Discovery of novel cathepsin K inhibitors for osteoporosis treatment using a deep learning-based strategy
Qi Li1,2, Xue-Chun Han1,2, Si-Rui Zhou1,2
1Beijing Key Laboratory of Diabetes Research and Care, Beijing Diabetes Institute, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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.
Insights
Researchers developed a deep learning model to find new Cathepsin K (CTSK) inhibitors for osteoporosis. Quercetin, γ-Linolenic acid (GLA), and Benzyl isothiocyanate (BITC) show promise as potent CTSK inhibitors and for reducing osteoclast formation.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Chemistry
Background:
- Cathepsin K (CTSK) is a key target for osteoporosis treatment due to its high expression in osteoclasts.
- Currently, no CTSK inhibitors are approved for clinical use, highlighting an unmet medical need.
Purpose of the Study:
- To identify novel Cathepsin K (CTSK) inhibitors using a combination of deep learning and experimental validation.
- To evaluate the inhibitory potential and mechanisms of action of identified compounds against CTSK.
- To assess the efficacy of lead compounds in inhibiting osteoclastogenesis.
Main Methods:
- Developed a predictive deep learning model (Chemprop) for CTSK inhibition.
- Screened 100 predicted molecules experimentally, followed by enzyme kinetics, molecular docking, and molecular dynamics simulations.
- Conducted in-vitro assays using RAW264.7 cells to evaluate inhibition of RANKL-induced osteoclastogenesis.
Main Results:
- Identified six compounds with concentration-dependent CTSK inhibitory activity.
- Quercetin, γ-Linolenic acid (GLA), and Benzyl isothiocyanate (BITC) emerged as the most potent inhibitors.
- Enzyme kinetics and molecular dynamics revealed distinct inhibition mechanisms and stable active site interactions for Quercetin and BITC.
- Quercetin and GLA significantly inhibited osteoclast formation in vitro.
Conclusions:
- A deep learning model for predicting CTSK inhibitors was successfully developed.
- Quercetin, GLA, and BITC are identified as promising therapeutic candidates for osteoporosis treatment.
- The study provides a foundation for developing novel CTSK-targeted osteoporosis therapies.
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