A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including...

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Designing chemical inhibitors for complex DNA Damage Response (DDR) networks is challenging.
• Existing methods like gene knockdown/knockout are insufficient for developing novel inhibitors.

Purpose of the Study:

• To present a Goldilocks (GL) computational protocol for efficient discovery of inhibitor tools and drug candidates targeting DDR networks.
• To enable cellular and structural biologists to identify potential therapeutics without extensive virtual screening or synthesis expertise.

Main Methods:

• Utilized a Goldilocks (GL) computational discovery protocol integrating experimental and predicted structures.
• Employed efficient virtual screening (VS) to analyze protein-protein interfaces (PPIs) and allosteric interactions.
• Screened a diverse library of 3,174 compounds, including FDA-approved drugs and Protein Data Bank fragments.

Main Results:

• Successfully identified DDR target sites and compounds for probing cancer biology.
• Discovered ligand binding sites beyond active sites by screening PPIs and multiple targets.
• Enabled early-structure and early-testing (ESET) experiments for academic laboratories.

Conclusions:

• The GL protocol and library facilitate strategic probing of multiple DDR network targets.
• Readily available compounds can be identified for early structural and activity testing, overcoming discovery bottlenecks.
• This approach can accelerate discovery for challenging targets, including emerging biothreats, and advance precision medicine.