Elimination of Systematic Mass Measurement Errors in Liquid Chromatography-Mass Spectrometry Based Proteomics Using Regression Models and a Priori Partial Knowledge of the Sample Content
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- 作者：Vladislav A. Petyuk ; Navdeep Jaitly ; Ronald J. Moore ; Jie Ding ; Thomas O. Metz ; Keqi Tang ; Matthew E. Monroe ; Aleksey V. Tolmachev ; Joshua N. Adkins ; Mikhail E. Belov ; Alan R. Dabney ; Wei-Jun Qian ; Da
- 刊名：Analytical Chemistry
- 出版年：2008
- 出版时间：February 1, 2008
- 年：2008
- 卷：80
- 期：3
- 页码：693 - 706
- 全文大小：1802K
- 年卷期：v.80,no.3(February 1, 2008)
- ISSN：1520-6882
文摘
The high mass measurement accuracy and precisionavailable with recently developed mass spectrometers isincreasingly used in proteomics analyses to confidentlyidentify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications andpeptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, itis necessary to limit systematic mass measurement errors.It is well known that errors in m/z measurements can beaffected by experimental parameters that include, forexample, outdated calibration coefficients, ion intensity,and temperature changes during the measurement.Traditionally, these variations have been corrected throughthe use of internal calibrants (well-characterized standardsintroduced with the sample being analyzed). In this paper,we describe an alternative approach where the calibrationis provided through the use of a priori knowledge of thesample being analyzed. Such an approach has previouslybeen demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluatedusing several commercially available instruments. Theapplied approach is shown to remove any noticeablebiases from the overall mass measurement errors anddecreases the overall standard deviation of the massmeasurement error distribution by 1.2-2-fold, dependingon instrument type. Subsequent reduction of the randomerrors based on multiple measurements over consecutivespectra further improves accuracy and results in anoverall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discoveryrates for peptide identifications using high-accuracy massmeasurements.