Receptor based 3D-QSAR to identify putative binders of Mycobacterium tuberculosis Enoyl acyl carrier protein reductase

In the current study, the applicability and scope of 3D-QSAR models (CoMFA and CoMSIA) to complement virtual screening using 3D pharmacophore and molecular docking is examined and applied to identify potential hits against Mycobacterium tuberculosis Enoyl acyl carrier protein reductase (MtENR). Initially CoMFA and CoMSIA models were developed using series of structurally related arylamides as MtENR inhibitors. Docking studies were employed to position the inhibitors into MtENR active site to derive receptor based 3D-QSAR models. Both CoMFA and CoMSIA yielded significant cross validated q² values of 0.663 and 0.639 and r² values of 0.989 and 0.963, respectively. The statistically significant models were validated by a test set of eight compounds with predictive r² value of 0.882 and 0.875 for CoMFA and CoMSIA. The contour maps from 3D-QSAR models in combination with docked binding structures help to better interpret the structure activity relationship. Integrated with CoMFA and CoMSIA predictive models structure based (3D-pharmacophore and molecular docking) virtual screening have been employed to explore potential hits against MtENR. A representative set of 20 compounds with high predicted IC₅₀ values were sorted out in the present study.     

Interactions of 5-deazapteridine Derivatives with Mycobacterium tuberculosis and with Human Dihydrofolate Reductases

Abstract

There are major differences between the structures of human dihydrofolate reductase (hDHFR) and Mycobacterium tuberculosis dihydrofolate reductase (mtDHFR). These differences may allow us to design more selective mtDHFR inhibitors. In this paper we study the reactions of six different compounds derived from 5-deazapteridine with human and bacterial enzymes. Results suggest that the addition of hydrophobic groups to the aminophenyl ring would increase mtDHFR-inhibitor affinity and selectivity.     

Elucidation of binding mode and three dimensional quantitative structure–activity relationship studies of a novel series of protein kinase B/Akt inhibitors

Protein kinase B (PKB; also known as Akt kinase) is located downstream in the PI-3 kinase pathway. Overexpression and constitutive activation of PKB/Akt leads to human prostate, breast and ovarian carcinomas. A series of 69 PKB/Akt inhibitors were examined to explore their binding modes using FlexX, and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed to provide structural insights into these compounds. CoMFA produced statistically significant results, with cross-validated q ² and non-cross validated correlation r ² coefficients of 0.53 and 0.95, respectively. For CoMSIA, steric, hydrophobic and hydrogen bond acceptor fields jointly yielded ‘leave one out’ q ²  = 0.51 and r ²  = 0.84. The predictive power of CoMFA and CoMSIA was determined using a test set of 13 molecules, which gave correlation coefficients, of 0.58 and 0.62, respectively. Molecular docking revealed that the binding modes of these molecules in the ATP binding sites of the Akt kinase domain were very similar to those of the co-crystallized ligand. The information obtained from 3D contour maps will allow the design of more potent and selective Akt kinase inhibitors.     

Binding conformations and QSAR of CA-4 analogs as tubulin inhibitors

A theoretical study on the binding conformations and the quantitative structure–activity relationship (QSAR) of combretastatin A4 (CA-4) analogs as inhibitors toward tubulin has been carried out using docking analysis and comparative molecular field analysis (CoMFA). The appropriate binding orientations and conformations of these compounds interacting with tubulin were revealed by the docking study; and a 3D-QSAR model showing significant statistical quality and satisfactory predictive ability was established, in which the correlation coefficient (R²) and cross-validation coefficient (q²) were 0.955 and 0.66, respectively. The same model was further applied to predict the pIC₅₀ values for 16 congeneric compounds as external test set, and the predictive correlation coefficient R²_pred reached 0.883. Other tests on additional validations further confirmed the satisfactory predictive power of the model. In this work, it was very interesting to find that the 3D topology structure of the active site of tubulin from the docking analysis was in good agreement with the 3D-QSAR model from CoMFA for this series of compounds. Some key structural factors of the compounds responsible for cytotoxicity were reasonably presented. These theoretical results can offer useful references for understanding the action mechanism and directing the molecular design of this kind of inhibitor with improved activity.     

