The Use of Protein—protein Interaction Networks for Genome Wide Protein Function Comparisons and Predictions
The concept of protein function is widely used by biologists. However, the means of the concept and its understanding can vary largely depending on the functional level under consideration (molecular, cellular, physiological, etc.) Function is therefore a complex notion and the development of efficient ways of representing function which can be computer-tractable is presently the goal of many research efforts. Moreover, genomic studies and new high-throughput methods of the post-genomic era provide the opportunity to shed a new light on the concept of protein function. Among them, the analysis of large protein—protein networks will permit the emergence of a more integrated view of protein function.
In this context, we have proposed a new method for protein function comparison and classification which, unlike usual methods based on sequence homology, permits the definition of functional classes of protein based solely on the identity of their interacting partners, thus giving access for the first time to function at the cellular level. This method, named PRODISTIN for Protein Distance based on Interactions, has been first applied to the Saccharomyces cerevisiae interactome (proteome-wide protein—protein interactions). An example of a classification/comparison is shown and discussed for a subset of S. cerevisiae proteins, accounting for 10% of its proteome (600 proteins). Functional classification trees have also been made for the Helicobacter pylori proteome, confirming the generic aspect of the method. We demonstrated that the method is robust (biologically and statistically) and can be used to predict function for unknown proteins and groups of proteins.
Finally, the potential use of protein—protein interaction data and of the PRODISTIN method in structural biology projects is presented and discussed. In the future, this method could also be potentially applied to other types of networks such as transcriptional and genetic networks.
KeywordsCarbohydrate Tyrosine Catalysis Adduct Polypeptide
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