Quantitative Structure (quantitative + structure)

Distribution by Scientific Domains
Distribution within Chemistry

Terms modified by Quantitative Structure

  • quantitative structure activity relationship

  • Selected Abstracts

    QSAR for Inhibition of Pseudomonas Species Lipase by 1-Acyloxy-3- N-n -octylcarbamyl-benzenes

    Shyh-Ying Chiou
    Abstract 1-Acyloxy-3- N-n -octylcarbamyl-benzenes (1,9) are synthesized to characterize the Quantitative Structure,Activity Relationship (QSAR) for the Third Acyl Group Binding Site (TACS) of Pseudomonas species lipase. Inhibitors 1,9 are characterized as pseudo or alternate substrate inhibitors of the enzyme. The inhibition constant (Ki) and carbamylation constant (k2) for the enzyme inhibitions by inhibitors 1,9 are determined. The carbamate carbons of the n -octylcarbamyl moieties of inhibitors 1,9 are nucleophilically attacked by the active site serine of the enzyme and the n -octylcarbamyl groups of inhibitors 1,9 are bound to the Acyl Group Binding Site (ACS) of the enzyme. Both pKi and log,k2 values are linearly corrected with the Hansch hydrophobicity , values of the substituents of the acyl moieties of inhibitors 1,7. The slopes for these corrections are 0.13 and 0.02, respectively. This result suggests that the enzyme inhibitions by inhibitors 1,7 have a common mechanism. Thus, all acyl moieties of inhibitors 1,7 should bind to the TACS of the enzyme since the acyl and carbamyl moieties of inhibitors 1,7 are meta to each other. This result also indicates that the major interaction between the acyl moiety of inhibitors 1,7 and the TACS of the enzyme is primarily the hydrophobic interaction. The more hydrophobic characters of inhibitors 1,7 are, the more tightly these inhibitors bind to the enzyme. In contrast, 1-triphenylacetoxy-3- N-n -octylcarbamyl-benzene (8) and 1-trimethylacetoxy-3- N-n -octylcarbamyl-benzene (9) do not bind to the TACS of the enzyme due to the fact that the inhibitions by both inhibitors are not linearly correlated with ,. It is possible that these two inhibitors are too bulky to fit into the TACS of the enzyme. [source]

    Identification of Novel CDK2 Inhibitors by QSAR and Virtual Screening Procedures

    MOLECULAR INFORMATICS, Issue 11-12 2008
    Ajay Babu, Padavala
    Abstract Quantitative Structure,Activity Relationship (QSAR) studies were carried out on a set of 46 imidazo[1,2-a]pyridines, imidazo[1,2-b]pyridazines and 2,4-bis anilino pyrimidines, and nitroso-6-aminopyrimidine and 2,6-diaminopyrimidine inhibitors of CDK2 (Cyclin-dependent Kinase2) using a multiple regression procedure. The activity contributions of these compounds were determined from regression equation and the validation procedures such as external set cross-validation r2, (R2cv,ext) and the regression of observed activities against predicted activities and vice versa for validation set were described to analyze the predictive ability of the QSAR model. An accurate and reliable QSAR model involving five descriptors was chosen based on the FIT Kubinyi function which defines the statistical quality of the model. The proposed model due to its high predictive ability was utilized to screen similar repertoire of compounds reported in the literature, and the biological activities are estimated. The screening study clearly demonstrated that the strategy presented shall be used as an alternative to the time-consuming experiments as the model tolerated a variety of structural modifications signifying its potential for drug design studies. [source]

    A 3-D QSAR Study of Catechol- O -Methyltransferase Inhibitors Using CoMFA and CoMSIA

