Publications by authors named "Jens Pettelkau"

CID-MS/MS cleavable cross-linkers hold an enormous potential for an automated analysis of cross-linked products, which is essential for conducting structural proteomics studies. The created characteristic fragment ion patterns can easily be used for an automated assignment and discrimination of cross-linked products. To date, there are only a few software solutions available that make use of these properties, but none allows for an automated analysis of cleavable cross-linked products.

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We describe the use of the (13)C-labeled artificial amino acid p-benzoyl-L-phenylalanine (Bpa) to improve the reliability of cross-linked product identification. Our strategy is exemplified for two protein-peptide complexes. These studies indicate that in many cases the identification of a cross-link without additional stable isotope labeling would result in an ambiguous assignment of cross-linked products.

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The topology of the GCAP-2 homodimer was investigated by chemical cross-linking and high resolution mass spectrometry. Complementary conducted size-exclusion chromatography and analytical ultracentrifugation studies indicated that GCAP-2 forms a homodimer both in the absence and in the presence of Ca(2+). In-depth MS and MS/MS analysis of the cross-linked products was aided by (15)N-labeled GCAP-2.

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The retinal guanylylcyclases ROS-GC 1 and 2 are regulated via the intracellular site by guanylylcyclase-activating proteins (GCAPs). The mechanisms of how GCAPs activate their target proteins remain elusive as exclusively structures of nonactivating calcium-bound GCAP-1 and -2 are available. In this work, we apply a combination of chemical cross-linking with amine-reactive cross-linkers and photoaffinity labeling followed by a mass spectrometric analysis of the created cross-linked products to study the interaction between N-terminally myristoylated GCAP-2 and a peptide derived from the catalytic domain of full-length ROS-GC 1.

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Chemical crosslinking in combination with mass spectrometry has matured into an alternative approach to derive low-resolution structural information of proteins and protein complexes. Yet, one of the major drawbacks of this strategy remains the lack of software that is able to handle the large MS datasets that are created after chemical crosslinking and enzymatic digestion of the crosslinking reaction mixtures. Here, we describe a software, termed StavroX, which has been specifically designed for analyzing highly complex crosslinking datasets.

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