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BioMed Research International
Volume 2018, Article ID 1564136, 11 pages
https://doi.org/10.1155/2018/1564136
Research Article

N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search

1National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100101, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
4Computer Network Information Center, Chinese Academy of Sciences, Beijing 100101, China

Correspondence should be addressed to Fuquan Yang; nc.ca.pbi@gnayqf and Yan Fu; nc.ca.ssma@ufy

Received 4 May 2018; Revised 16 July 2018; Accepted 29 July 2018; Published 14 August 2018

Academic Editor: Chunchao Zhang

Copyright © 2018 Zhiwu An et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, MS/MS spectra of deglycopeptides are used to create spectral library, MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.