Glycoprotein Microheterogeneity via N-Glycopeptide Identification

1
Glycoprotein Microheterogeneity via
N-Glycopeptide Identification

• Kevin Brown Chandler, Petr Pompach,
• Radoslav Goldman, Nathan Edwards
• Georgetown University Medical Center

2
The challenge

• Identify glycopeptides in large-scale tandem
  mass-spectrometry datasets
• Many glycopeptide enriched fractions
• Many tandem mass-spectra / fraction
• Good, but not great, instrumentation
• QStar Elite CID, good MS1/MS2 resolution
• Strive for hypothesis-generating analysis
• Site-specific glycopeptide characterization
• Glycoform occupancy in differentiated samples

3
Observations

• Oxonium ions (204, 366) help distinguish
  glycopeptides from peptides
• but do little to identify the glycopeptide
• Few peptide b/y-ions to identify peptides
• but intact peptide fragments are common
• If the peptide can be guessed, then
• the glycan's mass can be determined

4
Observations
5
Glycopeptide Search Strategy

• Glycan-Peptide to Spectrum Matches
• Multi-Peptide, Multi-Glycan Mass (Single
  Peptide),
• Single Glycan Mass, Single Glycan (Topology)

6
Compromises

• Single protein / Simple protein mixture
• Few peptides to distinguish
• Single N-glycan per peptide
• Subtraction from precursor
• Digest may not resolve site
• Need peptide/glycan fragments to distinguish
• Isobaric peptide-glycan pairs are not resolved
• Need peptide/glycan fragments to distinguish

7
Glycan Databases

• Link putative glycan masses to N-linked glycan
  structures (and organism, etc. )
• Human N-linked GlycomeDB
• Cartoonist structure enumeration
• CFG Mammalian Array (v5.0)
• In-house database (Oxford notation)
• Database(s) provide "biased" search space
• Coverage vs. "Reasonableness"
• Trade off Time, Specificity, Biology

8
Haptoglobin standard

• N-glycosylation motif (NX/ST)
• Site of GluC cleavage

Pompach et al. Journal of Proteome Research 11.3
(2012) 17281740.
9
Haptoglobin standard

• 11 HILIC fractions enriched for glycopeptides
• 11 x LC-MS/MS acquisitions ( 15k spectra)
• 2887/3288 MS/MS spectra have oxonium ion(s)
• 317 have "intact-peptide" fragment ions
• 263 spectra matched to peptide-glycan pairs
• 52 matched single-glycan
• 8 matched multi-peptide
• 27 distinct (mass) glycans on 11 peptides
• Glycans identified on all 4 haptoglobin sites

10
Algorithms Infrastructure

• Glycan databases indexed by composition, mass,
  N-linked, and motif/type
• Formats IUPAC, Linear Code, GlycoCT_condensed
• Implemented GlycomeDB, Cartoonist, CFG Array
• Monosaccharide decomposition of glycan mass
• Böcker et al. Efficient mass decomposition (2005)
• ?2 Goodness-of-fit test for precursor cluster
• Theoretical isotope cluster from composition.
• ICScore based on ?2 -test p-value.

11
False Discovery Rate (FDR)

• How confident can we be in these mass-matches?

12
False Discovery Rate (FDR)

• How confident can we be in these mass-matches?
  FDR 3.9 10 / 263 spectra

13
False Discovery Rate (FDR)

• How confident can we be in these mass-matches?
  FDR 3.9 10 / 263 spectra
• Estimate the number of errors by searching with
  non-N-linked motif (decoy) peptides too.
• Count spectra matched to decoy peptide-glycan
  pairs.
• Rescale decoy counts to balance the number of
  motif and non-motif peptides.

14
Tuning the filters

• Adjusting thresholds and parameters to
• Increase specificity (lower FDR, fewer spectra),
  or
• Increase sensitivity (more spectra, higher FDR)

15
Tuning the filters

• Oxonium ions
• Number intensity
• Match tolerance
• "Intact-peptide" fragments
• Number intensity
• Match tolerance
• Glycan composition
• ICScore
• Constrain search space
• Match tolerance

• Glycan database
• Constrain search space
• Match tolerance
• Precursor ion
• Non-monoisotopic selection
• Sodium adducts
• Charge state
• Peptide search space
• Semi-specific peptides
• Non-specific peptides
• Peptide MW range
• Variable modifications

16
Tuning the filters
17
Tuning the filters
18
GlycoPeptideSearch (GPS) 1.3

• Freely available implementation
• Windows, Linux
• Reads open-format spectra (mzXML, MGF)
• Pre-indexed Glycan databases
• Human Mammalian GlycomeDB
• Mammalian CFG Array (v5.0)
• User-Named (Oxford notation)
• In silico digest and N-linked motif
  identification
• Automatic target/decoy analysis for FDR
• http//edwardslab.bmcb.georgetown.edu/GPS

19
Where to from here?

• Demonstrate utility on new instrument platforms,
  proteins, samples
• Develop a scoring model for fragments
• Re-implement Cartoonist demerits
• Exploit relationships between
• MS2 spectra, MSn spectra
• Explore application to
• O-glycopeptides, N-glycans, O-glycans

20
Acknowledgements

• Edwards Lab (Georgetown)
• Kevin Brown Chandler NSF (Poster 32)
• Goldman Lab (Georgetown)
• Radoslav Goldman (Poster 6)
• Petr Pompach
• Miloslav Sanda (Poster 23)
• Marshal Bern (Xerox PARC)
• Cartoonist, Peptoonist
• Rene Ranzinger (CCRC)
• GlycomeDB