Insights from ligand and structure based methods in virtual screening of selective Ni-peptide deformylase inhibitors.

In recent years, there has been a growing interest in developing bacterial peptide deformylase (PDF) inhibitors as novel antibiotics. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse PDF inhibitors which is useful for designing of potential antibiotics. Twenty one structurally diverse compounds were considered for the generation of quantitative pharmacophore model using HypoGen of Catalyst, further model was validated using 78 compounds. Pharmacophore model demonstrated the importance of two acceptors, one donor and one hydrophobic feature toward the biological activity. The inhibitors were also docked into the binding site of PDF to comprehend the structural insights of the active site. Combination of ligand and structure based methods were used to find the potential antibiotics.

Molecular modeling evaluation of non-steroidal aromatase inhibitors.

A recent discovery of aromatase crystal structure triggered the efforts to design novel aromatase inhibitors for breast cancer therapy. While correlating docking scores with inhibitory potencies of known ligands, feeble robustness of scoring functions toward prediction was observed. This prompted us to develop new prediction models using stepwise regression analysis based on consensus of different docking and their scoring methods (GOLD, LIGANDFIT, and GLIDE). Quantitative structure-activity relationships were developed between the aromatase inhibitory activity (pIC(50) ) of flavonoid derivatives (n=39) and docking scores and docking descriptors. QSAR models have been validated internally [using leave-one-out cross-validated r(2)(cv) (LOO-Q2))] and externally to ensure the predictive capacity of the models. Model 2 [M2] developed using consensus of docking scores of scoring functions viz. ASP, potential of mean force and DOCK Score (r(2)(cv)=0.850, r(2) = 0.870, r(2)(pred) = 0.633, RMSE = 0.363 µm, r(2)(m(test)) =0.831, r(2)(m(overall)) =0.832) was found to be better in predicting aromatase inhibitory potency (pIC(50) ) compared to the Model 1 [M1] based on docking descriptors (r(2)(cv)= 0.848, r(2) = 0.825, r(2)(pred) =0.788, RMSE=0.421µm, r(2)(m(test)) =0.808, r(2)(m(overall)) =0.821). It has been observed that the natural flavonoids and their derivatives were less potent compared to these scaffolds with imidazolylmethyl substitution owing to the interaction of nitrogen atom of the imidazole ring toward the heme (Fe(3+) ) of the aromatase. Results confirm the potential of our methodology for the design of new potent non-steroidal aromatase inhibitors.

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3.

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149)and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM)and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.

Three dimensional pharmacophore modelling for c-Kit receptor tyrosine kinase inhibitors.

Three Dimensional Pharmacophore model was developed based on 24 currently available c-Kit inhibitors. The best pharmacophore model (Hypo1) consists of four features namely one hydrogen bond acceptor, one hydrophobic point and two ring aromatics. The correlation coefficient, root mean square deviation and cost difference were 0.973, 0.729 and 100.989 respectively, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of 40 known c-Kit inhibitors in our test set with a correlation coefficient of 0.709 with a cross validation of 95% confidence level. Accordingly, our model is reliable in identifying new compounds as c-Kit inhibitors.

Design, synthesis, and discovery of novel non-peptide inhibitor of Caspase-3 using ligand based and structure based virtual screening approach.

Caspase-3 belonging to a family of cysteine proteases is main executioner of apoptotic cascade pathway. The inhibitors of this protein are useful in the treatment of cardiomyopathy and neurodegenerative diseases. For the discovery of novel Caspase-3 non-peptide inhibitors from Maybridge database, ligand based and structure based virtual screening methods were used. Quantitative 3D pharmacophore models were generated using 25 known inhibitors of Caspase-3 and it was used as initial screen to retrieve the hits from the database. These compounds with high estimated activity were analyzed for drug like properties and docking studies were performed, to study the interaction between new hits and active site. One of the hits (AW01208), with good predictions was selected for synthesis and biological screening. This compound showed an inhibition activity against Caspase-3 in SKNH cell lines.

Three-dimensional quantitative structure-activity relationship studies on c-Src inhibitors based on different docking methods.

c-Src kinase play an important role in cell growth and differentiation and its inhibitors can be useful for the treatment of various diseases, including cancer, osteoporosis, and metastatic bone disease. Three dimensional quantitative structure-activity relationship (3D-QSAR) studies were carried out on quinazolin derivatives inhibiting c-Src kinase. Molecular field analysis (MFA) models with four different alignment techniques, namely, GLIDE, GOLD, LIGANDFIT and Least squares based methods were developed. glide based MFA model showed better results (Leave one out cross validation correlation coefficient r(2)(cv) = 0.923 and non-cross validation correlation coefficient r(2)= 0.958) when compared with other models. These results help us to understand the nature of descriptors required for activity of these compounds and thereby provide guidelines to design novel and potent c-Src kinase inhibitors.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of various benzodiazepine analogues of gamma-secretase inhibitors.

