PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation

Patrick, Ralph, Kobe, Bostjan, Lê Cao, Kim-Anh and Bodén, Mikael (2017) PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation. Bioinformatics, 33 12: 1773-1781. doi:10.1093/bioinformatics/btx072

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Author Patrick, Ralph
Kobe, Bostjan
Lê Cao, Kim-Anh
Bodén, Mikael
Title PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1460-2059
1367-4803
Publication date 2017-02-10
Year available 2017
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btx072
Open Access Status File (Author Post-print)
Volume 33
Issue 12
Start page 1773
End page 1781
Total pages 9
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Formatted abstract
Motivation: Genome-wide association studies are identifying single nucleotide variants (SNVs) linked to various diseases, however the functional effect caused by these variants is often unknown. One potential functional effect, the loss or gain of protein phosphorylation sites, can be induced through variations in key amino acids that disrupt or introduce valid kinase binding patterns. Current methods for predicting the effect of SNVs on phosphorylation operate on the sequence content of reference and variant proteins. However, consideration of the amino acid sequence alone is insufficient for predicting phosphorylation change, as context factors determine kinase-substrate selection.

Results: We present here a method for quantifying the effect of SNVs on protein phosphorylation through an integrated system of motif analysis and context-based assessment of kinase targets. By predicting the effect that known variants across the proteome have on phosphorylation, we are able to use this background of proteome-wide variant effects to quantify the significance of novel variants for modifying phosphorylation. We validate our method on a manually curated set of phosphorylation change-causing variants from the primary literature, showing that the method predicts known examples of phosphorylation change at high levels of specificity. We apply our approach to data-sets of variants in phosphorylation site regions, showing that variants causing predicted phosphorylation loss are over-represented among disease-associated variants.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Fri, 17 Feb 2017, 11:42:36 EST by Mrs Louise Nimwegen on behalf of School of Chemistry & Molecular Biosciences