Abstract
BackgroundCurrently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.MethodsIn the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.ResultsThe clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.ConclusionsAlthough the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.
Highlights
A huge amount of protein-protein interaction data is available from high throughput experimental methods
With early results showing that such clusters have functional relationships, such results may help to predict undiscovered interactions among proteins in the same cluster [3]
30– 60% false positives and 40–80% false negatives have been estimated for these methods [6,7]
Summary
A huge amount of protein-protein interaction data is available from high throughput experimental methods. Because of the use of high throughput experimental methods such as yeast two-hybrid screening [1], the number of reported protein-protein interactions (PPI) has increased dramatically. To extract meaningful information from this interaction data set, clustering of the interacting proteins is an established method. The protein interaction data obtained from high-throughput screening methods such as the yeast two-hybrid method [1] and affinity purification techniques [5] are highly error-prone. Predicting new interactions or drawing any conclusions from this interaction dataset requires validation of the interactions Another complementary source of information about the proteins is their individual structures. Looking at the 3D structure of each protein, especially the binding sites, in an interacting cluster can reveal information that can aid in validating the pair-wise interactions. What are the relative binding strengths of proteins within a cluster?
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