In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor.

Autor: Azevedo PHRA; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Peçanha BRB; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Flores-Junior LAP; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Alves TF; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Dias LRS; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Muri EMF; Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil., Lima CHDS; Universidade Federal do Rio de Janeiro, Instituto de Química, Rio de Janeiro, RJ, Brazil.
Jazyk: angličtina
Zdroj: Journal of biomolecular structure & dynamics [J Biomol Struct Dyn] 2024 Feb-Mar; Vol. 42 (3), pp. 1417-1428. Date of Electronic Publication: 2023 Apr 13.
DOI: 10.1080/07391102.2023.2199868
Abstrakt: O-linked N -acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.
Databáze: MEDLINE