SELPHI2.0: Data-driven extraction of human kinase-substrate relationships from omics datasets

In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods, and provides predictions for more kinases. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space. Our model is integrated into a web server, SELPHI2.0, to allow unbiased analysis of phosphoproteomics data, facilitating the design of downstream experiments to uncover mechanisms of signal transduction across conditions and cellular contexts. The SELPHI2.0 web server is freely available under: https://selphi2.com and all code for reproducing this project can be found at https://gitlab.ebi.ac.uk/petsalakilab/selphi_2.0 and https://github.com/alussana/selphi2.0-pssms. A Docker image of the web server can be found at: https://gitlab.ebi.ac.uk/petsalakilab/selphi_2.