CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis

PLoS Computational Biology
Marzieh AyatiMark R Chance

Abstract

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naïve Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases' substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing to a comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations.

References

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Citations

Jun 9, 2019·The Journal of Biological Chemistry·Joseph Y Ong, Jorge Z Torres
Apr 16, 2020·Database : the Journal of Biological Databases and Curation·Tyler CowmanMehmet Koyutürk
Jul 31, 2020·Bioinformatics·Marzieh AyatiMehmet Koyutürk
Feb 21, 2021·Nature Communications·Serhan YılmazMehmet Koyutürk
Feb 25, 2021·Cell Reports·Hani Jieun KimPengyi Yang

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Methods Mentioned

BETA
Xenograft
xenografts
interaction predictions

Software Mentioned

KSA
PhosphositePLUS
CoPhosK
KinomeXplorer
KinomeXporer
NetworKIN
PUEL
DB

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