    Chunzhi Ai
    Abstract Inhibitors of Catechol- O -Methyltransferase (COMT) play an important role in the treatment of Parkinson's Disease (PD). A new Three-Dimensional Quantitative Structure,Activity Relationship (3-D QSAR) analysis was performed on 36 previously reported COMT inhibitors employing Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methodologies to correlate the molecular fields and percent inhibition values and three predictive models were derived. The CoMFA and CoMSIA models with steric and electrostatic field yielded cross-validated rs of 0.585 and 0.528, respectively whereas the conventional rs were 0.979 and 0.891, respectively. The CoMSIA model with hydrophobic field exhibited a r of 0.544 and a r of 0.930. The individual inspection of 3-D contours generated from these models helps in understanding the possible region for structural modification of molecules to improve the inhibitory bioactivity. These 3-D QSAR models are also useful for designing and predicting novel COMT inhibitors. [source]

    Quantitative Structure,Activity Relationship Study on Fish Toxicity of Substituted Benzenes

    Zhiguo Gong
    Abstract Many chemicals cause latent harm, such as erratic diseases and change of climate, and therefore it is necessary to evaluate environmentally safe levels of dangerous chemicals. Quantitative Structure,Toxicity Relationship (QSTR) analysis has become an indispensable tool in ecotoxicological risk assessments. Our paper used QSTR to deal with the modeling of the acute toxicity of 92 substituted benzenes. The molecular descriptors representing the structural features of the compounds were calculated by CODESSA program. Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were utilized to construct the linear and the nonlinear QSTR models, respectively. The predictive results were in agreement with the experimental values. The optimal QSTR model which was established based on RBFNNs gave a correlation coefficient (R2) of 0.893, 0.876, 0.889 and Root-Mean-Square Error (RMSE) of 0.220, 0.205, 0.218 for the training set, the test set, and the whole set, respectively. RBFNNs proved to be a very good method to assess acute aquatic toxicity of these compounds, and more importantly, the RBFNNs model established in this paper has fewer descriptors and better results than other models reported in previous literatures. The current model allows a more transparent chemical interpretation of the acute toxicity in terms of intermolecular interactions. [source]

    Prediction of Flash Point Temperature of Pure Components Using a Quantitative Structure,Property Relationship Model

    Farhad Gharagheizi
    Abstract In this work, a general Quantitative Structure,Property Relationship (QSPR) model (, , and ) for the prediction of flash points of 1030 pure compounds is developed. Genetic Algorithm-based Multivariate Linear Regression (GA-MLR) technique is used to select four chemical structure-based molecular descriptors from a pool containing 1664 molecular descriptors. [source]

    Quantitative Structure,Activity Relationship Studies for the Binding Affinities of Imidazobenzodiazepines for the ,6 Benzodiazepine Receptor Isoform Utilizing Optimized Blockwise Variable Combination by Particle Swarm Optimization for Partial Least Squares Modeling

    Leqian Hu
    Abstract Binding affinities of a series of substituted imidazobenzodiazepines for the ,6 Benzodiazepine Receptor (BzR) isoform are investigated by the Optimized Blockwise Variable Combination (OBVC) by Particle Swarm Optimization (PSO) based on Partial Least Squares (PLS) modeling. The QSAR analysis result showed that MolRef, AlogP, MRCM**-3, Rotatable bonds (Rotlbonds), Hydrogen Bond Acceptors (Hbond acceptor), five Jurs descriptors, two Shadow indices descriptors and principal moment of inertia are the most important descriptors among all the investigated descriptors. One can change the molar refractivity, the polar interactions between molecules, the shape of the molecules, the principal moments of inertia about the principal axes of a molecule, the hydrophobic character of the molecule, the number of Rotlbonds and Hbond acceptors of the compounds to adjust the binding affinities of imidazobenzodiazepine for the ,6 BzR isoform. The Quantitative Structure,Activity Relationship (QSAR) analysis result was also compared with MLR, PLS, and hierarchical PLS algorithms. It has been demonstrated that OBVC by PSO for PLS modeling shows satisfactory performance in the QSAR analysis. [source]

    An Approach towards the Quantitative Structure,Activity Relationships of Caffeic Acid and its Derivatives