Docking-based 3D-QSAR (CoMFA,CoMFA-RG,CoMSIA) study on hydroquinoline and thiazinan-4-one derivatives as selective COX-2 inhibitors

A series of 26 selective COX-2 inhibitors which reported previously by our laboratory was selected to generate three-dimensional quantitative structure activity relationship (3D-QSAR) model. Active conformation of each molecule was predicted by docking studies and used for molecular alignment. Activity of 20 molecules as a train set was predicted using three methods including comparative molecular field analysis (CoMFA), CoMFA region focusing (CoMFA-RG) and comparative molecular similarity index analysis (CoMSIA). The best models of CoMFA-RG and CoMSIA revealed correlation coefficients r² of 0.955 and 0.947, the leave one out cross-validation coefficients q² of 0.573 and 0.574, respectively. In addition, CoMFA-RG and CoMSIA models were validated by a test set of six molecules with predicted coefficients r²_pred of 0.644 and 0.799, respectively. Contour maps of generated models provided fruitful information about structural aspect of molecules that affected their COX-2 inhibitory activity. Based on three models results, steric and electrostatic properties are the most important factors in controlling the activity of the molecules. Results of CoMFA-RG and CoMSIA models were utilized to design new molecules. Comparison of experimental and predicted pIC₅₀ values of designed molecules indicated that CoMFA-RG had the more predictive ability.

Communicated by Ramaswamy H. Sarma     

Chemical synthesis and in silico molecular modeling of novel pyrrolyl benzohydrazide derivatives: Their biological evaluation against enoyl ACP reductase (InhA) and Mycobacterium tuberculosis

In efforts to develop new antitubercular agents, we report here the synthesis of a series of novel pyrrole hydrazine derivatives. The molecules were evaluated against inhibitors of InhA, which is one of the key enzymes involved in type II fatty acid biosynthetic pathway of the mycobacterial cell wall as well as inhibitors of Mycobacterium tuberculosis H37Rv. The binding mode of compounds at the active site of enoyl-ACP reductase was explored using the surflex-docking method. The model suggests one or two H-bonding interactions between the compounds and the InhA enzyme. Some compounds exhibited good activities against InhA in addition to promising activities against M. tuberculosis.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

3D-QSAR and molecular modeling studies on 2,3-dideoxy hexenopyranosid-4-uloses as anti-tubercular agents targeting alpha-mannosidase

Ligand-based and structure-based methods were applied in combination to exploit the physicochemical properties of 2,3-dideoxy hex-2-enopyranosid-4-uloses against Mycobacterium tuberculosis H37Rv. Statistically valid 3D-QSAR models with good correlation and predictive power were obtained with CoMFA steric and electrostatic fields (r² = 0.797, q² = 0.589) and CoMSIA with combined steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (r² = 0.867, q² = 0.570) based on training set of 33 molecules with predictive r² of 0.808 and 0.890 for CoMFA and CoMSIA respectively. The results illustrate the requirement of optimal alkyl chain length at C-1 position and acceptor groups along hydroxy methyl substituent of C-6 to enhance the anti-tubercular activity of the 2,3-dideoxy hex-2-enopyranosid-4-uloses while any substitution at C-3 position exert diminishing effect on anti-tubercular activity of these enulosides. Further, homology modeling of M. tuberculosis alpha-mannosidase followed by molecular docking and molecular dynamics simulations on co-complexed models were performed to gain insight into the rationale for binding affinity of selected inhibitors with the target of interest. The comprehensive information obtained from this study will help to better understand the structural basis of biological activity of this class of molecules and guide further design of more potent analogues as anti-tubercular agents.     

Molecular docking guided 3D-QSAR CoMFA analysis of N-4-Pyrimidinyl-1H-indazol-4-amine inhibitors of leukocyte-specific protein tyrosine kinase

Inhibition of leukocyte-specific protein tyrosine kinase (Lck) activity offers one of the approaches for the treatment of T-cell mediated inflammatory disorders including rheumatoid arthritis, transplant rejection and inflammatory bowel disease. To explore the relationship between the structures of the N-4 Pyrimidinyl-1H-indazol-4-amines and their Lck inhibition, 3D-QSAR study using CoMFA analysis have been performed on a dataset of 42 molecules. The bioactive conformation of the template molecule, selected as the most potent molecule 23 from the series was obtained by performing molecular docking at the ATP binding site of Lck, which is then used to build the rest of the molecules in the series. The constructed CoMFA model is robust with of 0.603 and conventional r² of 0.983. The predictive power of the developed model was obtained using a test set of 10 molecules, giving predictive correlation coefficient of 0.921. CoMFA contour analysis was performed to obtain useful information about the structural requirements for the Lck inhibitors which could be utilized in its future design. Figure CoMFA steric contour map. Sterically favored areas (contribution level 80%) are represented by green polyhedra. Sterically disfavored areas (contribution level 20%) are represented by yellow polyhedra. The active molecule 23 shown in capped sticks. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Molecular docking and 3D-QSAR study on 4-(1H-indazol-4-yl) phenylamino and aminopyrazolopyridine urea derivatives as kinase insert domain receptor (KDR) inhibitors

