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      Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1

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          Abstract

          Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to “mine” information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154 proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.

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          The Pfam protein families database.

          Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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            Methanogenic archaea: ecologically relevant differences in energy conservation.

            Most methanogenic archaea can reduce CO(2) with H(2) to methane, and it is generally assumed that the reactions and mechanisms of energy conservation that are involved are largely the same in all methanogens. However, this does not take into account the fact that methanogens with cytochromes have considerably higher growth yields and threshold concentrations for H(2) than methanogens without cytochromes. These and other differences can be explained by the proposal outlined in this Review that in methanogens with cytochromes, the first and last steps in methanogenesis from CO(2) are coupled chemiosmotically, whereas in methanogens without cytochromes, these steps are energetically coupled by a cytoplasmic enzyme complex that mediates flavin-based electron bifurcation.
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              Prediction of lipoprotein signal peptides in Gram-negative bacteria.

              A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                05 March 2015
                2015
                : 6
                : 149
                Affiliations
                [1] 1Department of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA
                [2] 2Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles Los Angeles, CA, USA
                [3] 3Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA
                [4] 4UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA
                Author notes

                Edited by: Sonja-Verena Albers, University of Freiburg, Germany

                Reviewed by: Marco Moracci, National Research Council of Italy, Italy; Benjamin Harry Meyer, University of Freiburg, Germany

                *Correspondence: Robert P. Gunsalus, Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, 1602 Molecular Science Bldg., Los Angeles, CA 90095, USA e-mail: robg@ 123456microbio.ucla.edu ;
                Rachel R. Ogorzalek Loo, Molecular Biology Institute, University of California, 406 Boyer Hall, Los Angeles, Los Angeles, CA 90095, USA e-mail: rloo@ 123456mednet.ucla.edu

                This article was submitted to Microbial Physiology and Metabolism, a section of the journal Frontiers in Microbiology.

                †Present address: Deborah R. Leon, Mass Spectrometry Resource, Boston University School of Medicine, Boston, USA;

                A. Jimmy Ytterberg, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden;

                Pinmanee Boontheung, Halliburton, Houston, USA;

                Unmi Kim, BP Biofuels, San Diego, USA

                Article
                10.3389/fmicb.2015.00149
                4350412
                46d73f4e-0530-46b0-bb30-95e0710ab343
                Copyright © 2015 Leon, Ytterberg, Boontheung, Kim, Loo, Gunsalus and Ogorzalek Loo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 November 2014
                : 09 February 2015
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 95, Pages: 16, Words: 12091
                Categories
                Microbiology
                Original Research Article

                Microbiology & Virology
                s-layers,archaeal surface proteins,methanosarcina mazei,prokaryotic glycosylation,membrane proteins,concanavalin a

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