Amino acid adjacency matrix.

<p>The 20×20 amino acid adjacency matrix of the given βTM protein segment is shown. The matrix elements representing the frequency of the corresponding amino acid pairs in the segment are highlighted.</p

In Silico Assessment of Adverse Effects of a Large Set of 6-Fluoroquinol- ones Obtained from a Study of Tuberculosis Chemotherapy

<p>Among the different chemotherapeutic classes available today, the 6-fluoroquinolone (6-FQ) antibacterials are still one of the most effective cures in fighting tuberculosis (TB). Nowadays, the development of novel 6-FQs for treatment of TB mainly depends on understanding how the structural modifications of the main quinolone scaffold at specific positions affect the anti-mycobacterial activity. Alongside the structure-activity relationship (SAR) studies of the 6-FQ antibacterials, which can be considered as a golden rule in the development of novel active antitubercular 6-FQs, the structure side-effects relationship (SSER) of these drugs must be also taken into account. In the present study we focus on a proficient implementation of the existing knowledge-based expert systems for design of novel 6-FQ antibacterials with possible enhanced biological activity against Mycobaterium tuberculosis as well as lower toxicity. Following the SAR in silico studies of the quinolone antibacterials against M. tuberculosis performed in our laboratory, a large set of 6-FQs was selected. Several new 6-FQ derivatives were proposed as drug candidates for further research and development. The 6- FQs identified as potentially effective against M. tuberculosis were subjected to an additional SSER study for prediction of their toxicological profile. The assessment of structurally-driven adverse effects which might hamper the potential of new drug candidates is mandatory for an effective drug design. We applied publicly available knowledge-based (expert) systems and Quantitative Structure-Activity Relationship (QSAR) models in order to prepare a priority list of active compounds. A preferred order of drug candidates was obtained, so that the less harmful candidates were identified for further testing.</p> <p>TOXTREE expert system as well as some QSAR models developed in the framework of EC funded project CAESAR were used to assess toxicity. CAESAR models were developed according to the OECD principles for the validation of QSAR and they turn to be appropriate tools for in silico tests regarding five different toxicity endpoints. Those endpoints with high relevance for REACH are: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. We used the above-mentioned freely available models to select a set of less harmful active 6-FQs as candidates for clinical studies.</p

Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study

<p>Alongside the validation, the concept of applicability domain (AD) is probably one of the most important<br>aspects which determine the quality as well as reliability of the established quantitative structure–activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the Euclidean distance (ED) metric in the<br>structure-representation vector space. The performance of the method was evaluated and explained in a case study by using a pre-built and validated CP ANN model for prediction of the transport activity of the transmembrane protein bilitranslocase for a diverse set of compounds. The method was tested on two more datasets in order to confirm its performance for evaluation of the applicability domain in CP ANN models. The chemical compounds determined as potential outliers, i.e., outside of the CP ANN model AD, were confirmed in a comparative AD assessment by using the leverage approach. Moreover, the method offers a graphical depiction of the AD for fast and simple determination of the extreme points.</p

Cluster-based molecular docking study for in silico identification of novel 6-fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis

<p>A classical protein sequence alignment and homology modeling strategy were used for building three Mycobacterium tuberculosis-DNA gyrase protein models using the available topoII-DNA-6FQ crystal structure complexes originating from different organisms. The recently determined M. tuberculosis-DNA gyrase apoprotein structures and topoII-DNA-6FQ complexes were used for defining the 6-fluoroquinolones (6-FQs) binding pockets. The quality of the generated models was initially validated by docking of the cocrystallized ligands into their binding site, and subsequently by quantitative evaluation of their discriminatory performances (identification of active/inactive 6-FQs) for a set of 145 6-FQs with known biological activity values. The M. tuberculosis-DNA gyrase model with the highest estimated discriminatory power was selected and used afterwards in an additional molecular docking experiment on a mixed combinatorial set of 427 drug-like 6-FQ analogs for which the biological activity values were predicted using a prebuilt counter-propagation artificial neural network model. A novel three-level Boolean-based [T/F (true/false)] clustering algorithm was used to assess the generated binding poses: Level 1 (geometry properties assessment), Level 2 (score-based clustering and selection of the (T)-signed highly scored Level 1 poses), and Level 3 (activity-based clustering and selection of the most “active” (T)-signed Level 2 hits). The frequency analysis of occurrence of the fragments attached at R1 and R7 position of the (T)-signed 6-FQs selected in Level 3 revealed several novel attractive fragments and confirmed some previous findings. We believe that this methodology could be successfully used in establishing novel possible structure-activity relationship recommendations in the 6-FQs optimization, which could be of great importance in the current antimycobacterial hit-to-lead processes.</p

Cluster-based molecular docking study for in silico identification of novel 6-fluoroquinolones as potential inhibitors against Mycobacterium tuberculosis (Cover Page)

<p>The rapid development of drug resistance in microbes, the toxicity, and side effects of existing anti-infectious drugs are factors stimulating the effort directed toward a new generation of antibiotics. On page 790, Nikola Minovski, Andrej Perdih, Marjana Novic, and Tom Solmajer demonstrate how carefully validated in silico models using the recently determined structures of M. tuberculosis– DNA gyrase apoprotein and topoisomerase II-DNA-6-fluoroquinolones complexes are proficiently used for defining the drugs' binding pockets and the subsequent design of a series of novel inhibitors of DNA gyrase from the class of substituted 6-fluoroquinolones (shown on the cover).</p

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Additional file 2. File containing CPANNatNIC source files

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

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Additional file 10. File with read-across results for acute toxicity validation set

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Additional file 17. File containing results obtained for additional tests on eight datasets

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Additional file 16. File with read-across results for bio-concentration factor external set