Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Area of Science:

• Oncology
• Computational Chemistry
• Drug Discovery

Background:

• Cancer remains a leading cause of death globally, with significant challenges posed by resistance to conventional treatments.
• Cyclin-dependent kinase 11 (CDK11) is overexpressed in many cancers and associated with poor patient prognosis, making it a potential therapeutic target.

Purpose of the Study:

• To identify novel inhibitors of Cyclin-dependent kinase 11 (CDK11) through computational drug discovery.
• To evaluate the drug-likeness and binding potential of natural compounds against CDK11.

Main Methods:

• Utilized pharmacophore modeling, virtual screening, and molecular docking to screen a natural product database.
• Performed ADMET predictions, molecular dynamics simulations, and binding free energy analysis for promising candidates.
• Validated three pharmacophore models and assessed compound stability and interactions with CDK11.

Main Results:

• Identified several compounds with superior binding affinities to CDK11 compared to the reference inhibitor.
• Compound UNPD29888 demonstrated the strongest binding affinity.
• ADMET profiling indicated favorable drug-like properties for the identified candidates.

Conclusions:

• Computational predictions suggest UNPD29888 is a potent inhibitor of CDK11.
• The identified compounds hold potential for development as novel anti-cancer agents targeting CDK11.
• Further experimental validation is warranted to confirm the therapeutic efficacy of these compounds.