A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies

Computational chemistry is playing an increasingly important role in drug design and discovery, structural biology, and quantitative structure-activity relationship (QSAR) studies. For QSAR work, selecting an appropriate and accurate method to assign the electrostatic potentials of each atom in a molecule is a critical first step. So far several commonly used methods are available to assign charges. However, no systematic comparison of the effects of electrostatic potentials on QSAR quality has been made. In this study, twelve semi-empirical and empirical charge-assigning methods, AM1, AM1-BCC, CFF, Del-Re, Formal, Gasteiger, Gasteiger-Hückel, Hückel, MMFF, PRODRG, Pullman, and VC2003 charges, have been compared for their performances in CoMFA and CoMSIA modeling using several standard datasets. Some charge assignment models, such as Del-Re, PRODRG, and Pullman, are limited to specific atom and bond types, and, therefore, were excluded from this study. Among the remaining nine methods, the Gasteiger-Hückel charge, though commonly used, performed poorly in prediction accuracy. The AM1-BCC method was better than most charge-assigning methods based on prediction accuracy, though it was not successful in yielding overall higher cross-validation correlation coefficient (q(2)) values than others. The CFF charge model worked the best in prediction accuracy when q(2) was used as the evaluation criterion. The results presented should help the selection of electrostatic potential models in CoMFA and CoMSIA studies.

Comparison data sets for benchmarking QSAR methodologies in lead optimization

2D and 3D QSAR techniques are widely used in lead optimization-like processes. A compilation of 40 diverse data sets is described. It is proposed that these can be used as a common benchmark sample for comparisons of QSAR methodologies, primarily in terms of predictive ability. Use of this benchmark set will be useful for both assessment of new methods and for optimization of existing methods.

Partial charge calculation method affects CoMFA QSAR prediction accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.

Effect of steric molecular field settings on CoMFA predictivity

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard-Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R(2)) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard-Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.

Development of CoMFA models of affinity and selectivity to angiotensin II type-1 and type-2 receptors

The renin-angiotensin system (RAS) is of major importance in cardiovascular and renal regulation and has been an attractive target in drug discovery for a long time. The main receptors involved in the RAS are the Angiotensin type-1 (AT(1)) and type-2 (AT(2)) receptors, which are both activated by the endogenous octapeptide angiotensin II (AngII). This study describes the development of 3D-QSAR models for AT(1) and AT(2) receptor affinity and AT(1)/AT(2) receptor selectivity using CoMFA. A data set of 244 compounds, based on the triazolinone and quinazolinone structural classes was compiled from the literature. Before CoMFA could be performed, an alignment rule for the two structural classes was defined using the pharmacophore-searching program DISCOtech. Models were validated using a test set obtained by dividing the data set into a training set and test set using hierarchical clustering, based on the CoMFA fields, AT(1)-, AT(2)-receptor affinities, and AT(1)/AT(2) selectivity values. Predictive models with good statistics could be developed both for AT(1) and AT(2) receptor affinity as well as selectivity towards these receptors.

Systematic Statistical Comparison of Comparative Molecular Similarity Indices Analysis Molecular Fields for Computer-Aided Lead Optimization

Comparative molecular similarity indices analysis (CoMSIA) is a 3D quantitative structure-activity relationship technique used to determine structural and electronic features influencing biological activity. This proves particularly useful for facilitating lead optimization projects. This study aimed to compare CoMSIA models produced using different subsets of the CoMSIA molecular fields (steric, electrostatic, hydrophobic, hydrogen-bond donor, and hydrogen-bond acceptor) in a systematic and statistically valid manner. A total of 23 data sets sourced from the literature were used to compare molecular field contribution and model predictivity using leave-one-out cross-validated R2 values. Predictive ability varied in a highly statistically significant manner depending on the set of CoMSIA molecular fields used. In general, the greater the number of CoMSIA molecular fields included in the analysis, the better the model predictivity was. There is great redundancy in the information contained in the different CoMSIA molecular fields. When all five CoMSIA molecular fields are included, the hydrophobic and electrostatic fields had the largest and the steric field the smallest contribution. Data sets were clustered into four groups on the basis of the utility of molecular field sets to generate predictive models.

Proposal of a New Binding Orientation for Non-Peptide AT1 Antagonists: Homology Modeling, Docking and Three-Dimensional Quantitative Structure−Activity Relationship Analysis

A three-dimensional model of the AT1 receptor was constructed by means of a homology modeling procedure, using the X-ray structure of bovine rhodopsin as the initial template and taking into account the available site-directed mutagenesis data. The docking of losartan and its active metabolite EXP3174, followed by 1 ns of molecular dynamics (MD) simulation inserted into the phospholipid bilayer, suggested a different binding orientation for these antagonists from those previously proposed. Furthermore, the docking of several non-peptide antagonists was used as an alignment tool for the development of a three-dimensional quantitative structure-activity relationship (3D-QSAR) model, and the good results confirmed our binding hypothesis and the reliability of the model.

Selection of data sets for QSARs: analyses of Tetrahymena toxicity from aromatic compounds

The aim of this investigation was to develop a strategy for the formulation of a valid ecotoxicological-based QSAR while, at the same time, minimizing the required number of toxicological data points. Two chemical selection approaches-distance-based optimality and K Nearest Neighbor (KNN), were used to examine the impact of the number of compounds used in the training and testing phases of QSAR development (i.e. diversity and representivity, respectively) on the predictivity (i.e. external validation) of the QSAR. Regression-based QSARs for the ectotoxic potency for population growth impairment of aromatic compounds (benzenes) to the aquatic ciliate Tetrahymena pyriformis were developed based on descriptors for chemical hydrophobicity and electrophilicity. A ratio of one compound in the training set to three in the test set was applied. The results indicate that from a known chemical universe, in this case 385 derivatives, robust QSARs of equal quality may be developed from a small number of diverse compounds, validated by a representative test set. As a conservative recommendation it is suggested that there should be a minimum of 10 observations for each variable in a QSAR.

Design and synthesis of nonpeptide angiotensin II receptor antagonists featuring acyclic imidazole-mimicking structural units

Extensive molecular modelling studies, including conformational analysis and the comparison of molecular electrostatic potential distributions, wee used to evaluate structural parameters of new antagonists containing acyclic replacements of the N = C-N imidazole region. The synthesis and the biological screening of a series of acyl biphenyltetrazole derivatives were planned and realized to gain an insight into the structure-activity relationships of this unusual class of Angiotensin II antagonists.