Peptide Selection for Accurate Targeted Protein Quantification via a Dimethylation High-Resolution Mass Spectrum Strategy with a Peptide Release Kinetic Model

Qi Chen; Yirong Jiang; Yiping Ren; Meirong Ying; Baiyi Lu

doi:10.1021/acsomega.9b02002

Peptide Selection for Accurate Targeted Protein Quantification via a Dimethylation High-Resolution Mass Spectrum Strategy with a Peptide Release Kinetic Model

ACS Omega. 2020 Feb 18;5(8):3809-3819. doi: 10.1021/acsomega.9b02002. eCollection 2020 Mar 3.

Authors

Qi Chen^{1

2}, Yirong Jiang^{1

2}, Yiping Ren³, Meirong Ying⁴, Baiyi Lu^{1

2}

Affiliations

¹ National Engineering Laboratory of Intelligent Food Technology and Equipment, Key Laboratory for Agro-Products Postharvest Handling of Ministry of Agriculture, Key Laboratory for Agro-Products Nutritional Evaluation of Ministry of Agriculture, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
² Ningbo Research Institute, Zhejiang University, Ningbo 315100, China.
³ Yangtze Delta Region Institute of Tsinghua University, Jiaxing 314006, China.
⁴ Zhejiang Grain and Oil Product Quality Inspection Center, Hangzhou 310012, China.

Abstract

A crucial step in accurate targeted protein quantification using targeted proteomics is to determine optimal proteotypic peptides representing targeted proteins. In this study, a workflow of peptide selection to determine proteotypic peptides using a dimethylation high-resolution mass spectrum strategy with a peptide release kinetic model was investigated and applied in peptide selection of bovine serum albumin. After specificity, digestibility, recovery, and stability evaluation of tryptic peptides in bovine serum albumin, the optimal proteotypic peptide was selected as LVNELTEFAK. The quantification method using LVNELTEFAK gave a linear range of 1-100 ppm with the coefficient greater than 0.9990, and the detection limit of bovine serum albumin in milk was 0.78 mg/kg. Compared with the proteotypic peptides selected by Skyline, the method showed a better performance in method validation. The workflow exhibited high comprehensiveness and efficiency in peptide selection, facilitating accurate targeted protein quantification in the food matrix, which lack protein standards.