Park. et. al. The 15th HUPO Conference (2016) Taipei, Taiwan

IQ-GPA (GlycoProteome Analyzer): Automated Identification and Quantification of Site-Specific N-Glycosylation in Human Plasma

Gun Wook Park 1,2, Jin Young Kim 1 , Heeyoun Hwang 1 , Ju Yeon Lee 1 , Young Hee Ahn 3 , Hyun Kyoung Lee 1,2, Eun Sun Ji 1,4, Kwang Hoe Kim 1,2,
Hoi Keun Jeong 1,2 , Ki Na Yun 1,5, Yong-Sam Kim 6, Jeong-Heon Ko 6 , Hyun Joo An 2, Jae Han Kim 7, Young-Ki Paik 8, and Jong Shin Yoo 1,2

1 Biomedical Omics Group, Korea Basic Science Institute, Ochang, Republic of Korea
2 Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
3 Department of Biomedical Science,Cheongju University, Cheongju, Republic of Korea
4 Department of Chemistry, Hannam University, Daejeon, Republic of Korea
5 Department of Chemistry, Sogang University, Seoul, Republic of Korea
6 Cancer Biomarkers Development Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea
7 Department of Food Nutrition, Chungnam National University, Daejeon, Republic of Korea
8 Yonsei Proteome Research Center and Department of Integrated OMICS for Biomedical Science, and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea


We have developed Integrated GlycoProteomeAnalyzer1 for high throughput analysis of N-glycoproteome, which combines methods for tandem mass spectrometry with a database search and algorithmic suite. We created novel scoring algorithms with calculation of false discovery rate (FDR) and label-free quantification method using the combined intensities of top three isotope peaks at three highest MS spectral points (3TIQ).
Quantification Results id-GPA algorithm for identification of standard α1-glycoprotein (AGP) q-GPA algorithm for label-free quantitation of standard AGP Comparison between id-GPA & Byonic tools using standard AGP Schematic diagram for site-specific analysis of N-glycopeptides from human plasma glycoproteins by IQ-GPA

The resultant data were then computationally analyzed using specific algorithms within the IQ-GPA suite: glycopeptides were identified against the GPA database (id-GPA), quantified (q-GPA) using the 3TIQ, and finally compared between multiple samples (c-GPA). In IQ-GPA, scoring entailed three steps:
1) Selection of N-glycopeptide from 15 glycan-specific oxonium ions using HCD-MS/MS spectra; (M-score);
2) Selection of candidates by matching the isotope pattern to intact N-glycopeptides in the GPA-DB (S-score); and
3) Identification of N-glycopeptide from CID and HCD-MS/MS fragment ions (Y-score) with (FDR) < 1%.

Our method identified 123 N-glycoproteins present in plasma at concentration ranges over five orders of magnitude from highly abundant proteins such as immunoglobulin G (IgG, ~1 mg/ml) to low-abundance proteins such as AFP (~10 ng/ml).

Park. et al. Scientific Reports. (2016) 6:21175, DOI: 10.1038/srep21175.

By | 2017-09-04T17:16:05+00:00 September 30th, 2016|Poster, Publication|