Building a credibility-based framework for target discovery: Perspectives from tRNA synthetase-linked metabolic...

Area of Science:

• Computational Biology
• Bioinformatics
• Drug Discovery

Background:

• Systematic target identification is crucial for drug discovery but lacks standardized prioritization workflows.
• Aminoacyl-tRNA synthetases (ARSs) are implicated in metabolic diseases, necessitating target identification strategies.

Purpose of the Study:

• To develop and validate an information-based approach for prioritizing potential drug targets.
• To identify credible disease-associated targets, specifically focusing on ARS interactors in metabolic diseases.

Main Methods:

• Combined text mining of PubMed articles with network analysis and centrality-based prioritization.
• Constructed ARS interactor-disease networks using edge frequency and citation counts.
• Utilized five centrality indices and visualization tools to assess linkage reliability.

Main Results:

• Mined 1,407,654 PubMed articles to identify ARS interactors and disease associations.
• Identified 94 high-credibility disease-associated ARS interactors.
• Selected two targets, ESR1 and APP, for experimental validation.

Conclusions:

• The developed workflow provides a systematic approach for prioritizing drug targets across various disease areas.
• This method enhances the identification of credible disease-associated factors within any target space of interest.