    CHEMBIOCHEM, Issue 9 2004
    Rajeshwar P. Verma Dr.
    Abstract Caffeic acid and its derivatives are already known to possess a wide range of biological activities. We have developed quantitative structure,activity relationships (QSARs) for different series of caffeic acid derivatives (including caffeic acid) in order to understand the chemical,biological interactions governing antitumor activity against six different tumor cell lines, nitric oxide production, anti-HIV and enzymatic activities, and binding affinity to the lck domain. QSAR results have shown that the different activities of caffeic acid and its derivatives are largely dependent on their hydrophobicity or molar refractivity, with a bilinear correlation being the most important. [source]

    Quantitative Structure,Activity Relationship Models for Predicting Biological Properties, Developed by Combining Structure- and Ligand-Based Approaches: An Application to the Human Ether-a-go-go-Related Gene Potassium Channel Inhibition

    Alessio Coi
    A strategy for developing accurate quantitative structure,activity relationship models enabling predictions of biological properties, when suitable knowledge concerning both ligands and biological target is available, was tested on a data set where molecules are characterized by high structural diversity. Such a strategy was applied to human ether-a-go-go-related gene K+ channel inhibition and consists of a combination of ligand- and structure-based approaches, which can be carried out whenever the three-dimensional structure of the target macromolecule is known or may be modeled with good accuracy. Molecular conformations of ligands were obtained by means of molecular docking, performed in a previously built theoretical model of the channel pore, so that descriptors depending upon the three-dimensional molecular structure were properly computed. A modification of the directed sphere-exclusion algorithm was developed and exploited to properly splitting the whole dataset into Training/Test set pairs. Molecular descriptors, computed by means of the codessa program, were used for the search of reliable quantitative structure,activity relationship models that were subsequently identified through a rigorous validation analysis. Finally, pIC50 values of a prediction set, external to the initial dataset, were predicted and the results confirmed the high predictive power of the model within a quite wide chemical space. [source]

    ChemInform Abstract: Quantitative Structure,Activity Relationship (QSAR) Study of New Fluorovinyloxyacetamides.

    CHEMINFORM, Issue 14 2002
    Doo Ho Cho
    Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a "Full Text" option. The original article is trackable via the "References" option. [source]

    ChemInform Abstract: Investigation of 5-Nitrofuran Derivatives: Synthesis, Antibacterial Activity, and Quantitative Structure,Activity Relationships.

    CHEMINFORM, Issue 9 2002
    Jose Ricardo Pires
    Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a "Full Text" option. The original article is trackable via the "References" option. [source]

    QSARs for aromatic hydrocarbons at several trophic levels

    Walter Di Marzio
    Abstract Quantitative structure,activity relationships (QSARs) with aromatic hydrocarbons were obtained. Biological response was measured by acute toxicity of several aquatic trophic levels. The chemicals assayed were benzene, toluene, ethylbenzene, o -xylene, m -xylene, p -xylene, isopropylbenzene, n -propylbenzene, and butylbenzene. Acute toxicity tests were carried out with Scenedesmus quadricauda, as representative of primary producers; Daphnia spinulata, a zooplanctonic cladoceran; Hyalella curvispina, a benthic macroinvertebrate; and Bryconamericus iheringii, an omnivorous native fish. The EC50 or LC50 was calculated from analytical determinations of aromatic hydrocarbons. Nonlinear regression analysis between the logarithm of the octanol,water partition coefficient (log Kow) of each compounds and the toxicity end points was performed. QSARs were positively related to increases in log Kow at all trophic levels. Intertaxonomic differences were found in comparisons of algae with animals and of invertebrates with vertebrates. We observed that these differences were not significant with a log Kow higher than 3 for all organisms. Aromatic hydrocarbons with log Kow values of less than 3 showed different toxicity responses, with algae more resistant than fish and invertebrates. We concluded that this was a result of the narcotic mode of action related to liposolubility and the ability of the compound to reach its target site in the cell. The bioconcentration factor (BCF) achieved to start nonpolar narcosis fell almost 1 order of magnitude below the BCF expected from the log Kow. Predicted critical body residues for nonpolar narcosis ranged between 2 and 1 mM. © 2006 Wiley Periodicals, Inc. Environ Toxicol 21: 118,124, 2006. [source]

    Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology

    Roger Perkins
    Abstract Quantitative structure,activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure,activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance. [source]

    Studies on the quantitative relationship between the olfactory thresholds of pyrazine derivatives and their molecular structures

    Feng Luan
    Abstract Quantitative structure,property relationship (QSPR) investigation was performed for the study of olfactory thresholds of pyrazine derivatives. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. The six molecular descriptors selected by the best mutilinear regression (BMLR) in CODESSA were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The root mean squared errors (RMS) of logarithm of olfactory thresholds (p.p.m.) for the training, predicted and overall datasets were 0.5674, 0.6601 and 0.5860 for BMLR, 0.4720, 0.6861 and 0.5194 for RBFNN, and 0.5242, 0.6466 and 0.5495 for SVM, respectively. The prediction results were in agreement with the experimental values. The QSPR models provide a rapid, simple and valid way to predict the odour threshold of pyrazine derivatives. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Electronic and charge aspects of potential endocrine disruptors: Applications to pharmacological clustering

    James W. King
    Abstract Quantitative structure,activity relationships in a series of 37 substituted indoles with endocrine disruptor potential were performed using the structural indices FTe (electronic) and FTc (charge), in conjunction with a clustering technique, to relate substitution patterns to reported relative binding affinities for the calf estrogen receptor. Data clusters were generated by a primary numerically descending sort of the structure indices with a concurrent secondary numerically descending sort of the binding data. Reversal of the numerical descent of the latter served to delineate cluster boundaries. Analysis within the clusters defined the effect of substituents and their molecular positions on the pharmacological data. These results confirmed in detail a similar previous study in the same series using the more general FTm index and again suggested the same structure of a molecule with greater receptor binding ability than any in the database. The methodology used in these studies permits a rational presentation and subsequent interpretation of data that initially appear to be totally random and devoid of recoverable information content. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem, 2003 [source]

    Quantitative structure,activity relationship study on the inhibitors of fatty acid amide hydrolase

    Peng Lu
    Abstract A quantitative structure activity relationship (QSAR) analysis was performed on the values of a series of fatty acid amide hydrolase (FAAH) inhibitors. Six molecular descriptors selected by CODESSA software were used as inputs to perform heuristic method (HM) and support vector machine (SVM). The results obtained by SVM were compared with those obtained by the HM. The root mean square errors (RMSEs) for the training set given by HM and SVM were 0.555 and 0.404, respectively, which shows that the performance of the SVM model is better than that of the HM model. This paper provides a new and effective method for predicting the activity of FAAH inhibitors. Copyright © 2010 John Wiley & Sons, Ltd. [source]

    Quantitative structure,reactivity relationship studies on the catalyzed Michael addition reactions

    Bahram Hemmateenejad
    Abstract Quantitative structure,reactivity relationship (QSRR) can be considered as a variant of quantitative structure,property relationship (QSPR) studies, where the chemical reactivity of reactants or catalysts in a specified chemical reaction is related to chemical structure. In this manner, the Michael addition of some different substrates using different catalysts (SDS, silica gel, and ZrOCl2) was subjected to structure,reactivity relationship, quantitatively. Multiple linear regression (MLR) and partial least square (PLS) were used to perform the QSRR analysis. The resulted models for different catalyzed reactions showed that the catalysts probably act in different mechanisms since the models obtained for the catalysts included different parameters from substrate and enones. Overall, it was found that the reactivity in Michael addition reactions is controlled by coulombic (dipole and charge) interactions as well as the orbital energetic parameters. In the presence of different catalysts, the relative importance of these parameters is changed and hence the catalytic activity is changed. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Investigation of protein binding affinity and preferred orientations in ion exchange systems using a homologous protein library

