Developing a dynamic pharmacophore model for HIV-1 integrase

We present the first receptor-based pharmacophore model for HIV-1 integrase. The development of "dynamic" pharmacophore models is a new method that accounts for the inherent flexibility of the active site and aims to reduce the entropic penalties associated with binding a ligand. Furthermore, this new drug discovery method overcomes the limitation of an incomplete crystal structure of the target protein. A molecular dynamics (MD) simulation describes the flexibility of the uncomplexed protein. Many conformational models of the protein are saved from the MD simulations and used in a series of multi-unit search for interacting conformers (MUSIC) simulations. MUSIC is a multiple-copy minimization method, available in the BOSS program; it is used to determine binding regions for probe molecules containing functional groups that complement the active site. All protein conformations from the MD are overlaid, and conserved binding regions for the probe molecules are identified. Those conserved binding regions define the dynamic pharmacophore model. Here, the dynamic model is compared to known inhibitors of the integrase as well as a three-point, ligand-based pharmacophore model from the literature. Also, a "static" pharmacophore model was determined in the standard fashion, using a single crystal structure. Inhibitors thought to bind in the active site of HIV-1 integrase fit the dynamic model but not the static model. Finally, we have identified a set of compounds from the Available Chemicals Directory that fit the dynamic pharmacophore model, and experimental testing of the compounds has confirmed several new inhibitors.     

Pharmacophore modeling and virtual screening for the discovery of new fatty acid amide hydrolase inhibitors

A predictive pharmacophore model has been generated from a series of diverse fatty acid amide hydrolase (FAAH) inhibitors and the optimal pharmacophore model applied in virtual screening. The pharmacophore model was based on a training set of 21 compounds carefully selected from the published literatures. The optimal model Hypo-1 included four features (two hydrogen-bond acceptor units, one aromatic hydrophobic unit and one aromatic ring unit) and two excluded volumes. Cross-validation of the model confirmed that Hypo-1 was not generated by chance correlation. A large test set of 55 compounds showed that Hypo-1 performed well in classifying highly active and less active FAAH inhibitors. Superimposition analysis of the FAAH X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the model. Virtual screening generated a total of 976 hits from the Zinc Natural Products database, a hit rate of 1.04% and enrichment of 83.89. The acceptable hit rate further supports the use of Hypo-1 as a 3D query tool for virtual screening.     

Refining the multiple protein structure pharmacophore method: consistency across three independent HIV-1 protease models

Developing methods to incorporate protein flexibility into structure-based drug design is an important challenge. Our approach uses multiple protein structures (MPS) to create a receptor-based pharmacophore model of the desired target. We have previously demonstrated the success of the method by applying it to human immunodeficiency virus-1 protease (HIV-1p). Our models, based on an apo structure, discriminated known HIV-1p inhibitors from druglike inactive compounds and also accurately identified bound conformations of known inhibitors. Here, we test the method by applying it to all three unbound crystal structures of HIV-1p. We have also improved our method with denser probe mapping of the binding site and refined our selection criteria for pharmacophore elements. Our improved protocol has led to the development of a consistent 8-site pharmacophore model for HIV-1p, which is independent of starting structure, and a robust MPS pharmacophore method that is more amenable to automation.     

Computation of pharmacophore models for the prediction of mitogen-activated protein kinase activated protein kinase-2 inhibitory activity of pyrrolopyridines

This article is an attempt to formulate the three-dimensional pharmacophore modelling of pyrrolopyridine derivatives inhibiting mitogen-activated protein kinase activated protein kinase-2 (MK₂). To understand the essential structural features for MK₂ inhibitors, pharmacophore hypothesis were built on the basis of a set of known MK₂ inhibitors selected from literature using PHASE program. Three pharmacophore models with one hydrogen-bond acceptor (A), two hydrogen-bond donors (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features were developed. Amongst them the pharmacophore hypothesis ADDHR1 yielded a statistically significant 3D-QSAR model with 0.926 as R ² value and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.882 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore was expected to be useful for the design of selective MK₂ inhibitors.     

Ligand-based Pharmacophore Modeling; Atom-based 3D-QSAR Analysis and Molecular Docking Studies of Phosphoinositide-Dependent Kinase-1 Inhibitors

Phosphoinositide-dependent kinase-1 plays a vital role in the PI3-kinase signaling pathway that regulates gene expression, cell cycle growth and proliferation. The common human cancers include lung, breast, blood and prostate possess over stimulation of the phosphoinositide-dependent kinase-1 signaling and making phosphoinositide-dependent kinase-1 an interesting therapeutic target in oncology. A ligand-based pharmacophore and atom-based 3D-QSAR studies were carried out on a set of 82 inhibitors of PDK1. A six point pharmacophore with two hydrogen bond acceptors (A), three hydrogen bond donors (D) and one hydrophobic group (H) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least square statistics results. The training set correlation is characterized by partial least square factors (R² = 0.9557, SD = 0.2334, F = 215.5, P = 1.407e-32). The test set correlation is characterized by partial least square factors (Q² ext = 0.7510, RMSE = 0.5225, Pearson-R =0.8676). The external validation indicated that our QSAR model possess high predictive power with good value of 0.99 and value of 0.88. The docking results show the binding orientations of these inhibitors at active site amino acid residues (Ala162, Thr222, Glu209 and Glu166) of phosphoinositide-dependent kinase-1 protein. The binding free energy interactions of protein-ligand complex have been calculated, which plays an important role in molecular recognition and drug design approach.     

