Uso de fingerprints de farmacóforospotenciais para comparação de sítiosprotéicos e ligantes ativos

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Fábio Mendes dos Santos
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
UFMG
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/1843/ICBB-BDQPRQ
Resumo: Due to technological advances in the last decades databases containing information related to human health areas (such as genomic sequences, three-dimensional protein structures and small molecules) experienced a great growth. The amount of available information is so hignt that today a major concern is how to analyze such plethora ofdata. The correct interpretation of this information may contribute to the understanding of biological mechanisms of diseases, for classification and identification of proteins with similar biological activities and for the development of new drugs for treatment of known diseases and those who still will appear. Currently, there are serveral softwares for this purpose, each one with its own advantages and disadvantages. Basically, the search in libraries of small molecules in order to identify substances which are most likely to bind to a drug target is calledVirtual Screening (VS). It can use information from active ligands (Ligand-Based Virtual Screening - LBVs) or from biological targets (Target-Based Virtual Screening - TBVS). This work introduces a pharmacophore fingerprints methodology to analyse structuresof proteins and small ligands. We developed at NEQUIM (Nucleo de Estudos em Quimioinformática- UFMG) two softwares called PharmaSite (to calculate similarities between biological targets) and 3DPharma (for VS of small ligands). Furthermore, we propose a new methodology to build data models using bootstrap and cross-validation approaches. Several analysis were performed and our tools showed good results in both calculation of similarity between the active sites as in applications for recovery potentially active molecules in ligand databases.