Estudos quimiotaxonômicos e triagem virtual de flavonoides isolados da família asteraceae com potencial atividade leishmanicida

• Ano de defesa: 2018
• Autor(a) principal: Cavalcanti, Élida Batista Vieira Sousa
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Farmacologia; Programa de Pós-Graduação em Produtos Naturais e Sintéticos Bioativos; UFPB
• Palavras-chave em Português: Leishmania; Asteraceae; flavonoides; multitarget; Mapas Auto-Organizáveis; descritores moleculares; modelos de predição; docking; flavonoids; Self-Organizing Maps; molecular descriptors; prediction models; CNPQ::CIENCIAS BIOLOGICAS::FARMACOLOGIA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/11091

Neglected diseases affect millions of people around the world, one of them is leishmaniasis caused by protozoa of the genus Leishmania, whose treatment is still a challenge to science. Several research groups have confirmed that natural products have been a rich source of compounds with leishmanicidal activity, among them flavonoids, widely found in species of the Asteraceae family, which can therefore be used as taxonomic markers at lower hierarchical levels, in addition, in recent years there has been an increase in virtual screening studies that have demonstrated antiprotozoal activity of these compounds. The objective of this work is to combine virtual screening methodologies through machine learning using molecular descriptors and molecular docking in order to predict the potential leishmanicidal activity of flavonoids isolated from species of the Asteraceae family. Chapter 2 presents a publication in the book Multi-Scale Approaches in Drug Discovery (2017) that reports the properties and pharmacological potential of the flavonoids of Asteraceae. Through the literature review, it was possible to conclude that these compounds can become candidates for new drugs with multitarget activity against agents that cause protozoal diseases. Chapter 3 approaches the study conducted for the classification of Asteraceae tribes based on the number of occurrences of flavonoids in our internal database (available at www.sistematx.ufpb.br) using descriptors calculated by DRAGON 7.0 software. The 2371 botanical occurrences with respective 74 molecular fragment descriptors were used as input data in SOM Toolbox 2.0 (Matlab) to generate Self-Organizing Maps (SOMs), classifying five tribes: Anthemideae (A), Gnaphalieae (G), Tageteae (T), Senecioneae (S) and Carduoideae (CR). The positively contributed descriptors and the location of some molecules on the maps relative to each descriptor were verified, so SOM can be a useful tool in the search for flavonoids with their respective taxonomic information and biological activities.In chapter 4 the construction of prediction models was performed through the KNIME program using descriptors calculated by Volsurf software encompassing registered flavonoids in our database (in-house databank) associated with other databases (ChEMBL) containing compounds with activity against strains of Leishmania species. In Chapter 4, the construction of prediction models was performed through the KNIME program using descriptors calculated by the Volsurf software, including 889 flavonoids registered in our database (in-house databank) associated to other databases (ChEMBL) containing compounds with activity in front to strains of Leishmania species. Structure-based virtual screening was also performed through molecular docking of the same flavonoid database using 11 target enzymes from Leishmania species including a model of Arginase enzyme homology. Finally, through a consensus analysis of the two techniques, it was sought to normalize the probability values, to verify compounds potentially active against leishmaniasis. Thus, it was possible to predict the potential of the Asteraceae family of flavonoids are active against some species of 11 Leishmania, contributing to the in silico studies of natural products against neglected diseases caused by protozoa.