Global Snapshot of Protein Interaction Network -- A Percolation Based Approach

TL;DR

This paper employs a percolation-based stochastic approach to analyze the global connectivity features of yeast protein interaction networks, revealing differences between essential and non-essential proteins and variations across experimental methods.

Contribution

It introduces a novel percolation-based method to quantify protein connectivity and highlights fundamental differences in network roles of essential proteins and data from different experimental techniques.

Findings

Essential proteins have distinct global connectivity profiles.

Different experimental methods yield interaction data with different characteristics.

The approach provides insights into the biological significance of network connectivity.

Abstract

In this paper, we study the large-scale protein interaction network of yeast uti lizing a stochastic method based upon percolation of random graphs. In order to find the global features of connectivities in the network, we introduce numeric al measures that quantify (1) how strongly a protein ties with the other parts o f the network and (2) how significantly an interaction contributes to the integr ity of the network. Our study shows that the distribution of essential proteins is distinct from the background in terms of global connectivities. This observ ation highlights a fundamental difference between the essential and the non-esse ntial proteins in the network. Furthermore, we find that the interaction data o btained from different experimental methods such as immunoprecipitation and two- hybrid techniques possess different characteristics. We discuss the biological implications of these observations.