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The trans-Golgi network GRIP-domain proteins form alpha-helical homodimers.

ABSTRACT: A recently described family of TGN (trans-Golgi network) proteins, all of which contain a GRIP domain targeting sequence, has been proposed to play a role in membrane transport. On the basis of the high content of heptad repeats, GRIP domain proteins are predicted to contain extensive coiled-coil regions that have the potential to mediate protein-protein interactions. Four mammalian GRIP domain proteins have been identified which are targeted to the TGN through their GRIP domains, namely p230, golgin-97, GCC88 and GCC185. In the present study, we have investigated the ability of the four mammalian GRIP domain proteins to interact. Using a combination of immunoprecipitation experiments of epitope-tagged GRIP domain proteins, cross-linking experiments and yeast two-hybrid interactions, we have established that the GRIP proteins can self-associate to form homodimers exclusively. Two-hybrid analysis indicated that the N- and C-terminal fragments of GCC88 can interact with themselves but not with each other, suggesting that the GRIP domain proteins form parallel coiled-coil dimers. Analysis of purified recombinant golgin-97 by CD spectroscopy indicated a 67% alpha-helical structure, consistent with a high content of coiled-coil sequences. These results support a model for GRIP domain proteins as extended rod-like homodimeric molecules. The formation of homodimers, but not heterodimers, indicates that each of the four mammalian TGN golgins has the potential to function independently.

SUBMITTER: Luke MR

PROVIDER: S-EPMC1183463 | biostudies-other | 2005 Jun

REPOSITORIES: biostudies-other

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Project description:Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

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The trans-Golgi network GRIP-domain proteins form alpha-helical homodimers.

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