Identification of KKL-35 as a novel carnosine dipeptidase 2 (CNDP2) inhibitor by in silico screening
Homma, T.; Shinbara, K.; Osaki, T.
Show abstract
Extracellular glutathione (GSH) is degraded on the cell surface, in which the {gamma}-glutamyl residue is removed to generate cysteine-glycine (Cys-Gly) dipeptides that are subsequently transported to the cytoplasm. Carnosine dipeptidase II (CNDP2) is a cytoplasmic enzyme that hydrolyzes Cys-Gly and plays an important role in maintaining intracellular cysteine (Cys) homeostasis. CNDP2-mediated hydrolysis of Cys-Gly promotes Cys mobilization and contributes to the replenishment of intracellular GSH levels. CNDP2 is frequently overexpressed in various cancers and has been implicated in tumor cell proliferation and progression. This mechanism may enhance cancer cell survival by causing resistance to oxidative stress, which indicates that CNDP2 is a potential therapeutic target for cancer treatment. Although bestatin (BES) has been identified as a CNDP2 inhibitor, its limited specificity and suboptimal drug-like properties have limited its therapeutic potential. In this study, we performed an in silico screen of a small-molecule compound library and identified KKL-35 as a novel CNDP2-binding molecule. Molecular dynamics (MD) simulations suggested that KKL-35 interacts within the catalytic pocket. Biochemical assays confirmed that it inhibits CNDP2 enzymatic activity, albeit with lower potency compared with BES. Despite its modest intrinsic activity, KKL-35 exhibits favorable physicochemical and pharmacokinetic properties, which are characterized by a low topological polar surface area (TPSA), reduced molecular flexibility, and well-balanced lipophilicity. This positions it as an attractive and tractable starting point for lead optimization. Taken together, these findings establish KKL-35 as a validated CNDP2 inhibitor and a promising lead compound for the development of more selective therapeutics targeting CNDP2-mediated cancer cell metabolism.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 97%
- Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors 97%
- Prediction of Essential Binding Domains for the Endocannabinoid N-Arachidonoylethanolamine (AEA) in the Brain Cannabinoid CB1 receptor 96%
Similar papers in this journal
- Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase 97%
- Elucidation of Structural Mechanism of ATP Inhibition at the AAA1 Subunit of Cytoplasmic Dynein 1 Using a Chemical "Toolkit" 95%
- Identification of a cardiac glycoside exhibiting favorable brain bioavailability and potency for reducing levels of the cellular prion protein 95%
Similar papers in this journal
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 97%
- Chalcogen derivatives for the treatment of African trypanosomiasis: biological evaluation of thio and seleno- semicarbazones and their azole derivatives 97%
- Support Vector Machine based prediction models for drug repurposing and designing novel drugs for colorectal cancer 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.