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Björn Titz

Publications and source records attributed to Björn Titz.

3 recordsLinked to original sources

Novel conserved assembly factor of the bacterial flagellum.

TP0658 (FliW) and its orthologs, conserved proteins of unknown function in Treponema pallidum and other species, interact with a C-terminal region of flagellin (FlaB1-3 in T. pallidum; FliC in most other species). Mutants of orthologs in Bacillus subtilis and Campylobacter jejuni (yviF, CJ1075) showed strongly reduced motility. TP0658 stabilizes flagellin in a way similar to FliS, suggesting that TP0658 is a conserved assembly factor for the bacterial flagellum.

Bacillus subtilis↗

Transcriptional activators in yeast.

Eukaryotic transcription activation domains (ADs) are not well defined on the proteome scale. We systematicallly tested approximately 6000 yeast proteins for transcriptional activity using a yeast one-hybrid system and identified 451 transcriptional activators. We then determined their transcription activation strength using fusions to the Gal4 DNA-binding domain and a His3 reporter gene which contained a promoter with a Gal4-binding site. Among the 132 strongest activators 32 are known transcription factors while another 35 have no known function. Although zinc fingers, helix-loop-helix domains and several other domains are highly overrepresented among the activators, only few contain characterized ADs. We also found some striking correlations: the stronger the activation activity, the more acidic, glutamine-rich, proline-rich or asparagine-rich the activators were. About 29% of the activators have been found previously to specifically interact with the transcription machinery, while 10% are known to be components of transcription regulatory complexes. Based on their transcriptional activity, localization and interaction patterns, at least six previously uncharacterized proteins are suggested to be bona fide transcriptional regulators (namely YFL049W, YJR070C, YDR520C, YGL066W/Sgf73, YKR064W and YCR082W/Ahc2).

Amino Acids↗

What do we learn from high-throughput protein interaction data?

The biological significance of protein interactions, their method of generation and reliability is briefly reviewed. Protein interaction networks adopt a scale-free topology that explains their error tolerance or vulnerability, depending on whether hubs or peripheral proteins are attacked. Networks also allow the prediction of protein function from their interaction partners and therefore, the formulation of analytical hypotheses. Comparative network analysis predicts interactions for distantly related species based on conserved interactions, even if sequences are only weakly conserved. Finally, the medical relevance of protein interaction analysis is discussed and the necessity for data integration is emphasized.

Animals↗