1. Eat Raw & Fresh: Introducing isotopic Mass-to-charge Ratio and Envelope Fingerprinting (iMEF) and ProteinGoggle for Protein Database Search
Zhixin(Michael) Tian
CNCP
11/15/2012

2. What is mass? Monoisotopic mass (m/z, z=+1)
L. C. Dias, et al. J. Org. Chem. 2012, 77, 4046.

3. (13C/12C ratio’s variability)
Missing monoisotopic mass in protein
Monoisotopic mass
: most significant & accurate
Mass of the most abundant isotope
Error: ±1 Da or more
(mis-assignment of # of contributing heavy isotopes )
Average mass: Error: ±1 u at 16,000 u
(13C/12C ratio’s variability)
Monoisotopic mass (12C, 1H, 14N, 16O, 32S)
Average mass (average of isotopic peak masses weighted by abundance)
The increased probability for multiple heavy isotopes as the mass of a molecule increases causes a decrease in the relative abundance of the monoisotopic peak.
The observation of the monoisotopic peak is unlikely for molecules larger than 15 KDa.

4. Deisotoping (Deconvolution)
Algorithms: AID-MS, ESI-ISOCONV, LASSO, MapQuant, MasSPIKE, MATCHING, msInspect, Peplist, quadratic deisotoping, RAPID, THRASH, Wang’s method,
Zhang’s program, and ZSCORE
Steps:
Calculate background noise level
Determine charge state using FT/Patterson technique
Calculate theoretical profile
Fit with observed isotopic profile
Monoisotopic mass
Search Engines: ProSightPC, SEQUEST, Mascot, X!Tandem, InsPecT, OMSSA, Andromeda, pFind
2. C. D. Wenger, M. T. Boyne, J. T. Ferguson, D. E. Robinson, N. L. Kelleher, Versatile Online-Offline Engine for Automated Acquisition of High-Resolution Tandem Mass Spectra. Anal Chem 80, 8055 (Nov 1, 2008).
3. J. K. Eng, A. L. Mccormack, J. R. Yates, An Approach to Correlate Tandem Mass-Spectral Data of Peptides with Amino-Acid-Sequences in a Protein Database. J Am Soc Mass Spectr 5, 976 (Nov, 1994).
4. D. N. Perkins, D. J. C. Pappin, D. M. Creasy, J. S. Cottrell, Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551 (Dec, 1999).
5. S. Tanner et al., InsPecT: Identification of posttransiationally modified peptides from tandem mass spectra. Anal Chem 77, 4626 (Jul 15, 2005).
6. L. Y. Geer et al., Open mass spectrometry search algorithm. J Proteome Res 3, 958 (Sep-Oct, 2004).
7. J. Cox et al., Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment. J Proteome Res 10, 1794 (Apr, 2011).
8. D. Q. Li et al., pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry. Bioinformatics 21, 3049 (Jul 1, 2005).

5. Peptide Mass Fingerprinting (PMF)
Protein Database
RAW File
Input
MS Spectrum
(iE)
MS/MS Spectra
(iE)
A1/P1
A1/P2
A2/P3
Search Engine
Parent
(Theo. mass)
Parent
(Exp. mass)
A2/P4
Fragments
(Theo. mass)
Fragments
(Exp. mass)
Candidates
Output
Final IDs
Initial IDs

6. Ubiquitin - MS spectrum (profile)

7. Ubiquitin – MS/MS (ETD) Spectrum (Profile)

8. Database search with PMF using ProSightPC
NMFs = 92
NUMFs = 219
P score = 4.86E-98

9. Definition of P_Score
f - the total number of observed fragments (NMFs + NUMFs);
n - the number of matching fragments (NMFs).
x - the mean probability that a mass of an observed fragment ion will randomly match one from a generic protein
the mass of the average amino acid, weighted for its occurrence in proteins;
2 - the number of fragment ions generated from each bond cleavage, which is assumed to be 2 (b- and y-type ions or c-and z•-type ions);
Ma - the mass accuracy (a Ma of ±1 Da translates to a 2 Da window).
Neil L. Kelleher, et al. Nat. Biotechnol. 2001, 19, 952

10. Is “MFs” really good? ?

11. Is “NUMFs” really good? RAPID (28+49=77) THRASH (92+219=311)
PeakPicking:
SNRThreshold = 3.0
BackgroundRatio = 5.0
FitType = Lorentzian
DeconvPep:
MaxCharge = 25
ThScore = 0.0
AdvDeconv:
MaxAbundancePeak = 3
ScanNoModifier = 0
MaxMissPeak = 3
MassErr = 1.0E-05
ThClustExt = 0.0
IntsRangeErr = 0.5
Better “deisotoping”?
NO “deisotoping”?

