Da Costa A.L.P.,Labio Laboratorio Of Bioinformatica |
Da Costa A.L.P.,Pontifical Catholic University of Rio Grande do Sul |
Pauli I.,Labio Laboratorio Of Bioinformatica |
Pauli I.,Pontifical Catholic University of Rio Grande do Sul |
And 8 more authors.
Journal of Molecular Modeling | Year: 2012
InhA, the NADH-dependent 2-trans-enoyl-ACP reductase enzyme from Mycobacterium tuberculosis (MTB), is involved in the biosynthesis of mycolic acids, the hallmark of mycobacterial cell wall. InhA has been shown to be the primary target of isoniazid (INH), one of the oldest synthetic antitubercular drugs. INH is a prodrug which is biologically activated by the MTB catalaseperoxidase KatG enzyme. The activation reaction promotes the formation of an isonicotinyl-NAD adduct which inhibits the InhA enzyme, resulting in reduction of mycolic acid biosynthesis. As a result of rational drug design efforts to design alternative drugs capable of inhibiting MTB's InhA, the inorganic complex pentacyano(isoniazid)ferrate(II) (PIF) was developed. PIF inhibited both wild-type and INH-resistant Ile21Val mutants of InhA and this inactivation did not require activation by KatG. Since no threedimensional structure of the InhA-PIF complex is available to confirm the binding mode and to assess the molecular interactions with the protein active site residues, here we report the results of molecular dynamics simulations of PIF interaction with InhA. We found that PIF strongly interacts with InhA and that these interactions lead to macromolecular instabilities reflected in the long time necessary for simulation convergence. These instabilities were mainly due to perturbation of the substrate binding loop, particularly the partial denaturation of helices α6 and α7. We were also able to correlate the changes in the SASAs of Trp residues with the recent spectrofluorimetric investigation of the InhA-PIF complex and confirm their suggestion that the changes in fluorescence are due to InhA conformational changes upon PIF binding. The InhA-PIF association is very strong in the first 20.0 ns, but becomes very week at the end of the simulation, suggesting that the PIF binding mode we simulated may not reflect that of the actual InhAPIF complex. © Springer-Verlag 2011.
Winck A.T.,GPIN Grupo de Pesquisa em Inteligencia de Negocio |
Machado K.S.,Labio Laboratorio Of Bioinformatica |
de Souza O.N.,Labio Laboratorio Of Bioinformatica |
Ruiz D.D.,GPIN Grupo de Pesquisa em Inteligencia de Negocio
BMC Genomics | Year: 2013
Background: Data preprocessing is a major step in data mining. In data preprocessing, several known techniques can be applied, or new ones developed, to improve data quality such that the mining results become more accurate and intelligible. Bioinformatics is one area with a high demand for generation of comprehensive models from large datasets. In this article, we propose a context-based data preprocessing approach to mine data from molecular docking simulation results. The test cases used a fully-flexible receptor (FFR) model of Mycobacterium tuberculosis InhA enzyme (FFR_InhA) and four different ligands. Results: We generated an initial set of attributes as well as their respective instances. To improve this initial set, we applied two selection strategies. The first was based on our context-based approach while the second used the CFS (Correlation-based Feature Selection) machine learning algorithm. Additionally, we produced an extra dataset containing features selected by combining our context strategy and the CFS algorithm. To demonstrate the effectiveness of the proposed method, we evaluated its performance based on various predictive (RMSE, MAE, Correlation, and Nodes) and context (Precision, Recall and FScore) measures. Conclusions: Statistical analysis of the results shows that the proposed context-based data preprocessing approach significantly improves predictive and context measures and outperforms the CFS algorithm. Context-based data preprocessing improves mining results by producing superior interpretable models, which makes it well-suited for practical applications in molecular docking simulations using FFR models. © 2013 Winck et al.