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

The TAM family of receptor tyrosine
kinases is implicated
in multiple
distinct oncogenic signaling pathways. However, to date, there are
no FDA-approved small molecule inhibitors for the TAM kinases. Inhibitor
design and screening rely on tools to study the kinase activity. Our
goal was to address this gap by designing a set of synthetic peptide
substrates for each of the TAM family members: Tyro3, Axl, and Mer.
We used an in vitro phosphoproteomics workflow to determine the substrate
profile of each TAM kinase and input the identified substrates into
our data processing pipeline, KINATEST-ID, producing a position-specific
scoring matrix for each target kinase and generating a list of candidate
synthetic peptide substrates. We synthesized and characterized a set
of those substrate candidates, systematically measuring their initial
phosphorylation rate with each TAM kinase by LC-MS. We also used the
multimer modeling function of AlphaFold2 (AF2) to predict peptide–kinase
interactions at the active site for each of the novel candidate peptide
sequences against each of the TAM family kinases and observed that,
remarkably, every sequence for which it predicted a putative catalytically
competent interaction was also demonstrated biochemically to be a
substrate for one or more of the TAM kinases. This work shows that
kinase substrate design can be achieved using a combination of preference
motifs and structural modeling, and it provides the first demonstration
of peptide–protein interaction modeling with AF2 for predicting
the likelihood of constructive catalytic interactions.