Glycobiology Advance Access originally published online on March 6, 2009
Glycobiology 2009 19(7):707-714; doi:10.1093/glycob/cwp034
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De novo glycan structure search with the CID MS/MS spectra of native N-glycopeptides
2 Transplantation Laboratory & Infection Biology Research Program, Haartman Institute, University of Helsinki
3 Applied Numerics Oy, Ltd, Helsinki
4 Medicel Oy, Ltd, Espoo
5 HUSLAB, Helsinki University Central Hospital, Helsinki, Finland
1 To whom correspondence should be addressed: Tel: +358-50-3627125; Fax: +358-9-2411227; e-mail: risto.renkonen{at}helsinki.fi
Received on January 14, 2009; revised on February 27, 2009; accepted on February 27, 2009
The aim of our study is to automatically analyze the glycan and peptide structures of N-glycopeptides without a need to release glycans from the glycopeptides. Our wet laboratory raw data represent a series of MS/MS mass spectra obtained from a reverse-phase liquid chromatography run of size-exclusion-enriched tryptic-digested glycopeptides from glycoproteins. The MS/MS spectra are first analyzed in order to identify glycosylated peptides and N-glycan monosaccharide compositions present on each glycopeptide. We further developed a Branch-and-Bound algorithm to search de novo N-glycan structures, i.e., monosaccharide compositions and their ordered sequences from native glycopeptides. Our de novo algorithm is based on iterative growth and selection of a population of glycan structures and it does not use databases of known glycan structures. We validate the algorithm with (i) in silico-generated spectra, with or without deteriorating deletions, (ii) with a purified glycoprotein transferrin, and (iii) with a complex mixture of N-glycopeptides enriched from human plasma. Our Branch-and-Bound algorithm depicted glycan structures from all the above-mentioned three input data types. Due to the large diversity of glycan structures, the results typically contained several proposed structures matching almost equally well to the spectra. In conclusion, this algorithm automatically identifies glycopeptides and their structures from the MS/MS spectra and thus greatly reduces the number of possible glycan structures from the vast amount of potential ones.
Key words: de novo algorithm / glycan / glycopeptide / glycoprotein / mass spectrometry