Three-dimensional quantitative structure-activity relationship (3 D-QSAR) and docking studies on (benzothiazole-2-yl) acetonitrile derivatives as c-Jun N-terminal kinase-3 (JNK3) inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.     

3D-QSAR studies of farnesyltransferase inhibitors: a comparative molecular field analysis approach

3D-QSAR analysis has been performed on a series of previously synthesized benzonitrile derivatives, which were screened as farnesyltransferase inhibitors, using comparative molecular field analysis (CoMFA) with partial least-square fit to predict the steric and electrostatic molecular field interactions for the activity. The CoMFA study was carried out using a training set of 34 compounds. The predictive ability of the model developed was assessed using a test set of eight compounds (r(pred)(2) as high as 0.770). The analyzed 3D-QSAR CoMFA model has demonstrated a good fit, having r(2) value of 0.991 and cross-validated coefficient q(2) value as 0.619. The analysis of CoMFA contour maps provided insight into the possible modification of the molecules for better activity.     

Structural insights of PA-824 derivatives: ligand-based 3D-QSAR study and design of novel PA824 derivatives as anti-tubercular agents

Abstract

With the purpose of designing novel chemical entities with improved inhibitory potencies against drug-resistant Mycobacterium tuberculosis, the 3D- quantitative structure–activity relationship (QSAR) studies were carried out on biphenyl analogs of the tuberculosis (TB) drug, PA-824. Anti-mycobacterial activity (MABA) was considered for the 3D-QSAR studies using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The best CoMFA and CoMSIA models were found statistically significant with cross-validated coefficients (q²) of 0.784 and 0.768, respectively, and conventional coefficients (r²) of 0.823 and 0.981, respectively. The cross-validated and the external validation results revealed that both the CoMFA and CoMSIA models possesses high accommodating capacities and they would be reliable for predicting the pMIC values of new PA-824 derivatives. Based on the models and structural insights, a series of new PA-824 derivatives were designed and the anti-mycobacterial activities of the designed compounds were predicted based on the best 3D-QSAR model. The predicted data results suggest the designed compounds are more potent than existed ones.     

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r2, q2(loo) and r2(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r2(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.     

Synthesis and biological validation of novel pyrazole derivatives with anticancer activity guided by 3D-QSAR analysis

Using literature data on anticancer activity of pyrazole derivatives, 3D-QSAR models were developed and 3D-QSAR analysis was performed. The 3D-QSAR analysis enabled identification of molecular properties that have the highest impact on antitumor activity against lung cancer cells. The results of 3D-QSAR analysis were taken into account while new compounds were designed. Obtained 3D-QSAR models were used for prediction of activity of new compounds. In this way, design of new compounds was guided by 3D-QSAR analysis which was performed on literature data. Ten new pyrazole derivatives were synthesised and their antitumor activities against A549 and NCIH23 lung cancer cells were validated. In order to obtain full profile of anticancer activity, cells viability (MTS) assays were combined with cell proliferation (BrdU) assays which measure actively dividing cells in treated sample. Experimental measurements showed good agreement between predicted and measured activities for majority of compounds. Also, anticancer activities of new pyrazole derivatives pointed to the chemical groups that can be useful in designing antitumor molecules. Substitution of hydrazine linker with rigid, 1,2,4-oxadiazole moiety resulted in compound 10, which has low (if any) cytotoxic activity and high potential cytostatic activity. Therefore, compound 10 presents a good starting point for design of new, more potent and safer anticancer therapeutics.     

Understanding the structure-activity and structure-selectivity correlation of cyclic guanine derivatives as phosphodiesterase-5 inhibitors by molecular docking, CoMFA and CoMSIA analyses

Molecular docking and 3D-QSAR analyses were performed to understand how PDE5 and PDE6 interact with a series of (49) cyclic guanine derivatives. Using the conformations of the compounds revealed by molecular docking, CoMFA and CoMSIA analyses resulted in the first quantitative structure-activity relationship (QSAR) and first quantitative structure-selectivity relationship (QSSR) models (with high cross-validated correlation coefficient q(2) and conventional correlation coefficient r(2) values) for predicting the inhibitory activity against PDE5 and the selectivity against PDE6. The high q(2) and r(2) values, along with further testing, indicate that the obtained 3D-QSAR and 3D-QSSR models will be valuable in predicting both the inhibitory activity and selectivity of cyclic guanine derivatives for these protein targets. A set of 3D contour plots drawn based on the 3D-QSAR and 3D-QSSR models reveal some useful clues to improve both the activity and selectivity by modifying structures of the compounds. It has been demonstrated that both the steric and electrostatic factors should appropriately be taken into account in future rational design and development of more active and more selective PDE5 inhibitors for the therapeutic treatment of erectile dysfunction (ED).     

