"Planejamento de quinonas com atividade tripanossomicida" / Planning of quinone compounds with trypanocidal activity

AUTOR(ES)
DATA DE PUBLICAÇÃO

2007

RESUMO

A set of 25 quinone compounds with anti-trypanocidal activity was studied by using the Density Functional Theory (DFT) method in order to calculate electronic atomic and molecular properties to be correlated with the biological activity. The chemometric methods Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Stepwise Discriminant Analysis (SDA), Kth nearest neighbor (KNN) and Soft Independent Modeling of Class Analogy (SIMCA) were used to obtain possible relationships between the calculated descriptors and the biological activity studied and predict the anti-trypanocidal activity of new quinone compounds from a test set. Four descriptors were responsible for the separation between the active and inactive compounds: T5 (torsion angle), HOMO-1 (energy of the first molecular orbital below HOMO), QTS1 (sum of absolute values of the atomic charges) and VOLS2 (volume of the substituent at region B). These descriptors give information on the kind of interaction that occurs between the compounds and the biological receptor. The prediction study was done with a set of three new quinone compounds by using the PCA, HCA, SDA, KNN and SIMCA. Beside the five chemometric methods, the neural network method was used by employing the backpropagation algorithm. The four variables (T5, QTS1, VOLS2 and HOMO-1) were employed to validate the models constructed previously. The architecture of networks consisting of four neurons at input layers, ten neurons at intermediary layers and two neurons at output layers was adopted to observe the root mean square error between the true and desired output over the entire training set. The percentage of correct classification was 87.5%, and only one compound was predicted wrong in the test set, which indicates that the model is robust and could be able to make predictions. The docking studies were carried out with two different programs in the approach of ligands: the Autodock and FlexX. The docking results on trypanothione reductase enzyme showed that all studied compounds stay at second hydrophobic pocket in the outer region of the active site called the Z-site. The residues that could be specifically involved in the binding of ligands are Lys62, Thr66, Thr397, Thr463, Leu399, Ser464, Glu466 and Glu467, where the residues Thr66, Thr463 and Leu399 are conserved in different trypanothiones and could be used for the development of selective inhibitors against to the parasite enzyme.

ASSUNTO(S)

docagem dft chagas disease docking dft (density functional theory) doença de chagas

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