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

Abstract

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.

Communicated by Ramaswamy H. Sarma

Keywords:

• Drug repurposing
• machine learning
• molecular docking
• molecular dynamics simulation
• OGT

Acknowledgments

We would like to thank the Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP) for the resources for the Molecular Dynamics simulations. We also thank the Institutional Qualification Program of the Universidade Federal Fluminense (PQI-UFF 001/2018).

Authors’ Contributions

Pedro Henrique Rodrigues de Alencar Azevedo: Conceptualization, methodology, formal analysis, investigation, writing. Bruna Rachel de Britto Peçanha: methodology, formal analysis, investigation, writing.Tatiana Fialho Alves: formal analysis, investigation, writing. Luiz Augusto Pinheiro Flores-Junior: formal analysis, investigation, writing. Luiza Rosaria Souaa Dias: Review and editing, project administration, supervision. Estela Maris Freitas Muri: Review and editing, project administration, supervision. Camilo Henrique da Silva Lima: Review and editing, project administration, supervision.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES-BR) - Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil (CNPq-BR), and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).—Grants: E-26/200.930/2017, E-26/210.068/2021, SEI-260003/009792/2021, SEI-260003/007043/2022, and SEI-260003/003788/2022.