Identificação de novos peptídeos inibidores de TMPRSS2: uma abordagem computacional para prospecção de um tratamento antiviral

• Ano de defesa: 2023
• Autor(a) principal: Vivian Morais Paixão
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; Programa de Pós-Graduação em Bioinformatica; UFMG
• Palavras-chave em Português: TMPRSS2; docking; peptídeos; inibição; Bioinformática; Simulação de Acoplamento Molecular; Peptídeos; Betacoronavirus; Angiotensina II
• Link de acesso: http://hdl.handle.net/1843/64413; https://orcid.org/0000-0002-4154-304X

In 2020, a pandemic caused by SARS-CoV-2, a novel coronavirus, had a global impact on the economy and public health, resulting in nearly 7 million deaths worldwide. The virus requires the binding of its Spike protein to the Angiotensin-Converting Enzyme 2 (ACE2) receptor to infect host cells. This binding relies on the cleavage of the Spike protein, a process mediated by the enzymes Furin and TMPRSS2. This study focuses on TMPRSS2, a transmembrane serine protease found on various cell surfaces, including the prostate, lungs, and intestines, and associated with the pathogenesis of multiple diseases. Although approved drugs for COVID-19 treatment already exist, they present challenges such as high costs, side effects, and a growing loss of efficacy against new virus variants. Therefore, the primary objective of this study was to develop a methodology for the search and filtering of peptides as potential therapeutic approaches, with an emphasis on identifying peptides not previously described as inhibitors of this protease. Our methodology included a literature review, the search for peptides with binding characteristics to the catalytic site of TMPRSS2 using the Propedia database, filtering of the resulting sequences, molecular docking simulations of selected peptides, and the application of a code to filter the generated docking models. This led to the identification of 147 candidates, of which six emerged as the most promising. Subsequently, an analysis of interaction stability was performed. Three of the most promising candidates were optimized, generating new sequences with a higher probability of binding to the active site. These optimized peptides were later synthesized for in vitro assays. The results demonstrated that our methodology is capable of revealing docking poses that initially would not have been considered for evaluation, highlighting poses with a better fit to the active site. Moreover, three peptides with a history of protease inhibition were identified as potential candidates for TMPRSS2 inhibition, suggesting their applicability not only for COVID-19 treatment but also for other diseases in which TMPRSS2 plays a crucial role.