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Kinase Substrate Profiling Using a Proteome-wide Serine-Oriented Human Peptide Library
Author(s) -
Karl W. Barber,
Chad J. Miller,
Jay W. Jun,
Hua Jane Lou,
Benjamin E. Turk,
Jesse Rinehart
Publication year - 2018
Publication title -
biochemistry
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.43
H-Index - 253
eISSN - 1520-4995
pISSN - 0006-2960
DOI - 10.1021/acs.biochem.8b00410
Subject(s) - proteome , peptide , profiling (computer programming) , serine , human proteome project , substrate specificity , computational biology , chemistry , peptide library , biochemistry , proteomics , biology , computer science , peptide sequence , phosphorylation , enzyme , gene , operating system
The human proteome encodes >500 protein kinases and hundreds of thousands of potential phosphorylation sites. However, the identification of kinase-substrate pairs remains an active area of research because the relationships between individual kinases and these phosphorylation sites remain largely unknown. Many techniques have been established to discover kinase substrates but are often technically challenging to perform. Moreover, these methods frequently rely on substrate reagent pools that do not reflect human protein sequences or are biased by human cell line protein expression profiles. Here, we describe a new approach called SERIOHL-KILR (serine-oriented human library-kinase library reactions) to profile kinase substrate specificity and to identify candidate substrates for serine kinases. Using a purified library of >1 serine-oriented human peptides expressed heterologously in Escherichia coli, we perform in vitro kinase reactions to identify phosphorylated human peptide sequences by liquid chromatography and tandem mass spectrometry. We compare our results for protein kinase A to those of a well-established positional scanning peptide library method, certifying that SERIOHL-KILR can identify the same predominant motif elements as traditional techniques. We then interrogate a small panel of cancer-associated PKCβ mutants using our profiling protocol and observe a shift in substrate specificity likely attributable to the loss of key polar contacts between the kinase and its substrates. Overall, we demonstrate that SERIOHL-KILR can rapidly identify candidate kinase substrates that can be directly mapped to human sequences for pathway analysis. Because this technique can be adapted for various kinase studies, we believe that SERIOHL-KILR will have many new victims in the future.

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