MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.1

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1 MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples Parminder Kaur Bogdan Budnik, Konstantin Aizikov and Peter B. O Connor, Department of Electrical and Computer Engineering, Boston University Cardiovascular Proteomics Center, Boston University School of Medicine Mass Spectrometry Resource, Department of Biochemistry, Boston University School of Medicine Department of Bioinformatics, Boston University MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.1

2 Introduction Goal - Reducing complex mass spectra into monoisotopic mass lists Noise Baseline Modelling Isotopic Distribution (ID) Identification Charge State Determination Picking Experimental Isotopic Peaks Alignment of a Theoretical Isotopic Distribution (TID) with the Experimental Isotopic Distribution (EID) Generating the Monoisotopic Mass List Matching Observed Masses against Theoretical Fragment Masses from Given Sequence MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.2

3 Noise Baseline Modelling Baseline of a top down spectrum of bovine carbonic anhydrase (blue), noise mean vs m/z (white) Model based on the mean of the signal across m/z range MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.3

4 Isotopic Distribution (ID) Identification (a) (b) (a) Top down spectrum of BCA with red and green lines indicating start and end of IDs (b) Zoomed-in view Isotopic distribution identification uses (default) S/N=3 as a trigger threshold MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.4

5 Charge State Determination Isotopic Distributions obtained from previous step are passed as input for z determination Two new methods Maximum Likelihood (ML) method using Fourier Transform (FT) of EID Matched Filter Approach MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.5

6 ML method using FT of EID An EID is composed of complex exponentials with fundamental frequency corresponding to the charge state and its harmonics Peak locations are used to identify the charge state MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.6

7 Matched Filter (MF) Approach Parameters for generating TID (peak width, inter-point spacing, MAX Z, MIN Z) are based upon the data The TID (represented by T(Z) for charge state Z) that gives maximum value of cross-correlation coefficient with EID (E) generally represents the true charge state (1) arg max (2)!! (3) est arg max (4) MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.7

8 ! Typical MF Match (a) Raw Spectrum (b) Z=3 (c) Output List (a) EID of a fragment of BCA (b) TID with Z=3 (red) and EID (blue) TID shift corresponds to maximum value of cross-correlation coefficient (0.954) between the two (c) Snapshot of output listing corresponding to above fragment ( ) MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.8

9 Automated Comparison of Charge State Determination Methods Results using 775 isotopic distributions from myoglobin using 26 spectra with charge states ranging from 8-22 and from S/N of 1-100, comparison of different methods MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.9

10 Advantages of MF over ML Results are better 91%(MF) vs 88%(ML) Allows for pulling out EIDs from the observed signal even when signal contains multiple distributions Works better in case of overlapping distributions Since ML method uses FT map, it works better for higher z than for lower z, while MF works equally well for both cases MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.10

11 Picking Isotopic Peaks and ML alignment (a) (b) (c) (d) (e) (f) (a) Picking isotopic peaks of EID of myoglobin, Z=16 (b) TID of myoglobin, Alignment with (c) TID shifted by 5 (d) TID shifted by 6 (e) TID shifted by 7 (f) Probability of alignment as a function of TID indices MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.11

12 Testing ML Alignment with Low Ion Numbers (a) (b) (a) Alignment of myoglobin IDs using 3150 simulations (100 ions in each simulation) (b) A typical 100 ion distribution of myoglobin (5) index arg (6) where =Length of E (7) MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.12

13 Separating Overlapping Distributions (a) Raw Spectrum (b) Z=3, r=0.74 (c) Z=4, r=0.64 (d) Residual Signal MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.13

14 Low Charge State Overlapping Distributions from Top-Down Spectrum of BCA (a) Raw Spectrum (b) Z=4 (c) Residual MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.14

15 (d) Z=1 (e) Z=3 (f) Residual MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.15

16 Analysis of top-down spectrum of Ubch10 - Mixed Z Cases (a) Input Signal (b) Z=14, r=0.76 (c) Residual MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.16

17 (d) Z=14, r=0.74 (e) Residual (f) Z=1, r=0.5 MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.17

18 (g) Z=2, r=0.51 (h) Z=14, r=0.57 (i) Residual MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.18

19 (j) Z=1, r=0.54 (k) Z=2, r=0.5 (l) Z=14, r=0.58 MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.19

20 (m) Residual (n) Z=1,r=0.55 (o) Z=14,r=0.55 MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.20

21 (p) Final Residual Applying MasSPIKE to a particular noisy region of a top-down mass spectrum of a biologically derived protein Ubch10 (a) Input signal (b) z=14 detected (c) Residual after subtraction of (b) from (a) (d) z=14 detected in region m/z= (e) Residual signal (f), (g) & (h) z=1, 2 and 14 detected simultaneously (z=1 and 2 are probably false positives due to chemical noise) (i) Residual signal after subtraction of signal due to already determined charge states (j), (k) & (l) z=1, 2 and 14 detected simultaneously again, sharing three common peaks (m) Remaining signal (n) & (o) z=1 and 14 being detected (p) Final residual. Overall, 10 isotopic distributions were recovered in an 8 m/z window MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.21

22 Mass Spectrum of Hemoglobin of a normal person MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.22

23 Hemoglobin Variants Analysis Spectrum of Hemoglobin variants and comparison between theoretical and experimental masses MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.23

24 Conclusions Matched Filter method works best for charge state determination, helps in resolving overlapping distributions Maximum likelihood based alignment improves the accuracy of monoisotopic masses MasSPIKE has been tested against analysis of complex spectra from biologically derived proteins Once fully implemented in BUDA[5], MasSPIKE will substantially simplify interpretation of mass spectra MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.24

25 Acknowledgments Prof W. Clem Karl Dr Hua Huang Jason J. Cournoyer Raman Mathur Dr Roger Theberge Dr Mark McComb Prof Catherine E. Costello Dr David Perlman Dr Amit Juneja Dr Judith Jebanathirajah Dr Cheng Zhao Dr Cheng Lin Vera Ivleva Dr Jason Pittman Prof Richard Cohen This work was supported in part by Federal funds from the National Center for Research Resources under grant No. P41-RR10888 and the National Heart, Lung, and Blood Institute under Contract No. HHSN C. MasSPIKE (Mass SPectrum Interpretation and Kernel Extraction) for Biological Samples p.25

26 References [1] M W Senko; S C Beu; F W McLafferty, Automated Assignment of Charge States from Resolved Isotopic Peaks for Multiply Charged Ions, J. Am. Soc. Mass Spectrom.; 1995; 6, [2] A L Rockwood, Ultrahigh-Speed Calculation of Isotope Distributions, Anal Chem; 1996; 68; [3] D M Horn; R A Zubarev; F W McLafferty, Automated Reduction and Interpretation of High Resolution Electrospray Mass Spectra of Large Molecules, J. Am. Soc. Mass Spectrom.; 2000; 11; [4] P Kaur; P B O Connor, Use of Statistical Methods for Estimation of Total Number of Charges in a Mass Spectrometry Experiment, Anal Chem; 2004; 76; [5] P B O Connor, BUDA - Boston University Data Analysis