Muttineni Ravikumar

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 (pIC50) of flavonoid derivatives (n = 39) and docking scores and docking descriptors. QSAR models have been validated internally [using leave‐one‐out cross‐validated )] 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 ( = 0.850, r2 = 0.870,  = 0.633, RMSE = 0.363 μm, r2m(test) = 0.831, r2m(overall) = 0.832) was found to be better in predicting aromatase inhibitory potency (pIC50) compared to the Model 1 [M1] based on docking descriptors ( = 0.848, r2  =  0.825,  = 0.788, RMSE = 0.421 μm, r2m(test) = 0.808, r2m(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 (Fe3+) of the aromatase. Results confirm the potential of our methodology for the design of new potent non‐steroidal aromatase inhibitors.

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.

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.

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.

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.

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.

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.

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.

Research Article: 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 q2 value of 0.827 and r2 value of 0.990, in which q2 value of 0.827 and an r2 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.

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 q2 value of 0.827 and r2 value of 0.990, in which q2 value of 0.827 and an r2 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.