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.     

Pharmacophore modeling studies on N-hydroxyphenyl acrylamides and N-hydroxypyridin-2-yl-acrylamides as inhibitor of human cancer leukemia K562 cells

In order to understand the essential structural features for inhibitors of human cancer leukemia K562 cells, three-dimensional pharmacophore hypotheses were built on the basis of a set of inhibitors of human cancer leukemia K562 selected from literature using PHASE program. Five point pharmacophore with two hydrogen bond acceptor (A), one hydrogen bond donor (D), and two aromatic rings (R) as pharmacophoric features were developed. Among them, the pharmacophore hypothesis AADRR 62 yielded a statistically significant 3D-QSAR model with as R ² value 0.883 and Q ² value 0.528 and was considered to be the best pharmacophore hypothesis. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.765 was observed between experimental and predicted activity values of test set molecules.     

3D-QSAR studies of some tetrasubstituted pyrazoles as COX-II inhibitors

Pharmacophore mapping studies were undertaken for a series of molecules belonging to tetrasubstituted pyrazoles as canine COX-II inhibitors. A six point pharmacophore with 3 hydrogen bond acceptors (A), one hydrophobic group (H) and two aromatic rings (R) as pharmacophoric feature was developed. The pharmacophoric hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of r2 = 0.958. The developed pharmacophore model was externally validated by predicting the activity of test set molecules. The squared predictive correlation coefficient of 0.852 was observed between experimental and predicted activity values of test set molecules. The geometry and features of pharmacophore model describe the key structure-activity relationship of COX-II inhibitors, can predict their activities, and can thus be used to design novel inhibitors.     

Combined structure-based pharmacophore, virtual screening, and 3D-QSAR studies of structural diverse dehydrosqualene synthase inhibitors

Dehydrosqualene synthase (CrtM) is a key enzyme in the synthesis of presqualene diphosphate in Staphylococcus aureus. In the current study, a combination of structure-based pharmacophore and 3D-QSAR methods are used to clarify the essential quantitative structure–activity relationship (QSAR) of known CrtM inhibitors; the multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of CrtM based on twenty crystal structures of CrtM inhibitor complex. Performances of the MCBP-based virtual screening approach were applied to screen specs chemical databases (202, 408 compounds). Thirty-eight compounds were selected from the final hits and should be shifted to experimental studies. The MCBP model has been successfully used to identify the bioactive conformation and align 24 structurally diverse CrtM inhibitors. The QSAR analyses have been performed on these CrtM inhibitors based on MCBP guided alignment. These results may provide important information for further design and discovery of novel CrtM inhibitors.     

Pharmacophore development and docking studies of the hiv-1 integrase inhibitors derived from N-methylpyrimidones, Dihydroxypyrimidines, and bicyclic pyrimidinones

To elucidate the crucial structural features for the HIV-1 integrase inhibitors, a three-dimensional pharmacophore model was developed based on N-methyl pyrimidones, dihydroxypyrimidines, and bicyclic pyrimidinones derivatives using Phase. N-methyl pyrimidone derivative raltegravir, the first US-FDA approved drug by Merck, belongs to this series. The best-fitted common pharmacophore hypothesis was characterized by two acceptor, two hydrophobic, and two ring features having a correlation coefficient of 0.895, cross-validated Q(2) value of 0.631, and survival score of 8.862, suggesting that a highly predictive pharmacophore model was developed. The cross-validation studies using 23 test set molecules and fifteen structurally diverse HIV-integrase inhibitors give extra confidence about the correctness of the pharmacophore model. The cross-validation studies proved that our developed model can successfully differentiate between active and inactive HIV-integrase inhibitors. The docking studies were also carried out wherein the molecules were docked against the active site of HIV integrase to analyze the binding mode and the necessary structural requirement for their respective enzymatic inhibition. The results obtained from our studies provide a valuable tool for designing of new lead molecules with potent activity.     

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 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.     

