k‐Nearest neighbors optimization‐based outlier removal

Datasets of molecular compounds often contain outliers, that is, compounds which are different from the rest of the dataset. Outliers, while often interesting may affect data interpretation, model generation, and decisions making, and therefore, should be removed from the dataset prior to modeling efforts. Here, we describe a new method for the iterative identification and removal of outliers based on a k‐nearest neighbors optimization algorithm. We demonstrate for three different datasets that the removal of outliers using the new algorithm provides filtered datasets which are better than those provided by four alternative outlier removal procedures as well as by random compound removal in two important aspects: (1) they better maintain the diversity of the parent datasets; (2) they give rise to quantitative structure activity relationship (QSAR) models with much better prediction statistics. The new algorithm is, therefore, suitable for the pretreatment of datasets prior to QSAR modeling. © 2014 Wiley Periodicals, Inc.     

Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of <Emphasis Type="Italic">myasthenia gravis</Emphasis>

The Monte Carlo method was used for QSAR modeling of dimeric pyridinium compounds as acetylcholine esterase inhibitors. QSAR model was developed for a series of 39 dimeric pyridinium compounds. QSAR models were calculated with the representation of the molecular structure by the simplified molecular-input line-entry system. One split into the training and test set have been examined. The statistical quality of the developed model is very good. The calculated model for dimeric pyridinium derivatives had following statistical parameters: r ² = 0.9477 for the training set and r ² = 0.9332 the test set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new dimeric pyridinium compounds potential acetylcholine esterase inhibitors with the application of defined structural alerts has been presented.     

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization,Bayesian classification,and structural and physicochemical interpretation (SPCI) analysis

ABSTRACT

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.     

Impact assessment of the rational selection of training and test sets on the predictive ability of QSAR models

This study performed an analysis of the influence of the training and test set rational selection on the quality and predictively of the quantitative structure–activity relationship (QSAR) model. The study was carried out on three different datasets of Influenza Neuraminidase (H1N1) inhibitors. The three datasets were divided into training and test sets using three rational selection methods: based on k-means, Kennard–Stone algorithm and Activity and the results were compared with Random selection. Then, a total of 31,490 mathematical models were developed and those models that presented a determination coefficient higher than: r²_train > 0.8, r²_loo > 0.7, r²_test > 0.5 and minimum standard deviation (SD) and minimum root-mean square error (RMS) were selected. The selected models were validated using the internal leave-one-out method and the predictive capacity was evaluated by the external test set. The results indicate that random selection could lead to erroneous results. In return, a rational selection allows for obtaining more reliable conclusions. The QSAR models with major predictive power were found using the k-means algorithm and selection by activity.     

α-glucosidase inhibitors isolated from Mimosa pudica L.

The aim of the study was to isolate digestive enzymes inhibitors from Mimosa pudica through a bioassay-guided fractionation approach. Repeated silica gel and sephadex LH 20 column chromatographies of bioactive fractions afforded stigmasterol, quercetin and avicularin as digestive enzymes inhibitors whose IC₅₀ values as compared to acarbose (351.02 ± 1.46 μg mL^?1) were found to be as 91.08 ± 1.54, 75.16 ± 0.92 and 481.7 ± 0.703 μg mL^?1, respectively. In conclusion, M. pudica could be a good and safe source of digestive enzymes inhibitors for the management of diabetes in future.     

Comparative QSAR studies using HQSAR,CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC₅₀: 0.002–2.75 μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q² value of 0.693 and r² value of 0.981; a CoMFA q² value of 0.534 and r² value of 0.913; and a CoMSIA q² value of 0.512 and r² value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.     

Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors

Three-dimension quantitative structure activity relationship (3D-QSAR) was one of the major statistical techniques to investigate the correlation of biological activity with structural properties of candidate molecules, and the accuracy of statistic greatly depended on molecular alignment methodology. Exhaustive conformational search and successful conformational superposition could extremely improve the predictive accuracy of QSAR modeling. In this work, we proposed a solution to optimize QSAR prediction by multiple-conformational alignment methods, with a set of 40 flexible PTP1B inhibitors as case study. Three different molecular alignment methods were used for the development of 3D-QSAR models listed as following: (1) docking-based alignment (DBA); (2) pharmacophore-based alignment (PBA) and (3) co-crystallized conformer-based alignment (CCBA). Among these three alignments, it was indicated that the CCBA was the best and the fastest strategy in 3D-QSAR development, with the square correlation coefficient (r²) and cross-validated squared correlation coefficient (q²) of comparative molecular field analysis (CoMFA) were 0.992 and 0.694; the r² and q² of comparative molecular similarity indices analysis (CoMSIA) were 0.972 and 0.603, respectively. The alignment methodologies used here not only generated a robust QSAR model with useful molecular field contour maps for designing novel PTP1B inhibitors, but also provided a solution for constructing accurate 3D-QSAR model for various disease targets. Undoubtedly, such attempt in QSAR analysis would greatly help us to understand essential structural features of inhibitors required by its target, and so as to discover more promising chemical derivatives.     

Separation of Y from Sr by zirconium vanadate gel ion-exchanger sorbent: kinetics and thermodynamic study

A new zirconium vanadate (Zr–V) ion-exchanger was synthesized and characterized for fast and selective separation procedure of ⁹⁰Y from ⁸⁹Sr. The method was based on ⁹⁰Y(III) sorption from aqueous HCl solution containing ⁸⁹Sr(II) onto Zr–V gel exchanger. The kinetics of Y(III) sorption from HCl solution by Zr–V exchanger was subjected to Weber–Morris, Lagergren, Bhattacharya and Venkobachar, and Bt models. Initially, the uptake of Y(III) onto the exchanger was fast followed by kinetically first-order sorption with an overall rate constant, K _Lager = (3.55 ± 0.03) × 10^?4 min^?1. Film and intraparticle transport are the two steps that might influence Y(III) sorption. The negative values of ΔG of ⁹⁰Y retention dictate that, the process is a spontaneous. The negative values of ΔH and ΔS reflect the exothermic nature of ⁹⁰Y(IIsorption and the random uptake of ⁹⁰Y(III) onto Zr–V sorbent. Zr–V exchanger offers unique advantages of ⁹⁰Y(III) retention over conventional solid sorbents in rapid and effective separation of traces of ⁹⁰Y(III) from Sr. The exchanger was successfully packed in column for an effective separation of ⁹⁰Y.     

Combined 2D and 3D-QSAR,molecular modelling and docking studies of pyrazolodiazepinones as novel phosphodiesterase 2 inhibitors

Selective inhibition of phosphodiesterase 2 (PDE2) in cells where it is located elevates cyclic guanosine monophosphate (cGMP) and acts as novel analgesic with antinociceptive activity. Three-dimensional quantitative structure–activity relationship (QSAR) studies for pyrazolodiazepinone inhibitors exhibiting PDE2 inhibition were performed using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA, and two-dimensional QSAR study was performed using a Hologram QSAR (HQSAR) method. QSAR models were generated using training set of 23 compounds and were validated using test set of nine compounds. The optimum partial least squares (PLS) for CoMFA-Focusing, CoMSIA-SDH, Topomer CoMFA and HQSAR models exhibited good ‘leave-one-out’ cross validated correlation coefficient (q²) of 0.790, 0.769, 0.840 and 0.787, coefficient of determination (r²) of 0.999, 0.964, 0.979 and 0.980, and high predictive power (r²_pred) of 0.796, 0.833, 0.820 and 0.803 respectively. Docking studies revealed that those inhibitors able to bind to amino acid Gln859 by cGMP binding orientation called ‘glutamine-switch’, and also bind to the hydrophobic clamp of PDE2 isoform, could possess high selectivity for PDE2. From the results of all the studies, structure–activity relationships and structural requirements for binding to active site of PDE2 were established which provide useful guidance for the design and future synthesis of potent PDE2 inhibitors.     

