Kinase activity prediction from phosphoproteomics data
Predicting kinase activity has important consequences for advancing our understanding of signalling pathways and for the development of therapeutic kinase targets. Kinases modify the activity of proteins through the addition of phosphate groups and dysregulation of kinases are linked to a number of diseases. Until recently, predicting kinase activity has been limited by the availability of substrate sequences for modelling kinase specificity and large scale phosphoproteomes that adequately capture signalling states. This talk will discuss the development of an integrated network-based algorithm, KinSwing, for predicting kinase activity from phosphoproteomics data obtained by mass-spectrometry. I will discuss the motivation, the utility of KinSwing as an R package (KinSwingR), our findings in the context of understanding neuronal signalling and broader implications on understanding signalling networks.
Workshop: Predicting kinase activity from phospho-proteomics data with KinSwingR
High-throughput proteomics is providing new insight into biological systems. Protein phosphorylation is an important signalling mechanism, whereby kinases and phosphatases enzymatically modify protein activity through addition or removal of phosphate groups. This workshop will introduce the basics of phospho-proteomics and the utility of KinSwingR, an R package for prediction of kinase activity from phospho-proteomics data. Utility will be demonstrated with example phospho-proteomics datasets and will include visualisation of predicted kinase-substrate networks, visualisation of kinase models (motifs) and inference of kinase activity.
Each participant is required to bring their laptop with R/Rstudio with R version 3.5 (at least) installed as well as the following:
R packages to have installed:
- KinSwingR from BioConductor
See landing page for installation details: https://bioconductor.org/packages/release/bioc/html/KinSwingR.html
- devtools from CRAN
will be used for downloading additional data/packages for use in the workshop
Data will be exported and visualised
devtools::install_github(“awaardenberg/phosphoProcessR”, build_vignettes = TRUE)
All workshops materials have been uploaded to https://github.com/awaardenberg/training_material
Dr Ashley Waardenberg
Australian Tropical Health and Medicine (AITHM)
James Cook University
Ashley Waardenberg is a Research Fellow in Bioinformatics at the Australian Institute of Tropical Health and Medicine (AITHM). He is also a Theme Leader (Health and Disease in the Tropics) at the Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, and cofounder of the Australian Bioinformatics and Computational Biology Society (ABACBS). Ashley completed his PhD in Systems Biology (2012), jointly between CSIRO (Livestock Genomics) and Griffith University (Eskitis Institute), QLD, Australia and has held research positions at the Victor Chang Cardiac Research Institute (Sydney), Children’s Medical Research Institute (Sydney) and European Molecular Biology Laboratory (Germany). His research interests span methods development, integration, machine learning, visualisation and applications for understanding mechanisms of disease.