Enabling Systemic Identification and Functionality Profiling for Cdc42 Homeostatic Modulators.

Area of Science:

• Biochemistry and Pharmacology
• Computational Drug Discovery
• Molecular Biology

Background:

• Homeostatic modulation is crucial for therapeutics, but discovering effective modulators, especially for cytoplasmic targets like Cdc42, is challenging and often relies on random screening.
• Cdc42, a small GTPase, is implicated in various diseases including cancer and neuropsychiatric disorders, making it a significant therapeutic target.
• Existing homeostatic modulators (HMs) primarily target membrane proteins, with fewer options available for cytoplasmic targets.

Approach:

• Employed a combination of high-throughput in silico screening and experimental biochemical profiling to identify and classify Cdc42 homeostatic modulators (HMs).
• Utilized Cdc42-GEF assays to functionally profile over 2,500 compounds, categorizing them into five distinct HM classes based on their modulation of GTP loading.
• Developed and applied a modified molecular docking approach incorporating Preferential Binding Pocket Order (PBPO) for in silico classification of HMs.

Key Points:

• Discovered and classified five distinct classes of Cdc42 HMs: partial agonists, hormetic agonists, bona fide inhibitors, bona fide activators, and ligand-enhanced agonists.
• Demonstrated the therapeutic potential of novel HMs, showing efficacy in modulating actin remodeling and improving Alzheimer's disease-like behaviors in a mouse model.
• Validated the predictive power of computer-aided drug discovery (CADD) by showing that PBPO-based in silico analysis accurately reflected the experimentally determined HM classes.

Conclusions:

• Systemic identification and classification of Cdc42 HMs using a combined experimental and computational approach provide a robust model for discovering novel therapeutics.
• The study highlights the potential of targeting cytoplasmic proteins with HMs and demonstrates the utility of CADD in predicting and understanding HM functionality.
• This integrated strategy offers a pathway for actively discovering and profiling HMs across various protein-protein interaction landscapes, paving the way for future drug development.