Classifications of protein roles in the functional organization of the cell

Abstract

The availability of functional genomic data for numerous organisms provides an opportunity to comprehensively analyze the roles proteins play in the functional organization of the cell.

First, we study how simple network features of hub proteins (i.e., those with many physical interactions) are predictive of their roles in the functional organization of the cell. We analyze the protein-protein interaction networks of five organisms---S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli---and confirm significant and consistent functional and structural differences between hubs that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular within networks whereas the latter tend to be intermodular. However, we demonstrate that in each of these organisms, simple topological measures also reflect protein intra- and inter-modularity. Further, cross-interactomic analysis demonstrates that these topological characteristics of hubs tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks are a powerful means for understanding the dynamic roles of hubs in interactomes.

Second, we study the role of multifunctional genes (and proteins they encode) in the functional organization of the cell. Identifying multifunctional genes at a genome-wide level and studying their properties can shed light on the complexity of the molecular events that underpin cell function. However, to date, genome-wide analysis of multifunctional genes has been limited. We introduce a computational approach that uses known functional annotations to extract genes playing a role in multiple biological processes. We leverage functional genomics data sets for three organisms---H. sapiens, D. melanogaster, and S. cerevisiae---and show that, as compared to other genes, genes involved in multiple biological processes possess distinct physicochemical properties, are more broadly expressed, tend to be intermodular in protein interaction networks, tend to be more evolutionarily conserved, more likely to be essential, more likely to be involved in human disorders. These same features also hold for genes with multiple molecular functions. Our analysis is a step forward towards a better genome-wide understanding of gene multifunctionality.