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Thèse de Doctorat
DOI
10.11606/T.75.2007.tde-14052007-150816
Document
Auteur
Nom complet
Fabio Alberto de Molfetta
Adresse Mail
Unité de l'USP
Domain de Connaissance
Date de Soutenance
Editeur
São Carlos, 2007
Directeur
Jury
Silva, Alberico Borges Ferreira da (Président)
Costa, Fernando Batista da
Honorio, Káthia Maria
Montanari, Carlos Alberto
Romero, Roseli Aparecida Francelin
Titre en portugais
"Planejamento de quinonas com atividade tripanossomicida"
Mots-clés en portugais
DFT
docagem
doença de Chagas
Resumé en portugais
Desde a identificação do vírus da imunodeficiência humana (HIV, do inglês Human Immunodeficiency Virus) como agente causador da Síndrome da Imunodeficiência Adquirida (AIDS ? do inglês Acquired Immunodeficiency Syndrome), a busca para tratamentos seguros e eficazes contra o HIV transformou-se no principal foco para a descoberta de uma nova droga em todo o mundo. A AIDS aparece como um dos principais problemas de saúde pública para as próximas décadas, onde será o maior determinante de mortalidade na faixa etária entre 20 e 50 anos em praticamente todos os países do mundo. Tendo como objetivo relacionar a atividade de compostos biflavonóides anti-HIV-1 com algumas de suas propriedades moleculares, serão utilizados métodos de Mecânica Molecular e Química Quântica. O método de cálculo semi-empírico AM1 foi empregado para calcular um conjunto de propriedades moleculares dos 14 compostos biflavonóides com atividade anti-HIV-1. A seguir utilizar-se-á métodos estatísticos com a finalidade de separar os 14 compostos em duas classes, ativos e não ativos, de forma que se relacione qual as propriedades, dentre as calculadas, são responsáveis pela atividade dos compostos biflavonóides estudados. As técnicas estatísticas utilizadas foram a Análise de Componentes Principais (PCA: Principal Components Analysis), Análise Hierárquica de Agrupamentos (HCA: Hierarquical Clusters Analysis) e Análise de Discriminates por Passos (SDA: Stepwise Discriminant Analysis). Os estudos com PCA, HCA, e SDA mostraram que as variáveis HOMO (Highest Occupied Molecular Orbital - Orbital Molecular Ocupado de Maior Energia), LUMO (Lowest Unoccupied Molecular Orbital ? Orbital Molecular Desocupado de Menor Energia), e Área superficial são responsáveis pela separação dos compostos com alta e baixa atividade anti-HIV-1. O comportamento destas três propriedades pode ser útil na tentativa de se obter outros compostos biflavonóides com elevada atividade inibidora anti-HIV-1.
Titre en anglais
Planning of quinone compounds with trypanocidal activity
Mots-clés en anglais
Chagas disease
DFT (density functional theory)
docking
Resumé en anglais
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.
 
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FabioAMolfetta.pdf (4.70 Mbytes)
Date de Publication
2007-05-15
 
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