3D-QSAR studies of indole derivatives as phosphodiesterase IV inhibitors

Increased catalytic activity through ZnMo7O24/g-C3N4 heterostructured assemblies for greener indole condensation reaction at room temperature

As an economical conjugated polymer, graphitic carbon nitride (g-C3N4) has recently attracted much attention due to its exciting chemical and thermal stability and easy availability. Herein, we constructed a metal-coordinated graphitic carbon nitride (M-g-C3N4) catalyst through simple impregnation and calcination methods and used it as a new heterogeneous catalyst for the efficient synthesis of bis (indolyl) methanes and trisindolines under mild conditions. This reaction is performed efficiently in water as an environmentally friendly solvent at ambient conditions. The ZnMo7O24/g-C3N4 nanocomposite was synthesized by a simple method by immobilizing Mo7O24(NH4)6·4H2O and ZnCl2 on the surface of g-C3N4 under hydrothermal conditions. It was characterized by FT-IR, EDS, and electronic scanning microscopy (SEM). The metal doping of Mo and Zn on the surface of graphitic carbon nitride leads to the formation of a green catalyst that gives good to excellent yields of products in short reaction times with an easy working procedure. In addition, the ZnMo7O24/g-C3N4 catalyst could be reused at least five runs without apparent loss of efficiency.

QSAR models reveal new EPAC-selective allosteric modulators

Exchange proteins directly activated by cAMP (EPAC) are guanine nucleotide exchange factors for the small GTPases, Rap1 and Rap2. They regulate several physiological functions and mitigation of their activity has been suggested as a possible treatment for multiple diseases such as cardiomyopathy, diabetes, chronic pain, and cancer. Several EPAC-specific modulators have been developed, however studies that quantify their structure-activity relationships are still lacking. Here we propose a quantitative structure-activity relationship (QSAR) model for a series of EPAC-specific compounds. The model demonstrated high reproducibility and predictivity and the predictive ability of the model was tested against a series of compounds that were unknown to the model. The compound with the highest predicted affinity was validated experimentally through fluorescence-based competition assays and NMR experiments revealed its mode of binding and mechanism of action as a partial agonist. The proposed QSAR model can, therefore, serve as an effective screening tool to identify promising EPAC-selective drug leads with enhanced potency.

Exploring the Role of Water Molecules in the Ligand Binding Domain of PDE4B and PDE4D: Virtual Screening Based Molecular Docking of Some Active Scaffolds

BACKGROUND

The phosphodiesterase (PDE) is a superfamily represented by four genes: PDE4A, B,C, and D which cause the hydrolysis of phosphodiester bond of cAMP to yield inactive AMP. c-AMP catalyzing enzyme is predominant in inflammatory and immunomodulatory cells. Therapy to treat Chronic Obstructive Pulmonary Disease (COPD) with the use of PDE4 inhibitors is highly envisaged.

OBJECTIVE

A molecular docking experiment with large dataset of diverse scaffolds has been performed on PDE4 inhibitors to analyze the role of amino acid responsible for binding and activation of the secondary transmitters. Apart from the general docking experiment, the main focus was to discover the role of water molecules present in the ligand-binding domain.

METHODS

All the compounds were docked in the PDE4B and PDE4D active cavity to produce the free binding energy scores and spatial disposition/orientation of chemical groups of inhibitors around the cavity. Under uniform condition, the experiments were carried out with and without water molecules in the LBD. The exhaustive study was carried out on the Autodock 4.2 software and explored the role of water molecules present in the binding domain.

RESULTS

In presence of water molecule, Roflumilast has more binding affinity (-8.48 Kcal/mol with PDE4B enzyme and -8.91 Kcal/mol with PDE4D enzyme) and forms two hydrogen bonds with Gln443 and Glu369 and amino acid with PDE4B and PDE4D enzymes respectively. While in absence of water molecule its binding affinity has decreased (-7.3 Kcal/mol with PDE4B enzyme and -5.17 Kcal/mol with PDE4D enzyme) as well as no H-bond interactions were observed. Similar observation was made with clinically tested molecules.

CONCLUSION

In protein-ligand binding interactions, appropriate selection of water molecules facilitated the ligand binding, which eventually enhances the efficiency as well as the efficacy of ligand binding.

QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC₅₀ of CXCR2 receptors for which no experimental data is available.

Comparative Molecular Similarity Indices Analysis of 1-(Naphthylalky1)-1H-imidazole Analogs with Antiepileptic Activity

A three-dimensional quantitative structure-activity relationship (3D QSAR) of 44 structurally and functionally diverse series of 1- (Naphthylalkylimidazoles) as antiepileptic agents was studied using the Comparative molecular similarity indices analysis (CoMSIA) method. A training set containing 34 molecules served to establish the models. The optimum CoMSIA model obtained for the training set were all statistically significant, with cross-validated coefficients (q(2)) of 0.725 and conventional coefficients (r(2) (ncv)) of 0.998. The predictive capacities of the model were successfully validated by using a test set of 10 molecules that were not included in the training set. CoMSIA model (Model 1) obtained from the hydrophobic and Hbond acceptor field was found to have the best predictivity, with a predictive correlation coefficient (r(2) (pred)) of 0.67. The information obtained from this 3D-QSAR model can be used to guide the development of imidazoles as novel antiepileptic agents.

A novel structure-based multimode QSAR method affords predictive models for phosphodiesterase inhibitors

Quantitative structure-activity relationship (QSAR) methods aim to build quantitatively predictive models for the discovery of new molecules. It has been widely used in medicinal chemistry for drug discovery. Many QSAR techniques have been developed since Hansch's seminal work, and more are still being developed. Motivated by Hopfinger's receptor-dependent QSAR (RD-QSAR) formalism and the Lukacova-Balaz scheme to treat multimode issues, we have initiated studies that focus on a structure-based multimode QSAR (SBMM QSAR) method, where the structure of the target protein is used in characterizing the ligand, and the multimode issue of ligand binding is systematically treated with a modified Lukacova-Balaz scheme. All ligand molecules are first docked to the target binding pocket to obtain a set of aligned ligand poses. A structure-based pharmacophore concept is adopted to characterize the binding pocket. Specifically, we represent the binding pocket as a geometric grid labeled by pharmacophoric features. Each pose of the ligand is also represented as a labeled grid, where each grid point is labeled according to the atom types of nearby ligand atoms. These labeled grids or three-dimensional (3D) maps (both the receptor map (R-map) and the ligand map (L-map)) are compared to each other to derive descriptors for each pose of the ligand, resulting in a multimode structure-activity relationship (SAR) table. Iterative partial least-squares (PLS) is employed to build the QSAR models. When we applied this method to analyze PDE-4 inhibitors, predictive models have been developed, obtaining models with excellent training correlation (r(2) = 0.65-0.66), as well as test correlation (R(2) = 0.64-0.65). A comparative analysis with 4 other QSAR techniques demonstrates that this new method affords better models, in terms of the prediction power for the test set.

Development of improved models for phosphodiesterase-4 inhibitors with a multi-conformational structure-based QSAR method

Phosphodiesterase-4 (PDE-4) is an important drug target for several diseases, including COPD (chronic obstructive pulmonary disorder) and neurodegenerative diseases. In this paper, we describe the development of improved QSAR (quantitative structure-activity relationship) models using a novel multi-conformational structure-based pharmacophore key (MC-SBPPK) method. Similar to our previous work, this method calculates molecular descriptors based on the matching of a molecule's pharmacophore features with those of the target binding pocket. Therefore, these descriptors are PDE4-specific, and most relevant to the problem under study. Furthermore, this work expands our previous SBPPK QSAR method by explicitly including multiple conformations of the PDE-4 inhibitors in the regression analysis, and thus addresses the issue of molecular flexibility. The nonlinear regression problem resulted from including multiple conformations has been transformed into a linear equation and solved by an iterative partial least square (iPLS) procedure, according to the Lukacova-Balaz scheme. 35 PDE-4 inhibitors have been analyzed with this new method, and predictive models have been developed. Based on the prediction statistics for both the training set and the test set, these new models are more robust and predictive than those obtained by traditional ligand-based QSAR techniques as well as that obtained with the SBPPK method reported in our previous work. As a result, multiple predictive models have been added to the collection of QSAR models for PDE4 inhibitors. Collectively, these models will be useful for the discovery of new drug candidates targeting the PDE-4 enzyme.

