Utilizing machine learning to identify nifuroxazide as an inhibitor of ubiquitin-specific protease 21 in a drug repositioning strategy

Abstract

Ubiquitin-specific protease (USP), an enzyme catalyzing protein deubiquitination, is involved in biological processes related to metabolic disorders and cancer proliferation. We focused on constructing predictive models tailored to unveil compounds boasting USP21 inhibitory attributes. Six models, Extra Trees Classifier, Random Forest Classifier, LightGBM Classifier, XGBoost Classifier, Bagging Classifier, and a convolutional neural network harnessed from empirical data were selected for the screening process. These models guided our selection of 26 compounds from the FDA-approved drug library for further evaluation. Notably, nifuroxazide emerged as the most potent inhibitor, with a half-maximal inhibitory concentration of 14.9 ± 1.63 μM. The stability of protein-ligand complexes was confirmed using molecular modeling. Furthermore, nifuroxazide treatment of HepG2 cells not only inhibited USP21 and its established substrate ACLY but also elevated p-AMPKα, a downstream functional target of USP21. Intriguingly, we unveiled the previously unknown capacity of nifuroxazide to increase the levels of miR-4458, which was identified as downregulating USP21. This discovery was substantiated by manipulating miR-4458 levels in HepG2 cells, resulting in corresponding changes in USP21 protein levels in line with its predicted interaction with ACLY. Lastly, we confirmed the in vivo efficacy of nifuroxazide in inhibiting USP21 in mice livers, observing concurrent alterations in ACLY and p-AMPKα levels. Collectively, our study establishes nifuroxazide as a promising USP21 inhibitor with potential implications for addressing metabolic disorders and cancer proliferation. This multidimensional investigation sheds light on the intricate regulatory mechanisms involving USP21 and its downstream effects, paving the way for further exploration and therapeutic development.