Project Details
Projekt
SUMO-SIM interactions and their role in substrate recognition and autoregulation of SUMO-dependent ubiquitin ligases
Applicant
Professor Dr. Jürgen Dohmen
Subject Area
Cell Biology
Term
from 2008 to 2015
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 72189822
Studies on yeast and mammalian proteins in our as well as other laboratories have identified SUMO interaction motifs (SIMs) that mediate non-covalent interaction of proteins with the small ubiquitin-related modifier SUMO. Presence of such SIMs was found to be a characteristic feature of a novel class of ubiquitin ligases (ULS) that specifically recognize sumoylated proteins. We and others characterized the human RNF4 protein as the first mammalian ULS and identified the Promyelocytic Leukemia Protein (PML) as its substrate. Calorimetric assays revealed that RNF4 via its multiple SIMs preferentially binds to SUMO chains. Bioinformatical analyses identified several other mammalian proteins that combine ubiquitin ligase domains with SIMs. Several of these ligases affect SUMO conjugate levels upon expression in yeast or localized to PML bodies in human cells suggesting that SUMO-SIM interaction is relevant to their function. We observed both for yeast and human ULS proteins that they are themselves sumoylated, ubiquitylated and degraded suggesting that they subject to an auto-regulatory feedback control. We will use a combination of mutational biochemical analyses to characterize the role of individual or combinations of SIMs in the recognition of distinct types of SUMO modification in substrate recognition and auto-sumoylation of these ubiquitin ligases. We will further investigate how physiological conditions such as various stress types affects proteolytic control of ULS proteins in yeast and human cells. We have established a database screening protocol that allows to identify high probability SIMs and applied this method to predict SUMO interacting proteins encoded in the yeast genome. We will validate selected candidates and extend this approach by applying it to the analysis of the proteomes of humans and other model organisms. Furthermore, we will modify the approach to identify non-consensus SIMs und functionally related motifs.
DFG Programme
Priority Programmes
Participating Person
Professor Dr. Kay Hofmann