Combined comparative molecular field analysis,comparative molecular similarity indices analysis,molecular docking and molecular dynamics studies of histone deacetylase 6 inhibitors

Human histone deacetylase isoform 6 (HDAC6) has been shown to have an immense role in cell motility and aggresome formation and is being an attractive selective target for the treatment of multiple tumour types and neurodegenerative conditions. The discovery of selective HDAC6 inhibitors with new chemical functionalities is therefore of utmost interest to researchers. In order to examine the structural requirements for HDAC6‐specific inhibitors and to derive predictive model which can be used for designing new selective HDAC6 inhibitors, a three‐dimensional quantitative structure–activity relationship study was carried out on a diverse set of ligands using common feature‐based pharmacophore alignment followed by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models displayed high correlation of 0.978 and 0.991 for best CoMFA and CoMSIA models, respectively, and a good statistical significance. The model could be used for predicting activities of the test set compounds as well as for deriving useful information regarding steric, electrostatic, hydrophobic properties of the molecules used in this study. Further, the training and test set molecules were docked into the HDAC6 binding site and molecular dynamics was carried out to suggest structural requirements for design of new inhibitors.     

Molecular docking and 3D-QSAR studies on gag peptide analogue inhibitors interacting with human cyclophilin A

The interaction of a series gag peptide analogues with human cyclophilin A (hCypA) have been studied employing molecular docking and 3D-QSAR approaches. The Lamarckian Genetic Algorithm (LGA) and divide-and-conquer methods were applied to locate the binding orientations and conformations of the inhibitors interacting with hCypA. Good correlations between the calculated interaction free energies and experimental inhibitory activities suggest that the binding conformations of these inhibitors are reasonable. A novel interaction model was identified for inhibitors 11, 15, and 17 whose N-termini were modified by addition of the deaminovaline (Dav) group and the C-termini of 15 and 17 were modified by addition of a benzyl group. Accordingly, two new binding sites (sites A and D in Figure 1) were revealed, which show a strong correlation with inhibitor potency and thus can be used as a starting point for new inhibitor design. In addition, two predictive 3D-QSAR models were obtained by CoMFA and CoMSIA analyses based on the binding conformations derived from the molecular docking calculations. The reasonable r(cross)(2) (cross-validated) values 0.738 and 0.762 were obtained for CoMFA and CoMSIA models, respectively. The predictive ability of these models was validated by four peptide analogues test set. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding groove of hCypA. This indicates the reasonableness of the binding model of the inhibitors with hCypA. Considering all these results together with the valuable clues of binding from references published recently, reasonable pharmacophore elements have been suggested, demonstrating that the 3D-QSAR models about peptide analogue inhibitors are expected to be further employed in predicting activities of the novel compounds for inhibiting hCypA.     

Pharmacophore-based 3D-QSAR study of fungal chitin synthase inhibitors

To design new compounds with enhanced activity against the fungal chitin synthase enzyme, 3D-pharmacophore models were generated and QSAR study was carried out on 44 novel homoallylamines and related compounds, nikkomycin, maleimide, chalcones, and quinolin-2-one derivatives. A three-point pharmacophore with two hydrophobic (H) and one aromatic ring (R) as pharmacophore features was developed by PHASE module of Schrodinger molecular modeling suite. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R ² of 0.84 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q ² of 0.63 and Pearson-R value of 0.82 for a randomly chosen test set of nine compounds. The 3D-QSAR model explains the structure–activity relationship of these compounds which may help in the design and development of novel fungal chitin synthase inhibitors.     

Structural insights into human 5-lipoxygenase inhibition: combined ligand-based and target-based approach

The human 5-LOX enzyme and its interaction with competitive inhibitors were investigated by means of a combined ligand-based and target-based approach. First, a pharmacophore model was generated for 16 non redox 5-LOX inhibitors with Catalyst (HipHop module). It includes two hydrophobic groups, an aromatic ring, and two hydrogen bond acceptors. The 3D structure of human 5-LOX was then modeled based on the crystal structure of rabbit 15-LOX, and the binding modes of representative ligands were studied by molecular docking. Confrontation of the docking results with the pharmacophore model allowed the weighting of the pharmacophoric features and the integration of structural information. This led to the proposal of an interaction model inside the 5-LOX active site, consisting of four major and two secondary interaction points: on one hand, two hydrophobic groups, an aromatic ring, and a hydrogen bond acceptor, and, on the other hand, an acidic moiety and an additional hydrogen bond acceptor.     

作用于秋水仙碱结合位点的微管蛋白抑制剂药效团的构建

目的:构建作用于秋水仙碱结合位点的微管蛋白抑制剂药效团模型;初步分析该类抑制剂与靶点的作用方式。方法:使用Discovery Studio软件中的HypoGen模块对训练集进行药效团模型的构建。结果:最佳药效团模型的线性回归相关系数最高(0.981),包含1个氢键给体和4个疏水中心,利用测试集验证了该药效团模型的活性预测能力;通过分子与活性位点的对接得到了活性最好的两个化合物与此结合位点的具体作用方式。结论:得到的药效团模型具有较好的预测能力,有利于设计和开发新型微管蛋白抑制剂。