1. 2015.06.19 김지형

2. Introduction precursor peptides are dynamically selected for fragmentation with exclusion to prevent repetitive acquisition of MS/MS spectra for the same peptide. Increase the throughput of proteomic experiments Incurs fragmentation of peptide ions having weak intensities Wrong interpretation the portion of wrong interpretation of precursor ion mass is up to ∼ 40%. Overlapping isotopic clusters are often observed with complex proteome samples and resulted in wrong interpretation of their masses

3. Introduction Determining isotopic clusters and their monoisotopic masses is the first step in interpreting complex mass spectra generated by high- resolution mass spectrometers Fast, automated and accurate interpretation of the vastly large amount of MS data a fundamental and critical step in MS-based proteomic experiments remains the subject of much research activity.

4. Introduction Mann et al. : suggested a deconvolution algorithm to find charge states. Senko et al. : introduced a notion of an “average” amino acid called averagine suggested a computational method for determination of monoisotopic masses using it. Zscore : a fast and automated isotopic cluster identification algorithm based on a charge scoring scheme. ESI-ISOCONV MATCHING PepList LASSO AID-MS THRASH

5. Introduction THRASH Most widely used algorithms Employs the Fourier transform/Patterson method for charge determination and least-squares fitting to compare a peak cluster with an averagine isotopic distribution. Cons : the use of least-squares fitting and/or averagine isotopic distribution often leads to an inaccurate monoisotopic mass that is 1-2 Da different from the correct value

6. Methods 1.Present a probabilistic model of isotopic distribution of a polypeptide 2.Describe approximations of intensity ratio and intensity ratio product functions Intensity ratio function  Intensity ratio of two adjacent peaks Intensity ratio products function  Intensity ratio products of three adjacent peaks 3.Isotopic clusters and mono-isotopic masses was determined from suggested algorithm

7. Methods 1.Isotopic Distribution Model Notations A ={C,H,N,O,S} Set of atoms that compose a polypeptide X a : the +a isotope of an atom X ( for each atom X ∈ A ) +a(1,2,4) isotope P Xa = Existential probability For example P C 1 = 0.00107  1.107 % n x = the number of atom X in the polypeptide : elemental composition of a polypeptide

8. Methods 1.Isotopic Distribution Model Notations Isotopic Distribution of a polypeptide : the theoretical masses and intensities of the peaks generated by all instances of the polypeptide I k : the intensity of the kth peak in an isotopic distribution (k>=0) I 0 : the intensity of the monoisotopic peak I k (k >= 1) : the intensity of the peak whose mass difference from the monoisotopic peak is k. I0I0 IkIk

9. Methods 1.Isotopic Distribution Model Lemma1 The intensity I k in an isotopic distributions approximates to where

10. Methods 1.Isotopic Distribution Model Lemma1 We can compute I k by the coefficient of x k in the expansion of the following polynomial Intensity I k in an isotopic distribution of a polypeptide is regarded as the sum of existential probabilities of all polypeptide instances with mass difference k

11. Methods 1.Isotopic Distribution Model Lemma1 Intensity I 0 the probability of there being no isotopes in the constant term of polynomial P(x)

12. Methods 1.Isotopic Distribution Model Lemma1 Intensity I 1 the probability of there being only one +1 isotope coefficient of x in P(x)

13. Methods

16. 1.Isotopic Distribution Model Lemma1 The intensity I k in an isotopic distributions approximates to where

17. Methods

21. 2. Ratio Function and Ratio Product Functions Theorem 1 I k+1 /I k = cm + b Sampled about 100,000 tryptic peptides of 400 Da to 5,200 Da generated from UniProt DB 8.0 and computed the ratio I k+1 /I k for each peptide

22. Methods 2. Ratio Function and Ratio Product Functions Theorem 1 I k+1 /I k = cm + b The reason for choosing the threshold 1800 : A polypeptide within 1800 Da has the first and most abundant peak as its monoisotopic peak

