New insights into the substrate specificity of cholesterol oxidases for more aware application

Cholesterol oxidases (ChOxes) are enzymes that catalyze the oxidation of cholesterol to cholest-4-en-3-one. These enzymes find wide applications across various diagnostic and industrial settings. In addition, as a pathogenic factor of several bacteria, they have significant clinical implications. The current classification system for ChOxes is based on the type of bond connecting FAD to the apoenzyme, which does not adequately illustrate the enzymatic and structural characteristics of these proteins. In this study, we have adopted an integrative approach, combining evolutionary analysis, classic enzymatic techniques and computational approaches, to elucidate the distinct features of four various ChOxes from Rhodococcus sp. (RCO), Cromobacterium sp. (CCO), Pseudomonas aeruginosa (PCO) and Burkhoderia cepacia (BCO). Comparative and evolutionary analysis of substrate-binding domain (SBD) and FAD-binding domain (FBD) helped to reveal the origin of ChOxes. We discovered that all forms of ChOxes had a common ancestor and that the structural differences evolved later during divergence. Further examination of amino acid variations revealed SBD as a more variable compared to FBD independently of FAD coupling mechanism. Revealed differences in amino acid positions turned out to be critical in determining common for ChOxes properties and those that account for the individual differences in substrate specificity. A novel look with the help of chemical descriptors on found distinct features were sufficient to attempt an alternative classification system aimed at application approach. While univocal characteristics necessary to establish such a system remain elusive, we were able to demonstrate the substrate and protein features that explain the differences in substrate profile.

Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction

Mobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection step performed using the multivariate adaptive regression splines (MARS) tool. Although RT was found to be the most important, according to Kruskal-Wallis ANOVA analysis, it is insufficient to build a robust model, which justifies the need to expand the input layer with 2D descriptors. Therefore the following molecular descriptors: MPC10, WTPT-2, AATS8s, minaaCH, GATS7c, RotBtFrac, ATSC7v and ATSC1p, which were characterized by a high predicting potential were used to improve the classification performance. As a result of machine learning procedure ten of the most accurate neural networks were selected. The external validation showed that the final models are characterized by a high general accuracy score (85.71-96.43%). The high predicting abilities were also confirmed by the micro-averaged Matthews correlation coefficient (MAMCC) (0.73-0.88). To evaluate the applicability of the models, new retention times of selected POPs and EPs including pesticides, polycyclic aromatic hydrocarbons, pharmaceuticals, fragrances and personal care products were measured and used for mobility prediction. Further, the classifiers were used for photodegradation and chlorination products of two popular sunscreen agents, 2-ethyl-hexyl-4-methoxycinnamate and 2-ethylhexyl 4-(dimethylamino)benzoate.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String

A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.

Random Forest Model with Combined Features: A Practical Approach to Predict Liquid-crystalline Property

Quantitative structure-property relationships were developed to predict the liquid crystalline (LC) of a large dataset of aromatic organic compounds using machine learning algorithms and different molecular descriptors. The aim of this study was to find appropriate models and descriptors for the prediction of a large variety of liquid crystalline behaviors. Furthermore, descriptor calculations based on LC structural templates were proposed to understand the structural effects on the LC behaviors. The results suggest that random forest classifier and combined features which consists of structural templates were usable for LC behavior prediction. The best performance of prediction models showed high accuracy and F1 score (90 % and 93 %). Furthermore, the random forest has strong abilities to large input feature, quick training and easy model-tuning for constructing LC prediction model. Therefore, the prediction model allows experimentalists to seek the synthesis of a predicted molecule that would exhibit the desired LC properties to accelerate the progress in the discovery of new LC materials.

QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors

In the present work, predictive quantitative structure-property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of the structures of polymers' monomer units. Four models have been developed by applying partial least squares (PLS) regression with a different combination of six descriptors obtained via double cross-validation approaches. The predictive ability and robustness of the proposed models were checked using multiple validation strategies. Subsequently, the validated models were used for the generation of "intelligent" consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. The selected consensus models were used for the prediction of refractive index values of various classes of polymers. The final selected model was used to predict the refractive index of four small virtual libraries of monomers recently reported. We also used a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers. The obtained models showed a good predictive ability as evidenced from a very good external predicted variance.

From Classification to Regression Multitasking QSAR Modeling Using a Novel Modular Neural Network: Simultaneous Prediction of Anticonvulsant Activity and Neurotoxicity of Succinimides

Succinimides, which contain a pharmacophore responsible for anticonvulsant activity, are frequently used antiepileptic drugs and the synthesis of their new derivatives with improved efficacy and tolerability presents an important task. Nowadays, multitarget/tasking methodologies focused on quantitative-structure activity relationships (mt-QSAR/mtk-QSAR) have an important role in the rational design of drugs since they enable simultaneous prediction of several standard measures of biological activities at diverse experimental conditions and against different biological targets. Relating to this very topic, the mt-QSAR/mtk-QSAR methodology can give only binary classification models, and as such, in this study a regression mtk-QSAR (rmtk-QSAR) model based on a novel modular neural network (MNN) has been proposed. The MNN uses standard classification mtk-QSAR models as input modules, while the regression is performed by the output module. The rmtk-QSAR model has been successfully developed for the simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides, with a satisfactory accuracy in testing (R² = 0.87). Thus, the proposed mtk-QSAR regression method can be regarded as a viable alternative to the standard QSAR methodology.

A GMDH-type neural network with multi-filter feature selection for the prediction of transition temperatures of bent-core liquid crystals

The QSPR study on transition temperatures of five-ring bent-core LCs was performed using GMDH-type neural networks. A novel multi-filter approach, which combines chi square ranking, v-WSH and GMDH algorithm was used for the selection of descriptors.