Universität zu Köln

Vogt, Benjamin (2014). A bioinformatical approach for a reliable determination of short motifs for SUMO and Atg8 interaction in Saccharomyces cerevisiae. PhD thesis, Universität zu Köln.

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Abstract

Regulatory processes are initiated by posttranslational modifications of proteins which alter their activity, stability, localization or their interaction with other proteins. Among many other processes, decoding the SUMOylation signal or the recognition of lipidated Atg8 represent starting points to effective downstream signalling pathways, initiated by SUMO interacting motifs (SIMs) and Atg8 interacting motifs (AIMs) in protein sequences. The low information contents of SIMs and AIMs prevent their detection from spurious sequences. This thesis is about a detection approach for so far unknown SIMs and AIMs with bioinformatical methods. The first part of this thesis describes the bioinformatical SIM detection method. The overall method is common for all SIM types, whereas the single SIM detection screens apply to the characteristics for SIMa, SIMb and SIMr. Two sets of phylogenetic distances from budding yeast in combination with information theoretical approaches and sliding averages serve as a conservation measure. A combination of bioinformatical tools is used for an estimation whether a protein segment is unstructured, non-globular or globular. The bioinformatical approach in this thesis uses these conservation and structural features for the evolution of a functionality scoring measure for unknown SIM instances. Experimental interaction studies show so far unknown SUMO interaction for Dbp10, Drs1, Rfc1, Rad18 and Tdp1 from the bioinformatical SIM detection screens. Dbp10 and Drs1 are involved in ribosomal biogenesis in the nucleolus. A SIM in this biological context has not yet been reported, whereas SUMOylation is involved in the release of pre-ribosomal particles into the nucleoplasm. Tdp1, Rfc1 and Rad18 are involved in DNA replication and damage repair, where SUMOylation is a crucial activity factor for other proteins. The motif in Rfc1 identified from the bioinformatical detection screen is shown responsible for SUMO interaction in mutation studies. This motif also causes observable growth phenotype under chemically induced DNA damage stress. The motif in Rad18 was meanwhile identified by Parker and Ulrich. The second part of this study describes the application of analogous methods for a bioinformatical AIM detection approach. The conservation characteristics of established AIM are found similar to those for SIMs, whereas the structural context is harder to be represented with bioinformatical methods.