    Wai Keen Chung
    Abstract A library of cold shock protein B (CspB) mutant variants was employed to study protein binding affinity and preferred orientations in cation exchange chromatography. Single site mutations introduced at charged amino acids on the protein surface resulted in a homologous protein set with varying charge density and distribution. The retention times of the mutants varied significantly during linear gradient chromatography. While the expected trends were observed with increasing or decreasing positive charge on the protein surface, the degree of change was a strong function of the location and microenvironment of the mutated amino acid. Quantitative structure,property relationship (QSPR) models were generated using a support vector regression technique that was able to give good predictions of the retention times of the various mutants. Molecular descriptors selected during model generation were used to elucidate the factors affecting protein retention. Electrostatic potential maps were also employed to provide insight into the effects of protein surface topography, charge density and charge distribution on protein binding affinity and possible preferred binding orientations. The use of this protein mutant library in concert with the qualitative and quantitative analyses presented in the article provides an improved understanding of protein behavior in ion exchange systems. Biotechnol. Bioeng. 2009; 102: 869,881. © 2008 Wiley Periodicals, Inc. [source]

    Structure,activity relationships for the mutagenicity and carcinogenicity of simple and ,-, unsaturated aldehydes

    Romualdo Benigni
    Abstract Aldehydes are important industrial compounds that are used for the synthesis of chemicals and pharmaceuticals and as solvents, food additives, and disinfectants. Because of their reactivity, aldehydes are able to interact with electron-rich biological macromolecules and adverse health effects have been reported, including general toxicity, allergenic reactions, mutagenicity, and carcinogenicity. The cost, time, and number of animals necessary to adequately screen these chemicals places serious limitations on the number of aldehydes whose health potential can be studied and points to the need of using alternative methods for assessing, at least in a preliminary way, the risks associated with the use of aldehydes. A method of choice is the study of quantitative structure,activity relationships (QSARs). In the present work, we present QSAR models for the mutagenicity and carcinogenicity of simple aldehydes and ,-, unsaturated aldehydes. The models point to the role of electrophilicity, bulkiness, and hydrophobicity in the genotoxic activity of the aldehydes and lend themselves to the prediction of the activity of other untested chemicals of the same class. Environ. Mol. Mutagen. 42:136,143, 2003. © 2003 Wiley-Liss, Inc. [source]

    The extrapolation problem and how population modeling can help,

    Valery E. Forbes
    Abstract We argue that population modeling can add value to ecological risk assessment by reducing uncertainty when extrapolating from ecotoxicological observations to relevant ecological effects. We review other methods of extrapolation, ranging from application factors to species sensitivity distributions to suborganismal (biomarker and "-omics") responses to quantitative structure,activity relationships and model ecosystems, drawing attention to the limitations of each. We suggest a simple classification of population models and critically examine each model in an extrapolation context. We conclude that population models have the potential for adding value to ecological risk assessment by incorporating better understanding of the links between individual responses and population size and structure and by incorporating greater levels of ecological complexity. A number of issues, however, need to be addressed before such models are likely to become more widely used. In a science context, these involve challenges in parameterization, questions about appropriate levels of complexity, issues concerning how specific or general the models need to be, and the extent to which interactions through competition and trophic relationships can be easily incorporated. [source]

    Predicting single and mixture toxicity of petrogenic polycyclic aromatic hydrocarbons to the copepod Oithona davisae

    Carlos Barata
    Abstract In the present study, the acute toxicity of 10 polycyclic aromatic hydrocarbons (PAH) associated with the Prestige fuel oil spill (Spain, 2002) were evaluated, either as single substances or in mixtures, in adults of the copepod Oithona davisae. All but dimethylphenanthrene had negative effects on O. davisae survival at concentrations below their water solubility, with 48-h median lethal concentrations for naphthalene and pyrene of 56.1 and 0.8 ,mol/L, respectively, making these the least and most toxic compounds. Polycyclic aromatic hydrocarbons had narcotic effects on copepods, as evidenced by the lack of motility at lower concentrations than those causing death. Naphthalene showed the greatest narcotic effects, and phenanthrene showed minor effects. Acute toxicity of the tested PAHs was inversely related (r2 = 0.9) with their octanol,water partition coefficient, thereby confirming the validity of the baseline quantitative structure,activity regression models for predicting the toxicity of PAH compounds in copepod species. When supplied in mixtures, the toxic effect of PAHs was additive. These results indicate that the many PAHs in an oil spill can be considered unambiguous baseline toxicants (class 1) acting additively as nonpolar narcotics in copepods; hence, their individual and combined toxicity can be predicted using their octanol,water partition coefficient. [source]

