Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach

CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q² > 0.4, r² > 0.5 and r²_pred > 0.5. Based on better q² and r²_pred values, the best predictions were obtained for the CoMFA (model 5 q² = 0.488, r²_pred = 0.732), and CoMSIA (model 45 q² = 0.525, r²_pred = 0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.     

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.     

An in silico approach to elucidate structure based functional evolution of oxacillinase

Bacterial Oxacillinases (OXAs), genetically being extremely diverse and highly versatile in hydrolyzing antibiotics of different classes, holds utmost significant clinical importance. Hence, to analyze functional evolution of this enzyme, plausible changes in drug profile, affinity and binding stability of different subclasses of OXA with their preferred drugs, viz. penicillin, ceftazidime, imipenem/meropenem were investigated. Maximum-Likelihood dendrogram was constructed and based on tree topology, the least and most divergent variants of each clade were selected. Pocket characterization, enzyme structural stability and mutational effect were analyzed in silico. Modes of interaction of selected OXA variants with respective antibiotics were analyzed by Autodock4.0 and LIGPLOT. Comparative mobility profiling and subsequent ΔG° and Km calculations of representative OXA variants revealed that after RSBL evolution, perhaps, two competitive strategies evolved among the OXA variants. Either loops flanking helix5 gets stabilized or it becomes more flexible. Therefore, while OXA variants (e.g. OXA-2, OXA-32, OXA-23, OXA-133, OXA-24, OXA-25, OXA-51 and OXA-75) with highly stabilized loops flanking helix5 exhibited improved binding stability and affinity towards carbapenems, especially meropenem, OXA variants (e.g. OXA-10, OXA-251, OXA-48 and OXA-247) possessing highly flexibile loops flanking helix5 revealed their catalytic proficiency towards ceftazidime. Moreover, LIGPLOT and PROMALS3D jointly identified ten consensuses/conserved residues, viz. P68, A69, F72, K73, W105, V120, W164, L169, K216 and G218 to be critical for drug hydrolysis. Hence, novel inhibitors could be designed to target these sites.     

A statistical approach to delta layer depth profiling

We present a simple statistical model describing the removal and relocation of material during a sputter depth profiling experiment. All input parameters are determined from low‐fluence molecular dynamics simulations, making the model de facto parameter free. The model can be used to extrapolate data from the molecular dynamics simulations to projectile fluences relevant to sputter depth profiling experiments. As a result, the erosion of the surface is calculated in terms of fluence‐dependent filling factors of different sample layers. Using input data determined for the 20‐keV C₆₀ cluster bombardment of silicon, it is found that a steady‐state erosion profile is reached after removal of approximately 20 monolayer equivalents of material. Plotting the contribution of particles from a specific layer to the instantaneous sputtered flux, one can directly determine the delta layer response function predicted from such a model. It is shown that this function can be parameterized by the semiempirical Dowsett response function, and the resulting fitting parameters are compared with published depth profile data. The model is then used to study the role of different processes influencing the observed depth resolution. We find that the statistical nature of the sputtering process suffices to explain many features of experimentally measured delta layer depth profiles. Copyright © 2012 John Wiley & Sons, Ltd.     

A global metabolite profiling approach to identify protein-metabolite interactions

Understanding the biochemical functions of proteins is an important factor in elucidating their cellular and physiological functions. Due to the predominance of biopolymer interactions in biology, many methods have been designed to interrogate and identify biologically relevant interactions that proteins make to DNA, RNA, and other proteins. Complementary approaches that can elucidate binding interactions between proteins and small molecule metabolites will impact the understanding of protein-metabolite interactions and fill a need that is outside the scope of current methods. Here, we demonstrate the ability to identify natural protein-metabolite interactions from complex metabolite mixtures by combining a protein-mediated small molecule enrichment step with a global metabolite profiling platform.     

A new approach to express ToF SIMS depth profiling

We propose a new approach to express SIMS depth profiling on a TOF.SIMS‐5 time‐of‐flight mass spectrometer. The approach is based on the instrument capability to independently perform raster scans of sputter and probe ion beams. The probed area can be much smaller than the diameter of a sputter ion beam, like in the AES depth profiling method. This circumstance alleviates limitations on the sputter beam–raster size relation, which are critical in other types of SIMS, and enables analysis on a curved‐bottomed sputter crater. By considerably reducing the raster size, it is possible to increase the depth profiling speed by an order of magnitude without radically degrading the depth resolution. A technique is proposed for successive improvement of depth resolution through profile recovery with account for the developing curvature of the sputtered crater bottom in the probed area. Experimental study of the crater bottom form resulted in implementing a method to include contribution of the instrumental artifacts in a nonstationary depth resolution function within the Hofmann's mixing–roughness–information depth model. The real‐structure experiment has shown that the analysis technique combining reduction of a raster size with a successive nonstationary recovery ensures high speed of profiling at ~100 µm/h while maintaining the depth resolution of about 30 nm at a 5 µm depth. Copyright © 2015 John Wiley & Sons, Ltd.     

