Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation
- Authors
- Madar, Inamul Hasan; Ko, Seung-Ik; Kim, Hokeun; Mun, Dong-Gi; Kim, Sangtae; Smith, Richard D.; Lee, Sang-Won
- Issue Date
- 17-1월-2017
- Publisher
- AMER CHEMICAL SOC
- Citation
- ANALYTICAL CHEMISTRY, v.89, no.2, pp.1244 - 1253
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANALYTICAL CHEMISTRY
- Volume
- 89
- Number
- 2
- Start Page
- 1244
- End Page
- 1253
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/84901
- DOI
- 10.1021/acs.analchem.6b03874
- ISSN
- 0003-2700
- 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 dining 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.
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