Shaping dots and lines: Adding modularity into protein interaction networks using structural information

Abstract

Determining protein interaction networks and generating models to simulate network changes in time and space are crucial for understanding a biological system and for predicting the effect of mutants found in diseases. In this review we discuss the great potential of using structural information together with computational tools towards reaching this goal: the prediction of new protein interactions, the estimation of affinities and kinetic rate constants between protein complexes, and finally the determination of which interactions are compatible with each other and which interactions are exclusive. The latter one will be important to reorganize large scale networks into functional modular networks.

Structural and functional protein network analyses predict novel signaling functions for rhodopsin

“…Structural information can help discriminate between direct and indirect interactions in a given complex. More importantly, it can add a dynamical value to the classical interaction networks by determining if two or more predicted partners of any given protein or complex can simultaneously bind to a target, or if they instead compete for the same interaction surface (Kim et al , 2006; reviewed in Campagna et al , 2008 and Kiel et al , 2008). Integrating interaction data with protein expression information may assist in adding a dynamic dimension, and therefore a more realistic view, to the abstract ‘organism interactome’ (Hofmann et al , 2006).…”

Adding structural information to the von Hippel–Lindau (VHL) tumor suppressor interaction network

“…However, a large amount of data remains to be confirmed. We address the problem by combining information from experimental data with structural analysis [9][10][11][12]. Defining the pVHL interaction interfaces was the primary goal.…”

Structure Collisions between Interacting Proteins

“…Subsequently, Kim et al investigated the role of disorder in structural networks and discovered that disordered interface regions are more common in single-interface proteins [7]. Other studies also included structural information into their systematic analyses to increase the informative value of a given network or the reliability of protein interaction predictions [8], [9].…”