Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification. He, Z., Yang, C., Yang, C., Qi, R. Z., Tam, J. P., & Yu, W. In Proc. of Research in Computational Molecular Biology (RECOMB 2009), volume 5541, of Lect Notes Comput Sci, pages 16–30, 2009. Springer, Berlin.
abstract   bibtex   
In current proteome research, the most widely used method for protein mixture identification is probably peptide sequencing. Peptide sequencing is based on tandem Mass Spectrometry (MS/MS) data. The disadvantage is that MS/MS data only sequences a limited number of peptides and leaves many more peptides uncovered. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from single-stage MS data. Unfortunately, this technique is less accurate than the peptide sequencing method and can not handle protein mixtures, which hampers the widespread use of PMF. In this paper, we tackle the problem of protein mixture identification from an optimization point of view. We show that some simple heuristics can find good solutions to the optimization problem. As a result, we obtain much better identification results than previous methods. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF-based protein mixture identification. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures, especially in the identification of low-abundance proteins which are less likely to be sequenced by MS/MS scanning.
@InProceedings{he09optimization-based,
  author    = {Zengyou He and Chao Yang and Can Yang and Robert Z. Qi and Jason Po-Ming Tam and Weichuan Yu},
  title     = {Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification},
  booktitle = {Proc. of Research in Computational Molecular Biology (RECOMB 2009)},
  year      = {2009},
  volume    = {5541},
  series    = lncs,
  pages     = {16--30},
  publisher = Springer,
  abstract  = {In current proteome research, the most widely used method for protein mixture identification is probably peptide sequencing. Peptide sequencing is based on tandem Mass Spectrometry (MS/MS) data. The disadvantage is that MS/MS data only sequences a limited number of peptides and leaves many more peptides uncovered. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from single-stage MS data. Unfortunately, this technique is less accurate than the peptide sequencing method and can not handle protein mixtures, which hampers the widespread use of PMF. In this paper, we tackle the problem of protein mixture identification from an optimization point of view. We show that some simple heuristics can find good solutions to the optimization problem. As a result, we obtain much better identification results than previous methods. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF-based protein mixture identification. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures, especially in the identification of low-abundance proteins which are less likely to be sequenced by MS/MS scanning.},
  file      = {HeEtAl_OptimizationBasedPeptideMassFingerprinting_RECOMB_2009.pdf:2009/HeEtAl_OptimizationBasedPeptideMassFingerprinting_RECOMB_2009.pdf:PDF},
  keywords  = {ms, pmf, proteins, peptides},
  owner     = {Sebastian},
  timestamp = {2009.05.30},
}
Downloads: 0
About
Documentation
COVID-19 Tracker
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021