23. Methods

24. RP max (k,m) RP min (k,m) RP avg (k,m)

25. Methods 3. Algorithm Overview 1)Peak picking 2)Pseudocluster identification 3)Isotopic cluster identification and monoisotopic mass determination 4)Duplicate cluster removal

26. Methods 3. Algorithm Overview 1)Peak picking Remove noise and select relatively high intensity peaks from raw spectrum In our experiment, we used the peak picking algorithm of Decon2LS

27. Methods 3. Algorithm Overview 2)Pseudocluster identification Identifying pseudoclusters by scanning the selected peaks from low m/z to high m/z Finding all the pseudoclusters starting at all peaks first find pseudoclusters with a charge state 1+ and find the other pseudoclusters with higher charge states. Let X denote the m/z of current peak. Then the range of the next peak’s m/z will be [ X+(D-E)/C … X+(D+E)/C ] D : estimated mass difference between two adjacent peaks in an isotopic cluseter E : the error bound

28. Methods

29. 3. Algorithm Overview 3)Isotopic cluster identification and monoisotopic mass determination Monoisotopic Mass Calculation m : monoisotopic mass (most abundant peak in the pseudocluster) the most abundant peak : qth peak in the pseudocluster p : the number of missing peaks Score Calculation (score of pseudocluster) n : the number of peaks in the pseudocluster

30. Methods 3. Algorithm Overview 3)Isotopic cluster identification and monoisotopic mass determination Score Calculation (score of pseudocluster) Score about I k I k+2 / I k+1 2 Score about I k+1 / I k

31. Methods

32. R min < I' k+1 /I' k < R max  scoreR(k,m,p) > 0

33. Methods R min < I' k+1 /I' k < R max  scoreRP(k,m,p) > 0

34. 3. Algorithm Overview 3)Isotopic cluster identification and monoisotopic mass determination Methods Score ≤ 00< The pseudocluster is selected and becomes an isotopic cluster discarded

35. 3. Algorithm Overview 4) Duplicate cluster removal Because this algorithm consider all possible pseudoclusters, many pseudoclusters can be generated from a single isotopic cluster. Remove one of duplicate clusters as follows. Methods Most abundant peak same remove Charge state smaller remove lower same score remove lower

36. Results and Discussion Three programs were compared : RAPID, ICR2LS, Decon2LS Count the number of identified isotope clusters of known peptides whose amino acid sequences were identified by MS/MS It is difficult to pick out the isotopic clusters of known peptides because the MS data can contain many peptides whose monoisotopic clusters contain many peptides whose monoisotopic masses are very similar.

37. Results and Discussion So use the following method For each known peptide, find isotopic clusters of this peptide at the MS scan where this peptide was identified by MS/MS. If an isotopic cluster has the monoisotopic mass within a mass tolerance of 10 ppm, consider it a potentially correct isotopic cluster. Also look for peptide in adjacent scans. If no isotopic cluster is found within any of 10 consecutive scans, the cluster is discard

38. The number of isotopic clusters of 494 known peptides Results and Discussion new method10588 Decon2LS10104 ICR2LS9577

39. Results and Discussion

40. Reasons for different search results Some clusters are inherently ambiguous and each program can make different judgments. THRASH based algorithm  1~2 Da errors When the position of the most abundant peak is different from that of averagine

41. Results and Discussion

42. Identification of Overlapping Clusters there are many overlapping clusters  Hundreds of isotopic clusters crowded into a narrow range

43. Results and Discussion Clusters without sharing peaks were identified by all programs.

44. Results and Discussion Clusters with sharing peaks (blue) were only identified by paper’s method.

45. Results and Discussion Execution Time

46. Results and Discussion

47. Conclusion New probabilistic model & algorithm for determining isotopic distributions and monoisotopic masses Suggested algorithm found more isotopic clusters of identified peptides and Successfully resolved 1-2Da mismatch problem. Suggested algorithm Identified overlapping clusters well. Suggested algorithm was Faster than other algorithms.