A Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates.
Abeeb A Yekeen1,2, Cynthia J Meyer1,2, Melissa McCoy2
1Department of Biochemistry, University of Texas Southwestern Medical Center 5323 Harry Hines Blvd, Dallas, TX 75390-9038, USA.
Biorxiv : the Preprint Server for Biology
|July 9, 2025
Summary
Researchers developed Subtimizer, an AI pipeline for designing kinase peptide substrates. This approach significantly enhanced substrate activity and selectivity, aiding drug discovery and kinase research.
Area of Science:
- Biochemistry and Molecular Biology
- Computational Biology
- Drug Discovery
Background:
- Kinases are crucial regulators of cell signaling and key targets in drug development.
- Designing effective kinase peptide substrates for assays is challenging but vital for research.
Purpose of the Study:
- To present Subtimizer, a computational pipeline for structure-guided kinase peptide substrate design.
- To improve kinase substrate activity, specificity, and affinity for enhanced assay development.
Main Methods:
- Utilized AlphaFold-Multimer for protein structure modeling.
- Employed ProteinMPNN for sequence design and AlphaFold2 for interface evaluation.
- Applied the Subtimizer pipeline to five different kinases.
Main Results:
- Four out of five kinases showed significantly improved activity (up to 350%) with designed peptides.
- Designed peptides demonstrated over 2-fold reduction in Michaelis constant (Km), indicating enhanced enzyme-substrate affinity.
- Two designed peptides exhibited over 5-fold improvement in selectivity.
Conclusions:
- AI-driven, structure-guided protein design is effective for creating potent and selective kinase substrates.
- Subtimizer facilitates the development of improved kinase assays for drug discovery and kinome investigation.
