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

Abstract

While target identification is essential for successful drug discovery, no systematic workflow exists to prioritize potential targets for a given indication. Therefore, this study aims to develop an information-based approach combining text mining, network analysis, and centrality-based prioritization. As a case study, we applied this workflow to identify metabolic disease targets potentially linked to aminoacyl-tRNA synthetases (ARSs). From 1,407,654 PubMed articles, potential ARS interactors and their disease associations were mined. Using these data, the ARS interactor-disease networks were constructed based on edge frequency and citation count. To assess the reliability of these linkages, we used five centrality indices with novel visualization tools and identified 94 high-credibility disease-associated ARS interactors. Among them, two targets (ESR1 and APP) were selected for experimental validation. Although demonstrated in ARS-mediated metabolic diseases, this approach can be similarly used to identify disease-associated factors with credibility scores within any target space of interest.