Comparative QSAR analyses of competitive CYP2C9 inhibitors using three-dimensional molecular descriptors

One of the biggest challenges in QSAR studies using three-dimensional descriptors is to generate the bioactive conformation of the molecules. Com parative QSAR analyses have been performed on a dataset of 34 structurally diverse and competitive CYP2C9 inhibitors by generating their lowest energy conformers as well as additional multiple conformers for the calculation of molecular de scriptors. Three-dimensional descriptors account ing for the spatial characteristics of the molecules calculated using E-Dragon were used as the inde pendent variables. The robustness and the predic tive performance of the developed models were verified using both the internal [leave-one-out (LOO)] and external statistical validation (test set of 12 inhibitors). The best models (MLR using GET AWAY descriptors and partial least squares using 3D-MoRSE) were obtained by using the multiple conformers for the calculation of descriptors and were selected based upon the higher external pre diction (R2 test values of 0.65 and 0.63, respectively) and lower root mean square error of prediction (0.48 and 0.48, respectively). The predictive ability of the best model, i.e., MLR using GETAWAY de scriptors was additionally verified on an external test set of quinoline-4-carboxamide analogs and resulted in an R2 test value of 0.6. These simple and alignment-independent QSAR models offer the possibility to predict CYP2C9 inhibitory activity of chemically diverse ligands in the absence of X-ray crystallographic information of target protein structure and can provide useful insights about the ADMET properties of candidate molecules in the early phases of drug discovery.info:eu-repo/semantics/publishedVersio

QSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivatives

Peroxisome Proliferator Activated Receptor b/d (PPAR b/d), one of three PPAR isoforms is a member of nuclear receptor superfamily and ubiquitously expressed in several metabolically active tissues such as liver, muscle, and fat. Tissue specific expression and knock-out studies suggest a role of PPARd in obesity and metabolic syndrome. Specific and selective PPARd ligands may play an important role in the treatment of metabolic disorders. Indanylacetic acid derivatives reported as potent and specific ligands against PPARd have been studied for the Quantitative Structure – Activity Relationships (QSAR). Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. Four different approaches, i.e., random selection, hierarchical clustering, k-means clustering, and sphere exclusion method were used to classify the dataset into training and test subsets. Forward stepwise Multiple Linear Regression (MLR) approach was used to linearly select the subset of descriptors and establish the linear relationship with PPARd agonistic activity of the molecules. The models were validated internally by Leave One Out (LOO) and externally for the prediction of test sets. The best subset of descriptors was then fed to the Artificial Neural Networks (ANN) to develop non-linear models. Statistically significant MLR models; with R2 varying from 0.80 to 0.87 were generated based on the different training and test set selection methods. Training of ANNs with different architectures for the different training and test selection methods resulted in models with R2 values varying from 0.83 to 0.94, which indicates the high predictive ability of the models.info:eu-repo/semantics/publishedVersio

SitCon: binding site residue conservation visualization and protein sequence-to-function tool

We introduce SitCon (SITe CONservation), a program designed to explore conservation of functionally important sites in a series of hypothetically homologous candidate protein structures, given amino acid sequence as an input. This can especially be useful when looking for an unknown function of a protein. SitCon exploits the fact that binding sites of proteins are preserved better than the overall residue sequence conservation. To test the capability of unknown function prediction, we randomly chose known function proteins from Caenorhabditis elegans genome. To imitate a behavior of an unknown function target, only the low homology proteins with 0.01 E-score 100 were analyzed as templates. Out of 29 enzyme targets, SitCon was able to provide various hints about their function in at least 69% of the cases. For the eight nonenzyme targets, the predictions matched in only 25% of the cases. SitCon was also tested for a capability to predict presence or absence of metal-containing heterogroups in the target enzymes with 80% success rate. Because this algorithm is not based on specific protein signatures, it may allow detection of overlooked relationships between proteins. SitCon is also very effective as a tool allowing visual comparison of binding site residue conservation between the target and homologous templates side-by-side.info:eu-repo/semantics/publishedVersio

Quantitative structure-activity relationship models with receptor-dependent descriptors for predicting peroxisome proliferator-activated receptor activities of thiazolidinedione and oxazolidinedione derivatives

A quantitative structure–activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor a and the peroxisome proliferator activated receptor c agonistic activities of thiazo lidinedione and oxazolidinedione derivatives and quantitative descriptors, Vsite calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either com mon or specific to the superimposed binding sites of the targets under consideration. The quantita tive structure–activity relationship models were built by forward stepwise linear regression model ing for a training set of 27 compounds and vali dated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor a and of 0.89 for peroxisome proliferator-activated receptor c. The leave-one-out cross-validation and test set predictability squared correlation coeffi cient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor a and 0.89 and 0.50 for peroxisome proliferator-acti vated receptor c respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure–activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity.info:eu-repo/semantics/publishedVersio

