Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

Area of Science:

• Biochemistry and Molecular Biology
• Medicinal Chemistry
• Computational Drug Discovery

Background:

• Cyclin-dependent kinase 7 (CDK7) is vital for cell cycle progression and transcription.
• CDK7 overexpression is implicated in human cancers, making it a therapeutic target.
• Developing selective and cost-effective CDK7 inhibitors is crucial for cancer therapy.

Purpose of the Study:

• To identify novel small molecules with potential inhibitory activity against CDK7.
• To evaluate the binding affinity, selectivity, and pharmacokinetic properties of identified compounds.

Main Methods:

• Utilized a pharmacophore-based virtual screening approach on a library of 55,578 natural compounds.
• Validated pharmacophore models and employed them as 3D queries for screening.
• Conducted molecular docking, molecular dynamics simulations, and in silico pharmacokinetic predictions.

Main Results:

• Identified four promising hit compounds (ZINC20392430, SN00112175, SN00004718, SN00262261) with superior binding affinity to CDK7 compared to reference inhibitors.
• Detailed binding mode analysis revealed key interactions with critical CDK7 residues, including hinge region and ATP-binding site.
• In silico selectivity profiling against CDK2 and pharmacokinetic assessments indicated favorable properties for the identified hits.

Conclusions:

• The identified compounds demonstrate significant potential as novel therapeutic agents against CDK7.
• These hits exhibit promising binding characteristics and favorable in silico pharmacokinetic profiles.
• Further investigation of these compounds could lead to effective treatments for CDK7-driven cancers.