Virtual Screening Inhibitors of Ubiquitin-specific Protease 7 combining Pharmacophore Modeling and Molecular Docking

Exploring Ubiquitin-specific proteases as therapeutic targets in Glioblastoma

Glioblastoma (GB) remains a formidable challenge and requires new treatment strategies. The vital part of the Ubiquitin-proteasome system (UPS) in cellular regulation has positioned it as a potentially crucial target in GB treatment, given its dysregulation oncolines. The Ubiquitin-specific proteases (USPs) in the UPS system were considered due to the garden role in the cellular processes associated with oncolines and their vital function in the apoptotic process, cell cycle regulation, and autophagy. The article provides a comprehensive summary of the evidence base for targeting USPs as potential factors for neoplasm treatment. The review considers the participation of the UPS system in the development, resulting in the importance of p53, Rb, and NF-κB, and evaluates specific goals for therapeutic administration using midnight proteasomal inhibitors and small molecule antagonists of E1 and E2 enzymes. Despite the slowed rate of drug creation, recent therapeutic discoveries based on USP system dynamics hold promise for specialized therapies. The review concludes with an analysis of future wanderers and the feasible effects of targeting USPs on personalized GB therapies, which can improve patient hydration in this current and unattractive therapeutic landscape. The manuscript emphasizes the possibility of USP oncogene therapy as a promising alternative treatment line for GB. It stresses the direct creation of research on the medical effectiveness of the approach.

Virtual screening of natural products targeting ubiquitin-specific protease 7

Ubiquitin-specific protease 7 (USP7) is a promising prognostic and druggable target for cancer therapy. Inhibition of USP7 can activate the MDM2-P53 signaling pathway, thereby promoting cancer cell apoptosis. This study based on watvina molecular docking of virtual screening method and biological evaluation found the new USP7 inhibitors targeting catalytic active site. Three hits were screened from 3760 natural products and validated as USP7 inhibitors by enzymatic and kinetic assays. The IC50 values of scutellarein (Scu), semethylzeylastera (DML) and salvianolic acid C (SAC) were 3.017, 6.865 and 8.495 μM, respectively. Further, we reported that the hits could downregulate MDM2 and activate p53 signal pathway in HCT116 cells. Molecular dynamics simulation was used to investigate the binding mechanism of USP7 to Scu, the compound with the best performance, which formed stable contact with Val296, Gln297, Phe409, Tyr465 and Tyr514. These interactions are essential for maintaining the biological activity of Scu. Three natural products are suitable as lead compounds for the development of novel USP7 inhibitors, especially anti-colon cancer drugs.Communicated by Ramaswamy H. Sarma.

Current and future directions of USP7 interactome in cancer study

The ubiquitin-proteasome system (UPS) is an essential protein quality controller for regulating protein homeostasis and autophagy. Ubiquitination is a protein modification process that involves the binding of one or more ubiquitins to substrates through a series of enzymatic processes. These include ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), and ubiquitin ligases (E3). Conversely, deubiquitination is a reverse process that removes ubiquitin from substrates via deubiquitinating enzymes (DUBs). Dysregulation of ubiquitination-related enzymes can lead to various human diseases, including cancer, through the modulation of protein ubiquitination. The most structurally and functionally studied DUB is the ubiquitin-specific protease 7 (USP7). Both the TRAF and UBL domains of USP7 are known to bind to the [P/A/E]-X-X-S or K-X-X-X-K motif of substrates. USP7 has been shown to be involved in cancer pathogenesis by binding with numerous substrates. Recently, a novel substrate of USP7 was discovered through a systemic analysis of its binding motif. This review summarizes the currently discovered substrates and cellular functions of USP7 in cancer and suggests putative substrates of USP7 through a comprehensive systemic analysis.

Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison

Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening methods have now been successfully applied to discover USP7 inhibitors aiming at reducing costs and speeding up time in several studies. However, due to their unsatisfactory accuracy, it is still a difficult task to develop USP7 inhibitors. In this study, multiple supervised learning classifiers were built to distinguish active USP7 inhibitors from inactive ligands. Physicochemical descriptors, MACCS keys, ECFP4 fingerprints and SMILES were first calculated to represent the compounds in our in-house dataset. Two deep learning (DL) models and nine classical machine learning (ML) models were then constructed based on different combinations of the above molecular representations under three activity cutoff values, and a total of 15 groups of experiments (75 experiments) were implemented. The performance of the models in these experiments was evaluated, compared and discussed using a variety of metrics. The optimal models are ensemble learning models when the dataset is balanced or severely imbalanced, and SMILES-based DL performs the best when the dataset is slightly imbalanced. Meanwhile, multimodal data fusion in some cases can improve the performance of ML and DL models. In addition, SMOTE, unbiased decoy selection and SMILES enumeration can improve the performance of ML and DL models when the dataset is severely imbalanced, and SMOTE works the best. Our study established highly accurate supervised learning classification models, which would accelerate the development of USP7 inhibitors. Some guidance was also provided for drug researchers in selecting supervised models and molecular representations as well as handling imbalanced datasets.