An ensemble approach for in silico prediction of Ames mutagenicity

In this paper, we evaluate three learning algorithms based on supervised projections for molecular activity prediction. Using an approach based on supervised projections of the input space to construct ensembles of classifiers, three algorithms were tested. We constructed the projections by considering only instances that were misclassified by a previous classifier using the hidden layer of an Artificial Neural Network. We applied a supervised linear projection of the input space using a Nonparametric Discriminant Analysis method. Finally, we projected onto a subspace that minimizes the weighted error for each step. Using these three methods to construct ensembles of classifiers for the in silico prediction of Ames mutagenicity, we demonstrated the improved behavior of our proposal compared to classical methods.     

The group theoretical approach to nuclear quadrupole resonance

The phenomenon of nuclear quadrupole resonance is presented from the grouptheoretical point of view. This approach allows one to discuss the splitting of the nuclear spin states without prior wave-mechanical calculation. Nuclear quadrupole moments are also compared with moecular quadrupole moments and the differences discussed. Finally the selection rules are deduced.     

Automated carbohydrate profiling by capillary electrophoresis: a bioindustrial approach

Automated, high-resolution, quantitative, high-throughput analysis of mono- and oligosaccharides, produced by enzymatic digestion of cellohexaose (model substrate) and lignocellulosic biomass, is demonstrated using high-performance capillary electrophoresis in conjunction with a single-step fluorophore labeling strategy for sensitive laser-induced fluorescence detection. Unattended batch sample processing from 96-well plates enabled reliable industrial-scale carbohydrate analysis. Excellent resolution of mono- and oligosaccharides was achieved under suppressed electroosmotic flow conditions, using either covalently or dynamically coated fused-silica capillary columns. The proposed approach readily supports the demands of bioindustrial operation environments with respect to high-throughput carbohydrate profiling.     

Molecular and structural determinants of adamantyl susceptibility to HLA-DRs allelic variants: an in silico approach to understand the mechanism of MLEs

Class II major histocompatibility complex (MHC II) molecules as expressed by antigen-presenting cells are heterodimeric cell-surface glycoprotein receptors that are fundamental in initiating and propagating an immune response by presenting tumor-associated antigenic peptides to CD4⁺/T_H cells. The loading efficiency of such peptides can be improved by small organic compounds (MHC Loading Enhancers—MLEs), that convert the non-receptive peptide conformation of MHC II to a peptide-receptive conformation. In a reversible reaction, these compounds open up the binding site of MHC II molecules by specific interactions with a yet undefined pocket. Here, we performed molecular docking and molecular dynamics simulation studies of adamantyl compounds on the predicted cavity around the P1 pocket of 2 allelic variants of HLA-DRs. The purpose was to investigate the suitability of adamantyl compounds as MLEs at the dimorphic β86 position. Docking studies revealed that besides numerous molecular interactions formed by the adamantyl compounds, Asnβ82, Tyrβ83, and Thrβ90 are the crucial amino acid residues that are characterized as the “sensors” of peptide loading. Molecular dynamics simulation studies exposed the dynamical structural changes that HLA-DRs adopted as a response to binding of 3-(1-adamantyl)-5-hydrazidocarbonyl-1H-pyrazole (AdCaPy). The conformations of AdCaPy complexed with the Glyβ86 HLA-DR allelic variant are well correlated with the stabilized form of peptide-loaded HLA-DRs, further confirming the role of AdCaPy as a MLE. Hydrogen bonding interaction analysis clearly demonstrated that after making suitable contacts with AdCaPy, HLA-DR changes its local conformation. However, AdCaPy complexed with HLA-DR having Valβ86 at the dimorphic position did not accommodate AdCaPy as MLE due to steric hindrance caused by the valine.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.     

Expanded metabolomics approach to profiling endogenous carbohydrates in the serum of ovarian cancer patients

We applied hydrophilic interaction liquid chromatography coupled with tandem mass spectrometry to the quantitative analysis of serum from 58 women, including ovarian cancer patients, ovarian benign tumor patients, and healthy controls. All of these ovarian cancer and ovarian benign tumor patients have elevated cancer antigen 125, which makes them clinically difficult to differentiate the malignant from the benign. All of the 16 endogenous carbohydrates were quantitatively detected in the human sera, of which, eight endogenous carbohydrates were significantly different (P‐value < 0.05) between the ovarian cancer and healthy control. According to the receiver operating characteristic curve analysis, arabitol was the most potentially specific biomarker for discriminating ovarian cancer from healthy control, having an area under the curve of 0.911. A panel of metabolite markers composed of maltose, maltotriose, raffinose, and mannitol was selected, which was able to discriminate the ovarian cancer from the benign ovarian tumor counterparts, with an area under concentration‐time curve value of 0.832. Endogenous carbohydrates in the expanded metabolomics approach after the global metabolic profiling are characterized and are potential biomarkers for the early diagnosis of ovarian cancer.     