Vascular endothselial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as new promising targets for the design of novel anticancer agents. It is reported that 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives exhibit potent inhibitory activities toward KDR. To investigate how their chemical structures relate to the inhibitory activities and to identify the key structural elements that are required in the rational design of potential drug candidates of this class, molecular docking simulations and three-dimensional quantitative structure-activity relationship (3D-QSAR) methods were performed on 78 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives as KDR inhibitors. Surflex-dock was used to determine the probable binding conformations of all the compounds at the active site of KDR. As a result, multiple hydrophobic and hydrogen-bonding interactions were found to be two predominant factors that may be used to modulate the inhibitory activities. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed based on the docking conformations. The CoMFA model produced statistically significant results with the cross-validated correlation coefficient q² of 0.504 and the non-cross-validated correlation coefficient r² of 0.913. The best CoMSIA model was obtained from the combination of steric, electrostatic and hydrophobic fields. Its q² and r² being 0.595 and 0.947, respectively, indicated that it had higher predictive ability than the CoMFA model. The predictive abilities of the two models were further validated by 14 test compounds, giving the predicted correction coefficients r_pred² of 0.727 for CoMFA and 0.624 for CoMSIA, respectively. In addition, the CoMFA and CoMSIA models were used to guide the design of a series of new inhibitors of this class with predicted excellent activities. Thus, these models may be used as an efficient tool to predict the inhibitory activities and to guide the future rational design of 4-(1H-indazol-4-yl)phenylamino and aminopyrazolopyridine urea derivatives-based novel KDR inhibitors with potent activities.     

3D-QSAR studies of checkpoint kinase 1 inhibitors based on molecular docking and CoMFA

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on a series of substituted 1,4-dihydroindeno[1,2-c]pyrazoles inhibitors, using molecular docking and comparative molecular field analysis (CoMFA). The docking results from GOLD 3.0.1 provide a reliable conformational alignment scheme for the 3D-QSAR model. Based on the docking conformations and alignments, highly predictive CoMFA model was built with cross-validated q ² value of 0.534 and non-cross-validated partial least-squares analysis with the optimum components of six showed a conventional r ² value of 0.911. The predictive ability of this model was validated by the testing set with a conventional r ² value of 0.812. Based on the docking and CoMFA, we have identified some key features of the 1,4-dihydroindeno[1,2-c]pyrazoles derivatives that are responsible for checkpoint kinase 1 inhibitory activity. The analyses may be used to design more potent 1,4-dihydroindeno[1,2-c]pyrazoles derivatives and predict their activity prior to synthesis.     

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA,CoMSIA, HQSAR and molecular docking

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q²), non-cross-validated correlation coefficient (r²), and predictive correlation coefficient (r²_Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r²?=?0.967, q²?=?0.804, r²_Pred?=?0.827); the CoMSIA model had (r²?=?0.963, q²?=?0.752, r²_Pred?=?0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.     

3D-QSAR studies of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 receptor antagonists

3D-QSAR studies on the derivatives of 1-(3,3-diphenylpropyl)-piperidinyl amide and urea as CCR5 receptor antagonists were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule, the most active and pharmacokinetically stable molecule of the series, was obtained by systematic search and used to build structures of the molecules in the dataset. The best predictions for the CCR5-receptor were obtained with the CoMFA standard model (q ² = 0.787, r ² = 0.962) and CoMSIA model combined steric, electrostatic and hydrophobic fields (q ² = 0.809, r ² = 0.951). The predictive ability of CoMFA and CoMSIA were determined using a test set of 12 compounds giving predictive correlation coefficients of 0.855 and 0.83, respectively, indicating good predictive power. Further, the robustness of the model was verified by bootstrapping analysis. The contour maps produced by the CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series. Based on the CoMFA and CoMSIA analysis, we have identified some key features in the series that are responsible for CCR5 antagonistic activity which may be used to design more potent 1-(3,3-diphenylpropyl)-piperidinyl derivatives and predict their activity prior to synthesis. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Pair wise binding affinity: 3D QSAR studies on a set of triazolo [1, 5-a] quinoxalines as antagonists of AMPA and KA receptors

