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Screening of Natural Product-Derived USP7 Inhibitors for Cancer Therapy via Integrated Machine Learning and Molecular
1Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia.
Abstract:
Ubiquitination, a crucial cellular protein regulation process, is linked to various diseases, including cancer. Deubiquitinases (DUBs) can reverse ubiquitination, offering a therapeutic strategy. USP7, a DUB, is a key target in oncology due to its role in destabilizing p53, and small-molecule inhibitors could restore p53 activity and combat tumor growth. In this study, we integrated a machine learning (ML)-based screening approach with molecular docking and molecular dynamics (MD) simulations in order to identify potential small-molecule inhibitors of USP7. ML-based screening identified 22 active molecules from a library of 2301 natural compounds. Among the 22 active compounds, only fifteen compounds fulfilled the drug-likeness criteria. Subsequently, molecular docking found three compounds, PubChem 162957515, 114917, and 442879 as potential inhibitors based on binding affinity and interactions. Further, MD simulations and MM-PBSA analyses were performed to evaluate the stability and dynamic behavior of the complexes. Binding energy calculations Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) revealed that compounds PubChem 114917 and 162957515 exhibited strong binding affinities of -20.98 kcal/mol and -18.68 kcal/mol, respectively, implying that these compounds could serve as promising inhibitors for the development of anticancer therapeutics.
Insights
Researchers identified potential small-molecule inhibitors for USP7, a deubiquitinase (DUB) enzyme crucial in cancer. These compounds, discovered using machine learning and simulations, may help restore p53 activity and fight tumor growth.
Area of Science:
- Biochemistry
- Computational Chemistry
- Oncology
Background:
- Ubiquitination regulates cellular proteins and is implicated in cancer.
- Deubiquitinases (DUBs) reverse ubiquitination; USP7 is a key DUB target in oncology for its role in p53 destabilization.
- Small-molecule inhibitors of USP7 could restore p53 activity and inhibit tumor growth.
Purpose of the Study:
- To identify potential small-molecule inhibitors of USP7 using an integrated computational approach.
- To screen a natural compound library for USP7 inhibitors.
- To validate potential inhibitors through molecular docking and dynamics simulations.
Main Methods:
- Machine learning (ML)-based screening of 2301 natural compounds.
- Molecular docking to assess binding affinity and interactions.
- Molecular dynamics (MD) simulations and MM-PBSA for stability and binding energy calculations.
Main Results:
- ML screening identified 22 active compounds; 15 met drug-likeness criteria.
- Molecular docking pinpointed three compounds (PubChem 162957515, 114917, 442879) as potential inhibitors.
- MM-PBSA revealed strong binding affinities for PubChem 114917 (-20.98 kcal/mol) and 162957515 (-18.68 kcal/mol).
Conclusions:
- PubChem 114917 and 162957515 are promising candidates for USP7 inhibition.
- These compounds show potential for developing novel anticancer therapeutics.
- The integrated computational strategy effectively identified potential drug leads for USP7 targeting.
