Deciphering kinase-substrate relationships by analysis of domain-specific phosphorylation network

In silico prediction of site-specific kinase-substrate relationships (ssKSRs) is crucial for deciphering phosphorylation networks by linking kinomes to phosphoproteomes. However, currently available predictors for ssKSRs give rise to a large number of false-positive results because they use only a s...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2014-06, Vol.30 (12), p.1730-1738
Main Authors: Damle, Nikhil Prakash, Mohanty, Debasisa
Format: Article
Language:English
Subjects:
Animals
Humans
Mice
Phosphoproteins - chemistry
Phosphoproteins - metabolism
Phosphorylation
Protein Kinases - classification
Protein Kinases - metabolism
Protein Structure, Tertiary
Rats
Software
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Summary:In silico prediction of site-specific kinase-substrate relationships (ssKSRs) is crucial for deciphering phosphorylation networks by linking kinomes to phosphoproteomes. However, currently available predictors for ssKSRs give rise to a large number of false-positive results because they use only a short sequence stretch around phosphosite as determinants of kinase specificity and do not consider the biological context of kinase-substrate recognition. Based on the analysis of domain-specific kinase-substrate relationships, we have constructed a domain-level phosphorylation network that implicitly incorporates various contextual factors. It reveals preferential phosphorylation of specific domains by certain kinases. These novel correlations have been implemented in PhosNetConstruct, an automated program for predicting target kinases for a substrate protein. PhosNetConstruct distinguishes cognate kinase-substrate pairs from a large number of non-cognate combinations. Benchmarking on independent datasets using various statistical measures demonstrates the superior performance of PhosNetConstruct over ssKSR-based predictors. PhosNetConstruct is freely available at http://www.nii.ac.in/phosnetconstruct.html.
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btu112