Linked Life Data

19106086

Source:http://linkedlifedata.com/resource/pubmed/id/19106086

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-4-2
pubmed:abstractText
Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-to-spectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922; Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364-6373) as a substitute for the reference libraries used by the spectrum-to-spectrum search programs X!Hunter and BiblioSpec and similar results in comparison with the spectrum-to-sequence program Mascot. We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-12634793, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15221759, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15228325, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15253624, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15317041, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15335725, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-15789030, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-16194101, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-16478097, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-16889405, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-16906711, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-17018520, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-17327847, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-17608399, http://linkedlifedata.com/resource/pubmed/commentcorrection/19106086-18173222
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1535-9484
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
857-69
pubmed:dateRevised
2011-5-23
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A simulated MS/MS library for spectrum-to-spectrum searching in large scale identification of proteins.
pubmed:affiliation
Department of Chemistry and Biochemistry and paragraph signHoward Hughes Medical Institute, University of Colorado, Boulder, Colorado 80309, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural