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Precalibration of matrix‐assisted laser desorption/ionization‐time of flight spectra for peptide mass fingerprinting
Author(s) -
Wool Assaf,
Smilansky Zeev
Publication year - 2002
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/1615-9861(200210)2:10<1365::aid-prot1365>3.0.co;2-9
Subject(s) - calibration , peptide mass fingerprinting , mass spectrometry , ionization , matrix assisted laser desorption/ionization , mass spectrum , matrix (chemical analysis) , chemistry , chromatography , analytical chemistry (journal) , computer science , proteomics , desorption , statistics , mathematics , ion , biochemistry , organic chemistry , adsorption , gene
Identification of proteins using matrix‐assisted laser desorption/ionization‐time of flight (MALDI‐TOF) peptide mass fingerprinting (PMF) is a key technique in proteomics. The method is known to be sensitive as well as amenable to high‐throughput operation, but the resulting identifications suffer from a relatively low level of confidence. One way of increasing the confidence is by improving measurement accuracy using one of several calibration methods. This paper presents a new strategy for calibration of MALDI‐TOF PMF spectra that makes use of the phenomenon of peptide mass clustering, and enables spectrum calibration prior to the step of database interrogation, before or after peak extraction. Typically, mass errors are reduced by 40–60%. Accuracy improvement at this early stage can help avoid losing protein candidates, reduce the number of external calibration spots, eliminate internal calibrants, and reduce the number of candidates being scored, thereby reducing analysis time. Different variants of the method are discussed and compared to known calibration methods, such as relying on known calibrants or comparison to putative database candidates. In order to allow precise description of the method and to place the results in perspective, theoretical considerations of peptide databases and scoring functions are also discussed.