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

Abstract

Purpose:
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

Methods:
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%.

Results:
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|