12. What is a mass spectrum?
MS of Ubiquitin

13. The nature of the iE of an ion
x, y coordinates
Profile
Exp.
m/z
Abundance
6061
21811
52841
82342
93523
96019
75857
60680
42420
27294
14752
5685
1120
919
316
147
Centroid

14. What are in a protein database?
MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG
x, y coordinates
Exp.
m/z
Abundance
3.95
18.83
45.88
76.13
96.65
100.00
87.76
67.12
45.63
27.99
15.67
8.09
3.87
1.73
0.73
0.29
C378H630N105O118S1
Centroid

15. iMEF(isotopic m/z & Envelope Fingerprinting)
Protein Database
RAW File
Input
A2/P3
A2/P4
Parent
(Theo. mass)
Fragments
Parent
(Theo. iE)
Fragments
A/P1
A/P2
MS Spectrum
(iE)
MS/MS Spectra
(iE)
A1/P1
Parent
(Exp. mass)
Fragments
A1/P2
Search
Candidates
Output
Final IDs
Initial IDs

16. Top-down Screening – MS/MS2 ( Targeted Screening - MS2)
1st isotopic peak DB
A1/F1
Parent ion exp. iE
Parent ion theo. iE
A2 F2
Protein candidates
Fragment ion exp. iEs
Fragment ion theo. iEs
A2/F3
Preliminary protein IDs
2nd isotopic peak Y
3rd isotopic peak
Initial protein ID
NMFs PTM_Scores
Initial protein IDs
Final IDs
Remove
duplicates
Isotopic peak
exclusion list
Norm.
isotopic peaks
removed N
Combined initial protein IDs
Preliminary protein candidates N
Top-down Screening – MS/MS2
( Targeted Screening - MS2) N
iMEF = iMF (A1) + iEF (A2) Y Y Y N

17. Pre-Step 1: Customized database
MS Precursor ions
MS/MS fragment ions

18. Pre-Step 2: Noise level determination

19. Ubiquitin - MS spectrum (profile)

20. Ubiquitin – MS/MS (HCD) spectrum (profile)

21. Step 1: Profile to centroid (MS & MS2)

22. isolation window (±3 m/z units)
Step 2: iMF of precursor ion candidates
(4 ppm)
Top-down Screening
IPMD  15 ppm
isolation window (±3 m/z units)
… … … … … …

23. Step 3: iEF of precursor ion candidates
IPACO  5%
IPMD  15ppm
IPAD  30%

24. Targeted Screening IPMD  10 ppm
Step 4: iMF of fragment ion candidates
Targeted Screening
IPMD  10 ppm
(5 ppm)
C1;MAX_MZ= &C2;MAX_MZ= &C3;MAX_MZ= &C4;MAX_MZ= &C5;MAX_MZ= &C6;MAX_MZ= &C7;…

25. Step 5: iEF of fragment ion candidates
IPACO  5%
IPMD  10ppm
IPAD  50%

26. Exemplary PTM_Score assignment
Human histone H4_S1acK16acK20me2

27. IPMDO=20, IPMDOM=30, IPADO=20, IPADOM=200
ID of ubiquitin from ETD
NMFs = 91
IPACO=10, IPMD=15, IPAD=100
IPMDO=20, IPMDOM=30, IPADO=20, IPADOM=200
NMFs vs. IPACO NMFs vs. IPMD NMFs vs. IPAD

28. Pros and Cons Pros: As-strict-as-you-choose confidence
Strict quality control (QC)
Fine discrimination of close iEs
In-situ unwrapping of overlapped iEs
Cons:
More complex and bigger database
More data points for fingerprinting

29. Pros: As-strict-as-you-choose confidence
Comparison with ProSightPC

30. Layman’s choice of parameters
Default values with statistical significance!

31. Pros: Fine discrimination of close iEsb
b or b
(b6-22-H2O)3+
Exp. m/z
Theo. m/z
IPMD 16 11 -3 13 8 -6 18 -1

32. Pros: In-situ unwrapping of overlapped iEs
The abundance of an overlapped isotopic peak is divided into individual overlapped isotopic envelopes according to the calculated proportional abundance using the experimental abundance and theoretical relative abundance ratios
Proportional partition
k: # of overlapped isotopic peaks
m: # of isotopic peak in each iE
n: # of overlapped iEs

33. Other improvements and utilities
Bi-section method for fast indexing of candidates
LASSO-like approach to untangle overlapped iEs
Additional utilities:
A comprehensive confidence score
False discovery rate (FDR)
Customized ion types to look for new dissociation channels
Customized MODs for the search of new modification or labeled proteins
MS/MS spectrum annotation with matching fragments

34. Conclusions
An as-confident-as-you-choose protein database search algorithm, iMEF, has been created and implemented in the search engine ProteinGoggle
The principle of iMEF with ProteinGoggle is demonstrated with identification of ubiquitin from its tandem mass spectrum using ETD
iMEF as implemented in ProteinGoggle has been able to unwrap complex overlapping isotopic envelopes and confidently provide embedded fragment ions
iMEF could be adapted for peptide and glycan database search with customized databases

35. Acknowledgements DNL2003 Li Li Bo Wang Jing Li Xu Zhao
The KENES. Co. Ltd.
Miao Zhou
Shijin Liu
Bin Yang
Funding:
DICP “Research Start”
China “Youth 1000-talents Theme”

36. Thank you very much!