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      Global profiling of co- and post-translationally N-myristoylated proteomes in human cells

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          Abstract

          Protein N-myristoylation is a ubiquitous co- and post-translational modification that has been implicated in the development and progression of a range of human diseases. Here, we report the global N-myristoylated proteome in human cells determined using quantitative chemical proteomics combined with potent and specific human N-myristoyltransferase (NMT) inhibition. Global quantification of N-myristoylation during normal growth or apoptosis allowed the identification of >100 N-myristoylated proteins, >95% of which are identified for the first time at endogenous levels. Furthermore, quantitative dose response for inhibition of N-myristoylation is determined for >70 substrates simultaneously across the proteome. Small-molecule inhibition through a conserved substrate-binding pocket is also demonstrated by solving the crystal structures of inhibitor-bound NMT1 and NMT2. The presented data substantially expand the known repertoire of co- and post-translational N-myristoylation in addition to validating tools for the pharmacological inhibition of NMT in living cells.

          Abstract

          Protein N-myristoylation is a ubiquitous modification implicated in the regulation of multiple cellular processes. Here, Thinon et al. report the development of a general method to identify N-myristoylated proteins in human cells and identify over 100 endogenous post- and co-translational substrates of N-myristoyltransferase.

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          Most cited references49

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          Linking crystallographic model and data quality.

          In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.
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            The Gene Ontology (GO) project in 2006

            (2005)
            The Gene Ontology (GO) project () develops and uses a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see ). The GO Consortium continues to improve to the vocabulary content, reflecting the impact of several novel mechanisms of incorporating community input. A growing number of model organism databases and genome annotation groups contribute annotation sets using GO terms to GO's public repository. Updates to the AmiGO browser have improved access to contributed genome annotations. As the GO project continues to grow, the use of the GO vocabularies is becoming more varied as well as more widespread. The GO project provides an ontological annotation system that enables biologists to infer knowledge from large amounts of data.
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              A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics.

              MaxQuant is a quantitative proteomics software package designed for analyzing large mass spectrometric data sets. It is specifically aimed at high-resolution mass spectrometry (MS) data. Currently, Thermo LTQ-Orbitrap and LTQ-FT-ICR instruments are supported and Mascot is used as a search engine. This protocol explains step by step how to use MaxQuant on stable isotope labeling by amino acids in cell culture (SILAC) data obtained with double or triple labeling. Complex experimental designs, such as time series and drug-response data, are supported. A standard desktop computer is sufficient to fulfill the computational requirements. The workflow has been stress tested with more than 1,000 liquid chromatography/mass spectrometry runs in a single project. In a typical SILAC proteome experiment, hundreds of thousands of peptides and thousands of proteins are automatically and reliably quantified. Additional information for identified proteins, such as Gene Ontology, domain composition and pathway membership, is provided in the output tables ready for further bioinformatics analysis. The software is freely available at the MaxQuant home page.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                26 September 2014
                : 5
                : 4919
                Affiliations
                [1 ]Department of Chemistry, Imperial College London , Exhibition Road, London SW7 2AZ, UK
                [2 ]Department of Life Sciences, Imperial College London , Exhibition Road, London SW7 2AZ, UK
                [3 ]York Structural Biology Laboratory, Department of Chemistry, University of York , York YO10 5DD, UK
                [4 ]Department of Chemistry, Institute of Chemical Biology, Imperial College London , Exhibition Road, London SW7 2AZ, UK
                Author notes
                [*]

                These authors contributed equally to this work

                [†]

                Present address: The Rockefeller University, 1230 York Avenue, New York, New York, USA

                [‡]

                Present address: EUFETS GmbH, Vollmersbachstrasse 66, 55743 Idar-Oberstein, Germany

                [§]

                Present address: Department of Chemistry, TU München, Lichtenbergstrasse 4, D-85748 Garching, Germany

                [∥]

                Present address: Department of Chemistry, Kings College London, London SE1 1UL, UK

                Article
                ncomms5919
                10.1038/ncomms5919
                4200515
                25255805
                ec8c786e-901e-490c-bd4f-141fe855b869
                Copyright © 2014, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 05 March 2014
                : 05 August 2014
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