Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

doi:https://doi.org/10.1021/acs.analchem.6b03874

Title: Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation

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Abstract

Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, “multiplexed post-experiment monoisotopic mass refinement” (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/ MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.

Authors:

Madar, Inamul Hasan ^[1]; Ko, Seung-Ik ^[1]; Kim, Hokeun ^[1]; Mun, Dong-Gi ^[1]; Kim, Sangtae ^[2]; Smith, Richard D. ^[2];

^[1]

Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States

Publication Date:: 2016-12-22

Research Org.:: Pacific Northwest National Laboratory (PNNL), Richland, WA (US), Environmental Molecular Sciences Laboratory (EMSL)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

OSTI Identifier:: 1406696

Report Number(s):: PNNL-SA-129635
Journal ID: ISSN 0003-2700; 49531; 453040220

DOE Contract Number:: AC05-76RL01830

Resource Type:: Journal Article

Journal Name:: Analytical Chemistry

Additional Journal Information:: Journal Volume: 89; Journal Issue: 2; Journal ID: ISSN 0003-2700

Publisher:: American Chemical Society (ACS)

Country of Publication:: United States

Language:: English

Subject:: Environmental Molecular Sciences Laboratory

Citation Formats


                    Madar, Inamul Hasan, Ko, Seung-Ik, Kim, Hokeun, Mun, Dong-Gi, Kim, Sangtae, Smith, Richard D., and Lee, Sang-Won. Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation.  United States: N. p., 2016. 
        Web.  doi:10.1021/acs.analchem.6b03874.

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                    Madar, Inamul Hasan, Ko, Seung-Ik, Kim, Hokeun, Mun, Dong-Gi, Kim, Sangtae, Smith, Richard D., & Lee, Sang-Won. Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation.  United States.  https://doi.org/10.1021/acs.analchem.6b03874

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                    Madar, Inamul Hasan, Ko, Seung-Ik, Kim, Hokeun, Mun, Dong-Gi, Kim, Sangtae, Smith, Richard D., and Lee, Sang-Won. Thu .  
        "Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation".  United States.  https://doi.org/10.1021/acs.analchem.6b03874.

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@article{osti_1406696,

  title        = {Multiplexed Post-Experimental Monoisotopic Mass Refinement ( m PE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation},

  author       = {Madar, Inamul Hasan and Ko, Seung-Ik and Kim, Hokeun and Mun, Dong-Gi and Kim, Sangtae and Smith, Richard D. and Lee, Sang-Won},

  abstractNote = {Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, “multiplexed post-experiment monoisotopic mass refinement” (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/ MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.},

  doi          = {10.1021/acs.analchem.6b03874},

  url          = {https://www.osti.gov/biblio/1406696},
  journal      = {Analytical Chemistry},

  issn         = {0003-2700},

  number       = 2,

  volume       = 89,

  place        = {United States},

  year         = {2016},

  month        = {12}

}

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