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.     

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.     

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.     

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.     

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.     

Prompt nuclear analysis literature: A cumulative advantage approach

A quantitative model establishing a definite connection between literature growth and publication productivity distribution is applied to the example of prompt nuclear analysis.     

Synthesis of zeolites in the presence of radioisotopes: a novel approach to aqueous nuclear waste treatment

Zeolites A (LTA), gismondine (GIS) and the zeotype CoAPO₄-34 (CHA) were synthesised. During the syntheses, additions of Cs-137, Sr/Y-90, U and Th were made. Measurements of residual activities at the conclusion of the syntheses enabled the estimation of the zeolite efficiency to scavenge the isotopes from the initial solution. The majority of systems examined showed excellent radioisotope uptakes. PXRD was used to characterise the synthesis products.     

Low-field approach to double resonance in nuclear quadrupole resonance of spin-1 nuclei

Using double-resonance conditions, in which the Larmor frequency of a spin-1/2 nucleus is matched to one of the nuclear quadrupole resonance frequencies of a spin-1 nucleus, the authors demonstrate increased cross relaxation between the two nuclear spin species. They calculate the cross-relaxation rate using the motionally averaged heterogeneous dipole Hamiltonian as a perturbation to the combined quadrupole and Zeeman Hamiltonians. Using this cross-relaxation rate, in addition to hydrogen and nitrogen autorelaxation rates, expressions governing spin-1/2 and spin-1 spin-lattice relaxation are determined. With ammonium nitrate, containing nitrogen (spin-1) and hydrogen (spin-1/2), increased nitrogen signal and spin-lattice relaxation are demonstrated, using fields less than 120 G. The cross-relaxation rate is also measured and an overall signal/noise improvement by a factor of 2.3+/-0.1 is attained.     

Computational approach to nuclear magnetic resonance in 1-Alkyl-3-methylimidazolium ionic liquids

A quantum-chemical computational approach to accurately predict the nuclear magnetic resonance (NMR) properties of 1-alkyl-3-methylimidazolium ionic liquids has been performed by the gauge-including atomic orbitals method at the B3LYP/6-31++G** level using different simulated ionic liquid environments. The first molecular model chosen to describe the ionic liquid system includes the gas-phase optimized structures of ion pairs and separated ions of a series of imidazolium salts containing methyl, butyl, and octyl substituents and PF6-, BF4-, and Br- anions. In addition, a continuum polarizable model of solvation has been applied to predict the effects of the medium polarity on the molecular properties of 1,3-dimethylimidazolium hexafluorophosphate (MmimPF6). Furthermore, the specific acidic and basic solute-solvent interactions have been simulated by a discrete solvation model based on molecular clusters formed by MmimPF6 species and a discrete number of water molecules. The computational prediction of the NMR spectra allows a consistent interpretation of the dispersed experimental evidence in the literature. The following are main contributions of this work: (a) Theoretical results state the presence of a chemical equilibrium between ion-pair aggregates and solvent-separated counterions of 1-alkyl-3-methylimidazolium salts which is tuned by the solvent environment; thus, strong specific (acidic and basic) and nonspecific (polarity and polarizability) solvent interactions are predicted favoring the dissociated ionic species. (b) The calculated 1H and 13C NMR properties of these ionic liquids are revealed as highly dependent on the nature of solute-solvent interactions. Thus, the chemical shift of the hydrogen atom in position two of the imidazolium ring is deviated to high values by the specific interactions with water molecules, whereas nonspecific interaction with water (as a solvent) affects, in the opposite direction, this 1H NMR parameter. (c) Last, current calculations support the presence of hydrogen bonding between counterions, suggesting the importance of this interaction in the properties of the solvent in the 1-alkyl-3-methylimidazolium ionic liquids.     

Algebraic-diagrammatic construction polarization propagator approach to indirect nuclear spin-spin coupling constants

A new polarization propagator approach to indirect nuclear spin-spin coupling constantans is formulated within the framework of the algebraic-diagrammatic construction (ADC) approximation and implemented at the level of the strict second-order approximation scheme, ADC(2). The ADC approach possesses transparent computational procedure operating with Hermitian matrix quantities defined with respect to physical excitations. It is size-consistent and easily extendable to higher orders via the hierarchy of available ADC approximation schemes. The ADC(2) method is tested in the first applications to HF, N(2), CO, H(2)O, HCN, NH(3), CH(4), C(2)H(2), PH(3), SiH(4), CH(3)F, and C(2)H(4). The calculated indirect nuclear spin-spin coupling constants are in good agreement with the experimental data and results of the second-order polarization propagator approximation method. The computational effort of the ADC(2) scheme scales as n(5) with respect to the number of molecular orbitals n, which makes this method promising for applications to larger molecules.     

Probing genetic algorithms for feature selection in comprehensive metabolic profiling approach

Six different clones of 1-year-old loblolly pine (Pinus taeda L.) seedlings grown under standardized conditions in a green house were used for sample preparation and further analysis. Three independent and complementary analytical techniques for metabolic profiling were applied in the present study: hydrophilic interaction chromatography (HILIC-LC/ESI-MS), reversed-phase liquid chromatography (RP-LC/ESI-MS), and gas chromatography all coupled to mass spectrometry (GC/TOF-MS). Unsupervised methods, such as principle component analysis (PCA) and clustering, and supervised methods, such as classification, were used for data mining. Genetic algorithms (GA), a multivariate approach, was probed for selection of the smallest subsets of potentially discriminative classifiers. From more than 2000 peaks found in total, small subsets were selected by GA as highly potential classifiers allowing discrimination among six investigated genotypes. Annotated GC/TOF-MS data allowed the generation of a small subset of identified metabolites. LC/ESI-MS data and small subsets require further annotation. The present study demonstrated that combination of comprehensive metabolic profiling and advanced data mining techniques provides a powerful metabolomic approach for biomarker discovery among small molecules. Utilizing GA for feature selection allowed the generation of small subsets of potent classifiers.