MOTIVATION: Phosphoproteomics measurements are widely applied in cellular biology to detect changes in signalling dynamics. However, due to the inherent complexity of phosphorylation patterns and the lack of knowledge on how phosphorylations are related to functions, it is often not possible to directly deduce protein activities from those measurements. Here, we present a heuristic machine learning algorithm that infers the activities of kinases from Phosphoproteomics data using kinase-target information from the PhosphoSite- Plus database. By comparing the estimated kinase activity profiles to the measured phosphosite profiles it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite. RESULTS: We apply our approach to published datasets of the human cell cycle generated from HeLaS3 cells, and insulin signaling dynamics in mouse hepatocytes. In the first case, we estimate the activities of 118 at six cell cycle stages and derive 94 new kinase-phosphosite-links that can be validated through either database or motif information. In the second case, the activities of 143 kinases at eight time points is estimated and 49 new kinase-target-links are derived. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in Matlab and be downloaded from github. It makes use of the Optimization and Statistics toolboxes. https://github.com/marcel-mischnik/IKAP.git SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Choudhury, A. K. R., & Choudhury, A. K. R. (2014). 5 – Unusual visual phenomena and colour blindness. In Principles of Colour and Appearance Measurement (pp. 185–220). https://doi.org/10.1093/bioinformatics/btv699