A 3D QSAR analysis has been performed on a series of 67 benzodiazepine analogues reported as gamma-secretase inhibitors using molecular field analysis (MFA), with G/PLS to predict steric and electrostatic molecular field interaction for the activity. The MFA study was carried out using a training set of 54 compounds. The predictive ability of model developed was assessed using a test set of 13 compounds (r(2) pred as high as 0.729). The analyzed MFA model has demonstrated a good fit, having r(2) value of 0.858 and cross validated coefficient, r(2)cv value as 0.790. The analysis of the best MFA model provided insight into possible modification of the molecules for better activity.

Quantitative structure activity relationship and pharmacophore studies of adenosine receptor A2B inhibitors.

Adenosine receptor A2B (ADoR A2B) is an important G protein-coupled receptor (GPCR) of the rhodopsin family, and plays a pivotal role in gastrointestinal, neurological and hypersensitive disorders. QSAR and pharmacophore studies were carried out using 63 ADoR A2B inhibitor molecules to characterize molecular features and structural requirements for biological interaction. QSAR modelling using genetic algorithm- partial least squares (G/PLS) method identified molecular shape, size electrophilicity and conformational flexibility as important descriptors for these compounds affinity to the receptor. Further analysis of pharmacophore model revealed hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), hydrophobic aliphatic (HY-ala) and hydrophobic aromatic (HY-aro) as the crucial molecular features that predict binding affinity of these compounds to ADoR A2B. These observations provide important insights to the rationale development of novel and potent compounds against ADoR A2B.

Strategies for generating less toxic P-selectin inhibitors: pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits.

This paper describes the generation of ligand-based as well as structure-based models and virtual screening of less toxic P-selectin receptor inhibitors. Ligand-based model, 3D-pharmacophore was generated using 27 quinoline salicylic acid compounds and is used to retrieve the actives of P-selectin. This model contains three hydrogen bond acceptors (HBA), two ring aromatics (RA) and one hydrophobic feature (HY). To remove the toxic hits from the screened molecules, a counter pharmacophore model was generated using inhibitors of dihydrooratate dehydrogenase (DHOD), an important enzyme involved in nucleic acid synthesis, whose inhibition leads to toxic effects. Structure-based models were generated by docking and scoring of inhibitors against P-selectin receptor, to remove the false positives committed by pharmacophore screening. The combination of these ligand-based and structure-based virtual screening models were used to screen a commercial database containing 538,000 compounds.

Virtual screening of cathepsin k inhibitors using docking and pharmacophore models.

Cathepsin K is a lysosomal cysteine protease that is highly and selectively expressed in osteoclasts, the cells which degrade bone during the continuous cycle of bone degradation and formation. Inhibition of cathepsin K represents a potential therapeutic approach for diseases characterized by excessive bone resorption such as osteoporosis. In order to elucidate the essential structural features for cathepsin K, a three-dimensional pharmacophore hypotheses were built on the basis of a set of known cathepsin K inhibitors selected from the literature using catalyst program. Several methods are used in validation of pharmacophore hypothesis were presented, and the fourth hypothesis (Hypo4) was considered to be the best pharmacophore hypothesis which has a correlation coefficient of 0.944 with training set and has high prediction of activity for a set of 30 test molecules with correlation of 0.909. The model (Hypo4) was then employed as 3D search query to screen the Maybridge database containing 59,000 compounds, to discover novel and highly potent ligands. For analyzing intermolecular interactions between protein and ligand, all the molecules were docked using Glide software. The result showed that the type and spatial location of chemical features encoded in the pharmacophore are in full agreement with the enzyme inhibitor interaction pattern identified from molecular docking.

Pharmacophore modeling of diverse classes of p38 MAP kinase inhibitors.

Mitogen-activated protein (MAP) p38 kinase is a serine-threonine protein kinase and its inhibitors are useful in the treatment of inflammatory diseases. Pharmacophore models were developed using HypoGen program of Catalyst with diverse classes of p38 MAP kinase inhibitors. The best pharmacophore hypothesis (Hypo1) with hydrogen-bond acceptor (HBA), hydrophobic (HY), hydrogen-bond donor (HBD), and ring aromatic (RA) as features has correlation coefficient of 0.959, root mean square deviation (RMSD) of 1.069 and configuration cost of 14.536. The model was validated using test set containing 119 compounds and had high correlation coefficient of 0.851. The results demonstrate that results obtained in this study can be considered to be useful and reliable tools in identifying structurally diverse compounds with desired biological activity.

3D-QSAR and molecular docking studies of 1,3,5-triazene-2,4-diamine derivatives against r-RNA: novel bacterial translation inhibitors.

