1School of Chemistry and Molecular Biosciences2Institute for Molecular Bioscience3Australian Infectious Diseases Research Centre, The University of Queensland, St Lucia 4072, Australia4The University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia
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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.Availability and Implementation:The method is freely available as a web-service at the website http://bioinf.scmb.uq.edu.au/phosphopick/snpContact:email@example.comSupplementary information:Supplementary data are available at Bioinformatics online.