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5 results for “Liquid chromatography-mass spectrometry”
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089</em></p> <p> </p>
Data from: Liquid chromatography-mass spectrometry (LC-MS) data of a multi-epitope peptibody with bFGF/VEGFA
<p><span><span><span><span><span><span><span><span><span><span><span>The <span><span><span>protein </span></span></span><span><span><span>primary </span></span></span><span><span><span>structure of the recombinant </span></span></span>Peptibody were investigated systematically by Liquid Chromatography-Mass Spectrometry (LC-MS)<span><span><span>. T</span></span></span><span><span>he 15 amino acids of N-terminal were </span></span>Met-Gln-Lys-Arg-Lys-Arg-Lys-Lys-Ser-Arg-Tyr-Lys-Ser-Gly-Gly and <span><span>the C-terminal was Lys (K</span></span><span><span>), the same as</span></span> the theoretical sequence. <span><span>With more </span></span><a><span class="15"><span>protease</span></span></a><span><span>s, the whole sequence was detected at the coverage of </span></span>trypsin 87.5%, <span><span>c</span></span><span><span>hymotrypsin</span></span> 75.3% and <span><span>Glu-C</span></span> 76.7%<span><span>. The </span></span>peptide-mapping could be used as an valuable standard to certify the complete expression and primary structure of Peptibody. The pI and MW were 8.93 and 37.415 kDa, within the errors allowed . The binding specificity after production were analyzed using anti-VEGFA and anti-His antibodies.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Liquid chromatography-mass spectrometry (LC-MS) data of a multi-epitope peptibody with bFGF/VEGFA
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Study for the Liquid Chromatography-mass Spectrometry (LC-MS/MS) Assessment of Oxidative DNA Damage in Relation to Antioxidant Usage
ClinicalTrials.gov study NCT01038024. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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