Literature DB >> 19256536

Post analysis data acquisition for the iterative MS/MS sampling of proteomics mixtures.

Michael R Hoopmann1, Gennifer E Merrihew, Priska D von Haller, Michael J MacCoss.   

Abstract

The identification of peptides by microcapillary liquid chromatography-tandem mass spectrometry (microLC-MS/MS) has become routine because of the development of fast scanning mass spectrometers, data-dependent acquisition, and database searching algorithms. However, many peptides within the detection limit of the mass spectrometer remain unidentified because of limitations in MS/MS sampling speed despite the dynamic range and peak capacity of the instrument. We have developed an automated approach that uses the mass spectra from high resolution microLC-MS data to define the molecular species present in the mixture and directs the acquisition of MS/MS spectra to precursors that were missed in prior analyses. This approach increases the coverage of the molecular species sampled by MS/MS and consequently the number of peptides and proteins identified during the acquisition of technical or biological replicates using a simple one-dimensional chromatographic separation. The combination of a unique workflow and custom software contribute to the improved identification of molecular features detected in proteomics experiments of complex protein mixtures.

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Mesh:

Year:  2009        PMID: 19256536      PMCID: PMC2671646          DOI: 10.1021/pr800828p

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  32 in total

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5.  Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery.

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6.  Isoform analysis of LC-MS/MS data from multidimensional fractionation of the serum proteome.

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  28 in total

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6.  Design and application of a data-independent precursor and product ion repository.

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7.  Identification of peptide features in precursor spectra using Hardklör and Krönik.

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8.  Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

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