Comparison of Search Engine Contributions in Protein Mass Fingerprinting for Protein Identification

Peptide mass fingerprinting (PMF) is a valuable method for rapid and high-throughput protein identification using the proteomics approach. Automated search engines, such as Ms-Fit, Mascot, ProFound, and Peptldent, have facilitated protein identification through PMF. The potential to obtain a true MS...

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Bibliographic Details
Published in:Biotechnology and bioprocess engineering 2007-04, Vol.12 (2), p.125-130
Main Authors: Joo, W.A. (Korea University, Seoul, Republic of Korea), Lee, J.B. (Korea University, Seoul, Republic of Korea), Park, M.R. (Eulji University, Daejeon, Republic of Korea), Lee, J.W. (Korea University, Seoul, Republic of Korea), Kim, H.J. (Korea University, Seoul, Republic of Korea), Kim, C.W. (Korea University, Seoul, Republic of Korea), E-mail: cwkim@korea.ac.kr
Format: Article
Language:English
Subjects:
Algorithms
Contaminants
Data processing
Identification
Ions
Keratin
MALDI-TOF MS
Mass spectrometry
Mass spectroscopy
Peptide mapping
peptide mass fingerprinting
Peptides
PROTEINAS
PROTEINE
PROTEINS
proteomics
Search engines
searching engine
selectivity
sensibility
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Summary:Peptide mass fingerprinting (PMF) is a valuable method for rapid and high-throughput protein identification using the proteomics approach. Automated search engines, such as Ms-Fit, Mascot, ProFound, and Peptldent, have facilitated protein identification through PMF. The potential to obtain a true MS protein identification result depends on the choice of algorithm as well as experimental factors that influence the information content in MS data. When mass spectral data are incomplete and/or have low mass accuracy, the "number of matches" approach may be inadequate for a useful identification.
ISSN:1226-8372
1976-3816
DOI:10.1007/BF03028637