Linked Life Data

11795795

Source:http://linkedlifedata.com/resource/pubmed/id/11795795

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2002-1-17
pubmed:abstractText
We have developed a pattern recognition algorithm called SALSA (scoring algorithm for spectral analysis) for the detection of specific features in tandem MS (MS-MS) spectra. Application of the SALSA algorithm to the detection of peptide MS-MS ion series enables identification of MS-MS spectra displaying characteristics of specific peptide sequences. SALSA analysis scores MS-MS spectra based on correspondence between theoretical ion series for peptide sequence motifs and actual MS-MS product ion series, regardless of their absolute positions on the m/z axis. Analyses of tryptic digests of bovine serum albumin (BSA) by LC-MS-MS followed by SALSA analysis detected MS-MS spectra for both unmodified and multiple modified forms of several BSA tryptic peptides. SALSA analysis of MS-MS data from mixtures of BSA and human serum albumin (HSA) tryptic digests indicated that ion series searches with BSA peptide sequence motifs identified MS-MS spectra for both BSA and closely related HSA peptides. Optimal discrimination between MS-MS spectra of variant peptide forms is achieved when the SALSA search criteria are optimized to the target peptide. Application of SALSA to LC-MS-MS proteome analysis will facilitate the characterization of modified and sequence variant proteins.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0003-2700
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
74
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
203-10
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Peptide sequence motif analysis of tandem MS data with the SALSA algorithm.
pubmed:affiliation
Southwest Environmental Health Sciences Center, College of Pharmacy and Department of Computer Science, The University of Arizona, Tucson 85721-0207, USA. liebler@pharmacy.arizona.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.