Novel Substrate Prediction for the TAM Family of RTKs Using Phosphoproteomics and Structure-Based Modeling.

Area of Science:

• Biochemistry
• Molecular Biology
• Structural Biology

Background:

• The TAM family of receptor tyrosine kinases (Tyro3, Axl, Mer) plays a role in various oncogenic signaling pathways.
• Currently, there are no FDA-approved small molecule inhibitors targeting TAM kinases, hindering drug development.
• Effective inhibitor design and screening necessitate robust tools for studying kinase activity and substrate specificity.

Purpose of the Study:

• To address the lack of tools for TAM kinase research by designing novel synthetic peptide substrates.
• To characterize the substrate profiles of Tyro3, Axl, and Mer kinases.
• To validate the use of computational modeling, specifically AlphaFold2, in predicting kinase-substrate interactions.

Main Methods:

• Employed an in vitro phosphoproteomics workflow to identify TAM kinase substrate motifs.
• Utilized a data processing pipeline (KINATEST-ID) to generate position-specific scoring matrices.
• Synthesized and biochemically characterized candidate peptide substrates using LC-MS to measure phosphorylation rates.
• Applied AlphaFold2's multimer modeling to predict peptide-kinase interactions at the active site.

Main Results:

• Successfully designed and synthesized novel synthetic peptide substrates for Tyro3, Axl, and Mer kinases.
• Biochemical assays confirmed the phosphorylation of synthesized peptides by their target TAM kinases.
• AlphaFold2 predictions of catalytically competent peptide-kinase interactions strongly correlated with experimentally validated substrates.
• Demonstrated the utility of AlphaFold2 in predicting peptide-protein interactions relevant to kinase activity.

Conclusions:

• Kinase substrate design can be effectively achieved by integrating preference motif analysis with structural modeling.
• This study provides the first demonstration of AlphaFold2's capability in modeling peptide-protein interactions for predicting catalytic activity.
• The developed synthetic peptide substrates and validated computational approach offer valuable tools for future TAM kinase inhibitor research and development.