3D-QSAR and molecular docking studies of benzaldehyde thiosemicarbazone, benzaldehyde, benzoic acid, and their derivatives as phenoloxidase inhibitors

Phenoloxidase (PO), also known as tyrosinase, is a key enzyme in insect development, responsible for catalyzing the hydroxylation of tyrosine into o-diphenols and the oxidation of o-diphenols into o-quinones. Inhibition of PO may provide a basis for novel environmentally friendly insecticides. In the present study, we determined the inhibitory activities and IC50 values of 57 compounds belonging to the benzaldehyde thiosemicarbazone, benzaldehyde, and benzoic acid families against phenoloxidase from Pieris rapae (Lepidoptera) larvae. In addition, the inhibitory kinetics of 4-butylbenzaldehyde thiosemicarbazone against PO was measured in air-saturated solutions for the oxidation of L-3,4-dihydroxyphenylalanine (L-DOPA). The results indicated that the compound is a reversible noncompetitive inhibitor. The bioactivity results were used to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models using two molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). After carrying out superimposition using common substructure-based alignment, robust and predictive 3D-QSAR models were obtained from CoMFA (q2=0.926, r2=0.986) and CoMSIA (q2=0.933, r2=0.984) with six optimum components. The 3D-QSAR model built here will provide hints for the design of novel PO inhibitors. The molecular interactions between the ligands and the target were studied using a flexible docking method (FlexX). The best scored candidates were docked flexibly, and the interaction between the representative compound 4-butylbenzaldehyde thiosemicarbazone and the active site was elucidated in detail.     

The 3D-QSAR study of antitumor arylsulfonylimidazolidinone derivatives by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies for a series of arylsulfonylimidazolidinone derivatives having antitumor activity were conducted using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The in vitro cytotoxicity against human lung carcinoma (A549) exhibited a strong correlation with steric and electrostatic factors of the molecules. Four different types of models have been built using CoMFA and CoMSIA method with AM1 charge or Gasteiger-Huckel charge. By comparison of the statistical results of these models, model I obtained by CoMFA study with AM1 showed the best predictability of the antitumor activities based on the cross-validated value (0.642), conventional r2 (0.981), standard error of estimate (0.106) and PRESS value (0.170).     

3D-QSAR studies on c-Src kinase inhibitors and docking analyses of a potent dual kinase inhibitor of c-Src and c-Abl kinases

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses were carried out on quinazoline, quinoline, and cyanoquinoline derivatives inhibiting c-Src kinase. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were developed. The conventional r2 values for CoMFA and CoMSIA are 0.93 and 0.89, respectively. In addition, a homology model of c-Src kinase with the activation loop resembling the active conformation was constructed using the crystal structure of the kinase domain of Lck. The ATP binding pocket of the active form of c-Src is similar to that of the c-Abl kinase in which the activation loop resembles that of an active form. One of the potent c-Src and c-Abl dual kinase inhibitors (77 or SKI-606) was docked inside the active sites of both c-Src and c-Abl. The orientation and hydrogen bonding interactions of 77 are similar in both kinases. The results of 3D-QSAR analyses and structure based studies will be useful for the design of novel c-Src and c-Abl dual kinase inhibitors.     

Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors

Tumour progression locus-2 (Tpl2) is a serine/threonine kinase, which regulates the expression of tumour necrosis factor α. The article describes the development of a robust pharmacophore model and the investigation of structure-activity relationship analysis of quinoline-3-carbonitrile derivatives reported for Tpl2 kinase inhibition. A five point pharmacophore model (ADRRR) was developed and used to derive a predictive atom-based 3-dimensional quantitative structure activity relationship (3D-QSAR) model. The obtained 3D-QSAR model has an excellent correlation coefficient value (r(2)= 0.96), Fisher ratio (F = 131.9) and exhibited good predictive power (q(2) = 0.79). The QSAR model suggests that the inclusion of hydrophobic substituents will enhance the Tpl2 kinase inhibition. In addition, H-bond donating groups, negative ionic groups and electron withdrawing groups positively contribute to the Tpl2 kinase inhibition. Further, pharmacophoric model was validated by the receiver operating characteristic curve analysis and was employed for virtual screening to identify six potential Tpl2 kinase inhibitors. The findings of this study provide a set of guidelines for designing compounds with better Tpl2 kinase inhibitory potency.     

