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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.
Current Issues in Molecular Biology
|June 26, 2026
Summary
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.
