Identification of Potential p38γ Inhibitors via In Silico Screening, In Vitro Bioassay and Molecular Dynamics...

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• Protein kinase p38γ is crucial for cancer cell proliferation by phosphorylating the retinoblastoma tumor suppressor protein.
• Inhibiting p38γ with small molecules offers a promising strategy for anti-cancer drug development.

Purpose of the Study:

• To develop a systematic virtual screening framework for identifying potential p38γ inhibitors.
• To discover novel small molecules targeting p38γ for cancer therapy.

Main Methods:

• Utilized a combination of machine learning (quantitative structure-activity relationship modeling) and traditional computer-aided drug discovery (molecular docking, ligand-based methods).
• Employed negative design for filtering and molecular dynamics simulations to assess binding stability.
• Screened for inhibitors of protein kinase p38γ.

Main Results:

• Identified a promising compound that inhibits p38γ activity at nanomolar concentrations.
• The identified compound demonstrated efficacy in reducing hepatocellular carcinoma cell growth in vitro at low micromolar concentrations.
• Validated the compound's binding stability with p38γ through simulations.

Conclusions:

• The identified compound is a potent inhibitor of p38γ and hepatocellular carcinoma cell growth.
• This compound serves as a potential scaffold for developing novel anti-cancer therapeutics targeting p38γ.
• The virtual screening framework successfully identified a promising lead compound for further drug development.