Understanding the antitumor activity of novel tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors using CoMFA and CoMSIA

New molecular insights into the tyrosyl-tRNA synthase inhibitors: CoMFA, CoMSIA analyses and molecular docking studies

Drug resistance caused by excessive and indiscriminate antibiotic usage has become a serious public health problem. The need of finding new antibacterial drugs is more urgent than ever before. Tyrosyl-tRNA synthase was proved to be a potent target in combating drug-resistant bacteria. In silico methodologies including molecular docking and 3D-QSAR were employed to investigate a series of newly reported tyrosyl-tRNA synthase inhibitors of furanone derivatives. Both internal and external cross-validation were conducted to obtain high predictive and satisfactory CoMFA model (q 2 = 0.611, r 2pred  = 0.933, r 2m  = 0.954) and CoMSIA model (q 2 = 0.546, r 2pred  = 0.959, r 2m  = 0.923). Docking results, which correspond with CoMFA/CoMSIA contour maps, gave the information for interactive mode exploration. Ten new molecules designed on the basis of QSAR and docking models have been predicted more potent than the most active compound 3-(4-hydroxyphenyl)-4-(2-morpholinoethoxy)furan-2(5H)-one (15) in the literatures. The results expand our understanding of furanones as inhibitors of tyrosyl-tRNA synthase and could be helpful in rationally designing of new analogs with more potent inhibitory activities.

Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors

Neuraminidase (NA) is a major glycoprotein of influenza virus which is essential for viral infection. It offers a potential target for antiviral drug design and discovery. To develop novel potent neuraminidase inhibitors (NAI), Surflex-Dock was employed to dock 40 hydrophobic p-aminosalicylic acid derivatives into the active site of NA. The 3D-quantitative structure-activity relationship studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were carried out on 40 molecules. Both CoMFA (q(2) = 0.628, r(2) = 0.697) and CoMSIA (q(2) = 0.746, r(2) = 0.816) gave reasonable results. A preliminary pharmacokinetic profile of these NAI was also performed on the basis of Volsurf predictions. The results obtained from this study will be useful in the design of novel potent NAI.

CoMFA and CoMSIA analysis of tetrahydroquinolines as potential antimalarial agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used on a dataset of compounds, some of them having been reported to inhibit Plasmodium falciparum protein, farnesyltransferase. The co-crystal structure of the lead molecule, BMS-214662 bound to Rat-PFT was used as a template. CoMFA yielded a good model, with r²(ncv) = 0.909, r²(cv) = 0.617 and was validated using an external set r²(pred) = 0.748). It compared favourably with CoMSIA. In the CoMFA model the steric and electrostatic fields exerted an almost equal influence on activity. The contour maps indicated the necessity for sterically large electropositive groups with electronegative tail to be present in these molecules for activity, and sterically large electronegative moieties on the sulfonamide linker. By incorporating these features some new compounds have been identified for further investigation.

Development of highly predictive 3D-QSAR CoMSIA models for anthraquinone and acridone derivatives as telomerase inhibitors targeting G-quadruplex DNA telomere

G-quadruplex structures of DNA represent a potentially useful target for anticancer drugs. Telomerase enzyme, involved in immortalization of cancer cells is inhibited by stabilization of G-quadruplex at the ends of chromosomes. Anthraquinone and acridone derivatives are promising G-quadruplex ligands as telomerase inhibitors. So far, optimization of these ligands remained hampered due to the lack of creditable quantitative structure-activity relationships. To understand the structural basis of anthraquinone and acridone derivatives, a predictive 3D-QSAR model has been developed for the first time for telomerase inhibitory activity of G4 ligands, employing comparative molecular similarity indices analysis (CoMSIA). Considering the proposition that the basic nitrogens in these compounds should exist in protonated form at physiological pH the protonated forms of the reported compounds were analyzed and investigated. The QSAR model from conformational template Conf1 exhibited best correlative and predictive properties. The actual predictive abilities of the QSAR model were thoroughly validated through an external validation test set of compounds. The statistics indicate a significantly high prediction power of the best model (r(2), 0.721), supporting the proposed molecular mechanism of DNA G-quadruplex ligands.

Three-dimensional quantitative structure-farnesyltransferase inhibition analysis for some diaminobenzophenones

A 3D-QSAR investigation of 95 diaminobenzophenone yeast farnesyltransferase (FT) inhibitors selected from the work of Schlitzer et al. showed that steric, electrostatic, and hydrophobic properties play key roles in the bioactivity of the series. A CoMFA/CoMSIA combined model using the steric and electrostatic fields of CoMFA together with the hydrophobic field of CoMSIA showed significant improvement in prediction compared with the CoMFA steric and electrostatic fields model. The similarity of the 3D-QSAR field maps for yeast FT inhibition activity (from this work) and for antimalarial activity data (from previous work) and the correlation between those activities are discussed.

Imidazole-containing farnesyltransferase inhibitors: 3D quantitative structure-activity relationships and molecular docking

One of the most promising anticancer and recent antimalarial targets is the heterodimeric zinc-containing protein farnesyltransferase (FT). In this work, we studied a highly diverse series of 192 Abbott-initiated imidazole-containing compounds and their FT inhibitory activities using 3D-QSAR and docking, in order to gain understanding of the interaction of these inhibitors with FT to aid development of a rational strategy for further lead optimization. We report several highly significant and predictive CoMFA and CoMSIA models. The best model, composed of CoMFA steric and electrostatic fields combined with CoMSIA hydrophobic and H-bond acceptor fields, had r (2) = 0.878, q (2) = 0.630, and r (pred) (2) = 0.614. Docking studies on the statistical outliers revealed that some of them had a different binding mode in the FT active site based on steric bulk and available active site space, explaining why the predicted activities differed from the experimental activities.

