An Integrative HCD/CID Scoring Scheme for Improved Characterization of Site-Specific Protein
N -Glycosylation
Anoop M. Mayampurath1, Yin Wu1, Zaneer M. Segu 2,3 , Milos V. Novotny 2,3, Yehia Mehcref 2,3, Haixu Tang 1,3
1School
of Informatics, 2Department of Chemistry and 3National Center for Glycomics and Glycoproteomics
Indiana University, Bloomington, IN 47405
Overview
 We present GlyPID 2.0, a software tool for
accurate characterization of protein glycosylation
through a combination of the following
techniques
• Accurate precursor ion mass calculated
using THRASH–based deisotoping methods
[2, 3]
• A CID score calculated from CollisionInduced Dissociation (CID) spectra, similar to
previous version of GlypID [1]. The model is
based on spacing between fragment peaks
indicative of glycan fragmentation.
• A new HCD score calculated from Highenergy C-trap Dissociation (HCD) spectra
scoring technique based on the presence of
characterestic glycan peaks.
HCD is
available on the LTQ Orbitrap XL.
 GlyPID also reports the type of glycosylation
(High Mannose, Complex etc.) .
 The tool is open source and thus can be
tailored to different needs.
Introduction
Protein glycosylation is a common posttranslational modification, estimated to occur in
over 50% of human proteins. Platforms such as
liquid chromatography with tandem mass
spectrometry (LC/MS/MS) typically employ
Collision-Induced Dissociation (CID) for the
characterization of glycoproteins. We have
previously developed GlyPID that enables the
automatic mapping of N-glycolsylation sites and
their microhetrogenties from LC/MS/MS data.
This was achieved by combining the clusters of
precursor ions and their fragmentation patterns.
Here, we use the newly developed LTQ Orbitrap
XL instrument that simultaneously performs highenergy-C-trap dissociation (HCD) and CID. An
integrative scoring scheme is presented and
implemented as part of GlypID 2.0 that combines
HCD and CID data with the accurate monoisotopic
precursor mass, thus allowing a confident
characterization of protein glycosylation.
Methods
Results
 Accurate Precursor Ion Mass
- Used DeconMSn [2] methodology to accurately detect precursor ion mass
Precursor mono mass : 4600.839
Precursor CS : 3
THRASH
precursor
 CID Scoring
-The De Novo CID scoring algorithm finds all
connected-monosaccharide paths in the CID
spectrum using fragment mass differences[3].
-The largest oligosaccharide sequence subset
identified using the dynamic programming
algorithm to find the longest path in the
spectrum graph (see example above)
Class
137
m/z
Hex
GlcNAc
NeuAC
(-H20)
NeuAc
Hex+
GlcNAc
NeuAc+He
x+GlcNAc
High
Mannose
0
S
0
0
0
W
0
ComplexAsaliated
0
W
M
0
0
S
0
ComplexSaliated
M
M
M
M
M
M
M
Hybrid
M
S
M
M
M
M
M
S – Strong (0.99); M- Moderate (0.6-0.7); W – weak (0.2-0.3)
 HCD Scoring
- The algorithm looks for the presence of
seven characteristic monosacharride
peaks from HCD fragments,
(see example above) and models the
number of detected characteristic peaks
as a binomial distribution.
- A p-value score is then calculated to
assign confidence to the identification.
 Prediction of the classes of N-glycosylation
-The classes of N-glycosylation (High-Mannose,
Complex-asaliated, Complex-saliated or Hybrid)
can be predicted by matching observed HCD
spectra against theoretical characteristic peak
distribution.
- As an example, high mannose will have a
prominent peak at Hex (from Mannose) and a
weak peak at Hex+GlcNAc.
Example (from glycosylated Fetuin sample datset) of integrative
scoring for an ion (given in 2D view with monoisotopic mass on Y
axis and LC scan on X axis) of mass 4600.84. Presence of
monosaccharide peaks (in HCD spectra) and monosocharride
path (in CID spectra) suggests that this could be a glycosylated
peptide.
Ongoing and Future work
Efforts are currently being made to combine the two scoring models
together to provide a unified score, to increase glycosylation
identification confidence by using the glycan type determined by HCD
scoring model to validate the CID scoring model, to determine
microheterogenties, and to implement user-friendly visualization
controls.
Software link:
References
1.
2.
3.
Mayampurath et. al., DeconMSn: a software tool for accurate parent ion monoisotopic
mass determination for tandem mass spectra. Bioinformatics. 2008 Apr 1;24(7):1021-3
Horn, et. al. Automated Reduction and Interpretation of High Resolution Electrospray Mass
Spectra of Large Molecules. J. Am. Soc. Mass Spectrom. 2000, 11, 320-332.
Wu et. al. A Comp. Approach for the Identification of Site-Specific Protein Glycosylations
Through Ion-Trap Mass Spectrometry, Lecture Notes in Comp Sci, 2007, 4532:96-107.
Acknowledgements
This work is supported by NSF award DBI-0642897, and National Center for Glycomics and
Glycoproteomics, funded by NIH/NCRR grant 5P41RR018942. This work was also partially
supported by the Indiana Metabolomics and Cytomics Initiative
METACyt).