QSAR analyses of 3-(4-benzylpiperidin-1-yl)-N-phenylpropylamine derivatives as potent CCR5 antagonists

Homoisoflavonoids as potential antiangiogenic agents for retinal neovascularization

A number of people worldwide have been suffering from ocular neovascularization that may be treated by a variety of drugs but these may possess adverse effects. Therefore, small antiangiogenic molecules with higher potency and lower toxic effects are intended. However, homoisoflavonoids of natural origin show the potential antiangiogenic effect in ocular neovascularization. These homoisoflavonoids are judged quantitatively in terms of statistical validation through multi-chemometric modeling approaches for the betterment and refinement of their structures required for higher antiangiogenic activity targeted to ocular neovascularization. These approaches may be utilized to design better antiangiogenic homoisoflavonoids.

A GMDH-type neural network with multi-filter feature selection for the prediction of transition temperatures of bent-core liquid crystals

The QSPR study on transition temperatures of five-ring bent-core LCs was performed using GMDH-type neural networks. A novel multi-filter approach, which combines chi square ranking, v-WSH and GMDH algorithm was used for the selection of descriptors.

Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed.

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch's equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (b(j)). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov((i)μ(k)) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box-Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25,000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

Computational study on the interaction between CCR5 and HIV-1 entry inhibitor maraviroc: insight from accelerated molecular dynamics simulation and free energy calculation

C-C chemokine receptor type 5 (CCR5) is the co-receptor of human immunodeficiency virus type 1 (HIV-1) and plays an important role in HIV-1 virus infection. Maraviroc has been proved to be effective for anti-HIV-1 by targeting CCR5. Understanding the detailed interaction mechanism between CCR5 and Maraviroc will be of great help to the rational design of a more potential inverse agonist to block HIV-1 infection. Here, we performed molecular dynamics (MD) simulation and accelerated MD simulation (aMD) to study the interaction mechanism between CCR5 and Maraviroc based on a recently reported crystal structure. The results of MD simulation demonstrate that Maraviroc can form stable hydrogen bonds with residues Tyr37(1.39), Tyr251(6.51) and Glu283(7.39). The results of aMD simulation indicate that the carboxamide moiety is more flexible than the tropane group of Maraviroc in the pocket of CCR5. The electrostatic potential analysis proves that Maraviroc can escape from the pocket of CCR5 along the negative electrostatic potential pathway during the dissociation process. The free energy calculation illustrates that there exist three binding pockets during the dissociation process of Maraviroc. Our results will be useful for understanding the interaction mechanism between CCR5 and Maraviroc as well as for the rational design of a more potent inverse agonist.

Experimental and computational studies of fatty acid distribution networks

A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution.

Prediction of PKCθ inhibitory activity using the Random Forest Algorithm

This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R(2) (pred) of 0.72, a standard error of prediction (SEP) of 0.45, for an external prediction set of 51 inhibitors which were not used in the development of QSAR models. By using the RF built-in measure of the relative importance of the descriptors, an important predictor-the number of group donor atoms for H-bonds (with N and O)-has been identified to play a crucial role in PKCθ inhibitory activity. We hope that the developed RF model will be helpful in the screening and prediction of novel unknown PKCθ inhibitory activity.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks

Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.

Development of predictive in silico model for cyclosporine- and aureobasidin-based P-glycoprotein inhibitors employing receptor surface analysis

P-glycoprotein (Pgp) is implicated in multiple drug resistance (MDR) exhibited by several types of cancer against a multitude of anticancer chemotherapeutic agents. This problem prompted several research groups to search for effective P-gp inhibitors. Cyclosporine A (CsA), aureobasidin A (AbA) and related analogues were reported to possess potent inhibitory actions against Pgp. In this work we employed receptor surface analysis (RSA) to construct two satisfactory receptor surface models (RSMs) for cyclosporine- and aureobasidin-based Pgp inhibitors. These pseudoreceptors were combined to achieve satisfactory three-dimensional quantitative structure activity relationship (3D-QSAR) for 68 different cyclosporine and aureobasidin derivatives. Upon validation against an external set of 16 randomly selected Pgp inhibitors, the optimal 3D-QSAR was found to be self-consistent and predictive (r(LOO)(2)=0.673, r(PRESS)(2)=0.600). The resulting 3D-QSAR was employed to probe the structural factors that control the inhibitory activities of cyclosporine and aureobasidin analogues against Pgp.

