Inference of differential kinase interaction networks with KINference

Differential kinase interaction networks (DKINs) are networks containing kinase-substrate links that are differentially active between two conditions. Existing methods are either able to predict condition-agnostic kinase-substrate links or condition-specific differential kinase activity, but do not provide differential kinase-substrate links. Moreover, existing methods for predicting kinase-substrate links usually rely on curated biochemical knowledge. Thus, there is a lack of data-driven DKIN inference methods that are also applicable when prior knowledge is scarce.

To address this need, we present KINference. KINference combines computation of a baseline KIN representing the space of all possible kinase-substrate links with filters applied to nodes and edges to identify differentially active subnetworks that are relevant in the context of a specific phosphoproteomics dataset. For the node filters, we rely on functional relevance and differential phosphorylation scores; for the edge filters, we make use of prize-collecting Steiner trees and correlations between phosphorylation sites of kinases and their target proteins. Tests on two phosphoproteomics datasets (kinase inhibition in breast cancer cells, SARS-CoV-2 infection in Calu-3 cells) show that the proposed filters produce significant results in terms of overlap with known interactions between kinases and phosphorylation sites. Furthermore, a case study on the SARS-CoV-2 infection data, suggests a potential host pathway linked to virus replication, showcasing the process of hypothesis generation utilizing DKINs computed by KINference.

KINference is available as an R package at https://github.com/bionetslab/KINference and https://doi.org/10.5281/zenodo.15411150. Scripts to reproduce the results are available at https://github.com/bionetslab/KINference-Evaluation-Scripts and https://doi.org/10.5281/zenodo.15424599.