The glutamate receptor system is implicated in the development and maintenance of epileptic seizures and it has been reported that compounds showing high affinity for both AMPA and KA binding sites are more potent anticonvulsants than compounds having selective affinity toward AMPA or KA receptor. These outcomes make such inhibitors future potential antiepileptic drugs. So, the pair wise binding affinity for AMPA and KA receptors inhibition was proposed by using the addition between biological activities of ligands. This approach for evaluation of pair wise binding affinity was exemplified using set of triazolo [1,5-a] quinoxaline for AMPA and KA receptors. The biological activity towards AMPA and KA receptors (expressed as -log IC_5O) was taken as a dependent variable for building CoMFA and CoMSIA models. The resulting models show the ways of increasing binding affinity to both AMPA and KA receptors as potential target for epilepsy. The statistically significant results show that pair wise CoMFA and CoMSIA models are better then individual models. The resulting cross-validated r²_CV value 0.806 for CoMFA is greater then 0.780 for CoMSIA pair wise model. The non-cross validated run giving a coefficient of determination r² value of 0.946 and 0.908 for CoMFA and CoMSIA respectively, provided a good correlation between the observed and computed affinities of the compounds.     

CoMFA and CoMSIA 3D-QSAR studies on S6-(4-nitrobenzyl)mercaptopurine riboside (NBMPR) analogs as inhibitors of human equilibrative nucleoside transporter 1 (hENT1)

3D-QSAR (CoMFA and CoMSIA) studies were performed on human equlibrative nucleoside transporter (hENT1) inhibitors displaying K_i values ranging from 10,000 to 0.7 nM. Both CoMFA and CoMSIA analysis gave reliable models with q² values >0.50 and r² values >0.92. The models have been validated for their stability and robustness using group validation and bootstrapping techniques and for their predictive abilities using an external test set of nine compounds. The high predictive r² values of the test set (0.72 for CoMFA model and 0.74 for CoMSIA model) reveals that the models can prove to be a useful tool for activity prediction of newly designed nucleoside transporter inhibitors. The CoMFA and CoMSIA contour maps identify features important for exhibiting good binding affinities at the transporter, and can thus serve as a useful guide for the design of potential equilibrative nucleoside transporter inhibitors.     

Studies on α(1→5) linked octyl arabinofuranosyl disaccharides for mycobacterial arabinosyl transferase activity

The appearance multi-drug resistant Mycobacterium tuberculosis (MTB) throughout the world has prompted a search for new, safer and more active agents against tuberculosis. Based on studies of the biosynthesis of mycobacterial cell wall polysaccharides, octyl 5-O-(α- -arabinofuranosyl)-α- -arabinofuranoside analogues were synthesized and evaluated as inhibitors for M. tuberculosis and Mycobacterium avium. A cell free assay system has been used for the evaluation of these disaccharides as substrates for mycobacterial arabinosyltransferase activity.     

Simple and rapid method for detection of nitrate reductase activity of Mycobacterium tuberculosis and Mycobacterium canettii grown in the Bactec MGIT960 system

Mycobacterium tuberculosis reduces nitrate very strongly as compared to Mycobacterium bovis and M. bovis BCG. Nitrate reductase, in conjunction with niacin accumulation, constitutes one of the major biochemical tests used in clinical microbiology laboratories to differentiate M. tuberculosis from other members of the M. tuberculosis complex, as well as nontuberculous Mycobacteria. Determination of nitrate reductase activity is currently performed using cultures grown on solid media with a slow detection time and the need for large quantities of bacilli, as otherwise the test is not reliable. Hereby, we propose a nitrate reduction test coupled to Bactec MGIT960 system as a simple, rapid and economic method with a total gain of time of about 3 to 4 weeks over the conventional solid medium. In our study, almost all the M. tuberculosis and Mycobacterium canettii strains gave a strongly positive nitrate reductase result within 1 day of positive detection by the MGIT960 system. In contrast, M. bovis, M. bovis BCG and M. africanum strains remained negative even after 14 days of incubation. The possibility to detect nitrate reductase within 1 to 3 days of a positive culture using MGIT960 opens new perspectives with the possibility of confirming M. tuberculosis — starting directly from pathological specimens.