Background: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeutic target for osteoporosis. However, there are currently no CTSK inhibitors available for clinical use.
Research design and methods: The authors employed a combination of deep learning approaches and experimental methods to identify novel CTSK inhibitors. Firstly, the authors utilized Chemprop to develop a predictive model for predicting CTSK inhibition. Subsequently, the top 100 predicted molecules were selected for experimental validation, with the most potent inhibitors chosen for further analysis, including enzyme kinetics, molecular docking, molecular dynamics simulations, and RANKL-induced osteoclastogenesis assays.
Results: The authors identified six compounds exhibiting concentration-dependent CTSK inhibitory effects, with Quercetin, γ-Linolenic acid (GLA), and Benzyl isothiocyanate (BITC) demonstrating the highest potency. Enzyme kinetics studies revealed that these inhibitors employ distinct mechanisms of CTSK inhibition. Molecular dynamics simulations further showed that Quercetin and BITC form stable interactions at the CTSK active site. Moreover, in-vitro studies demonstrated that Quercetin and GLA significantly inhibit RANKL-induced osteoclastogenesis in RAW264.7 cells.
Conclusions: This study led to the development of a deep learning model capable of predicting CTSK inhibitors and identified Quercetin, GLA, and BITC as promising candidates for the treatment of osteoporosis.
Keywords: Cathepsin K; deep learning; enzyme kinetics; molecule docking; osteoporosis.