Uncovering Biological Network Function via Graphlet Degree Signatures

TL;DR

This paper introduces a graph theoretic approach using graphlet degree signatures to analyze protein-protein interaction networks, revealing that local network structures are closely related to protein functions and aiding in classifying uncharacterized proteins.

Contribution

The paper presents a novel graphlet degree signature method for comparing local network structures, linking them to biological functions and classifying uncharacterized proteins in PPI networks.

Findings

Proteins with similar local network structures tend to share functions.

The method groups proteins into functionally coherent clusters.

Application to large-scale data infers functions for unclassified proteins.

Abstract

Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins aggregate to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines. We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method groups topologically similar proteins under this measure in a PPI network and shows that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research.