TRIAGEM VIRTUAL DE MOLÉCULAS COM POTENCIAL INIBIDOR DA GLUTAREDOXINA A1 DE CORYNEBACTERIUM PSEUDOTUBERCULOSIS

• Ano de defesa: 2021
• Autor(a) principal: Charlene Marcondes Avelar
• Orientador(a): Marcos Serrou do Amaral
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
• País: Brasil
• Palavras-chave em Português: Dinâmica Molecular, Modelagem por Homologia, Virtual Screening.
• Link de acesso: https://repositorio.ufms.br/handle/123456789/4130

Caseous Lymphadenitis is an infectious disease caused by the pathogenic microorganism Corynebacterium pseudotuberculosis, causing great economic losses in livestock worldwide. The absence of effective therapies against the pathogen paves the way for the investigation of possible drug candidates. We know that the generation of oxidative stress in bacteria hinders its reproduction and can lead to death.C. pseudotuberculosis produces the protein Glutaredoxin A1 (Grxa1), which functions as a reduction buffer against reactive oxygen species (ROS) and, in this context, by understanding the aspects of bacterial defenses, in silico approaches can assist in the inhibition strategies of this mechanism. The present study aims to investigate possible inhibitors of C. pseudotuberculosis Grxa1 protein, using computational biophysics techniques. In the first step, a model of the three-dimensional structure of the protein in its active form was generated by homology from the uniprot D9Q987 code with the PDB 2LQO template obtained through the Swissmodel server. After the construction of the structure, three replicas were refined from molecular dynamics simulations with the Amber18 program for 200 ns. The representation took place through cluster analysis, using the K-means method with a range of 2 to 10 clusters. Using the DrugBank molecule bank, with about 8,823 thousand molecules, Virtual Screening was performed with the Autodockvina program. By the criterion of affinity (lower interaction energy) the best compounds were classified and the analyses were performed using the software Pymol and Discovery Studio, and relevant amino acids were identified, as well as intermolecular interactions, types and bond intensities in protein-ligand interaction. These compounds formed a set of 26 candidates for inhibitors of C. pseudotuberculosis protein Grxa1. The results serve as a basis for future studies, allowing the development of more efficient medicines, or even bringing benefits in biotechnological applications.