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Comparison of search engine contributions in protein mass fingerprinting for protein identification

Authors
Joo, Won-ALee, Jeong-BokPark, MiraLee, Jae-WonKim, Hyun-JungKim, Chan-Wha
Issue Date
3월-2007
Publisher
KOREAN SOC BIOTECHNOLOGY & BIOENGINEERING
Keywords
peptide mass fingerprinting; searching engine; MALDI-TOF MS; selectivity; sensibility
Citation
BIOTECHNOLOGY AND BIOPROCESS ENGINEERING, v.12, no.2, pp.125 - 130
Indexed
SCIE
SCOPUS
KCI
Journal Title
BIOTECHNOLOGY AND BIOPROCESS ENGINEERING
Volume
12
Number
2
Start Page
125
End Page
130
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/125804
DOI
10.1007/BF03028637
ISSN
1226-8372
Abstract
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 PeptIdent, 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. Several studies have evaluated factors influencing the quality of mass spectrometry (MS) experiments. Missed cleavages, post-translational modifications of peptides and contaminants (e.g., keratin) are important factors that can affect the results of MS analyses by influencing the identification process as well as the quality of the MS spectra. We compared search engines frequently used to identify proteins from Homo sapiens and Halobacterium salinarum by evaluating factors, including database and mass tolerance, to develop an improved search engine for PMF. This study may provide information to help develop a more effective algorithm for protein identification in each species through PMF. (c) KSBB.
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