Aminoglycoside mimetics inhibit bacterial translation by interfering with the ribosomal decoding site. To elucidate the structural properties of these compounds important for antibacterial activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to a set of 56 aminoglycosides mimetics. The successful CoMFA model yielded the leave-one-out (LOO) cross-validated correlation coefficient (q(2)) of 0.708 and a non-cross-validated correlation coefficient (r(2)) of 0.967. CoMSIA model gave q(2)=0.556 and r(2)=0.935. The CoMFA and CoMSIA models were validated with 36 test set compounds and showed a good r(pred)(2) of 0.624 and 0.640, respectively. Contour maps of the two QSAR approaches show that electronic effects dominantly determine the binding affinities. These obtained results were agreed well with the experimental observations and docking studies. The results not only lead to a better understanding of structural requirements of bacterial translation inhibitors but also can help in the design of novel bacterial translation inhibitors.

Three-dimensional quantitative structure-activity relationship modeling of gamma-secretase inhibitors using molecular field analysis.

Three-dimensional quantitative structure-activity relationship analysis of a set of 79 analogs of gamma-secretase inhibitors was performed by molecular field analysis with genetic partial least squares method to investigate the substitutional requirements to derive a predictive model and for the favorable receptor-drug interaction that may be used for the designing of a novel gamma-secretase inhibitors. The developed molecular field analysis model has a good fit, with r2 value of 0.952 and cross-validated coefficient, r2(cv), value of 0.931. Predictive ability of the developed model was further assessed using test set of 19 compounds and r2(pred) was found to be 0.665.

CoMFA and docking studies on triazolopyridine oxazole derivatives as p38 MAP kinase inhibitors.

With the objective to design new chemical entities with enhanced inhibitory potencies against p38 MAP alpha kinase, the 3D-QSAR and Comparative Molecular Field Analysis (CoMFA) studies were carried out on triazolopyridine oxazole compounds as inhibitors of these kinase is presented here. The developed model gave q(2) value of 0.707 and r(2) value of 0.942 for CoMFA. The high leave-one-out (LOO) cross-validated correlation coefficient q(2) reveals that the model is a useful tool for the prediction of test set of 19 compounds that were not included in the training set of 55 compounds. The results not only lead to better understanding of structural requirements of p38 alpha inhibitors but also can help in the design of new potent inhibitors. The binding mode of the compounds at the active site of p38 MAP alpha kinase was explored using Glide docking program and hydrogen-bonding interactions were observed between the inhibitors and the target. The details of amino acid interactions of the active site are discussed briefly and correlated with the contour plots.

3D-QSAR studies of triazafluorenone inhibitors of metabotropic glutamate receptor subtype 1.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 46 triazafluorenone derivatives, inhibiting metabotropic glutamate receptor subtype 1 (mGluR1). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 35 compounds and its predictive ability was assessed using a test set of 11 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.908 and 0.798 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)(2) values of 0.707 and 0.580 for MFA and RSA, respectively. The results of 3D-QSAR methodologies provide a powerful tool directed to the design of novel and selective triazafluorenone inhibitors.

Exploring three-dimensional quantitative structural activity relationship (3D-QSAR) analysis of SCH 66336 (Sarasar) analogues of farnesyltransferase inhibitors.

3D-QSAR analysis of a set of 37 analogues of SCH 66336 (Sarasar) was performed by most widely used computational tool, molecular field analysis (MFA) to investigate the substitutional requirements for the favorable receptor-drug interaction and to derive a predictive model that may be used for the designing of a novel farnesyltransferase inhibitors (FTIs). Regression analysis was carried out using genetic partial least squares (G/PLS) method. A highly predictive and statistically significant model was generated. The predictive ability of the model developed was assessed using a test set of six compounds (r(2)(pred) as high as 0.791). The analyzed MFA model has demonstrated a good fit, having r(2) value of 0.967 and cross-validated coefficient r(2)(cv) value as 0.921.

Comparative molecular field analysis of quinoline derivatives as selective and noncompetitive mGluR1 antagonists.

A 3D- QSAR model os Comparative Molecular Field Analysib (CoMFA) of 45 quinoline derivatives as metaborropic glutamate receptor subtype 1 (mGluR1) inhibitors wew investigated. The CoMFA analysis provided a model with q(2) value of 0.827 and r(2) value of 0.990, in which q(2) value of 0.827 and an r(2) value of 0.990, in which the good correlation between the inhibitory activities and the steric and electrostatic molecular field around the analoques was observed. The predictive ability of the models was validated using the set of 12 compounds that were not included in the training set of 33 compounds. These results provided further understanding of the relationship between the structural features of quinolone derivatives and its activities, which should be applicable to design and find new potential mGluR1 inhibitors.

Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors.

Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. 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. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.