FAAH inhibitory activity and five compounds (2-6) inhibiting FAAH were found with IC50 values between 4 and 44 microM. In addition, the hit molecules from the virtual screening of CB2 receptor ligands (reported previously in Salo et al. J. Med. Chem. 2005, 48, 7166) were also tested in our FAAH assay, and four active compounds (7-10) were found with IC50 values between 0.52 and 22 microM. Additionally, compound 7 inhibited MGL-like enzymatic activity with an IC50 value of 31 microM.     

Three-dimensional quantitative structure-activity relationship analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors using a pharmacophore generation approach

A 3D pharmacophore model able to quantitatively predict inhibition constants was derived for a series of inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR), a validated target for antimalarial therapy. The data set included 52 inhibitors, with 23 of these comprising the training set and 29 an external test set. The activity range, expressed as Ki, of the training set molecules was from 0.3 to 11 300 nM. The 3D pharmacophore, generated with the HypoGen module of Catalyst 4.7, consisted of two hydrogen bond donors, one positive ionizable feature, one hydrophobic aliphatic feature, and one hydrophobic aromatic feature and provided a 3D-QSAR model with a correlation coefficient of 0.954. Importantly, the type and spatial location of the chemical features encoded in the pharmacophore were in full agreement with the key binding interactions of PfDHFR inhibitors as previously established by molecular modeling and crystallography of enzyme-inhibitor complexes. The model was validated using several techniques, namely, Fisher's randomization test using CatScramble, leave-one-out test to ensure that the QSAR model is not strictly dependent on one particular compound of the training set, and activity prediction in an external test set of compounds. In addition, the pharmacophore was able to correctly classify as active and inactive the dihydrofolate reductase and aldose reductase inhibitors extracted from the MDDR database, respectively. This test was performed in order to challenge the predictive ability of the pharmacophore with two classes of inhibitors that target very different binding sites. Molecular diversity of the data sets was finally estimated by means of the Tanimoto approach. The results obtained provide confidence for the utility of the pharmacophore in the virtual screening of libraries and databases of compounds to discover novel PfDHFR inhibitors.     

Combination of Pharmacophore Matching, 2D Similarity Search,and In Vitro Biological Assays in the Selection of Potential 5‐HT6 Antagonists from Large Commercial Repositories

Rapid in silico selection of target‐focused libraries from commercial repositories is an attractive and cost‐effective approach. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compound databases, but the generated library requires further focusing. We report here a combination of the 2D approach with pharmacophore matching which was used for selecting 5‐HT₆ antagonists. In the first screening round, 12 compounds showed >85% antagonist efficacy of the 91 screened. For the second‐round (hit validation) screening phase, pharmacophore models were built, applied, and compared with the routine 2D similarity search. Three pharmacophore models were created based on the structure of the reference compounds and the first‐round hit compounds. The pharmacophore search resulted in a high hit rate (40%) and led to novel chemotypes, while 2D similarity search had slightly better hit rate (51%), but lacking the novelty. To demonstrate the power of the virtual screening cascade, ligand efficiency indices were also calculated and their steady improvement was confirmed.     

Identification of novel inhibitors of HIV-1 integrase using pharmacophore-based virtual screening combined with molecular docking strategies

Human immunodeficiency virus type-1 (HIV-1) integrase is one of three enzymes encoded by the HIV genome for effective viral replication, and therefore an attractive target for chemotherapeutic interventions in the development of AIDS treatment. In this study, chemical feature-based pharmacophore models of different classes of integrase strand transfer inhibitors have been developed. The best HypoRefine pharmacophore models, Hypo1, which have the best correlation coefficients (0.92) and the lowest RMSs (0.78), contain two hydrogen bond acceptor lipids, one hydrogen bond donor, and one hydrophobic aromatic with four excluded volumes. After filtering by Lipinski’s rule of five, the best pharmacophore model was utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to docking studies by GOLD program to refine the retrieved hits. Finally, 4 top ranked compounds based on GOLD score fitness function and rescoring by X-score were investigated for compliance with the standard ranges through in silico ADME studies.     

