Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization
Xiaoxuan Zhang1,2, Meile Zhang1,2, Yihao Li1,2
1College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.
Abstract:
P21-activated kinase 4 (PAK4) plays a crucial role in the proliferation and metastasis of various cancers. However, developing selective PAK4 inhibitors remains challenging due to the high homology within the PAK family. Therefore, developing highly selective PAK4 inhibitors is critical to overcoming the limitations of existing inhibitors. We analyzed the structural differences in the binding pockets of PAK1 and PAK4 by combining cross-docking and molecular dynamics simulations to identify key binding regions and unique structural features of PAK4. We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. Based on this, we applied a fragment-replacement strategy under electrostatic-surface-matching conditions to obtain Compd 26. This optimization significantly improved electrostatic interactions and reduced binding energy, highlighting its potential for selectivity. Our findings provide a novel approach for developing selective PAK4 inhibitors and lay the theoretical foundation for future anticancer drug design.
Insights
Developing selective P21-activated kinase 4 (PAK4) inhibitors is crucial for cancer treatment. This study identified a novel inhibitor, Compd 26, by analyzing structural differences and employing advanced computational methods for improved selectivity.
Area of Science:
- Oncology
- Structural Biology
- Computational Chemistry
Background:
- P21-activated kinase 4 (PAK4) is implicated in cancer proliferation and metastasis.
- Developing selective PAK4 inhibitors is challenging due to high homology within the PAK kinase family.
Purpose of the Study:
- To identify novel, highly selective PAK4 inhibitors.
- To explore structural differences between PAK1 and PAK4 binding pockets for targeted drug design.
Main Methods:
- Cross-docking and molecular dynamics simulations to analyze PAK1 and PAK4 binding pockets.
- Shape and protein conformation ensemble screening, followed by deep-learning-driven docking with GNINA.
- Fragment-replacement strategy with electrostatic-surface matching for inhibitor optimization.
Main Results:
- Identified key binding regions and unique structural features of PAK4.
- Discovered candidate molecule STOCK7S-56165 through virtual screening.
- Optimized STOCK7S-56165 to Compd 26, enhancing electrostatic interactions and reducing binding energy for improved selectivity.
Conclusions:
- A novel computational approach was developed for designing selective PAK4 inhibitors.
- Compd 26 demonstrates significant potential as a selective PAK4 inhibitor for anticancer drug development.
- This study provides a theoretical foundation for future anticancer drug discovery targeting PAK4.
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