Effect of mutations to amino acid A301 and F361 in thermostability and catalytic activity of the β-galactosidase from <Emphasis Type="Italic">Bacillus subtilis</Emphasis> VTCC-DVN-12-01

Background

Beta-galactosidase (EC 3.2.1.23), a commercially important enzyme, catalyses the hydrolysis of β-1,3- and β-1,4-galactosyl bonds of polymer or oligosaccharidesas well as transglycosylation of β-galactopyranosides. Due to catalytic properties; β-galactosidase might be useful in the milk industry to hydrolyze lactose and produce prebiotic GOS. The purpose of this study is to characterize β-galactosidase mutants from B. subtilis.

Results

Using error prone rolling circle amplification (epRCA) to characterize some random mutants of the β-galactosidase (LacA) from B. subtilisVTCC-DVN-12-01, amino acid A301 and F361 has been demonstrated significantly effect on hydrolysis activity of LacA. Mutants A301V and F361Y had markedly reduced hydrolysis activity to 23.69 and 43.22 %, respectively. Mutants the site-saturation of A301 reduced catalysis efficiency of LacA to 20–50 %, while the substitution of F361 by difference amino acids (except tyrosine) lost all of enzymatic activity, indicating that A301 and F361 are important for the catalytic function. Interestingly, the mutant F361Y exhibited enhanced significantly thermostability of enzyme at 45–50 °C. At 45 °C, LacA-361Y retained over 93 % of its original activity for 48 h of incubation, whereas LacA-WT and LacA-301Vwere lost completely after 12 and 24 h of incubation, respectively. The half-life times of LacA-361Y and LacA-301 V were about 26.8 and 2.4 times higher, respectively, in comparison to the half-life time of LacA-WT. At temperature optimum 50 °C, LacA-361Y shows more stable than LacA-WT and LacA-301 V, retaining 79.88 % of its original activities after 2 h of incubation, while the LacA-WT and LacA-301 V lost all essential activities. The half-life time of LacA-361Y was higher 12.7 and 9.39 times than that of LacA-WT and LacA-301 V, respectively. LacA-WT and mutant enzymes were stability at pH 5–9, retained over 90 % activity for 72 h of incubation at 30 °C. However, LacA-WT showed a little bit more stability than LacA-301 V and LacA-361Y at pH 4.

Conclusions

Our findings demonstrated that the amino acids A301V and F361 play important role in hydrolysis activity of β -galactosidase from B. subtilis. Specially, amino acid F361 had noteworthy effect on both catalytic and thermostability of LacA enzyme, suggesting that F361 is responsible for functional requirement of the GH42 family.

     

Comparative molecular field analysis and comparative molecular similarity index analysis studies on 1H NMR chemical shift of NH group of diaryl triazene derivatives

Comparative molecular field analysis (CoMFA), comparative molecular field analysis region focusing (CoMFA‐RF) for optimizing the region for the final partial least square analysis, and comparative molecular similarity indices analysis (CoMSIA) methods were employed to develop three‐dimensional quantitative structure–activity relationship (3D‐QSAR) models of ¹H NMR chemical shift of NH proton of diaryl triazene derivatives. The best orientation was searched by all‐orientation search (AOS) strategy to minimize the effect of the initial orientation of the structures. The predictive abilities of CoMFA‐RF and CoMSIA models were determined using a test set of ten compounds affording predictive correlation coefficients of 0.721 and 0.754, respectively, indicating good predictive power. For further model validation, cross validation (leave one out), progressive scrambling, and bootstrapping were also applied. The accuracy and speed of obtained 3D‐QSAR models for the prediction of ¹H NMR chemical shifts of NH group of diaryl triazene derivatives were greater compared to some computational well‐known procedures. Copyright © 2013 John Wiley & Sons, Ltd.