A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors

We describe the application of a new QSAR (quantitative structure-activity relationship) formalism to the analysis and modeling of PDE-4 inhibitors. This new method takes advantage of the X-ray structural information of the PDE-4 enzyme to characterize the small molecule inhibitors. It calculates molecular descriptors based on the matching of their pharmacophore feature pairs with those (the reference) of the target binding pocket. Since the reference is derived from the X-ray crystal structures of the target under study, these descriptors are target-specific and easy to interpret. We have analyzed 35 indole derivative-based PDE-4 inhibitors where Partial Least Square (PLS) analysis has been employed to obtain the predictive models. Compared to traditional QSAR methods such as CoMFA and CoMSIA, our models are more robust and predictive measured by statistics for both the training and test sets of molecules. Our method can also identify critical pharmacophore features that are responsible for the inhibitory potency of the small molecules. Thus, this structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors. The success of this study has also laid a solid foundation for systematic QSAR modeling of the PDE family of enzymes, which will ultimately contribute to chemical genomics research and drug discovery targeting the PDE enzymes.

Systematic Statistical Comparison of Comparative Molecular Similarity Indices Analysis Molecular Fields for Computer-Aided Lead Optimization

Comparative molecular similarity indices analysis (CoMSIA) is a 3D quantitative structure-activity relationship technique used to determine structural and electronic features influencing biological activity. This proves particularly useful for facilitating lead optimization projects. This study aimed to compare CoMSIA models produced using different subsets of the CoMSIA molecular fields (steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor) in a systematic and statistically valid manner. A total of 23 data sets sourced from the literature were used to compare molecular field contribution and model predictivity using leave-one-out cross-validated R2 values. Predictive ability varied in a highly statistically significant manner depending on the set of CoMSIA molecular fields used. In general, the greater the number of CoMSIA molecular fields included in the analysis, the better the model predictivity was. There is great redundancy in the information contained in the different CoMSIA molecular fields. When all five CoMSIA molecular fields are included, the hydrophobic and electrostatic fields had the largest and the steric field the smallest contribution. Data sets were clustered into four groups on the basis of the utility of molecular field sets to generate predictive models.

Comparative molecular similarity indices analysis (CoMSIA) studies of 1,2-naphthoquinone derivatives as PTP1B inhibitors

Protein tyrosine phosphatase-1B (PTP1B) has been demonstrated to play a key role in the negative signalling pathway of insulin. Potent and orally active PTP1B inhibitors are considered to be promising pharmacological agents for the treatment of type-2 diabetes and resistance to weight gain. CoMSIA studies have been preformed on 1,2-naphthoquinone derivatives that are reported to be potential non-peptidic inhibitors of PTP1B. For the selection of dataset to develop the model, the reported molecules were subjected to property filters and segregated into training and test set. As the crystal structure of PTP1B-naphthoquinone derivative is not known, the most active molecule was subjected to simulated annealing dynamics method and the lowest energy conformer was reminimised and considered as the bioactive conformation. Database-inertial alignment was followed for aligning the molecules. Different CoMSIA models were built to get the best related field.

Three-dimensional quantitative structure (3-D QSAR) activity relationship studies on imidazolyl and N-pyrrolyl heptenoates as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitors by comparative molecular similarity indices analysis (CoMSIA)

A comparative molecular similarity indices analysis (CoMSIA) of a set of 29 imidazolyl and N-pyrrolyl heptenoates have been performed to find out the structural requirements for 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR) inhibitory activity. The HMG like side chain, a common moiety of statins, was used to align the molecules. The results guide to design new chemical entities with high potency.

3D-QSAR studies of pyruvate dehydrogenase kinase inhibitors based on a divide and conquer strategy

PDHK is a highly specific enzyme, which inhibits PDC thereby reducing the conversion of pyruvate to AcetylCoA leading to increased glucose and lactate level contributing to various pathological disease states. 3D-QSAR CoMFA studies were performed on diverse PDHK inhibitors based on maximum common substructural alignments of different classes of molecules with the selected reference molecule using a divide and conquer strategy. Statistically robust CoMFA model was obtained with a cross-validated correlation coefficient of 0.561 and conventional correlation coefficient of 0.990. Predictive correlation coefficient r2(pred) was found to be 0.875.