SAGE: A Segment-Anchor-Guided Exploration Framework for the Optimization of CDK7 Inhibitors as Promising Cancer...

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Oncology

Background:

• Cyclin-dependent kinase 7 (CDK7) is a critical regulator of cell cycle and transcription, making it a significant target in cancer therapy.
• The existing covalent inhibitor THZ1, while effective, has limitations including a short half-life and potential off-target effects.

Purpose of the Study:

• To design novel, noncovalent CDK7 inhibitors with improved selectivity and safety profiles.
• To address the limitations of current covalent CDK7 inhibitors through computational drug design.

Main Methods:

• Utilized a computational workflow combining virtual screening, molecular dynamics (MD) simulations, and free energy perturbation (FEP) methods.
• Analyzed the inhibitory mechanism of THZ1 using MD simulations to identify key molecular fragments.
• Incorporated fragments from known inhibitors to engineer extensive noncovalent interactions within the CDK7 binding pocket.

Main Results:

• Identified three novel noncovalent CDK7 inhibitors.
• These new inhibitors exhibit binding affinities comparable to or exceeding that of THZ1.
• The computational framework successfully elucidated THZ1's mechanism and guided the design of improved inhibitors.

Conclusions:

• The study introduces promising noncovalent CDK7 inhibitors as potential cancer therapeutics.
• The developed computational strategy offers a robust and accelerated approach for discovering targeted kinase inhibitors.
• This work provides a foundation for developing safer and more effective cancer treatments.