Protein interaction networks and biology: towards the connection

TL;DR

This paper investigates the relationship between true protein interactions and experimental datasets, revealing mathematical properties that can assess dataset reliability and biological mechanisms behind protein recruitment.

Contribution

It demonstrates that the natural adjacency matrix of protein interaction networks has a separable form, linking degree distribution moments to short loops, aiding dataset validation.

Findings

Relations between degree moments and short loops established

Mathematical tools for dataset reliability assessment developed

Insights into biological recruitment mechanisms suggested

Abstract

Protein interaction networks (PIN) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy (MS) and yeast two-hybrid (Y2H). We show that the most natural adjacency matrix of protein interaction networks has a separable form, and this induces precise relations between moments of the degree distribution and the number of short loops. These relations provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.