Combination of pharmacophore modeling and 3D-QSAR analysis of potential glyoxalase-I inhibitors as anticancer agents

Glyoxalase system is an ubiquitous system in human cells which has been examined thoroughly for its role in different diseases. It comprises two enzymes; Glyoxalase I (Glo-I) and Glyoxalase II (Glo-II) which perform detoxifying endogenous harmful metabolites, mainly methylglyoxal (MG) into non-toxic bystanders. In silico computer Aided Drug Design approaches were used and ninety two diverse pharmacophore models were generated from eighteen Glyoxalase I crystallographic complexes. Subsequent QSAR modeling followed by ROC evaluation identified a single pharmacophore model which was able to predict the expected Glyoxalase I inhibition. Screening of the National Cancer Institute (NCI) database using the optimal pharmacophore Hypo(3VW9) identified several promising hits. Thirty eight hits were successfully predicted then ordered and evaluated in vitro. Seven hits out of the thirty eight tested compounds showed more than 50% inhibition with low micromolar IC₅₀.     

Comparative pharmacophore modeling of human adenosine receptor A1 and A3 antagonists

Adenosine receptors are promising therapeutic targets in drug discovery. In this study, three-dimensional pharmacophore models of human adenosine receptor A1 and A3 antagonists were developed based on 26 and 23 diverse compounds, respectively. The best A1 pharmacophore model (A 1 _Hopy1) consists of four features: one hydrogen bond donor, one hydrophobic point and two ring aromatics, while the best A 3 pharmacophore model (A3 _Hopy1) also has four features: one hydrogen bond acceptor, one hydrophobic point and two ring aromatics. The correlation coefficients were 0.840 for A 1 test set with 146 diverse compounds and 0.827 for A3 test set with 238 diverse compounds. In the simulated virtual screening experiments, high enrichment factors of 6.51 and 6.90 were obtained for A 1 _Hopy1 and A3 _Hopy1 models, respectively. Moreover, two models also showed high subtype-selectivity in the simulated virtual screening experiments. These results could be helpful for the discovery of novel potent and selective A 1 and A3 antagonists.     

Homology modeling,docking and structure-based pharmacophore of inhibitors of DNA methyltransferase

DNA methyltransferase 1 (DNMT1) is an emerging epigenetic target for the treatment of cancer and other diseases. To date, several inhibitors from different structural classes have been published. In this work, we report a comprehensive molecular modeling study of 14 established DNTM1 inhibitors with a herein developed homology model of the catalytic domain of human DNTM1. The geometry of the homology model was in agreement with the proposed mechanism of DNA methylation. Docking results revealed that all inhibitors studied in this work have hydrogen bond interactions with a glutamic acid and arginine residues that play a central role in the mechanism of cytosine DNA methylation. The binding models of compounds such as curcumin and parthenolide suggest that these natural products are covalent blockers of the catalytic site. A pharmacophore model was also developed for all DNMT1 inhibitors considered in this work using the most favorable binding conformations and energetic terms of the docked poses. To the best of our knowledge, this is the first pharmacophore model proposed for compounds with inhibitory activity of DNMT1. The results presented in this work represent a conceptual advance for understanding the protein–ligand interactions and mechanism of action of DNMT1 inhibitors. The insights obtained in this work can be used for the structure-based design and virtual screening for novel inhibitors targeting DNMT1.     

A novel strategy to assemble the beta-diketo acid pharmacophore of HIV integrase inhibitors on purine nucleobase scaffolds

Claisen condensation, the key step in constructing the pharmacophore of aryl beta-diketo acids (DKA) as integrase inhibitors, fails in certain cases of highly electron-deficient heterocycles such as purines. A general synthetic strategy to assemble the DKA motif on the purine scaffold has been accomplished. The synthetic sequence entails a palladium-catalyzed cross-coupling, a C-acylation involving a tandem addition/elimination reaction, and a novel ferric ion-catalyzed selective hydrolysis of an enolic ether in the presence of a carboxylic acid ester.     

