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      Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling*

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          We present a methodology using in vivo crosslinking combined with HPLC-MS for the global analysis of endogenous protein complexes by protein correlation profiling. Formaldehyde crosslinked protein complexes were extracted with high yield using denaturing buffers that maintained complex solubility during chromatographic separation. We show this efficiently detects both integral membrane and membrane-associated protein complexes,in addition to soluble complexes, allowing identification and analysis of complexes not accessible in native extracts. We compare the protein complexes detected by HPLC-MS protein correlation profiling in both native and formaldehyde crosslinked U2OS cell extracts. These proteome-wide data sets of both in vivo crosslinked and native protein complexes from U2OS cells are freely available via a searchable online database ( Raw data are also available via ProteomeXchange (identifier PXD003754).

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          Most cited references 45

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          Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

          We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at Copyright 2001 Academic Press.
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            STRING v10: protein–protein interaction networks, integrated over the tree of life

            The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database ( aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at

                Author and article information

                Mol Cell Proteomics
                Mol. Cell Proteomics
                Molecular & Cellular Proteomics : MCP
                The American Society for Biochemistry and Molecular Biology
                July 2016
                25 April 2016
                25 April 2016
                : 15
                : 7
                : 2476-2490
                From the ‡Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom;
                §Biological Chemistry and Drug Discovery Division, School of Life Sciences, University of Dundee, Dundee, United Kingdom
                Author notes
                ‖ To whom correspondence should be addressed: Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow St, Dundee, United Kingdom. Tel.: +44-01382385473; E-mail: a.i.lamond@ .

                ¶ These authors contributed equally to this work.

                © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

                Author's Choice—Final version free via Creative Commons CC-BY license.

                Funded by: Royal Society of Edinburgh
                Award ID: Royal Society of Edinburgh - Scottish Government Personal Research Fellow
                Funded by: Wellcome Trust
                Award ID: 083524/Z/07/Z
                Award ID: 097945/B/11/Z
                Award ID: 073980/Z/03/Z
                Award ID: 08136/Z/03/Z
                Award ID: 0909444/Z/09/Z
                Award ID: 090944/Z/09/Z
                Technological Innovation and Resources

                Molecular biology


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