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Signal Peptide Hydrophobicity Modulates Interaction with the Twin-Arginine Translocase.

Qi Huang | Tracy Palmer
mBio | 2017

The general secretory pathway (Sec) and twin-arginine translocase (Tat) operate in parallel to export proteins across the cytoplasmic membrane of prokaryotes and the thylakoid membrane of plant chloroplasts. Substrates are targeted to their respective machineries by N-terminal signal peptides that share a tripartite organization; however, Tat signal peptides harbor a conserved and almost invariant arginine pair that is critical for efficient targeting to the Tat machinery. Tat signal peptides interact with a membrane-bound receptor complex comprised of TatB and TatC components, with TatC containing the twin-arginine recognition site. Here, we isolated suppressors in the signal peptide of the Tat substrate, SufI, that restored Tat transport in the presence of inactivating substitutions in the TatC twin-arginine binding site. These suppressors increased signal peptide hydrophobicity, and copurification experiments indicated that they restored binding to the variant TatBC complex. The hydrophobic suppressors could also act in cis to suppress substitutions at the signal peptide twin-arginine motif that normally prevent targeting to the Tat pathway. Highly hydrophobic variants of the SufI signal peptide containing four leucine substitutions retained the ability to interact with the Tat system. The hydrophobic signal peptides of two Sec substrates, DsbA and OmpA, containing twin lysine residues, were shown to mediate export by the Tat pathway and to copurify with TatBC. These findings indicate that there is unprecedented overlap between Sec and Tat signal peptides and that neither the signal peptide twin-arginine motif nor the TatC twin-arginine recognition site is an essential mechanistic feature for operation of the Tat pathway.IMPORTANCE Protein export is an essential process in all prokaryotes. The Sec and Tat export pathways operate in parallel, with the Sec machinery transporting unstructured precursors and the Tat pathway transporting folded proteins. Proteins are targeted to the Tat pathway by N-terminal signal peptides that contain an almost invariant twin-arginine motif. Here, we make the surprising discovery that the twin arginines are not essential for recognition of substrates by the Tat machinery and that this requirement can be bypassed by increasing the signal peptide hydrophobicity. We further show that signal peptides of bona fide Sec substrates can also mediate transport by the Tat pathway. Our findings suggest that key features of the Tat targeting mechanism have evolved to prevent mistargeting of substrates to the Sec pathway rather than being a critical requirement for function of the Tat pathway.

Pubmed ID: 28765221

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Associated grants

  • Agency: Wellcome Trust, United Kingdom

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UniProt (tool)

RRID:SCR_002380

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

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Phobius (tool)

RRID:SCR_015643

Web application for combined transmembrane topology and signal peptide prediction. Used for whole genome annotation of signal peptides and transmembrane regions. Predictor is based on hidden Markov model (HMM) that models different sequence regions of signal peptide and different regions of transmembrane protein in series of interconnected states.

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SignalP (tool)

RRID:SCR_015644

Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.

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