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.     

Ligand-based luminescence in lead-containing complexes: The effect of conjugated organic ligands on fluorescence

The asymmetrically substituted heterocyclic alkyne 5-(pyridin-3-ylethynyl) picolinate (P3PA) was synthesized via a one-pot Sonogashira coupling. After purification, P3PA was reacted with Pb(NO₃)₂ into the two-dimensional coordination polymer Pb(P3PA)₂(H₂O)₂ (1) under hydrothermal conditions. Coordination polymer 1 crystallizes into the monoclinic space group I2/a, with a = 15.5775(10) Å, b = 5.9949(4) Å, c = 24.8380(16) Å, and β = 90.7500(10)°. In addition, the luminescent fluorene-based ligands, fluorene-9-carboxylate (FCA) and fluorenone-2,7-dicarboxylate (FDCA), were incorporated into hybrid materials via coordination to the lead (II) cation. Coordination polymer Pb(FCA)₂ (2) crystallizes in the monoclinic space group P2₁/c (a = 8.0518(9) Å, b = 25.338(3) Å, c = 10.5842(12) Å, β = 94.913(2)°), and forms a 1-D polymeric chain. Coordination polymer Pb₃(FDCA)₃(H₂O)₃ (3) crystallizes in the triclinic space group P1 (a = 6.6990(5) Å, b = 10.1529(7) Å, c = 13.6935(9) Å, α = 81.884(2)°, β = 87.260(2)°, γ = 84.399(2)°), and forms pillared 3-D layers. These three compounds have been identified via single crystal X-ray diffraction and characterized via powder X-ray diffraction, UV–Vis spectroscopy, and fluorescence spectroscopy.     

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure–activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.     

High-resolution chromatography/time-of-flight MSE with in silico data mining is an information-rich approach to reactive metabolite screening

Electrophilic reactive metabolite screening by liquid chromatography/mass spectrometry (LC/MS) is commonly performed during drug discovery and early-stage drug development. Accurate mass spectrometry has excellent utility in this application, but sophisticated data processing strategies are essential to extract useful information. Herein, a unified approach to glutathione (GSH) trapped reactive metabolite screening with high-resolution LC/TOF MS(E) analysis and drug-conjugate-specific in silico data processing was applied to rapid analysis of test compounds without the need for stable- or radio-isotope-labeled trapping agents. Accurate mass defect filtering (MDF) with a C-heteroatom dealkylation algorithm dynamic with mass range was compared to linear MDF and shown to minimize false positive results. MS(E) data-filtering, time-alignment and data mining post-acquisition enabled detection of 53 GSH conjugates overall formed from 5 drugs. Automated comparison of sample and control data in conjunction with the mass defect filter enabled detection of several conjugates that were not evident with mass defect filtering alone. High- and low-energy MS(E) data were time-aligned to generate in silico product ion spectra which were successfully applied to structural elucidation of detected GSH conjugates. Pseudo neutral loss and precursor ion chromatograms derived post-acquisition demonstrated 50.9% potential coverage, at best, of the detected conjugates by any individual precursor or neutral loss scan type. In contrast with commonly applied neutral loss and precursor-based techniques, the unified method has the advantage of applicability across different classes of GSH conjugates. The unified method was also successfully applied to cyanide trapping analysis and has potential for application to alternate trapping agents.     

Antiviral phytochemicals identification from Azadirachta indica leaves against HCV NS3 protease: an in silico approach

Hepatitis C virus (HCV) is a major health problem across the world affecting the people of all age groups. It is the main cause of hepatitis and at chronic stage causes liver cirrhosis and hepatocellular carcinoma. Various therapeutics are made against HCV but still there is a need to find out potential therapeutics to combat the virus. The goal of this study is to identify the phytochemicals of Azadirachta indica leaves having antiviral activity against HCV NS3 protease through molecular docking and simulation approach. Results show that the compound 3-Deacetyl-3-cinnamoyl-azadirachtin possesses good binding properties with HCV NS3/4A protease. It can be concluded from this study that Deacetyl-3-cinnamoyl-azadirachtin may serve as a potential inhibitor against NS3/4A protease.