Estudos in silico da interação da enzima InhA de Mycobacterium tuberculosis com pequenas moléculas do tipo fármaco

• Ano de defesa: 2011
• Autor(a) principal: Pauli, Ivani
• Orientador(a): Souza, Osmar Norberto de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Porto Alegre
• Palavras-chave em Português: BIOLOGIA MOLECULAR; BIOLOGIA CELULAR; ENZIMAS; TUBERCULOSE; MEDICAMENTOS
• Link de acesso: http://hdl.handle.net/10923/1434

The inhA gene from Mycobacterium tuberculosis (Mtb), encodes for an enoyl acyl carrier protein reductase, InhA, a key enzyme of the mycobacterial type II fatty acid elongation cycle and has been validated as an effective target for the development of anti-microbial agents. InhA catalyzes the NADH-dependent reduction of trans double bond between positions C2 and C3 of fatty acyl substrates. It is the target of isoniazid, a first line drug in the tuberculosis treatment. Mutations in InhA structural gene are associated with isoniazid resistance in vivo. Even though mutations within the inhA gene are known to facilitate isoniazid resistance, InhA remains a good candidate for drug design because: (i) the vast majority of the mutations found in isoniazid-resistant clinical isolates are associated with the isoniazid activator (KatG catalase-peroxidase); (ii) only one enoyl-ACP reductase is found in Mtb, unlike some of the other enzymes of bacterial FAS-II systems; (iii) the longer substrate chain length specificity of InhA distinguishes it from the enoyl-ACP reductases from other sources. Our goal with this work was to analyze in detail the structural and physicochemical available information about Mtb InhA using bioinformatics tools. As a result, we developed a pharmacophoric model based on the InhA substrate binding cavity that allowed the application of a virtual screening methodology focused in selecting ligands that satisfied these features, allowing so, a best complementarity with the target protein. Besides we tested the hability of four docking algorithms to find similar conformation to a molecule, providing clues that this would be the conformation closest that adopted in vivo. Finally, molecular dynamics simulations were employed to achieve a better comprehension of the interaction between InhA and a known inhibitor.