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).

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

scholarly journals PSV-25 Detection of heart and aorta tissue peptide markers by the multiple-reaction monitoring method

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 362-363
Author(s):  
Daniil Khvostov ◽  
Natalya Vostrikova ◽  
Irina M Chernukha
Keyword(s):  
Amino Acid ◽  
High Performance ◽  
Multiple Reaction Monitoring ◽  
Meat Product ◽  
Meat Products ◽  
Amino Acid Sequences ◽  
Composition Analysis ◽  
Reaction Monitoring ◽  
Russian Science ◽  
Monitoring Method

Abstract Functional, particularly personalized meat-based foods are of more in demand by a consumer today. Functional additives, such as plant components and animal proteins from bovine or porcine tissues have been successfully used. With many ingredients added to foods, it is important to provide quality and composition monitoring to confirm the products’ authenticity, to identify undeclared or rarely used types of raw meat in product formulations. For example, if animal heart tissue is a component of a product formulation or if aorta tissue presents in a product due to improper trimming. Different methods are used to identify raw materials, including new approaches in proteomics and peptidomics that are considered the most effective modern methods nowadays. The purpose of the study is meat product composition analysis and special biomarker peptide identification to confirm the presence of heart and aorta tissue in a finished meat product. Over 20 amino acid sequences were checked based on earlier obtained data. Those amino acid sequences were analyzed with a high-performance liquid chromatography with mass spectrometric detection as described. The MS settings were selected using the Skyline. Signal-to-Noise ratio (S/N) over 10 units were used to choose the best peptide candidates. Seven peptides were found in porcine hearts. The best candidate was peptide VNVDEVGGEALGR (S/N - 73.10±5.3) from β-Hemoglobin. Two marker peptides from serum albumin were selected for pork aorta: TVLGNFAAFVQK (S/N 53.51±2.4) and EVTEFAK (S/N 31.69±4.1). These biomarkers showed the best detection and specificity. The multiply reaction monitoring method made it possible to identify the most/best specific peptides—biomarkers that could confirm the heart and/or aorta in meat products. The method can be used for comparative research or identification of best peptides that are specific to any type of animal tissue. The work was supported by the Russian Science Foundation, project no. 16-16- 10073.

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