A Data-Driven Signaling Network Inference Approach for Phosphoproteomics

Abstract

Proteins are rapidly and dynamically post-transcriptionally modified as cells respond to changes in their environment. For example, protein phosphorylation is mediated by kinases while dephosphorylation is mediated by phosphatases. Quantifying and predicting interactions between kinases, phosphatases, and target proteins over time will aid the study of signaling cascades under a variety of environmental conditions. Here, we describe methods to statistically analyze label-free phosphoproteomic data and infer posttranscriptional regulatory networks over time. We provide an R-based method that can be used to normalize and analyze label-free phosphoproteomic data using variance stabilizing normalization and a linear mixed model across multiple time points and conditions. We also provide a method to infer regulator-target interactions over time using a discretization scheme followed by dynamic Bayesian modeling computations to validate our conclusions. Overall, this pipeline is designed to perform functional analyses and predictions of phosphoproteomic signaling cascades.

Keywords: Bayesian modeling; Kinase regulatory networks; Label-free phosphoproteomics; Post-translational modifications.