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 PhosphoSitePlus 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 signalling 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 are estimated and 49 new kinase-target links are derived.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics