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.
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.