Discovery of novel Bruton's tyrosine kinase inhibitors using a hybrid protocol of virtual screening approaches based on SVM model, pharmacophore and molecular docking

Bruton's tyrosine kinase has emerged as a potential target for the treatment for B-cell malignancies and autoimmune diseases. Discovery of Bruton's tyrosine kinase inhibitors has thus attracted much attention recently. In this investigation, we introduced a hybrid protocol of virtual screening methods including support vector machine model-based virtual screening, pharmacophore model-based virtual screening and docking-based virtual screening for retrieving new Bruton's tyrosine kinase inhibitors from commercially available chemical databases. Performances of the hybrid virtual screening approach were evaluated against a test set, which results showed that the hybrid virtual screening approach significantly shortened the overall screening time, and considerably increased the hit rate and enrichment factor compared with the individual method (SB-VS, PB-VS and DB-VS) or their combinations by twos. This hybrid virtual screening approach was then applied to screen several chemical databases including Specs (202,408 compounds) and Enamine (980,000 compounds) databases. Thirty-nine compounds were selected from the final hits and have been shifted to experimental studies.     

Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors

One pharmacophore model and three quantitative structure-activity relationship models were developed on a series of benzimidazole and imidazole inhibitors of histone deacetylase 2. The goodness of hit score value of the best pharmacophore model was 0.756, which indicated that it is reliable to be used for virtual screening. The built pharmacophore model was used to search the NCI database. The hit compounds were subjected to molecular docking. The results showed that 25 compounds had high scores and strong interactions with histone deacetylase 2. In three-dimensional quantitative structure-activity relationship studies, good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecular field analysis. Some putative active compounds were proposed based on compound no. 41. Twenty-six compounds had high scores and good interactions when they were docking into histone deacetylase 2.     

采用药效团模型和分子对接方法筛选新型的组蛋白甲基转移酶G9a抑制剂

目的利用药效团模型和分子对接方法对商业化合物库ChemDiv中的G9afocused-libraries进行筛选,希望发现新骨架结构的G9a抑制剂。方法首先,使用Discovery studio 3.1软件分别构建基于配体的药效团模型和基于配体-受体复合物的药效团模型,并根据构建的2个模型再重新定义2个新的药效团模型。然后,构建测试集并测试药效团模型的预测能力。最后,选取最优药效团模型对G9afocused-libraries进行筛选,对筛选出的化合物使用CDOCKER分子对接进行分析与评价。结果测试结果显示,所构建的药效团模型具有一定的预测能力,通过该药效团筛选得到了2个结构新颖的潜在的G9a抑制剂。结论所构建的药效团模型具有一定的可靠性,虚拟筛选发现的G9a抑制剂还需进一步的实验证明。     

3D QSAR pharmacophore-based virtual screening and molecular docking studies for the discovery of potential PDK1 inhibitors

Many researches discover that 3-phosphoinositide-dependent protein kinase-1 (PDK1) is overexpressed in many cancer cells and a promising target for developing novel anticancer drugs. The aim of this study is to identify novel scaffolds and utilize them in designing potent PDK1 inhibitors. 3D pharmacophore models were established based on the known PDK1 inhibitors. The best pharmacophore model, Hypo1, was selected, validated, and used in virtual screening. The obtained compounds were subjected to Lipinski’s rule of five, ADMET filtrations, and molecular docking studies. Finally, five molecules with high Genetic Optimization for Ligand Docking fitness scores and interactions with critical active site amino acids were identified. These hit compounds may act as novel leads for PDK1 inhibitors design.     

新型脂肪酰胺水解酶抑制剂的设计，合成和活性评价

目的 设计合成新型的脂肪酰胺水解酶（FAAH）抑制剂,并对其活性评价。 方法 通过已有FAAH酶抑制剂的结构和活性构建药效团模型,对部分ZINC数据库粗筛;通过与FAAH酶分子对接,对粗筛所获得的小分子化合物的活性进行打分评价并,确定拟合成目标化合物的母核结构（2-氧代苯并吡喃-7-酯）;采用酰化、缩合反应,合成系列目标化合物;通过体外酶抑制活性实验检测其活性。 结果 化合物（±）-2-（2-苯氧基乙酰基氨基）-丙酸-2-氧代苯并吡喃-7-酯（2b）的IC50值为95.24 μmol.L-1,（±）-1-（2-苯氧基-乙酰基）-吡咯烷-2-羧酸-2-氧代苯并吡喃-7-酯（2g）的IC50值为17.34μmol.L-1,具有较好的抑制FAAH酶活性的作用。 结论 本文活性化合物的结构与目前报道的脂肪酰胺水解酶抑制剂结构差异较大,有望成为新类型先导化合物。     