    Toxicity assessment of mono-substituted benzenes and phenols using a Pseudomonas initial oxygen uptake assay

    Ded-Shih Huang
    Abstract A methodology is presented for assessing the toxicity of chemical substances through their inhibitory action toward the Pseudomonas initial oxygen uptake (PIOU) rate. The current studies reveal that the PIOU assay is rapid, cost-efficient, and easy to perform. The oxygen uptake rate was found to be associated with a putative benzoate transporter and highly dependent on benzoate concentration. The putative benzoate transporter has been shown to follow Michaelis,Menten kinetics. Most phenols were found to be noncompetitive inhibitors of the benzoate transporter. The inhibition constant (Ki) of these noncompetitive inhibitors can be related to the concentration causing 50% oxygen uptake inhibition in Pseudomonas putida. Modeling these data by using the response,surface approach leads to the development of a quantitative structure,activity relationship (QSAR) for the toxicity of phenols ((1/Ki) = ,0.435 (±0.038) lowest-unoccupied-molecular orbital + 0.517 (±0.027)log KOW ,2.340 (±0.068), n = 49, r2 = 0.930, s = 0.107, r2adj = 0.926, F = 303.1). A comparison of QSAR models derived from the Ki data of the PIOU method and the toxicity data of 40-h Tetrahymena pyrifomis growth inhibition assay (Tetratox) indicated that there was a high correlation between the two approaches (r2 = 0.925). [source]

    Prediction of uptake dynamics of persistent organic pollutants by bacteria and phytoplankton

    Sabino Del Vento
    Abstract Phytoplankton and bacteria play an important role on the biogeochemical cycles of persistent organic pollutants (POPs). However, experimental data and quantitative knowledge of the kinetics of uptake and depuration of most POPs by bacteria and phytoplankton are scarce. In the present paper, a procedure to predict the sorption kinetics to bacteria and phytoplankton is developed. The prediction method is the combination of a mechanistic model for sorption and quantitative structure,activity relationships relating bioconcentration factors and membrane permeability to the chemical physical-chemical properties. The model consists of two compartments where the first compartment is the cellular surface and the second compartment is the cell biomass or matrix. Equations for estimating uptake and depuration rate constants into the matrix and adsorption and desorption rate constants onto the surface are obtained. These expressions depend on the physical-chemical properties of the chemical, the environmental temperature, the microorganism size, and species-specific quality of organic matter. While microorganism shape has a secondary influence on uptake dynamics, microorganism size and chemical hydrophobicity arise as the key factors controlling the kinetics of POP incorporation into bacteria and plankton. Uptake, depuration, adsorption, and desorption rate constants are reported for POPs such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated dioxins and furans (PCDD/Fs), and POPs of emerging concern, such as polybrominated diphenyl ethers (PBDEs). Finally, implications of uptake and depuration dynamics on the biogeochemical cycling of POPs are discussed. [source]

    Mutagenic probes of the role of Ser209 on the cavity shaping loop of human monoamine oxidase A

