ASCQ_ME: a new engine for peptide mass fingerprint directly from mass spectrum without mass list extraction

Transcription

1 ASCQ_ME: a new engine for peptide mass fingerprint directly from mass spectrum without mass list extraction Jean-Charles BOISSON1, Laetitia JOURDAN1, El-Ghazali TALBI1, Cécile CREN-OLIVE2 et Christian ROLANDO2 Université des Sciences et Technologies de Lille 1 LIFL, Laboratoire d Informatique Fondamentale de Lille, UMR COM, Chimie Organique et Macromoléculaire, UMR CNRS 8009

2 Outline Actual identification methods (MS level). Characteristics of our approach. Global scheme. Digestion algorithm. Spectrum simulation (Fast Fourier Transform). Scoring. The ASCQ_ME application. Performances. Conclusions and perspectives. 2

3 The protein identification (MS level) Data : MS spectrum list of mass/intensity peaks. Mono isotopic peaks extraction phase : Proprietary application. For the most interesting samples human intervention is still required Time consuming Potential risk of information lose. Identification with different scoring methods : Mascot Sequest ProteinProspector and lately correlation of the 3 scoring methods using a metascoring algorithm 3

4 Characteristics of our approach Direct interrogation from the MS spectrum Suppression of the mono isotopic extraction step. Identification by correlating the experimental spectrum with the theoretical one. Complete algorithm no external source code. Sources and algorithm proofs available. OPEN SOURCE 4

5 Global scheme Protein databases (FASTA format) Digestion Simulated spectrum Isotopic distribution computation Experimental spectrum Set of peptides (chemical formula) Scoring 5

6 Digestion phase Development of a linear iterative algorithm. time only proportional to the protein size. Generic algorithm no limitations for the configuration parameters (number of misscleavage, enzyme used, ). Dynamic grammar No limitation on the complexity of rules Detection of consensus sequence (fixed or variation) The cleavage may be triggered by the amino acid composition after or before the cleavage site Proof of the completeness of the digestion tree available. 6

7 Example of digestion tree 7

8 Example of digestion Cytochrome C bovine : 0 miss-cleavage 21 peptides. 1 miss-cleavage 41 peptides. 2 miss-cleavage 66 peptides. 10 miss-cleavage 176 peptides. Titine ( amino acids): 0 miss-cleavage peptides (1 s). 10 miss-cleavage peptides (15 s). 20 miss-cleavage peptides (45 s). Max miss-cleavage peptides (2h). 8

9 The simulated spectra generator Based on the algorithm proposed by A.L. Rockwood (1995) for the computation of isotopic distribution. Use of Fourier transform. Spectrum generation from the isotopic distribution of each peptide. Exact algorithm whatever the number of atoms. No combinatorial explosion. The mass of the monositopic peak comes from addition, multiplication of the atomic mass, the isotope peak distribution and mass from the algorithm. The right size for the FFT is approximately the size of the original MS spectrum. 9

10 Generation of the simulated spectra Each atom has its basic isotopic distribution (example : Cn, Hm, ). passage in the Fourier (frequency) space. atom quantity multiplications with the atom basic isotopic distribution. return to the Euclidian (mass) space. multiplication with the isotopic distribution already computed for the current peptide. 10

11 A basic example : the Cl2 distribution Fourier Transform atom quantity multiplication (here 2) Inverse Fourier Transform 11

12 Scoring Study of the correlation between the theoretical peptides and the experimental spectrum. Filtering to know which peptides are useful for the identification. Visual representation for an easy interpretation of the results. 12

13 Scoring : explanations Based on the convolution of the two spectra by scalar multiplication of the two vectors. Convolution made peptide by peptide partial score of each peptide. First version: a naïve scoring with fixed threshold for determining the significant peaks. 13

14 The ASCQ_ME application Combination of the digestion algorithm and the scoring. Entirely governed by a text-only configuration file (20 parameters for the first version). On the web soon : Site for online identification requests. Download of the complete sources and the different libraries composing ASQC_ME. 14

15 Example of configuration file 15

16 Results example (1/5) (score 1st version) Full spectrum display MS spectrum of Cytochrome C bovine (MALDI TOF) Simulated spectrum Correlation spectrum 16

17 Results example (2/5) (score 1st version) significant peptide MS spectrum of Cytochrome C bovine (MALDI TOF) Simulated spectrum Correlation spectrum 17

18 Results example (3/5) (score 1st version) peptides mix MS spectrum of Cytochrome C bovine (MALDI TOF) Simulated spectrum Correlation spectrum 18

19 Results example(4/5) (score 1st version) peptide in the noise MS spectrum of Cytochrome C bovine (MALDI TOF) Simulated spectrum Correlation spectrum 19

20 Results example (5/5) (score 1st version) Peptide participation in the scoring 20

21 Scoring : second version Non significant peptides are due to the scoring with the noise in the experimental spectrum But the implementation of a dynamic threshold is not obvious, as for noisy spectra the distinction is not so clear. Incorporation of threshold based on peak shape detection the experimental and the calculated spectra intensity variation must be in a given ratio (1/3 to 3) or the peak scoring is rejected. 21

22 Results example (1/4) (score 2nd version) Full spectrum (native human Apo AI) MS spectrum of Apo AI human (MALDI TOF) Simulated spectrum Correlation spectrum 22

23 Results example (2/4) (score 2nd version) significant peptide MS spectrum of Apo AI human (MALDI TOF) Simulated spectrum Correlation spectrum 23

24 Results example(3/4) (score 2st version) peptide in the noise MS spectrum of Apo AI human (MALDI TOF) Simulated spectrum Correlation spectrum 24

25 Results example (4/4) (score 2nd version) peptide participation in the scoring 25

26 Result file for user viewing (1/2) 26

27 Result file for user viewing (2/2) 27

28 ASCQ_ME Performances First release : Computation of digestion and isotopic distribution performed for each protein (most time consuming task). average 1 second per protein (mono processor machine Xeon 2 GHz, 2 Go memory). MS spectrum of cytochrome C, Swissprot databank (august 2005), 10 miss-cleavages maximum for the digestion, filter BOVINE (average 1600 proteins). 28 min for the identification. 28

29 Conclusions and perspectives (1/2) New approach for the protein identification Based only on the MS spectrum. Without mono isotopic peaks extraction. Digestion algorithm based on a formal proof. Dynamic grammar including consensus sequence detection. All the algorithm including post-translational modifications is based on chemical formula (and not on numerical mass). Spectrum simulation with isotopic distribution. Shape recognition in the scoring for detecting only significant peaks and not noise. 29

30 Conclusions and perspectives (2/2) Optimization of the algorithm speed by generating a peptide library. Scoring optimization to eliminate calibration errors. Adaptation of the algorithm to MS/MS data. Implementation of statistics tools in order to validate the results. More realistic simulated spectrum by including a factor response according to the nature of the peptide (hydrophobic, basic). 30

31 Questions? Thank you for your attention 31