An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork

TL;DR

This paper introduces an extension of hypergraph centrality measures to analyze protein complex hypernetworks, revealing insights into protein essentiality and complex classification beyond traditional dyadic interaction models.

Contribution

It proposes a novel node and edge nonlinear hypergraph centrality model that accounts for polyadic interactions and enables classification of protein complexes.

Findings

Certain model variations predict protein essentiality more accurately.

The hypergraph approach extends the centrality-lethality rule to complex networks.

Identifies small sets of densely essential proteins.

Abstract

The use of graph centrality measures applied to biological networks, such as protein interaction networks, underpins much research into identifying key players within biological processes. This approach however is restricted to dyadic interactions and it is well-known that in many instances interactions are polyadic. In this study we illustrate the merit of using hypergraph centrality applied to a hypernetwork as an alternative. Specifically, we review and propose an extension to a recently introduced node and edge nonlinear hypergraph centrality model which provides mutually dependent node and edge centralities. A Saccharomyces Cerevisiae protein complex hypernetwork is used as an example application with nodes representing proteins and hyperedges representing protein complexes. The resulting rankings of the nodes and edges are considered to see if they provide insight into the essentiality of the proteins and complexes. We find that certain variations of the model predict essentiality more accurately and that the degree-based variation illustrates that the centrality-lethality rule extends to a hypergraph setting. In particular, through exploitation of the models flexibility, we identify small sets of proteins densely populated with essential proteins. One of the key advantages of applying this model to a protein complex hypernetwork is that it also provides a classification method for protein complexes, unlike previous approaches which are only concerned with classifying proteins.