MMass as a software tool for the annotation of cyclic peptide tandem mass spectra.
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ABSTRACT:
SUBMITTER: Niedermeyer TH
PROVIDER: S-EPMC3441486 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co-isolation and thus prone to false identifications. The deconvolution approach matched complementary b-, y-ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co-isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20-35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.
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Project description:Glycosaminoglycans (GAGs) covalently linked to proteoglycans (PGs) are characterized by repeating disaccharide units and variable sulfation patterns along the chain. GAG length and sulfation patterns impact disease etiology, cellular signaling, and structural support for cells. We and others have demonstrated the usefulness of tandem mass spectrometry (MS2) for assigning the structures of GAG saccharides; however, manual interpretation of tandem mass spectra is time-consuming, so computational methods must be employed. In the proteomics domain, the identification of monoisotopic peaks and charge states relies on algorithms that use averagine, or the average building block of the compound class being analyzed. Although these methods perform well for protein and peptide spectra, they perform poorly on GAG tandem mass spectra, because a single average building block does not characterize the variable sulfation of GAG disaccharide units. In addition, it is necessary to assign product ion isotope patterns to interpret the tandem mass spectra of GAG saccharides. To address these problems, we developed GAGfinder, the first tandem mass spectrum peak finding algorithm developed specifically for GAGs. We define peak finding as assigning experimental isotopic peaks directly to a given product ion composition, as opposed to deconvolution or peak picking, which are terms more accurately describing the existing methods previously mentioned. GAGfinder is a targeted, brute force approach to spectrum analysis that uses precursor composition information to generate all theoretical fragments. GAGfinder also performs peak isotope composition annotation, which is typically a subsequent step for averagine-based methods. Data are available via ProteomeXchange with identifier PXD009101.
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Project description:MotivationIn proteomics, database search programs are routinely used for peptide identification from tandem mass spectrometry data. However, many low-quality spectra cannot be interpreted by any programs. Meanwhile, certain high-quality spectra may not be identified due to incompleteness of the database or failure of the software. Thus, spectrum quality (SPEQ) assessment tools are helpful programs that can eliminate poor-quality spectra before the database search and highlight the high-quality spectra that are not identified in the initial search. These spectra may be valuable candidates for further analyses.ResultsWe propose SPEQ: a spectrum quality assessment tool that uses a deep neural network to classify spectra into high-quality, which are worthy candidates for interpretation, and low-quality, which lack sufficient information for identification. SPEQ was compared with a few other prediction models and demonstrated improved prediction accuracy.Availability and implementationSource code and scripts are freely available at github.com/sor8sh/SPEQ, implemented in Python.
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