Inferring disruption of directed graphs using LIKA reveals altered protein phosphorylation networks in schizophrenia

Authors

Zhang, L., Demarco, A. G., Ghafari, K., Devlin, B., MacDonald, M. L., Roeder, K.

Abstract

Motivation: Kinases regulate a multitude of protein functions, and their dysregulation is pivotal for many human diseases. Direct measurement of kinase activity, however, is often challenging; therefore, inferring activity from the behavior of their substrates is a widely adopted strategy. Nonetheless, traditional methods typically oversimplify the underlying network, ignoring that any particular substrate can be phosphorylated by multiple kinases. Results: We present LIKA, a likelihood-based framework for inferring kinase activity from phosphoproteomic data. By modeling the many-to-many structure of kinase-substrate interactions, LIKA achieves high efficiency, even with limited data, while capturing network complexity. Simulation and cell line analyses confirm the robustness and accuracy of LIKA. Importantly, analysis of a phosphoproteomic dataset from schizophrenia and control subjects reveals novel dysregulated kinases. Availability and Implementation: The implementation code and publicly available data are provided at: https://github.com/lujingz/LIKA.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Aug 2026.

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