1. Constructing high resolution consensus spectra for a peptide library
Sergey L. Sheetlin,
Yuri A. Mirokhin, Dmitrii V. Tchekhovskoi, Xiaoyu Yang, Stephen E. Stein
NIST Mass Spectrometry Data Center, Biomolecular Measurement Division,
National Institute of Standards and Technology

2. Tandem mass spectrometry
Sample
Ionization
m/z ion sorting
MS1 precursor ion
CID fragmentation
m/z ion sorting
MS2 product ions
Detection (measuring m/z)
m/z: mass-to-charge ratio
CID: Collision-Induced Dissociation
MS: Mass Spectrum

3. Example of chromatogram (using Thermo Xcaliber Qual Browser)
Relative abundance
Time (min)

4. Example of MS1 spectrum (using Thermo Xcaliber Qual Browser)
Relative abundance

5. Example of high resolution MS2 spectrum (shown with NIST MS search program)
Relative abundance

6. Peptides Glu-Thr-Lys ETK Glutamylthreonyllysine C15H28N4O7
Short chains of amino acids connected by amide bonds
Glu-Thr-Lys
ETK
Glutamylthreonyllysine
C15H28N4O7

7. Peptide libraries
NIST MS Search libraries of peptide spectra are available at

8. Nomenclature for peptide CID fragment ions
R R Rn Rn
| | | |
H2N-CH-CO-NH-CH-CO- …...-NH-CH-CO-NH-CH-CO2H
I. A. Papayannopoulos. The interpretation of collision-induced dissociation mass spectra of peptides. Mass Spectrometry Review, 14:49–73, 1995.

9. Computation of fragment masses

10. Peptides monoisotopic masses
20 amino acids residues and their monoisotopic masses
Ala
Arg
Asn
Asp
Cys
Glu
Gln
Gly
His
Ile A R N D C E Q G H I
Leu
Lys
Met
Phe
Pro
Ser
Thr
Trp
Tyr
Val L K M F P S T W Y V
87.032

11. Protein modifications for mass spectrometry
In vivo Posttranslational modifications (PTM) are covalent modifications of proteins after its translation. PTMs play role in activity, function of proteins and their interaction with other molecules.
Modifications caused by sample preparation.
Modification name
Composition
Monoisotopic mass
Carbamidomethyl
C2H3NO
iTRAQ4plex
H12C413C3N15NO
Oxidation O
Deamidation
H-1N-1O
Phosphorylation
HO3P
Glu->pyro-Glu
H-2O-1
Gln->pyro-Glu
H-3N-1
Protein modifications for mass spectrometry

12. Fragments neutral losses
Common losses are H2O, NH3, CO, H3PO4,iTRAQ (H12C413C3N15NO).
Relative abundance

13. Peptide “GHVIAAR”; charge 2; modification iTRAQ4plex on the first AA ‘G’
Relative abundance

14. Experimental and theoretical isotopic peaks
Relative abundance
Valkenborg, D., Mertens, I., Lemiere, F., Witters, E., Burzykowski, T.: The isotopic distribution conundrum. Mass Spectrom. Rev. 31(1), 96–109 (2012)

15. Annotation of peaks of experimental spectra

16. Experimental and theoretical densities
Probability density function

17. Experimental and theoretical densities
Probability density function

18. Experimental and theoretical densities
Probability density function

19. Error of annotation of experimental peaks
Probability density function

20. Statistics of different types of fragment ions (based on limited set of data)

21. Clustering experimental spectra
Set of unidentified MS2 spectra
Identification (peptide sequencing)
Grouping results by charge, peptide, modifications, collision energy
Filtering
Clusters of replicate spectra

22. Peptide sequencing algorithms
Database search: comparing theoretical spectra for sequences from a database with the query spectrum
MS-GF+, Mascot etc.
De novo sequencing: trying to find a peptide optimal in terms of some measure of similarity between its theoretical spectrum and the query spectrum
PEAKS, NovoHMM etc.
Library search: direct comparison of the query spectrum with identified spectra from a library
NIST Mass Spectral Library etc.

23. Example of experimental spectra from the same cluster
Relative abundance

24. Computing peaks of consensus spectra
MS-GF+
𝑑 1
𝑋 1
𝑑 2
𝑋 2
𝑑 3
Replicate spectra
𝑋 3
𝑑 5
𝑋 5
Consensus

25. Computing peaks of consensus spectra

26. Example of log-likelihood

27. Average fraction of replicates
Hypothesis: “good” peaks of the consensus spectrum have properties similar to annotated peaks

28. Filtering peaks of consensus spectra
Replicate number
Is there a peak in the replicate corresponding to the given consensus peak with abundance A? 1
Yes 2 …
N-1 No N
Bernoulli distribution:
Yes with probability p;
No with probability 1-p

29. Density of peaks for different relative abundancies

30. Comparison of consensus and best replicate spectra

31. Further directions
Adjusting the parameters of the method for optimal performance of the existing search algorithms
Building peptide libraries of consensus spectra

32. Acknowledgements NIST MS Data Center Yuri A. Mirokhin
Dmitrii V. Tchekhovskoi
Xiaoyu Yang
Stephen E. Stein
William E. Wallace