CoMFA and CoMSIA 3D-QSAR analysis of DMDP derivatives as anti-cancer agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on three dimensional quantitative structure-activity relationship (3D-QSAR) studies were conducted on a series (78 compounds) of 2, 4-diamino-5-methyl-5-deazapteridine (DMDP) derivatives as potent anticancer agents. The best prediction were obtained with a CoMFA standard model (q(2) = 0.530, r(2) = 0.903) and with CoMSIA combined steric, electrostatic, hydrophobic and hydrogen bond donor fields (q(2) = 0.548, r(2) = 0.909). Both models were validated by a test set of ten compounds producing very good predictive r(2) values of 0.935 and 0.842, respectively. CoMFA and CoMSIA contour maps were then used to analyze the structural features of ligands to account for the activity in terms of positively contributing physiochemical properties such as steric, electrostatic, hydrophobic and hydrogen bond donor fields. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to the biological activity in this series of analogs. This study suggests that the highly electropositive substituents with low steric tolerance are required at 5 position of the pteridine ring and bulky electronegatve substituents are required at the meta-position of the phenyl ring. The information obtained from CoMFA and CoMSIA 3-D contour maps can be used for the design of deazapteridine-based analogs as anticancer agents.     

Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists

Molecular modeling and 3D-QSAR studies were performed on 31 indolomorphinan derivatives to evaluate their antagonistic behaviors on kappa opioid receptor and provide information for further modification of this kind of compounds. Best predictions were obtained with CoMFA standard model (q2 = 0.693, N = 4, r2 = 0.900) and CoMSIA combined model (q2 = 0.617, N = 4, r2 = 0.904). Both models were further validated by an external test set of eight compounds with satisfactory predictions: r2 = 0.607 for CoMFA and r2 = 0.701 for CoMSIA. In addition, the 3D structure of human kappa opioid receptor was constructed based on the crystal structure of bovine rhodopsin, and the CoMSIA contour plots were then mapped into the structural model of kappa opioid receptor-GNTI complex to identify key residues, which might account for kappa antagonist potency and selectivity. The roles of nonconserved Glu297 and conserved Lys227 of human kappa opioid receptor were then discussed.     

Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking studies were carried out to explore the binding of 73 inhibitors to dipeptidyl peptidase IV (DPP-IV), and to construct highly predictive 3D-QSAR models using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The negative logarithm of IC₅₀ (pIC₅₀) was used as the biological activity in the 3D-QSAR study. The CoMFA model was developed by steric and electrostatic field methods, and leave-one-out cross-validated partial least squares analysis yielded a cross-validated value (r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} ) of 0.759. Three CoMSIA models developed by different combinations of steric, electrostatic, hydrophobic and hydrogen-bond fields yielded significant r_cv² {\hbox{r}}_{{\rm{cv}}}^{\rm{2}} values of 0.750, 0.708 and 0.694, respectively. The CoMFA and CoMSIA models were validated by a structurally diversified test set of 18 compounds. All of the test compounds were predicted accurately using these models. The mean and standard deviation of prediction errors were within 0.33 and 0.26 for all models. Analysis of CoMFA and CoMSIA contour maps helped identify the structural requirements of inhibitors, with implications for the design of the next generation of DPP-IV inhibitors for the treatment of type 2 diabetes.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r(2) (q(2))=0.651, non-cross-validated r(2)=0.980, standard error of estimate (s)=0.207, F=129.3. For the optimal CoMSIA model the following values were found: q(2)=0.619, r(2)=0.994, s=0.104, F=372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.     

3D-QSAR study of Chk1 kinase inhibitors based on docking

Checkpoint kinase 1 (Chk1), a kind of a serine/threonine protein kinase, plays a significant role in DNA damage-induced checkpoints. Chk1 inhibitors have been demonstrated to abrogate the S and G2 checkpoints and disrupt the DNA repair process, which results in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain at the G1 phase via p53 to repair their DNA damages, and are less influenced by the abrogation of S and G2 checkpoint. Therefore, selective inhibitors of Chk1 may be of great therapeutic value in cancer treatment. In this paper, in order to understand the structure-activity relationship of macro-cyclic urea Chk1 inhibitors, a study combined molecular docking and 3D-QSAR modeling was carried out, which resulted in two substructure-based 3D-QSAR models, including the CoMFA model (r(2), 0.873; q(2), 0.572) and CoMSIA model (r(2), 0.897; q(2), 0.599). The detailed microscopic structures of Chk1 binding with inhibitors were performed by molecular docking. Two docking based 3D-QSAR models were developed (CoMFA with r(2), 0.887; q(2), 0.501; CoMSIA with r(2), 0.872; q(2), 0.520). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures would be helpful to better understand the structure-activity relationship. All the conclusions drawn from both the 3D-QSAR contour maps and molecular docking were in accordance with the experimental activity dates. The results suggested that the developed models and the obtained CHk1 inhibitor binding structures might be reliable to predict the activity of new inhibitors and reasonable for the future drug design.