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 2

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 3

4. Observations 4

5. Glycopeptide Search Strategy Glycan-Peptide to Spectrum Matches Multi-Peptide, Multi-Glycan Mass (Single Peptide), Single Glycan Mass, Single Glycan (Topology) 5

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 6

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 7

8. Haptoglobin (HPT_HUMAN) NLFLNHSE*NATAK MVSHHNLTTGATLINE VVLHPNYSQVDIGLIK Haptoglobin standard 8 N-glycosylation motif (NX/ST) * Site of GluC cleavage Pompach et al. Journal of Proteome Research 11.3 (2012): 1728–1740.

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 9

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) χ 2 Goodness-of-fit test for precursor cluster Theoretical isotope cluster from composition. ICScore based on χ 2 -test p-value. 10

11. False Discovery Rate (FDR) How confident can we be in these mass- matches? 11

12. False Discovery Rate (FDR) How confident can we be in these mass- matches? FDR: 3.9% [ ~ 10 / 263 spectra ] 12

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. 13

14. Tuning the filters… Adjusting thresholds and parameters to Increase specificity (lower FDR, fewer spectra), or Increase sensitivity (more spectra, higher FDR) 14

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 15

16. Tuning the filters… 16

17. Tuning the filters… 17

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 18

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 MS 2 spectra, MS n spectra Explore application to O-glycopeptides, N-glycans, O-glycans 19

20. 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 Acknowledgements 20