Document Type
Article
Publication Date
2-27-2019
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.
Recommended Citation
Ayati M, Wiredja D, Schlatzer D, Maxwell S, Li M, Koyuturk M, et al. (2019) CoPhosK: A method for comprehensive kinase substrate annotation using co-phosphorylation analysis. PLoS Comput Biol 15(2): e1006678. https://doi. org/10.1371/journal.pcbi.1006678
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
PLos One Computational Biology
DOI
10.1371/journal.pcbi.1006678
Comments
© 2019 Ayati et al.