DUGi

Validació de protocols de docking molecular per a l’enzim hidrolasa d’epòxid soluble

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Soluble epoxide hydrolase (sEH) is an enzyme that catalyzes the hydrolysis of epoxyeicosatrienoic acids (EETs) to their corresponding diols and plays a fundamental role at the cellular level. In recent years, numerous studies have indicated that inhibiting the sEH enzyme can control pain processes. For this reason, sEH inhibitors are considered a promising treatment for various diseases, and their design is a topic of pharmacological interest. In recent years, many bioinformatics tools have been developed to predict protein structures and molecular docking programs to identify drug-receptor interactions. To understand the molecular principles that determine these interactions, it is essential to have a fast and precise molecular docking protocol, as well as the ability to visualize the interactions. A protocol that allows these computational tools to be efficiently applied in the drug design process for the sEH enzyme has not yet been found. The main objective of this study is to validate the performance of various molecular docking protocols and assess whether they can accurately predict the interaction between drug and receptor for the design of soluble epoxide hydrolase (sEH) enzyme inhibitors. It is noteworthy that sEH inhibitors share essential structural characteristics for their activity, such as the presence of urea or amide groups and aromatic rings. This analysis allows suggesting improvements in the design of new inhibitors, aiming to develop more effective drugs. The results obtained in this study through molecular docking have demonstrated that this method can correctly predict the ligand’s orientation with respect to the protein in the region of interest, although there may be cases where the prediction is not entirely accurate. Furthermore, the study has shown that, in general, more negative free binding energy values are associated with greater inhibitory activity. Subsequently, these results have been compared using different molecular docking methods, observing similar results. Additionally, these methods can adequately separate the more active inhibitors from the less active ones, although they do not correctly rank the most active inhibitors. This study highlights the usefulness of computational tools to predict ligand-protein interactions in sEH and the importance of continuing research to improve existing drugs or design more selective and potent ones

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