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Automated methods for improved protein identification by peptide mass fingerprinting

Levander, Fredrik LU ; Rognvaldsson, T; Samuelsson, J and James, Peter LU (2004) In Proteomics 4(9). p.2594-2601
Abstract
In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more... (More)
In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
protein, mass spectrometry, automation, database searching, identification
in
Proteomics
volume
4
issue
9
pages
2594 - 2601
publisher
John Wiley & Sons
external identifiers
  • wos:000223801300010
  • pmid:15352234
  • scopus:4444233697
ISSN
1615-9861
DOI
10.1002/pmic.200300804
language
English
LU publication?
yes
id
d673aaba-b562-44d9-bdcd-d3311d361d29 (old id 267279)
date added to LUP
2007-08-03 09:40:17
date last changed
2017-02-19 03:41:18
@article{d673aaba-b562-44d9-bdcd-d3311d361d29,
  abstract     = {In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.},
  author       = {Levander, Fredrik and Rognvaldsson, T and Samuelsson, J and James, Peter},
  issn         = {1615-9861},
  keyword      = {protein,mass spectrometry,automation,database searching,identification},
  language     = {eng},
  number       = {9},
  pages        = {2594--2601},
  publisher    = {John Wiley & Sons},
  series       = {Proteomics},
  title        = {Automated methods for improved protein identification by peptide mass fingerprinting},
  url          = {http://dx.doi.org/10.1002/pmic.200300804},
  volume       = {4},
  year         = {2004},
}