Exploring molecular shape analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors

HIV-1 integrase inhibitory activity data of styrylquinoline derivatives have been subjected to 3D-QSAR study by molecular shape analysis (MSA) technique using Cerius(2) version 4.8 software (Accelrys). For the selection of test set compounds, initially a QSAR analysis was done based on topological and structural descriptors and K-means clustering technique was used to classify the entire data set (n=36). Clusters were formed from the factor scores of the whole data set comprising of topological and structural descriptors without the biological activity, and based on the clusters, the data set was divided into training and test sets (n=26 and n=10, respectively) so that all clusters are properly represented in both training and test sets. In the molecular shape analysis, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape reference compound (based on active conformation); (4) performing pair-wise molecular superimposition using maximum common subgroup [MCSG] method; (5) measuring molecular shape commonality using MSA descriptors; (6) determination of other molecular features by calculating spatial and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by standard statistical techniques. The best model obtained from stepwise regression and GFA techniques shows 51.6% predicted variance (leave-one-out) and 57.3% explained variance. In case of FA-PLS regression, the best relation shows 54.0% predicted variance and 57.9% explained variance. The R(2)(pred) and R(2)(test) values for the GFA derived model are 0.611 and 0.664, respectively, while the best FA-PLS model has R(2)(pred) and R(2)(test) values of 0.602 and 0.656, respectively. These models show the importance of Jurs descriptors (total polar surface area, relative polar surface area, relative hydrophobic surface area, relative positive charge), fraction area of the molecular shadow in the XZ plane (ShadowXZfrac), common overlap steric volume and the ratio of common overlap steric volume to volume of individual molecules. Statistically reliable MSA models obtained from this study suggest that this technique could be useful to design potent HIV-1 integrase inhibitors.

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.

Comparative QSAR modeling of CCR5 receptor binding affinity of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas

The present QSAR study attempts to explore the structural and physicochemical requirements of substituted 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas for CCR5 binding affinity using linear free energy-related (LFER) model of Hansch. QSAR models have been developed using electronic (Hammett sigma), hydrophobicity (pi), and steric (molar refractivity and STERIMOL L, B1, and B5) parameters of phenyl ring substituents of the compounds along with appropriate dummy variables. Whole molecular descriptor like partition coefficient (logP(calcd)) was also tried as an additional descriptor. Statistical techniques like stepwise regression, multiple linear regression with factor analysis as the data preprocessing step (FA-MLR), partial least squares with factor analysis as the preprocessing step (FA-PLS), principal component regression analysis (PCRA), multiple linear regression with genetic function approximation (GFA-MLR), and genetic partial least squares (G/PLS) were applied to identify the structural and physicochemical requirements for the CCR5 binding affinity. The generated equations were statistically validated using leave-one-out technique. The quality of equations obtained from stepwise regression, FA-MLR, FA-PLS, and PCRA is of acceptable statistical range (explained variance ranging from 71.9% to 80.4%, while predicted variance ranging from 67.4% to 77.0%). The GFA-derived models show high intercorrelation among predictor variables used in the equations while the G/PLS model shows lowest statistical quality among all types of models. The best models were also subjected to leave-25%-out crossvalidation.

Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques

A linear quantitative-structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.

Quantitative structure activity relationship of benzoxazinone derivatives as neuropeptide Y Y5 receptor antagonists

Quantitative structure activity relationship (QSAR) has been established for 30 benzoxazinone derivatives acting as neuropeptide Y Y5 receptor antagonists. The genetic algorithm and multiple linear regression were used to generate the relationship between biological activity and calculated descriptors. Model with good statistical qualities was developed using four descriptors from topological, thermodynamic, spatial and electrotopological class. The validation of the model was done by cross validation, randomization and external test set prediction.