Multiple Reaction Monitoring Cubed for Protein Quantification at the Low Nanogram/Milliliter Level in Nondepleted Human Serum

2009 ◽  
Vol 81 (22) ◽  
pp. 9343-9352 ◽  
Author(s):  
T. Fortin ◽  
A. Salvador ◽  
J. P. Charrier ◽  
C. Lenz ◽  
F. Bettsworth ◽  
...  
Keyword(s):  
Human Serum ◽  
Multiple Reaction Monitoring ◽  
Protein Quantification ◽  
Reaction Monitoring

Evaluation of Interspecimen Trypsin Digestion Efficiency Prior to Multiple Reaction Monitoring-Based Absolute Protein Quantification with Native Protein Calibrators

2013 ◽  
Vol 12 (12) ◽  
pp. 5760-5774 ◽  
Author(s):  
Irene van den Broek ◽  
Nico P. M. Smit ◽  
Fred P. H. T. M. Romijn ◽  
Arnoud van der Laarse ◽  
André M. Deelder ◽  
...  
Keyword(s):  
Multiple Reaction Monitoring ◽  
Protein Quantification ◽  
Trypsin Digestion ◽  
Reaction Monitoring ◽  
Native Protein ◽  
Digestion Efficiency

Quantitative analysis of genetically modified soya using multiple reaction monitoring mass spectrometry with endogenous peptides as internal standards

2018 ◽  
Vol 25 (1) ◽  
pp. 50-57 ◽  
Author(s):  
Shobha Devi ◽  
Yi-Cheng Lin ◽  
Yen-Peng Ho
Keyword(s):  
Mass Spectrometry ◽  
Genetically Modified ◽  
Multiple Reaction Monitoring ◽  
Linear Regression Analysis ◽  
Exchange Chromatography ◽  
Protein Quantification ◽  
Reaction Monitoring ◽  
Label Free ◽  
Internal Standards ◽  
Phosphate Synthase

A simple label-free method was developed for the quantification of the herbicide-resistant gene-related protein 5-enolpyruvylshikimate-3-phosphate synthase using multiple reaction monitoring liquid chromatography–mass spectrometry. Sample pretreatment procedures including ion exchange chromatography and CaCl2 precipitation were used to purify the 5-enolpyruvylshikimate-3-phosphate synthase protein. Quantification of various percentages of genetically modified soya (0.5–100%) was performed by selecting suitable endogenous soybean peptides as internal standards. Results indicated that Gly P (QGDVFVVPR) and Lec P (LQLNK) are useful internal standards for the quantification of low and high percentages of genetically modified soya, respectively. Linear regression analysis of both calibration curves yielded good linearity with R2 of 0.99. This approach is a convenient and accurate quantification method for genetically modified soya at a level as low as 0.5% (less than the current EU threshold for labeling genetically modified soya).

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