Development of predictive 3D-QSAR CoMFA and CoMSIA models for beta-aminohydroxamic acid-derived tumor necrosis factor-alpha converting enzyme inhibitors

A three-dimensional quantitative structure-activity relationship study was performed on a series of beta-aminohydroxamic acid-derived tumor necrosis factor-alpha converting enzyme inhibitors employing comparative molecular field analysis and comparative molecular similarity indices analysis techniques to investigate the structural requirements for the inhibitors, and derive a predictive model that could be used for the design of novel tumor necrosis factor-alpha converting enzyme inhibitors. log P was used as an additional descriptor in the comparative molecular field analysis analysis to study the effects of lipophilic parameters on activity. Inclusion of log P did not improve the models significantly. The statistically significant model was established with 45 molecules, which were validated by a test set of 11 compounds. Ligand molecular superimposition on the template structure was performed by the atom-/shape-based root mean square fit and database alignment methods. Docked conformer based alignment (V) yielded the best predictive comparative molecular field analysis model = 0.673, = 0.860, F-value = 86.073, predictive r (2) = 0.642, with two components, standard error of prediction = 0.394 and standard error of estimates = 0.243 while the comparative molecular similarity indices analysis model yielded = 0.635, = 0.858, F-value = 84.451, predictive r (2) = 0.441 with three components, standard error of prediction = 0.393 and standard error of estimates = 0.245. The contour maps obtained from three-dimensional quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular field analysis models exhibited good external predictivity as compared with that of comparative molecular similarity indices analysis models. The model generated through comparative molecular field analysis was validated with the IK-682. The data generated from this study may guide our efforts in designing and predicting the tumor necrosis factor-alpha converting enzyme inhibitory activity of novel molecules.

3D-quantitative structure-activity relationship studies on benzothiadiazepine hydroxamates as inhibitors of tumor necrosis factor-alpha converting enzyme

A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.

QSAR study on tetrahydroquinoline analogues as plasmodium protein farnesyltransferase inhibitors: a comparison of rationales of malarial and mammalian enzyme inhibitory activities for selectivity

The quantitative structure-activity relationships of Plasmodium falciparum and Rat protein farnesyltransferase (PFT) inhibitory activities of 6-cyano-1-(3-methyl-3H-imidazoly-4-ylmethyl)-3-substituted-1,2,3,4-tetrahydroquinoline (THQ) analogues are investigated in order to explore the similarities/deviations between the two enzymes for these analogues. The structure space of a ligand (BMS-214662) bound to Rat-PFT (PDB code 1SA5) has been used as the conformational space of the compounds under investigation. The study has been carried out using the combinatorial protocol in multiple linear regression with several 2D- and 3D-descriptors from molecular operating environment (MOE) representing the physicochemical and electronic features of the compounds. The molecular potential energy and partially charged van der Waals surface areas have taken part in the PFT models. They suggested in favor of molecular arrangement with minimum energy and low positively/negatively charged surfaces for optimum Pf-PFT inhibitory activity. Furthermore, less hydrophobic compounds are preferred for the activity. The Rat-PFT inhibitory activity models suggested in favor of more negatively as well as more positively charged surface area descriptors for the better activity. The PLS analysis carried out on the descriptors of the Pf-PFT and Rat-PFT models suggested that among the parameters, the partially charged surface areas in the range -0.20 to -0.15 (PEOE_VSA-3) and -0.30 to -0.25 (PEOE_VSA-5), hydrophobicity (a_hyd, logP(o/w) and SlogP_VSA4), and electronic energy (PM3_Eele) of the molecules hold promise for modulating the Pf-PFT/R-PFT inhibitory activities of the compounds. This suggested the possibility of modulating the Pf-PFT/R-PFT inhibitory activities and bringing about selectivity in the THQ analogues for the malarial parasite enzyme.

Insights into the structural requirements of farnesyltransferase inhibitors as potential anti-tumor agents based on 3D-QSAR CoMFA and CoMSIA models

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on three different chemical series reported as selective farnesyltransferase (FTase) inhibitors employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) techniques to investigate the structural requirements for substrates and derive a predictive model that may be used for the design of novel FTase inhibitors. Removal of outliers improved the predictive power of models developed for all three structurally diverse classes of compounds. 3D-QSAR models were derived for 3-aminopyrrolidinone derivatives (training set N=38, test set N=7), 2-amino-nicotinonitriles (training set N=46, test set N=13) and 1-aryl-1'-imidazolyl methyl ethers (training set N=35, test set N=5). The CoMFA models with steric and electrostatic fields exhibited r(2)(cv) 0.479-0.803, r(2)(ncv) 0.945-0.993, r(2)(pred) 0.686-0.811. The CoMSIA models displayed r(2)(cv) 0.411-0.814, r(2)(ncv) 0.923-0.984, r(2)(pred) 0.399-0.787. 3D contour maps generated from these models were analyzed individually, which provide the regions in space where interactive fields may influence the activity. The superimposition of contour maps on the active site of farnesyltransferase additionally helps in understanding the structural requirements of these inhibitors. 3D-QSAR models developed may guide our efforts in designing and predicting the FTase inhibitory activity of novel molecules.