A Specific Pharmacophore Model of Aurora B Kinase Inhibitors and Virtual Screening Studies Based on it

In this study, 3D‐pharmacophore models of Aurora B kinase inhibitors have been developed by using HipHop and HypoGen modules in Catalyst software package. The best pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9911), consists of one hydrogen‐bond acceptor, one hydrogen‐bond donor, one hydrophobic aliphatic moiety and one ring aromatic feature. Hypo1 was validated by test set and cross‐validation methods. And the specificity of Hypo1 to Aurora B inhibitors was examined with the use of selective inhibitors against Aurora B and its paralogue Aurora A. The results clearly indicate that Hypo1 can differentiate selective inhibitors of Aurora B from those of Aurora A, and the ring aromatic feature likely plays some important roles for the specificity of Hypo1. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD) for identifying new inhibitors of Aurora B. The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits, and some compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase.

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.     

Identification of Novel HCV RNA‐dependent RNA polymerase Inhibitors Using Pharmacophore‐Guided Virtual Screening

We performed pharmacophore‐guided virtual screening experiments using FlexX‐Pharm to identify novel inhibitors of hepatitis C virus RNA‐dependent RNA polymerase. Pharmacophore model generated from our previous analysis of the binding modes as well as structure‐based three‐dimensional quantitative structure–activity relationship studies of aryl diketoacid analogues was used. In pharmacophore‐guided virtual screening study, among 37 447 compounds in LeadQuest chemical library, 40 compounds were selected as novel candidates of hepatitis C virus RNA‐dependent RNA polymerase inhibitors, and their biological activities were evaluated. Especially, T29 was chosen for further development.     

Identification of promising DNA GyrB inhibitors for Tuberculosis using pharmacophore‐based virtual screening,molecular docking and molecular dynamics studies

In this study, we searched for potential DNA GyrB inhibitors using pharmacophore‐based virtual screening followed by molecular docking and molecular dynamics simulation approaches. For this purpose, a set of 248 DNA GyrB inhibitors was collected from the literature and a well‐validated pharmacophore model was generated. The best pharmacophore model explained that two each of hydrogen bond acceptors and hydrophobicity regions were critical for inhibition of DNA GyrB. Good statistical results of the pharmacophore model indicated that the model was robust in nature. Virtual screening of molecular databases revealed three molecules as potential antimycobacterial agents. The final screened promising compounds were evaluated in molecular docking and molecular dynamics simulation studies. In the molecular dynamics studies, RMSD and RMSF values undoubtedly explained that the screened compounds formed stable complexes with DNA GyrB. Therefore, it can be concluded that the compounds identified may have potential for the treatment of TB.     

Generation of predictive pharmacophore models for CCR5 antagonists: study with piperidine- and piperazine-based compounds as a new class of HIV-1 entry inhibitors

Predictive pharmacophore models were developed for a large series of piperidine- and piperazine-based CCR5 antagonists as anti-HIV-1 agents reported by Schering-Plough Research Institute in recent years. The pharmacophore models were generated using a training set consisting of 25 carefully selected antagonists based on well documented criteria. The activity spread, expressed in K(i), of training set molecules was from 0.1 to 1300 nM. The most predictive pharmacophore model (hypothesis 1), consisting of five features, namely, two hydrogen bond acceptors and three hydrophobic, had a correlation (r) of 0.920 and a root mean square of 0.879, and the cost difference between null cost and fixed cost was 44.46 bits. The model was cross-validated by randomizing the data using the CatScramble technique. The results confirmed that the pharmacophore models generated from the test set were not due to chance correlation. The best model (hypothesis 1) was validated using test set molecules (total of 78) and performed well in classifying active and inactive molecules correctly. The model was further validated by mapping onto it a diverse set of six CCR5 antagonists identified by five different pharmaceutical companies. The best model correctly predicted these compounds as being highly active. These multiple validation approaches provide confidence in the utility of the predictive pharmacophore model developed in this study as a 3D query tool in virtual screening to retrieve new chemical entities as potent CCR5 antagonists. The model can also be used in predicting biological activities of compounds prior to undertaking their costly synthesis.