Identificação de novos potenciais inseticidas e antivirais utilizando ferramentas computacionais e banco de dados de produtos naturais

• Ano de defesa: 2021
• Autor(a) principal: Rodrigues, Gabriela Cristina Soares
• 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: Metabólitos secundários; Bioinseticidas; Terpenos; QSAR; Análise combinada baseada no ligante e na estrutura; Docking molecular; Drosophila melanogaster; Reticulitermes chinensis; Aphis gossypii; Musca domestica; Mythimna separata; Secondary metabolites; Biopesticides; Terpenes; Combined ligand and structure-based analysis; Molecular docking; CNPQ::CIENCIAS BIOLOGICAS::FARMACOLOGIA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/23360

Secondary metabolites are an important source of compounds for pest management, providing an alternative to replace synthetic, persistent and toxic insecticides. Among the classes of natural products, the terpenes stand out, which cover a wide variety of substances of plant origin with ecological importance such as plant defensives and with biological activity against different insects. Terpenes have also been investigated as antiviral agents and with the emergence of the pandemic caused by Sars-Cov-2, several researchers focused their research on identifying potential compounds for the treatment of COVID-19. The application of computational approaches helps to understand the physicochemical properties of active principles, model and elucidate chemical phenomena and predict the toxicity and biological activity for different effects. Therefore, the objective of this work is to apply QSAR studies to find new structures from databases of natural products with potential insecticidal and antiviral activity with the aid of computational tools from Chemoinformatics. In chapter 2, the bioinsecticide potential of forty-two monoterpenes against Drosophila melanogaster and Reticulitermes chinensis Snyder was evaluated. QSAR modeling was performed for both organisms, while docking and molecular dynamics were used only for Drosophila melanogaster. The results suggest that pulegone, citronellal, carvacrol, linalyl acetate, neryl acetate, citronellil acetate and geranyl acetate may be considered some potential candidates for pesticides. In chapter 3, he sought to find natural products with insecticidal potential against Musca domestica and Mythimna separata. For this, predictive QSAR models were developed using MuDRA, PLS and RF approaches, homology models and virtual screening of 117 natural products. The results showed that Pimarane diterpenes, Abietan diterpenes, Dimeric diterpenes and Scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae) can be considered as potential insecticides. In chapter 4, we performed computer-aided predictions for compounds with known insecticidal activity against Aphis gossypii and Drosophila melanogaster and the results revealed that the compounds bistenuifolin L and bistenuifolin K were potentially active against A. gossypii enzymes; and salvisplendin C and salvixalapadiene are potentially active against D. melanogaster. While in Chapter 5, we used computational methods to perform a virtual screening of candidate diterpenes with potential to act as CoV inhibitors. For this, a set of 1,955 diterpenes, derived from a subfamily Nepetoideae (Lamiaceae), were selected using the SistematX tool (https://sistematx.ufpb.br), which were used to make predictions. From the ChEMBL database, 3 sets of chemical structures were selected to build predictive models. The results showed that the consensus analysis approach, based on ligand and structure, was able to select 19 compounds as potential CoV inhibitors, including isotansinone IIA (01), tansinlactone (02), isocryptotansinone (03) and tanshinketolactone (04), which did not show toxicity within the evaluated parameters.