Molecular modelling studies on cinnoline-based BTK inhibitors using docking and structure-based 3D-QSAR

ABSTRACT

BTK inhibitors have been proved as an effective target for B-cell malignancies. Ibrutinib is the most advanced irreversible BTK inhibitor for treating mantle cell lymphoma/chronic lymphocytic leukaemia but with existing drug resistance and adverse effects. To design novel effective and safety reversible BTK inhibitors, 115 newly cinnoline analogues were selected to perform molecular docking and 3D-QSAR study because of the main scaffold similarity to Ibrutinib. Both established CoMFA and CoMSIA models obtained high predictive and satisfactory value. CoMFA/CoMSIA contour maps demonstrated that bulky substitutions are preferred at R₁ and R₃ positions, and introducing hydrophilic and negative electrostatic substitutions at R₁ positions is important for improving BTK inhibitory activities. These results will be useful to provide clues for rationally designing novel and high potency BTK inhibitors.     

Combined 3D-QSAR modeling and molecular docking study on indolinone derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

3-Phosphoinositide-dependent protein kinase-1 (PDK1) is a promising target for developing novel anticancer drugs. In order to understand the structure-activity correlation of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2)=0.907; q(2)=0.737) and CoMSIA model (r(2)=0.991; q(2)=0.824), for predicting the biological activity of new compounds. The detailed microscopic structures of PDK1 binding with inhibitors have been studied by molecular docking. We have also developed docking-based 3D-QSAR models (CoMFA with q(2)=0.729; CoMSIA with q(2)=0.79). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. This is the first report on 3D-QSAR modeling of PDK1 inhibitors. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained PDK1-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.     

3D-QSAR and molecular modeling of HIV-1 integrase inhibitors

Three-dimensional quantitative structure-activity relationship (3D QSAR) methods were applied on a series of inhibitors of HIV-1 integrase with respect to their inhibition of 3-processing and 3-end joining steps in vitro.The training set consisted of 27 compounds belonging to the class of thiazolothiazepines. The predictive ability of each model was evaluated using test set I consisting of four thiazolothiazepines and test set II comprised of seven compounds belonging to an entirely different structural class of coumarins. Maximum Common Substructure (MCS) based method was used to align the molecules and this was compared with other known methods of alignment. Two methods of 3D QSAR: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were analyzed in terms of their predictive abilities. CoMSIA produced significantly better results for all correlations. The results indicate a strong correlation between the inhibitory activity of these compounds and the steric and electrostatic fields around them. CoMSIA models with considerable internal as well as external predictive ability were obtained. A poor correlation obtained with hydrophobic field indicates that the binding of thiazolothiazepines to HIV-1 integrase is mainly enthalpic in nature. Further the most active compound of the series was docked into the active site using the crystal structure of integrase. The binding site was formed by the amino acid residues 64-67, 116, 148, 151-152, 155-156, and 159. The comparison of coefficient contour maps with the steric and electrostatic properties of the receptor shows high level of compatibility.     

3D-QSAR and Pharmacophore modeling of 3,5-disubstituted indole derivatives as Pim kinase inhibitors

Structural Chemistry - Indole derivatives are reported in the literature for their excellent kinase inhibition activity, so understanding their structural requirement is important....     

A structure-based 3D-QSAR study of anthrapyrazole analogues of the anticancer agents losoxantrone and piroxantrone

A series of 13 anthrapyrazole compounds that are analogues of piroxantrone and losoxantrone were synthesized, and their cell growth inhibitory effects, DNA binding, topoisomerase IIalpha mediated (EC 5.99.1.3) cleavage of DNA, and inhibition of DNA topoisomerase IIalpha decatenation catalytic activities were determined. Cell growth inhibitory activity was well-correlated with DNA binding, suggesting that these compounds may act by targeting DNA. However, cell growth inhibition was not well-correlated with the inhibition of topoisomerase IIalpha catalytic activity, suggesting that these anthrapyrazoles did not act solely by inhibiting the catalytic activity of topoisomerase II. Most of the analogues were able to induce DNA cleavage, and thus, it was concluded that they acted, at least in part, as topoisomerase II poisons. Structure-based three-dimensional quantitative structure-activity analyses (3D-QSAR) were carried out on the aligned structures of the anthrapyrazoles docked into DNA using comparative molecular field analysis (CoMFA) and comparative molecular similarity index (CoMSIA) analyses in order to determine the structural features responsible for their activity. Both CoMFA and CoMSIA yielded statistically significant models upon partial least-squares analyses. The 3D-QSAR analyses showed that hydrogen-bond donor interactions and electrostatic interactions with the protonated amino side chains of the anthrapyrazoles led to high cell growth inhibitory activity.     

