Identification and Validation of New DNA-PKcs Inhibitors through High-Throughput Virtual Screening and Experimental...

Area of Science:

• Biochemistry
• Molecular Biology
• Pharmacology

Background:

• DNA-Protein Kinase catalytic subunit (DNA-PKcs) is vital in DNA repair and cellular responses.
• Aberrant DNA-PKcs activity correlates with cancer occurrence and progression, making it a key therapeutic target.

Purpose of the Study:

• To discover novel small molecules that inhibit DNA-PKcs activity.
• To explore the potential of these inhibitors as anticancer therapeutics.
• To investigate their impact on DNA repair pathways and gene editing techniques.

Main Methods:

• Utilized a deep learning-based screening pipeline combined with molecular dynamics (MD) simulations to identify potential DNA-PKcs inhibitors.
• Conducted experimental validation of candidate molecules, assessing their efficacy in inhibiting DNA-PKcs-mediated cell proliferation.
• Performed structure-activity relationship (SAR) studies to understand molecular interactions and binding sites.

Main Results:

• Identified eight candidate small molecules, with three (5025-0002, M769-1095, V008-1080) demonstrating effective inhibition of DNA-PKcs.
• Determined IC50 values for the active compounds, indicating varying potencies (152.6 μM, 30.71 μM, 74.84 μM).
• Observed that V008-1080 selectively enhanced CRISPR/Cas9-mediated homology-directed repair (HDR) while suppressing non-homologous end joining (NHEJ).

Conclusions:

• The study successfully identified novel small molecule inhibitors of DNA-PKcs using an integrated deep learning and MD simulation approach.
• The discovered compounds, particularly V008-1080, show promise as anticancer agents and tools for modulating DNA repair pathways.
• This research validates the efficacy of the DeepBindGCN_RG pipeline for drug discovery and provides a foundation for developing targeted cancer therapies and advancing gene editing technologies.