Protein Hypernetworks: a Logic Framework for Interaction Dependencies and Perturbation Effects in Protein Networks

TL;DR

This paper introduces protein hypernetworks, a logic-based framework that models interaction dependencies in protein networks, improving predictions of complexes, functional necessity, and synthetic lethality in yeast.

Contribution

The paper presents a novel propositional logic framework for modeling interaction dependencies in protein networks, enhancing analysis of complex biochemical systems.

Findings

Improved prediction of protein complexes.

Better inference of protein functional necessity.

Enhanced prediction of synthetic lethal interactions.

Abstract

Motivation: Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of such systems emerge not from the protein interactions themselves but from the dependencies between these interactions. Therefore, a comprehensive approach for integrating and using information about such dependencies is required. Results: We present an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining protein hypernetworks. First we demonstrate how this framework straightforwardly improves the prediction of protein complexes. Next we show that modeling protein perturbations in hypernetworks, rather than in networks, allows to better infer the functional necessity of proteins for yeast. Furthermore, hypernetworks improve the prediction of synthetic lethal interactions in yeast, indicating their capability to capture high-order functional relations between proteins. Conclusion: Protein hypernetworks are a consistent formal framework for modeling dependencies between protein interactions within protein networks. First applications of protein hypernetworks on the yeast interactome indicate their value for inferring functional features of complex biochemical systems.