Phosphoproteomics data-driven signalling network inference: does it work?

Abstract

AbstractThe advent in high throughput global phosphoproteome profiling has led to wide phosphosite coverage and therefore the need to predict kinase substrate associations from these datasets. However, for multiple substrates, the regulatory kinase is unknown due to biased and incomplete interactome databases. In this study we compare the performance of six pairwise measures to predict kinase substrate associations using a purely data driven approach on publicly available dynamic time resolved and perturbation phosphoproteome data using mass spectrometry profiling. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both database-derived and predicted interactomes was poor. To expand into the wider interactome space, the performance of these measures was evaluated against a network compiled from pairs of substrates regulated by the same kinase (substrate-substrate associations). Similar to the kinase substrate predictions, a purely statistical approach to predict substrate-substrate associations was also poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a boost in performance and to the inference of statistically significant substrate-substrate associations. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase substrate associations.