苯并呋喃／噻吩联二苯类PTP1B抑制剂三维构效关系研究

主要采用比较分子力场分析方法（CoMFA）对苯并呋喃／噻吩联二苯类PTP1B (protein tyrosine phosphatase 1B)抑制剂进行了三维构效关系的研究，考察了 静电场、立体场和氢键场对构效关系的影响，交叉系数q^2的值达到0．58，表明 CoMFA得到的构效关系模型比较理想，同时test set中分子的预测活性也表明，模 型具有较好的预测能力，研究还表明，氢键场的加入不一定有利于模型的改善，通 过对分子场等值面图的分析，可以观察到叠合分子周围立体场和静电场对化合物活 性的影响，为改进原有化合物的结构，提高它们的活性提供了指导，还尝试采用比 较分子相似性指数分析方法（CoMFA）对这一系列化合物作了研究，结果表明虽然 CoMFA中加入了疏水场，但是对于研究的体系，CoMFA的模型质量并没有显著提高。     

Molecular modelling studies on adamantane-based Ebola virus GP-1 inhibitors using docking,pharmacophore and 3D-QSAR

The pathogenic Ebola virus (EBOV) causes a potential health risk and global spread. To date, few drugs are available for the treatment of Ebola virus disease (EVD) that allow researchers to use computational methods for designing potential drugs. The developed PHASE-based common six-point pharmacophore hypothesis (AADHPR_1) showed the necessity of two hydrogen bond acceptor features, one hydrogen bond donor feature, one hydrophobic group feature, one positively ionizable and one aromatic ring feature for further designing. We developed best 3D-QSAR models with high regression coefficients for the training (r²>0.82) and test (Q²>0.5) sets for both atoms-based and field-based 3D-QSAR models. The molecule 1A-4 (docking score = –4.711 kcal/mol) was obtained as best docked (SP mode) on Ebola virus envelope glycoprotein (PDB ID-3CSY) as compared with the standards oseltamivir (docking score = –4.39 kcal/mol) and zanamivir (docking score = –3.392 kcal/mol). The obtained ZINC hit ZINC58935541 showed a good docking score of –4.892 kcal/mol. The ZINC58935541 molecule also showed a strong binding affinity towards the receptor cavity of Ebola virus envelope glycoprotein when simulated for 1.2 ns. The good QikProp parameters reflect the fact that this molecule, upon optimization into a lead, might become a good candidate for the treatment of EVD.     

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.     

Molecular dynamics simulation, free energy calculation and structure-based 3D-QSAR studies of B-RAF kinase inhibitors

(V600E)B-RAF kinase is the most frequent onco-genic protein kinase mutation in melanoma and is a promising target to treat malignant melanoma. In this work, a molecular modeling study combining QM-polarized ligand docking, molecular dynamics, free energy calculation, and three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed on a series of pyridoimidazolone compounds as the inhibitors of (V600E)B-RAF kinase to understand the binding mode between the inhibitors and (V600E)B-RAF kinase and the structural requirement for the inhibiting activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by QM-polarized ligand docking strategy. The obtained models have a good predictive ability in both internal and external validation. Furthermore, molecular dynamics simulation and free energy calculations were employed to determine the detailed binding process and to compare the binding mode of the inhibitors with different activities. The binding free energies calculated by MM/PBSA gave a good correlation with the experimental biological activity. The decomposition of free energies by MM/GBSA indicates the van der Waals interaction is the major driving force for the interaction between the inhibitors and (V600E)B-RAF kinase. The hydrogen bond interactions between the inhibitors with Glu501 and Asp594 of the (V600E)B-RAF kinase help to stabilize the DFG-out conformation. The results from this study can provide some insights into the development of novel potent (V600E)B-RAF kinase inhibitors.     

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor

The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven homology modeling approach, we have built a model of the human adenosine A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine antagonists finding a good agreement between the theoretical and the experimental predictions.     

A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study using Comparative Molecular Similarity Indices Analysis (CoMSIA) was conducted on a series of 3-azolylmethylindoles as anti-leishmanial agents. Evaluation of 24 compounds synthesized in our laboratory served to establish the model. A random search was performed on the library of compounds, and molecules of the training set were aligned on common elements of template molecule 13, one of the most active compounds. The best predictions were obtained from multifit procedure with a CoMSIA model combining steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (q ²?=?0.594, r ²?=?0.897). The model was validated using an external test set of 7 compounds giving a satisfactory predictive r ² value of 0.649. Information obtained from CoMSIA contour maps could be used for further design of more promising inhibitors.     

A 3D-QSAR CoMSIA study on 3-azolylmethylindoles as anti-leishmanial agents

A three-dimensional quantitative structure-activity relationship (3D-QSAR) study using Comparative Molecular Similarity Indices Analysis (CoMSIA) was conducted on a series of 3-azolylmethylindoles as anti-leishmanial agents. Evaluation of 24 compounds synthesized in our laboratory served to establish the model. A random search was performed on the library of compounds, and molecules of the training set were aligned on common elements of template molecule 13, one of the most active compounds. The best predictions were obtained from multifit procedure with a CoMSIA model combining steric, electrostatic, hydrophobic and hydrogen bond acceptor fields (q2 = 0.594, r2 = 0.897). The model was validated using an external test set of 7 compounds giving a satisfactory predictive r2 value of 0.649. Information obtained from CoMSIA contour maps could be used for further design of more promising inhibitors.     

A structure-based design approach to advance the allyltyrosine-based series of HIV integrase inhibitors

As of mid-2017, only one structure of the human immunodeficiency virus (HIV) integrase core domain co-crystallised with an active site inhibitor was reported. In this structure (1QS4), integrase is complexed with a diketo-acid based strand-transfer inhibitor (INSTI). This structure has been a preferred platform for the structure-based design of INSTIs despite concerns relating to structural irregularities arising from crystallographic packing effects. A survey of the current pool of 297 reported integrase catalytic core structures indicated that the anatomy of the active site in the complex structure 1QS4 exhibits subtle variations relative to all other structures examined. Consequently, the 1QS4 structure was employed for docking studies. From the docking of twenty-seven allyltyrosine analogues, a 3-point inhibitor binding motif required for activity was established and successfully utilised in the development of a tripeptide displaying an EC₅₀ value of 10 ± 5 μM in HIV infected human T-cells. Additional docking of “in-house” compound libraries unearthed a methyl ester based nitrile derivative displaying an IC₅₀ value of 0.5 μM in a combined 3′-processing and strand-transfer assay.     

Design,synthesis and computational approach to study novel pyrrole scaffolds as active inhibitors of enoyl ACP reductase (InhA) and Mycobacterium tuberculosis antagonists

Novel 54 pyrrolyl acetohydrazide analogues were designed, synthesized and screened for antitubercular activity against InhA. Enoyl-ACP reductase/InhA one of the significant enzymes implicated in type II FAS (fatty acid synthase) biosynthetic pathway of bacterial outer cell membrane, in addition Mycobacterium tuberculosis H37Rv inhibition potency has proven to be one of the most promising drug target used for designing and testing against TB. In silico molecular modeling was achieved using Surflex-docking method to recognize important binding sites of the enoyl-ACP reductase. 3D-QSAR studies like CoMFA and CoMSIA approaches were studied to create 3D-QSAR depictions for InhA inhibitors. Based on docking results the synthesized molecules are oriented towards the core of the active site. The pyrolyl acetohydrazides exhibited one or two H-bonding connections with InhA enzyme. Molecule 8l (MIC 0.4 μg/mL, InhA- 70% at 50 μM) showed H-bonding connections with Tyr158 and NAD⁺ in a similar mode to that of ligand pyrrolidine carboxamide. The tested molecules also showed good antibacterial (Escherichia coli, Staphylococcus aureus) activity (MIC 0.4–25 μg/mL), while the study against A549 lung adenocarcinoma cell line confirms nontoxic nature of the reported molecules. The QSAR model from CoMFA and CoMSIA through the database configuration showed the most excellent data. The prognostic ability of CoMFA and CoMSIA representations was developed by a test set of 15 molecules that produced cross validated correlation coefficients (q²) of 0.642 and 0.701, respectively. This study gives insight into structural requirement needed for the development of more active InhA inhibitors through in silico approach.