RepTB: a gene ontology based drug repurposing approach for tuberculosis

Area of Science:

• Computational Biology
• Drug Discovery
• Bioinformatics

Background:

• Tuberculosis (TB) remains a leading infectious cause of death globally.
• Traditional drug discovery is time-consuming and costly, necessitating alternative approaches.
• Identifying novel anti-TB compounds is crucial for combating drug resistance.

Purpose of the Study:

• To develop and validate RepTB, a drug repurposing strategy for TB.
• To predict novel drug-target interactions (DTIs) for TB using molecular function correlations.
• To identify potential drug repurposing candidates for TB treatment.

Main Methods:

• Constructed a Gene Ontology-based network with drug and target nodes.
• Utilized Network Based Inference (NBI) to compute association scores for DTIs.
• Employed a combined evidence approach to prioritize drug repurposing candidates, focusing on essential Mtb targets without human homologs.

Main Results:

• Identified four key TB targets (FolP1, Tmk, Dut, MenB) for further validation.
• Observed chemical structure similarity between predicted and known drugs for prioritized targets.
• Reported novel chemical spaces for prioritized targets, suggesting new therapeutic avenues.

Conclusions:

• RepTB effectively predicts novel drug-target interactions for TB drug repurposing.
• The approach prioritizes essential Mtb targets, enhancing the likelihood of identifying viable drug candidates.
• RepTB shows promise for identifying drug repurposing candidates for TB and other diseases.