    FEBS JOURNAL, Issue 16 2009
    Jin Wang
    The available literature implicating human monoamine oxidase A (MAO A) in apoptotic processes reports levels of MAO A protein that do not correlate with activity, suggesting that unknown mechanisms may be involved in the regulation of catalytic function. Bioinformatic analysis suggests Ser209 as a possible phosphorylation site that may be relevant to catalytic function because it is adjacent to a six-residue loop termed the ,cavity shaping loop' from structural data. To probe the functional role of this site, MAO A Ser209Ala and Ser209Glu mutants were created and investigated. In its membrane-bound form, the MAO A Ser209Glu phosphorylation mimic exhibits catalytic and inhibitor binding properties similar to those of wild-type MAO A. Solubilization in detergent solution and purification of the Ser209Glu mutant results in considerable decreases in these functional parameters. By contrast, the MAO A Ser209Ala mutant exhibits similar catalytic properties to those of wild-type enzyme when purified. Compared to purified wild-type and Ser209Ala MAO A proteins, the Ser209Glu MAO A mutant shows significant differences in covalent flavin fluorescence yield, CD spectra and thermal stability. These structural differences in the purified MAO A Ser209Glu mutant are not exhibited in quantitative structure,activity relationship patterns using a series of para -substituted benzylamine analogs similar to the wild-type enzyme. These data suggest that Ser209 in MAO A does not appear to be the putative phosphorylation site for regulation of MAO A activity and demonstrate that the membrane environment plays a significant role in stabilizing the structure of MAO A and its mutant forms. [source]

    Foundation of quantum similarity measures and their relationship to QSPR: Density function structure, approximations, and application examples

    Ramon Carbó-Dorca
    Abstract This work presents a schematic description of the theoretical foundations of quantum similarity measures and the varied usefulness of the enveloping mathematical structure. The study starts with the definition of tagged sets, continuing with inward matrix products, matrix signatures, and vector semispaces. From there, the construction and structure of quantum density functions become clear and facilitate entry into the description of quantum object sets, as well as into the construction of atomic shell approximations (ASA). An application of the ASA is presented, consisting of the density surfaces of a protein structure. Based on this previous background, quantum similarity measures are naturally constructed, and similarity matrices, composed of all the quantum similarity measures on a quantum object set, along with the quantum mechanical concept of expectation value of an operator, allow the setup of a fundamental quantitative structure,activity relationship (QSPR) equation based on quantum descriptors. An application example is presented based on the inhibition of photosynthesis produced by some naphthyridinone derivatives, which makes them good herbicide candidates. © 2004 Wiley Periodicals, Inc. Int J Quantum Chem, 2005 [source]

    Neural network modeling of physical properties of chemical compounds

    J. Kozio
    Abstract Three different models relating structural descriptors to normal boiling points, melting points, and refractive indexes of organic compounds have been developed using artificial neural networks. A newly elaborated set of molecular descriptors was evaluated to determine their utility in quantitative structure,property relationship (QSPR) studies. Applying two data sets containing 190 amines and 393 amides, neural networks were trained to predict physical properties with close to experimental accuracy, using the conjugated gradient algorithm. Obtained results have shown a high predictive ability of learned neural networks models. The fit error for the predicted properties values compared to experimental data is relatively small. © 2001 John Wiley & Sons, Inc. Int J Quant Chem 84: 117,126, 2001 [source]

    Modeling the semi-empirical electrotopological index in QSPR studies for aldehydes and ketones

    Érica Silva Souza
    Abstract The semi-empirical electrotopological index, ISET, used for quantitative structure,retention relationship (QSRR) models firstly developed for alkanes and alkenes, was remodeled for organic functions such as ketones and aldehydes. The ISET values for hydrocarbons are calculated through the atomic charge values obtained from a Mulliken population analysis using the semi-empirical AM1 method and their correlation with the SETi values attributed to the different types of carbon atoms according to experimental data. For ketones and aldehydes the interactions between the molecules and the stationary phase are slowly increased relative to the hydrocarbons, due to the charge redistribution that occurs in the presence of heteroatoms. For these polar molecules the increase in the interactions was included in the calculation of the ISET values through the dipole moment of the whole molecule and also through an equivalent local dipole moment related to the net charges of the atoms of the CO and HCO functional groups. Our findings show that the best definition of an equivalent local dipole moment is clearly dependent on the specific features of the charge distribution in the polar region of the molecules (e.g. ketones and aldehydes), which allows them to be distinguished. Thus, the QSRR models for 15 aldehydes and 42 ketones obtained using the remodeled ISET were of good quality as shown by the statistical parameters. The ability of this remodeled index to include charge distribution and structural details opens a new way to study the correlations between the molecular structure and retention indices in gas chromatography. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    An alignment-free methodology for modelling field-based 3D-structure activity relationships using inductive logic programming

