27 research outputs found

    High-throughput peptide quantification using mTRAQ reagent triplex

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    <p>Abstract</p> <p>Background</p> <p>Protein quantification is an essential step in many proteomics experiments. A number of labeling approaches have been proposed and adopted in mass spectrometry (MS) based relative quantification. The mTRAQ, one of the stable isotope labeling methods, is amine-specific and available in triplex format, so that the sample throughput could be doubled when compared with duplex reagents.</p> <p>Methods and results</p> <p>Here we propose a novel data analysis algorithm for peptide quantification in triplex mTRAQ experiments. It improved the accuracy of quantification in two features. First, it identified and separated triplex isotopic clusters of a peptide in each full MS scan. We designed a schematic model of triplex overlapping isotopic clusters, and separated triplex isotopic clusters by solving cubic equations, which are deduced from the schematic model. Second, it automatically determined the elution areas of peptides. Some peptides have similar atomic masses and elution times, so their elution areas can have overlaps. Our algorithm successfully identified the overlaps and found accurate elution areas. We validated our algorithm using standard protein mixture experiments.</p> <p>Conclusions</p> <p>We showed that our algorithm was able to accurately quantify peptides in triplex mTRAQ experiments. Its software implementation is compatible with Trans-Proteomic Pipeline (TPP), and thus enables high-throughput analysis of proteomics data.</p

    MOD(i) : a powerful and convenient web server for identifying multiple post-translational peptide modifications from tandem mass spectra

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    MOD(i) () is a powerful and convenient web service that facilitates the interpretation of tandem mass spectra for identifying post-translational modifications (PTMs) in a peptide. It is powerful in that it can interpret a tandem mass spectrum even when hundreds of modification types are considered and the number of potential PTMs in a peptide is large, in contrast to most of the methods currently available for spectra interpretation that limit the number of PTM sites and types being used for PTM analysis. For example, using MOD(i), one can consider for analysis both the entire PTM list published on the unimod webpage () and user-defined PTMs simultaneously, and one can also identify multiple PTM sites in a spectrum. MOD(i) is convenient in that it can take various input file formats such as .mzXML, .dta, .pkl and .mgf files, and it is equipped with a graphical tool called MassPective developed to display MOD(i)'s output in a user-friendly manner and helps users understand MOD(i)'s output quickly. In addition, one can perform manual de novo sequencing using MassPective

    Fast Multi-blind Modification Search through Tandem Mass Spectrometry

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    DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning

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    Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep

    Role of Nitric Oxide in the Pathogenesis of Encephalomyocarditis Virus-Induced Diabetes in Mice▿

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    The D variant of encephalomyocarditis virus (EMC-D virus) causes diabetes in mice by destroying pancreatic β cells. In mice infected with a low dose of EMC-D virus, macrophages play an important role in β-cell destruction by producing soluble mediators such as interleukin-1β (IL-1β), tumor necrosis factor alpha (TNF-α), and nitric oxide (NO). To investigate the role of NO and inducible NO synthase (iNOS) in the development of diabetes in EMC-D virus-infected mice, we infected iNOS-deficient DBA/2 mice with EMC-D virus (2 × 102 PFU/mouse). Mean blood glucose levels in EMC-D virus-infected iNOS-deficient mice and wild-type mice were 205.5 and 466.7 mg/dl, respectively. Insulitis and macrophage infiltration were reduced in islets of iNOS-deficient mice compared with wild-type mice at 3 days after EMC-D virus infection. Apoptosis of β cells was decreased in iNOS-deficient mice, as evidenced by reduced numbers of terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling-positive cells. There were no differences in mRNA expression of antiapoptotic molecules Bcl-2, Bcl-xL, Bcl-w, Mcl-1, cIAP-1, and cIAP-2 between wild-type and iNOS-deficient mice, whereas expression of proapoptotic Bax and Bak mRNAs was significantly decreased in iNOS-deficient mice. Expression of IL-1β and TNF-α mRNAs was significantly decreased in both islets and macrophages of iNOS-deficient mice compared with wild-type mice after EMC-D virus infection. Nuclear factor κB was less activated in macrophages of iNOS-deficient mice after virus infection. We conclude that NO plays an important role in the activation of macrophages and apoptosis of pancreatic β cells in EMC-D virus-infected mice and that deficient iNOS gene expression inhibits macrophage activation and β-cell apoptosis, contributing to prevention of EMC-D virus-induced diabetes
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