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12
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ShareScore release 0.9.0
Dataset results
12 results for “peptide mass”
Peptide Mass Fingerprint Library of Monoclonal Murine anti-SARS-CoV-2 Antibodies
<p>The dataset contains fingerprints of monoclonal antibodies that can be used to identify the antibodies by using the open-source software ABID 2.0 <a href="https://bam.de/ABID">https://bam.de/ABID</a><br> More information can be found in the publication where this data was used to rapidly distinguish between 35 monoclonal murine Anti-SARS-CoV-2 antibodies: <a href="https://doi.org/10.3390/antib11020027">https://doi.org/10.3390/antib11020027 </a></p>
Dataset for What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?
<p>The data set to paper: </p> <p>What are key factors for detection of peptides using mass spec-trometry on boron-doped diamond surfaces?</p> <p>Juvissan Aguedo1, Marian Vojs2, Martin Vrška2, Marek Nemcovic3, Zuzana Pakanova3, Katerina Aubrechtova Dragounova4, Oleksandr Romanyuk4, Alexander Kromka4, Marian Varga5, Michal Hatala6, Marián Marton2 and Jan Tkac1,*</p> <p>1 Institute of Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia<br>2 Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia<br>3 Centre of Excellence for Glycomic, Slovak Academy of Sciences, Bratislava, Slovakia<br>4 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>5 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia<br>6 Department of Graphic Arts Technology and Applied Photochemistry, Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovakia</p> <p>* corresponding author: jan.tkac@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 5. 2023 - 31. 5. 2024</p> <p>All the data shown in the pictures are provided with a described sample in X-Y or X-Y-Z format.<br>The respective figure to which the data belong is always provided in high resolution.<br>The data are in the following formats: <br>Scheme 1: pdf<br>Figure 1: pdf<br>Figure 2: pdf, csv<br>Figure 3: pdf<br>Figure 4: pdf, csv<br>Figure 5: pdf<br>Figure 6: pdf, csv<br>Figure 7: pdf, csv<br>Figure 8: pdf, csv<br>Figure 9: pdf, csv<br><br></p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.3390/nano14151241</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>
Sensitive mass spectrometric determination of kinin-kallikrein system peptides in light of COVID-19
<p>This dataset provides information on the obtained MS/MS data generated during the conducted validation runs.</p> <p> </p> <p>Abstract of the corresponding paper:</p> <p>The outbreak of COVID-19 has raised interest in the kinin–kallikrein system, as symptoms were postulated to be in connection. Viral blockade of the angiotensin-converting-enzyme 2 impedes degradation of the active kinin des-Arg(9)-bradykinin, which thus increasingly activates bradykinin receptors known to promote inflammation, cough, and edema – symptoms that are commonly observed in COVID-19. However, lean and reliable investigation of the postulated alterations is currently hindered by non-specific peptide adsorption, lacking sensitivity, and cross-reactivity of applicable assays. Here, an LC-MS/MS method was established to determine the following kinins in respiratory lavage fluids: kallidin, bradykinin, des-Arg(10)-kallidin, des-Arg(9)-bradykinin, bradykinin 1-7, bradykinin 2-9 and bradykinin 1-5. This method was fully validated according to regulatory bioanalytical guidelines of the European Medicine Agency and the US Food and Drug Administration and has a broad calibration curve range (up to a factor of 10<sup>3</sup>), encompassing low quantification limits of 4.4–22.8 pg/mL (depending on the individual kinin). The application of the developed LC-MS/MS method to nasal lavage fluid allowed for the rapid (~2 hours), comprehensive and low-volume (100 µL) determination of kinins for the first time. Hence, this novel assay may support current efforts to investigate the pathophysiology of COVID-19, but can also be extended to other diseases.</p>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Ultra-Accurate Correlation Between Precursor and Fragment Ions in Two-lDimensional Mass Spectrometry: Acetylated vs. Trimethylated Histone Peptides
<p>Two-dimensional mass spectrometry (2D MS) is a method<br> for tandem<br> mass spectrometry in which precursor and fragment ions are correlated<br> by manipulating ion radii rather than by ion isolation. A 2D mass<br> spectrum contains the fragmentation patterns of all analytes in a<br> sample, acquired in parallel. We report ultrahigh-resolution narrowband<br> 2D mass spectra of a mixture of two histone peptides with the same<br> sequence, one of which carries an acetylation and the other a trimethylation<br> (m/z 0.006 difference). We reduced<br> the distance between data points in the precursor ion dimension and<br> compared the accuracy of the precursor-fragment correlation with the<br> resolving power. We manage to perform label-free quantification on<br> the histone peptide mixture and show that precursor and fragment ions<br> can be accurately correlated even though the precursor ions are not<br> resolved. Finally, we show that increasing the resolution of a 2D<br> mass spectrum in the precursor ion dimension too far can lead to a<br> decline in the signal-to-noise ratio.</p>
Data from: A proteomic method to extract, concentrate, digest, and enrich peptides from fossils with colored (humic) substances for mass spectrometry analyses
Humic substances are break-down products of decaying organic matter that co-extract with proteins from fossils. These substances are difficult to separate from proteins in solution, and interfere with analyses of fossil proteomes. We introduce a method combining multiple recent advances in extraction protocols to both concentrate proteins from fossil specimens with high humic content, and remove humics, producing clean samples easily analyzed by mass spectrometry (MS). This method includes: 1) a non-demineralizing extraction buffer that eliminates protein loss during the demineralization step in routine methods; 2) filter-aided sample preparation (FASP) of peptides, which concentrates and digests extracts in one filter, allowing the separation of large humics after digestion; 3) centrifugal stage-tipping, which further clarifies and concentrates samples in a uniform process performed simultaneously on multiple samples. We apply this method to a moa fossil (~800¬–1000 yr) dark with humic content, generating colorless samples and enabling the detection of more proteins with greater sequence coverage than previous MS analyses on this same specimen. This workflow allows analyses of low-abundance proteins in fossils containing humics, and thus may widen the range of extinct organisms and regions of their proteomes we can explore with MS.
Data for Theoretical assessment of indistinguishable peptides in mass spectrometry-based proteomics
<p>This dataset provides comprehensive spectral libraries for the theoretical assessment of indistinguishable peptides in mass spectrometry-based proteomics. The data were generated through various experimental conditions. Each file contains the peptide fragmentation patterns and retention times predicted with Prosit at different normalized collision energies (NCE) and charge states.</p>
Glucagon Like Peptide 1 Receptor (GLP1R) Expression and Beta-cell Mass in Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT04733508. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: A proteomic method to extract, concentrate, digest, and enrich peptides from fossils with colored (humic) substances for mass spectrometry analyses
Open the record for dataset details and reuse information.
Differential Peptide Subtraction (DPS): mass spectrometry for human cancer virus discovery
GEO Series GSE157610. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Using RNA-Seq to create sample-specific proteomic databases that enable mass spectrometric discovery of splice junction peptides
GEO Series GSE45428. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
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International Brain Laboratory public data
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OpenNeuro
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