Flexibility of a biotinylated ligand in artificial metalloenzymes based on streptavidin—an insight from molecular dynamics simulations with classical and ab initio force fields

In the field of enzymatic catalysis, creating activity from a non catalytic scaffold is a daunting task. Introduction of a catalytically active moiety within a protein scaffold offers an attractive means for the creation of artificial metalloenzymes. With this goal in mind, introduction of a biotinylated d6-piano-stool complex within streptavidin (SAV) affords enantioselective artificial transfer-hydrogenases for the reduction of prochiral ketones. Based on an X-ray crystal structure of a highly selective hybrid catalyst, displaying significant disorder around the biotinylated catalyst [η6-(p-cymene)Ru(Biot-p-L)Cl], we report on molecular dynamics simulations to shed light on the protein–cofactor interactions and contacts. The results of these simulations with classical force field indicate that the SAV-biotin and SAV-catalyst complexes are more stable than ligand-free SAV. The point mutations introduced did not affect significantly the overall behavior of SAV and, unexpectedly, the P64G substitution did not provide additional flexibility to the protein scaffold. The metal-cofactor proved to be conformationally flexible, and the S112K or P64G mutants proved to enhance this effect in the most pronounced way. The network of intermolecular hydrogen bonds is efficient at stabilizing the position of biotin, but much less at fixing the conformation of an extended biotinylated ligand. This leads to a relative conformational freedom of the metal-cofactor, and a poorly localized catalytic metal moiety. MD calculations with ab initio potential function suggest that the hydrogen bonds alone are not sufficient factors for full stabilization of the biotin. The hydrophobic biotin-binding pocket (and generally protein scaffold) maintains the hydrogen bonds between biotin and protein

Comparison of criteria used to access carcinogenicity in CPANN QSAR models <i>versus</i> the knowledge-based expert system Toxtree

<div><p>The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.</p></div

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

<div><p>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 <i>Q</i>_<i>V</i>² 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.</p></div

Robust modelling of acute toxicity towards fathead minnow (<i>Pimephales promelas)</i> using counter-propagation artificial neural networks and genetic algorithm

<p>Large worldwide use of chemicals has caused great concern about their possible adverse effects on human health, flora and fauna. Increased production of new chemicals has also increased demand for their risk assessment. Traditionally, results from animal tests have been used to assess toxicity of chemicals. However, such methods are ethically questionable since they involve killing and causing suffering of the test animals. Therefore, new <i>in silico</i> methods are being sought to replace the traditional <i>in vivo</i> and <i>in vitro</i> testing methods. In this article we report on one method that can be used to build robust models for the prediction of compounds’ properties from their chemical structure. The method has been developed by combining a genetic algorithm, a counter-propagation artificial neural network and cross-validation. It has been tested using existing data on toxicity to fathead minnow (<i>Pimephales promelas</i>). The results show that the method may give reliable results for chemicals belonging to the applicability domain of the developed models. Therefore, it can aid the risk assessment of chemicals and consequently reduce demand for animal tests.</p

Quantitative structure-activity relationships (QSARs) using the novel marine algal toxicity data of phenols

<p>The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to<br>marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly<br>correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (LogP). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure–activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q2test=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided<br>an excellent external predictivity (Q2test=0.93).<br>The proposed algal QSARs were further tested in their predictivity using an external set comprising<br>toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (Cortho) not only had high external predictivity (Q2ext=0.74), but it also had excellent external data set coverage (%97).</p