Triagem virtual em banco de dados de ligantes considerando propriedades físico-químicas de um modelo de receptor totalmente flexível

• Autor(a) principal: Quevedo, Christian Vahl
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da PUC_RS
• Texto Completo: http://tede2.pucrs.br/tede2/handle/tede/7178
• topic: MINERAÇÃO DE DADOS (INFORMÁTICA); ENZIMAS - TECNOLOGIA; INFORMÁTICA; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Pharmacophore models have been widely used in the virtual screening, allowing to select ligands that containing the spatial arrangement of essential physico-chemical properties. These properties are obtained from the evaluation of similar interactions identified in known receptor-ligand complexes. Currently, these pharmacophore models based on ligands are dependent on the physicochemical characteristics present in the known receptorligand complex. Thus, the pharmacophore model generated can overlook the proteins that have no known ligands complexed and whose physical and chemical properties do not establish interaction in the evaluated complex. That is, regions in the cavity that do not interact with ligands that generate the pharmacophore model and that may allow the interaction of structurally diverse ligands are not included in the selective search. Furthermore, several authors have shown that not taking the protein’s flexibility into account during the selection of drug candidates limits the result’s accuracy significantly. Thus, this thesis presents a new method for performing a virtual screening of ligands based on the evaluation of the 3D physico-chemical properties of the substrate binding pocket, and without the presence of complexed ligands, of representative structures of a Fully-Flexible Receptor (FFR) model. This method allows identifying 3D pharmacophoric models of flexible regions, which cannot be obtained from 3D pharmacophore models developed only from crystal structures of the ligand-receptor complex. A list of pharmacophoric hypothesis is proposed to select a set of ligands ZINC DB. Tests of this method’s efficacy were based on cross-docking experiments with the FFR model of 19.5 ns of the InhA enzyme from Mycobacterium tuberculosis. Molecular docking experiments with selected ligands showed that 95.0% of this group were negative values FEB, with 20.6% of these values that the best values obtained with FEB docking experiments with the crystalline structure that generated the rated model. These promising results show that the developed method may be an important support tool for researchers in the search for new drug candidates, accelerating the selection of possible candidates to be tested with FFR models of target molecules. The method presented also provides a great way to evaluate FFR models, enabling the domain expert to identify whether the obtained regions are really accessible in the investigated protein.