# Signing protein–protein interaction networks

## TL;DR

This paper introduces a new method to determine whether protein interactions activate or repress each other, improving models of cell signaling.

## Contribution

The novel contribution is SIGNAL, an algorithm that annotates protein–protein interaction signs using cause-effect data and network propagation.

## Key findings

- SIGNAL uses a multiplicative model to predict interaction signs based on gene expression changes.
- The algorithm was validated using known annotations and successfully predicted knockout effects on gene expression and telomere length.

## Abstract

Protein–protein interactions (PPIs) provide the skeleton for signaling pathways in the cell. Their experimental measurement, however, reveals only the existence of an interaction without any information on its functional roles. A key step in developing a working logical model of cell signaling is annotating activation/repression (sign) of an interaction.

Here, we develop SIGN Annotation aLgorithm (SIGNAL), a method for annotating PPI networks with signs based on cause-effect data. The approach is based on a multiplicative model in which the effect of a pathway is assumed to be the product of the signs along its edges. The algorithm uses network propagation techniques to quantify the influence of each edge on gene expression changes, and the resulting features are fed to a classifier for sign prediction. We validate our method using known annotations and demonstrate the utility of SIGNAL for predicting the effect of a knockout on gene expression and on telomere length.

SIGNAL code is available at https://github.com/L-F-S/PPI_Network_Signer.