    Bård Buttingsrud
    Abstract Traditional 3D-quantitative structure,activity relationship (QSAR)/structure,activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine-learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first-order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Controlling coverage of D-optimal onion designs and selections

    Ing-Marie Olsson
    Abstract Statistical molecular design (SMD) is a powerful approach for selection of compound sets in medicinal chemistry and quantitative structure,activity relationships (QSARs) as well as other areas. Two techniques often used in SMD are space-filling and D-optimal designs. Both on occasions lead to unwanted redundancy and replication. To remedy such shortcomings, a generalization of D-optimal selection was recently developed. This new method divides the compound candidate set into a number of subsets (,layers' or ,shells'), and a D-optimal selection is made from each layer. This improves the possibility to select representative molecular structures throughout any property space independently of requested sample size. This is important in complex situations where any given model is unlikely to be valid over the whole investigated domain of experimental conditions. The number of selected molecules can be controlled by varying the number of subsets or by altering the complexity of the model equation in each layer and/or the dependency of previous layers. The new method, called D-optimal onion design (DOOD), will allow the user to choose the model equation complexity independently of sample size while still avoiding unwarranted redundancy. The focus of the present work is algorithmic improvements of DOOD in comparison with classical D-optimal design. As illustrations, extended DOODs have been generated for two applications by in-house programming, including some modifications of the D-optimal algorithm. The performances of the investigated approaches are expected to differ depending on the number of principal properties of the compounds in the design, sample sizes and the investigated model, i.e. the aim of the design. QSAR models have been generated from the selected compound sets, and root mean squared error of prediction (RMSEP) values have been used as measures of performance of the different designs. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Omeprazole and analogue compounds: a QSAR study of activity against Helicobacter pylori using theoretical descriptors,

    JOURNAL OF CHEMOMETRICS, Issue 8-10 2002
    Aline Thais Bruni
    Abstract Omeprazole and analogues were studied with respect to their activity as inhibitors of urease Helicobacter pylori. Conformational analysis was performed according to the method proposed by Bruni et al. Theoretical descriptors were calculated by an ab initio method (6,31G** basis set). Since several minimum energy structures were obtained for each compound, and the calculated descriptors proved to be sensitive to the structural conformation, different criteria were proposed for conformation selection. Three data sets were generated wherein conformations were grouped according to minimum heat of formation, minimum electronic energy and structural similarity. For these three sets, experimental per cent of control was used to develop quantitative structure,activity models by PLS. Their cross-validation and correlation coefficients were very good (Q2,=,0.97 and R2,=,0.99 on average) and the standard error of validation was much smaller in comparison with results from the literature. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    A systematic evaluation of the benefits and hazards of variable selection in latent variable regression.

    Part I. Search algorithm, simulations, theory
    Abstract Variable selection is an extensively studied problem in chemometrics and in the area of quantitative structure,activity relationships (QSARs). Many search algorithms have been compared so far. Less well studied is the influence of different objective functions on the prediction quality of the selected models. This paper investigates the performance of different cross-validation techniques as objective function for variable selection in latent variable regression. The results are compared in terms of predictive ability, model size (number of variables) and model complexity (number of latent variables). It will be shown that leave-multiple-out cross-validation with a large percentage of data left out performs best. Since leave-multiple-out cross-validation is computationally expensive, a very efficient tabu search algorithm is introduced to lower the computational burden. The tabu search algorithm needs no user-defined operational parameters and optimizes the variable subset and the number of latent variables simultaneously. Copyright © 2002 John Wiley & Sons, Ltd. [source]