A Role for Non-Covalent SUMO Interaction Motifs in Pc2/CBX4 E3 Activity

Background

Modification of proteins by the small ubiquitin like modifier (SUMO) is an essential process in mammalian cells. SUMO is covalently attached to lysines in target proteins via an enzymatic cascade which consists of E1 and E2, SUMO activating and conjugating enzymes. There is also a variable requirement for non-enzymatic E3 adapter like proteins, which can increase the efficiency and specificity of the sumoylation process. In addition to covalent attachment of SUMO to target proteins, specific non-covalent SUMO interaction motifs (SIMs) that are generally short hydrophobic peptide motifs have been identified.

Methodology/Principal Findings

Intriguingly, consensus SIMs are present in most SUMO E3s, including the polycomb protein, Pc2/Cbx4. However, a role for SIMs in SUMO E3 activity remains to be shown. We show that Pc2 contains two functional SIMs, both of which contribute to full E3 activity in mammalian cells, and are also required for sumoylation of Pc2 itself. Pc2 forms distinct sub-nuclear foci, termed polycomb bodies, and can recruit partner proteins, such as the corepressor CtBP. We demonstrate that mutation of the SIMs in Pc2 prevents Pc2-dependent CtBP sumoylation, and decreases enrichment of SUMO1 and SUMO2 at polycomb foci. Furthermore, mutational analysis of both SUMO1 and SUMO2 reveals that the SIM-interacting residues of both SUMO isoforms are required for Pc2-mediated sumoylation and localization to polycomb foci.

Conclusions/Significance

This work provides the first clear evidence for a role for SIMs in SUMO E3 activity.     

A correlated motif approach for finding short linear motifs from protein interaction networks

Background  

An important class of interaction switches for biological circuits and disease pathways are short binding motifs. However, the biological experiments to find these binding motifs are often laborious and expensive. With the availability of protein interaction data, novel binding motifs can be discovered computationally: by applying standard motif extracting algorithms on protein sequence sets each interacting with either a common protein or a protein group with similar properties. The underlying assumption is that proteins with common interacting partners will share some common binding motifs. Although novel binding motifs have been discovered with such approach, it is not applicable if a protein interacts with very few other proteins or when prior knowledge of protein group is not available or erroneous. Experimental noise in input interaction data can further deteriorate the dismal performance of such approaches.