Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity

Quantitative structure–activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482–491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.     

Qualitative and quantitative structure–activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure–activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood–brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r²) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r² > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.     

The investigation of the quantitative structure–activity relationships between the structure of hydrazine derivatives and the reduction of Np(VI)

The 3D structure of hydrazine derivatives was optimized and their energy was calculated by density functional theory with B3LYP method and 6-311 + (3d, 3p) basis set. The results show that the reaction relationship between the structure of hydrazine derivatives and Np(VI) could be explained by two quantitative structure–activity relationships equations. In Eq. 1, the lowest unoccupied molecular orbital energy is a major factor affecting the reduction rate, and it is negatively correlated with the reaction rate. In Eq. 2, the molecular dipole moment and hydrophobic parameters are the most important factors affecting the reduction rate. The molecular dipole moment is negatively correlated with the reaction rate, but the hydrophobic parameter is positively correlated with the reaction rate.     

Ranking and similarity for quantitative structure–retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons

Quantitative structure–(chromatographic) retention relationship (QSRR) models for prediction of Lee retention indices for polycyclic aromatic hydrocarbons (PAHs) were gathered from the literature and the predictive performances of models were compared. Numerous Lee retention indices (46) were served as a reliable basis for ranking by a recently developed novel method of ordering based on the sum of ranking differences (SRD) [TrAC, Trends Anal. Chem. 29 (2010) 101–109], by which the best model can be selected easily. Two kinds of references for ranking were accepted, average (consensus) and the experimental retention indices. Leave-many-out cross validation of the SRD procedure provides an easy way to group similar models. Significant differences among models can be revealed by using Wilcoxon's matched pair test.     

Review of the quantitative structure–activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules

There is an increasing interest in the use of quantitative structure–activity relationship (QSAR) approaches as a progressive tool in modelling and prediction of many physicochemical properties in host–guest interactions of macrocyclic complexes. A review is presented on the QSAR modelling of macrocyclic compounds formation constants, which focus on two most interesting macrocycles, e.g. crown ethers and cyclodextrins (CDs), with different guest molecules. The review starts with a short overview on experimental methods of stability constant measurement, followed by a short explanation of the QSAR methodologies. In the next section, we focus on and discuss QSAR techniques that used to predict the stability (binding) constants or free energy complexation of some most interesting macrocyclic compounds, e.g. CDs and crown ethers, with different guest molecules including anionic, cationic and neutral molecules.     

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.     

Prediction of the auto-ignition temperature of binary liquid mixtures based on the quantitative structure–property relationship approach

Journal of Thermal Analysis and Calorimetry - The auto-ignition temperature (AIT) is one of the most important parameters in flammability risk assessment and management in the chemical process....     

Antioxidant properties of phenolic Schiff bases: structure–activity relationship and mechanism of action

Phenolic Schiff bases are known for their diverse biological activities and ability to scavenge free radicals. To elucidate (1) the structure–antioxidant activity relationship of a series of thirty synthetic derivatives of 2-methoxybezohydrazide phenolic Schiff bases and (2) to determine the major mechanism involved in free radical scavenging, we used density functional theory calculations (B3P86/6-31+(d,p)) within polarizable continuum model. The results showed the importance of the bond dissociation enthalpies (BDEs) related to the first and second (BDE_d) hydrogen atom transfer (intrinsic parameters) for rationalizing the antioxidant activity. In addition to the number of OH groups, the presence of a bromine substituent plays an interesting role in modulating the antioxidant activity. Theoretical thermodynamic and kinetic studies demonstrated that the free radical scavenging by these Schiff bases mainly proceeds through proton-coupled electron transfer rather than sequential proton loss electron transfer, the latter mechanism being only feasible at relatively high pH.     

Chemometrics approach for the prediction of structure–activity relationship for membrane transporter bilitranslocase

Membrane transport proteins are essential for cellular uptake of numerous salts, nutrients and drugs. Bilitranslocase is a transporter, specific for water-soluble organic anions, and is the only known carrier of nucleotides and nucleotide-like compounds. Experimental data of bilitranslocase ligand specificity for 120 compounds were used to construct classification models using counter-propagation artificial neural networks (CP-ANNs) and support vector machines (SVMs). A subset of active compounds with experimentally determined transport rates was used to build predictive QSAR models for estimation of transport rates of unknown compounds. Several modelling methods and techniques were applied, i.e. CP-ANN, genetic algorithm, self-organizing mapping and multiple linear regression method. The best predictions were achieved using CP-ANN coupled with a genetic algorithm, with the external validation parameter Q_V² of 0.96. The applicability domains of the models were defined to determine the chemical space in which reliable predictions can be obtained. The models were applied for the estimation of bilitranslocase transport activity for two sets of pharmaceutically interesting compounds, antioxidants and antiprions. We found that the relative planarity and a high potential for hydrogen bond formation are the common structural features of anticipated substrates of bilitranslocase. These features may serve as guidelines in the design of new pharmaceuticals transported by bilitranslocase.     

A quantitative structure–mobility relationship of organic acids using solvation parameters

A quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R²) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N?=?5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism.