Structural and functional protein network analyses predict novel signaling functions for rhodopsin

Kiel, Christina; Vogt, Andreas; Campagna, Anne; Chatr‐aryamontri, Andrew; Lange, Magdalena; Beer, Monika; Bolz, Sylvia; Mack, Andreas F.; Kinkl, Norbert; Serrano, Luís; Ueffing, Marius

doi:10.1038/msb.2011.83

Cited by 33 publications

(38 citation statements)

References 91 publications

(141 reference statements)

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“…The mass spectrometry analysis performed on samples immunoprecipitated with the CNGA1 and CNGB1 antibodies failed to detect ankyrin G peptides. In fact, ankyrin G has not been found in massspectrometric-based proteomic studies of photoreceptor outer segments (Kiel et al, 2011;Kwok et al, 2008;Reidel et al, 2011;Skiba et al, 2013). Considering the size of this protein it is surprising that these proteomics studies have failed to detect peptides from ankyrin G in outer segment preparations.…”

Section: Comparison Of 41g and Cngs Distribution In Photoreceptorsmentioning

confidence: 96%

Interaction of 4.1G and cGMP-gated channels in rod photoreceptor outer segments

Cheng

Molday

2013

Journal of Cell Science

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SummaryIn photoreceptors, the assembly of signaling molecules into macromolecular complexes is important for phototransduction and maintaining the structural integrity of rod outer segments (ROSs). However, the molecular composition and formation of these complexes are poorly understood. Using immunoprecipitation and mass spectrometry, 4.1G was identified as a new interacting partner for the cyclic-nucleotide gated (CNG) channels in ROSs. 4.1G is a widely expressed multifunctional protein that plays a role in the assembly and stability of membrane protein complexes. Multiple splice variants of 4.1G were cloned from bovine retina. A smaller splice variant of 4.1G selectively interacted with CNG channels not associated with peripherin-2-CNG channel complex. A combination of truncation studies and domain-binding assays demonstrated that CNG channels selectively interacted with 4.1G through their FERM and CTD domains. Using immunofluorescence, labeling of 4.1G was seen to be punctate and partially colocalized with CNG channels in the ROS. Our studies indicate that 4.1G interacts with a subset of CNG channels in the ROS and implicate this protein-protein interaction in organizing the spatial arrangement of CNG channels in the plasma membrane of outer segments.

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Section: Comparison Of 41g and Cngs Distribution In Photoreceptorsmentioning

confidence: 96%

Interaction of 4.1G and cGMP-gated channels in rod photoreceptor outer segments

Cheng

Molday

2013

Journal of Cell Science

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“…Alternatively, Kiel and coworkers describe a new conceptual analysis pipeline that combines experimental proteomic data, literature mining, computational analyses, and structural information to generate a multiscale signal transduction network based on tissue-specific gene expression and domain-domain interaction data for the study of rhodopsin and its interactions (169).…”

Section: Computational Assessment Of Protein Interaction Datamentioning

confidence: 99%

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Armean

Lilley

Trotter³

2013

Molecular & Cellular Proteomics

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Advances in sensitivity, resolution, mass accuracy, and throughput have considerably increased the number of protein identifications made via mass spectrometry. Despite these advances, state-of-the-art experimental methods for the study of protein-protein interactions yield more candidate interactions than may be expected biologically owing to biases and limitations in the experimental methodology. In silico methods, which distinguish between true and false interactions, have been developed and applied successfully to reduce the number of false positive results yielded by physical interaction assays. Such methods may be grouped according to: (1) the type of data used: methods based on experiment-specific measurements (e.g., spectral counts or identification scores) versus methods that extract knowledge encoded in external annotations (e.g., public interaction and functional categorisation databases); (2) the type of algorithm applied: the statistical description and estimation of physical protein properties versus predictive supervised machine learning or text-mining algorithms; (3) the type of protein relation evaluated: direct (binary) interaction of two proteins in a cocomplex versus probability of any functional relationship between two proteins (e.g., cooccurrence in a pathway, sub cellular compartment); and (4) initial motivation: elucidation of experimental data by evaluation versus prediction of novel protein-protein interaction, to be experimentally validated a posteriori. This work reviews several popular computational scoring methods and software platforms for protein-protein interactions evaluation according to their methodology, comparative strengths and weaknesses, data representation, accessibility, and availability.

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“…It is now clear that cell regulatory decisions are made by molecular switching events in large but highly dynamic, often coalescing, protein complexes (1,2). Protein complex isolation by affinity purification is a common technique, used for the identification of the protein composition of these molecular machines (3), contributing to the elucidation of spatial and temporal patterns of large protein networks and functional modules within these networks (4). Interaction data derived from different protein complex analyses are the basis for predictions of biological pathways or disease mechanisms concerning those proteins (5).…”

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confidence: 99%

Elution Profile Analysis of SDS-induced Subcomplexes by Quantitative Mass Spectrometry

Texier

Toedt

Gorza

et al. 2014

Molecular & Cellular Proteomics

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Analyzing the molecular architecture of native multiprotein complexes via biochemical methods has so far been difficult and error prone. Protein complex isolation by affinity purification can define the protein repertoire of a given complex, yet, it remains difficult to gain knowledge of its substructure or modular composition. Here, we introduce SDS concentration gradient induced decomposition of protein complexes coupled to quantitative mass spectrometry and in silico elution profile distance analysis. By applying this new method to a cellular transport module, the IFT/lebercilin complex, we demonstrate its ability to determine modular composition as well as sensitively detect known and novel complex components. We show that the IFT/lebercilin complex can be separated into at least five submodules, the IFT complex A, the IFT complex B, the 14 -3-3 protein complex and the CTLH complex, as well as the dynein light chain complex. Understanding the orchestration and dynamics of cellular function on the molecular level is one of the challenges in biology. It is now clear that cell regulatory decisions are made by molecular switching events in large but highly dynamic, often coalescing, protein complexes (1, 2). Protein complex isolation by affinity purification is a common technique, used for the identification of the protein composition of these molecular machines (3), contributing to the elucidation of spatial and temporal patterns of large protein networks and functional modules within these networks (4). Interaction data derived from different protein complex analyses are the basis for predictions of biological pathways or disease mechanisms concerning those proteins (5). Still, in most cases it is difficult or even impossible to determine how, or even if the copurified proteins assemble as a single module in a cell, limiting the fine-grained description of the complex structure (6, 7). The possibility to integrate module and submodule information in higher order protein networks is extremely valuable for their understanding and opens the route to define pathways of information flow within and between discrete molecular machines. Zooming in on a protein mutated in early childhood blindness, lebercilin, we have previously identified proteins of the intraflagellar transport (IFT) machinery to interact with lebercilin (5). IFT appears as a physical entity driving vesicular trafficking through the connecting cilium that bridges the inner and the outer segment of vertebrate photoreceptors (8). IFT, like many other multiprotein complexes, is an example for a functionally fairly well described molecular machine with yet unknown molecular topology and mechanical properties.To determine composition, as well as protein complex topology of lebercilin and its interaction with IFT components, we developed a novel workflow, which we termed "elution profile analysis of SDS-induced subcomplexes by quantitative From the ‡Division

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Structural and functional protein network analyses predict novel signaling functions for rhodopsin

Abstract: Proteomic analyses, literature mining, and structural data were combined to generate an extensive signaling network linked to the visual G protein-coupled receptor rhodopsin. Network analysis suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking.

Cited by 33 publications

References 91 publications

Interaction of 4.1G and cGMP-gated channels in rod photoreceptor outer segments

Interaction of 4.1G and cGMP-gated channels in rod photoreceptor outer segments

Popular Computational Methods to Assess Multiprotein Complexes Derived From Label-Free Affinity Purification and Mass Spectrometry (AP-MS) Experiments

Elution Profile Analysis of SDS-induced Subcomplexes by Quantitative Mass Spectrometry

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