Generation of artificial neural networks models in anticancer study

Artificial neural networks (ANNs) have several applications; one of them is the prediction of biological activity. Here, ANNs were applied to a set of 32 compounds with anticancer activity assayed experimentally against two cancer cell lines (A2780 and T-47D). Using training and test sets, the obtained correlation coefficients between experimental and calculated values of activity, for A2780, were 0.804 and 0.829, respectively, and for T-47D, we got 0.820 for the training set and 0.927 for the test set. Com paring multiple linear regression and ANN models, the latter were better suited in establishing relationships between compounds’ structure and their anticancer activity.info:eu-repo/semantics/publishedVersio

Caffeoylquinic acids as inhibitors for HIV-I protease and HIV-I integrase: a molecular docking study

Caffeoylquinic acids are ubiquitous phenolic compounds with several health benefits to humans and they have been shown to be promisinganti-HIV compounds. In this work, molecular docking was used to study the inhibition of HIV-I integrase and protease using caffeoylquinic acids. It was possible to establish that the naturally occurring caffeoylquinic acids are not suitable as inhibitors for protease but are very good inhibitors for integrase. A new binding site was found for 3, 4-O-di-Caffeoylquinic acid between the chains of HIV-I integrase that could possibly lead to a disruption of the catalytic process of HIV-I integrase.info:eu-repo/semantics/publishedVersio

Protein-ligand docking study: diterpenes from Juniperus brevifolia as anticancer and antimicrobial agentes

REDCAT: Natural Products and related Redox Catalysts: Basic Research and Applications in Medicine and Agriculture, Aveiro, 25-27 Novembro de 2012.From leaves of Juniperus brevifolia, an endemic conifer from Azores, were isolated and structurally characterized, several dehydroabietane and sandaracopimarane derivatives. Some of them (1-4), displayed antiproliferative activity against cancer cell lines (HeLa, A-549 and MCF-7) and bactericidal effect against Bacillus cereus at different concentrations tested. However, it is not known how these compounds interact with most often proteins involved in the antimicrobial and cytotoxic mechanisms. Protein-ligand docking is mainly used to predict (energy and conformation wise) how small molecules bind to a protein of known 3D structure and to predict possible molecular targets for a set of compounds. In this work, the docking studies were performed, using the FlexScreen program, in order to pick molecular targets from a large set of common anticancer (63) and antimicrobial (39) targets to the selected compounds 1-4. The predicted interactions established between the compounds under study and the anticancer targets revealed that the compounds 1 and 3 interact preferentially with phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 2, whereas compounds 2 and 4 interact preferentially with human mitochondrial peptide deformylase and -tubulin, respectively. Studying the interactions between the compounds 1 and 3 and the antimicrobial targets we predict that these compounds interact preferentially with RNA polymerase and peptide deformylase. These results provide additional understanding of the cytotoxic and antimicrobial effects of diterpenes studied. These preliminary computational docking predictions of therapeutic targets were established working with just 4 compounds, and to obtain more reliable predictions the number of compounds needs to be increased.Thanks are due to the University of Azores, FCT, FEDER, BIOPHARMAC - MAC/1/C104 and Project PEst-OE/QUI/UI0674/2011

Prediction of terpenoid toxicity based on a quantitative structure–activity relationship model

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2 ) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.info:eu-repo/semantics/publishedVersio

Phosphorylation of mineralocorticoid receptor ligand binding domain impairs receptor activation and has a dominant negative effect over non-phosphorylated receptors

Post-translational modification of steroid receptors allows fine-tuning different properties of this family of proteins, including stability, activation, or interaction with co-regulators. Recently, a novel effect of phosphorylation on steroid receptor biology was described. Phosphorylation of human mineralocor ticoid receptor (MR) on Ser-843, a residue placed on the ligand binding domain, lowers affinity for agonists, producing inhibi tion of gene transactivation. We now show that MR inhibition by phosphorylation occurs even at high agonist concentration, suggesting that phosphorylation may also impair coupling between ligand binding and receptor activation. Our results demonstrate that agonists are able to induce partial nuclear translocation of MR but fail to produce transactivation due at least in part to impaired co-activator recruitment. The inhibi tory effect of phosphorylation on MR acts in a dominant-nega tive manner, effectively amplifying its functional effect on gene transactivation.info:eu-repo/semantics/publishedVersio

Structure and ligand-based design of P-glycoprotein inhibitors: a historical perspective

Computer-assisted drug design (CADD) is a valuable approach for the discovery of new chemical entities in the field of cancer therapy. There is a pressing need to design and develop new, selective, and safe drugs for the treatment of multidrug resistance (MDR) cancer forms, specifically active against P-glycoprotein (P-gp). Recently, a crystallographic structure for mouse P-gp was obtained. However, for decades the design of new P-gp inhibitors employed mainly ligand-based approaches (SAR, QSAR, 3D-QSAR and phar macophore studies), and structure-based studies used P-gp homology models. However, some of those results are still the pillars used as a starting point for the design of potential P-gp inhibitors. Here, pharmacophore mapping, (Q)SAR, 3D-QSAR and homology modeling, for the discovery of P-gp inhibitors are reviewed. The importance of these methods for understanding mechanisms of drug resistance at a molecular level, and design P-gp inhibitors drug candidates are discussed. The examples mentioned in the review could provide insights into the wide range of possibilities of using CADD methodologies for the discovery of efficient P-gp inhibitors.info:eu-repo/semantics/publishedVersio