Simulated evolution of protein-protein interaction networks with realistic topology

TL;DR

This paper presents a model for the evolution of eukaryotic protein-protein interaction networks based on gene duplication and neofunctionalization, showing that such networks naturally develop modularity and specific topological features without requiring selection pressures.

Contribution

The study introduces a biologically motivated model that accurately reproduces key properties of real PPI networks across multiple species, highlighting the role of simple evolutionary mechanisms.

Findings

PPI networks evolve modular structures without specific selection.

Proteins typically have about 6 degrees of separation.

PPI network diameter is predicted to grow over time.

Abstract

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein's neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution.