Golgi α-mannosidase: opposing structures of Drosophila melanogaster and novel human model using molecular dynamics simulations and docking at different pHs

• Autor(a) principal: Drogalin, Artem
• Data de Publicação: 2023
• Outros Autores: Monteiro, Luís S., Alves, Maria José Chão, Castro, Tarsila Gabriel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/86973
• topic: Golgi a-mannosidase II; Iminosugars; Molecular dynamics simulations; Molecular docking; pH; Structural assessment; Inhibitors; Ciências Naturais::Ciências Químicas; Saúde de qualidade

The search for Golgi a-mannosidase II (GMII) potent and specific inhibitors has been a focus of many studies for the past three decades since this enzyme is a key target for cancer treatment. a-Mannosidases, such as those from Drosophila melanogaster or Jack bean, have been used as functional models of the human Golgi a-mannosidase II (hGMII) because mammalian mannosidases are difficult to purify and characterize experimentally. Meanwhile, computational studies have been seen as privileged tools able to explore assertive solutions to specific enzymes, providing molecular details of these macromolecules, their protonation states and their interactions. Thus, modelling techniques can successfully predict hGMII 3D structure with high confidence, speeding up the development of new hits. In this study, Drosophila melanogaster Golgi mannosidase II (dGMII) and a novel human model, developed in silico and equilibrated via molecular dynamics simulations, were both opposed for docking. Our findings highlight that the design of novel inhibitors should be carried out considering the human model’s characteristics and the enzyme operating pH. A reliable model is evidenced, showing a good correlation between Ki/IC50 experimental data and theoretical DGbinding estimations in GMII, opening the possibility of optimizing the rational drug design of new derivatives