Protein Sequencing and Identification by Mass Spectrometry

Transcription

1 Protein Sequencing and Identification by Mass Spectrometry

2 Tandem Mass Spectrometry De Novo Peptide Sequencing Spectrum Graph Protein Identification via Database Search Identifying Post Translationally Modified Peptides Spectral Convolution Spectral Alignment

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4 H...-HN-CH-CO-NH-CH-CO-NH-CH-CO- OH R i-1 R i R i+1 N-terminus C-terminus AA residue i-1 AA residue i AA residue i+1

5 Collision Induced Dissociation H + H...-HN-CH-CO... NH-CH-CO-NH-CH-CO- OH R i-1 R i R i+1 Prefix Fragment Suffix Fragment Peptides tend to fragment along the backbone. Fragments can also loose neutral chemical groups like NH 3 and H 2 O.

6 Proteases, e.g. trypsin, break protein into peptides. A Tandem Mass Spectrometer further breaks the peptides down into fragment ions and measures the mass of each piece. Mass Spectrometer accelerates the fragmented ions; heavier ions accelerate slower than lighter ones. Mass Spectrometer measure mass/charge ratio of an ion.

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8 Peptide Mass (D) = 415 Peptide without Mass (D) = 397

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12 Reconstruct peptide from the set of masses of fragment ions (mass-spectrum)

13 b 2 -H 2 O b 3 - NH 3 a b a b 3 HO NH + 3 R O R O R O R H -- N --- C --- C --- N --- C --- C --- N --- C --- C --- N --- C -- COOH H H H H H H H y 3 y 2 y 1 y 3 -H 2 O y 2 - NH 3

14 G V D L K H 2 O K L D V G 57 Da = G 99 Da = V mass 0 The peaks in the mass spectrum: Prefix and Suffix Fragments. Fragments with neutral losses (-H 2 O, -NH 3 ) Noise and missing peaks.

15 G V D L K Peptide MS/MS Identification: Intensity mass 0

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17 GTDIMR MPSERGTDIMRPAKID... PAKID MPSER HPLC To MS/MS protein peptides

18 Matrix-Assisted Laser Desorption/Ionization (MALDI) From lectures by Vineet Bafna (UCSD)

19 Relative Abundance RT: LC Time(min) NL: 1.52E8 BasePeak F: + c Full ms [ ] S#: 1707 RT: AV: 1 NL: 2.41E7 F: + c Full ms [ ] Relative Abundance m/z MS Scan S#: 1708 RT: AV: 1 NL: 5.27E6 T: + c d Full ms [ ] collision Ion MS-1 cell MS-2 Relative Abundance MS/MS Source Scan m/z

20 S e MS/MS instrument S#: 1708 RT: AV: 1 NL: 5.27E6 T: + c d Full ms [ ] q u e n c e Database search Sequest de Novo interpretation Sherenga Relative Abundance m/z

21 Tandem Mass Spectrometry (MS/MS): mainly generates partial N- and C-terminal peptides Spectrum consists of different ion types because peptides can be broken in several places. Chemical noise often complicates the spectrum. Represented in 2-D: mass/charge axis vs. intensity axis

22 S # : R T : AV: 1 N L : E 6 T : + c d F u ll m s [ ] Database Relative Abundance De Novo 3 0 Search m /z Mass, Score Database of known peptides MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT, HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE, ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA, EKLNKAATYIN.. A A L C V V W P G G W Database of all peptides = 20 n AAAAAAAA,AAAAAAAC,AAAAAAAD,AAAAAAAE, AAAAAAAG,AAAAAAAF,AAAAAAAH,AAAAAAI, AVGELTI, AVGELTK, AVGELTL, AVGELTM, YYYYYYYS,YYYYYYYT,YYYYYYYV,YYYYYYYY E E T L L D T R T K K AVGELTK

23 De Novo vs. Database Search: A Paradox The database of all peptides is huge O(20 n ). The database of all known peptides is much smaller O(10 8 ). However, de novo algorithms can be much faster, even though their search space is much larger! A database search scans all peptides in the database of all known peptides search space to find best one. De novo eliminates the need to scan database of all peptides by modeling the problem as a graph search.

24 S#: 1708 RT: AV: 1 NL: 5.27E6 T: + c d Full ms [ ] Relative Abundance m/z Sequence

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26 (cont d)

27 (cont d)

28 How to create vertices (from masses) How to create edges (from mass differences) How to score paths How to find best path

29 b Mass/Charge (M/Z)

30 a Mass/Charge (M/Z)

31 a is an ion type shift in b Mass/Charge (M/Z)

32 y Mass/Charge (M/Z)

33 Mass/Charge (M/Z)

34 Mass/Charge (M/Z)

35 noise Mass/Charge (M/Z)

36 Mass/Charge (M/z)

37 Some Mass Differences between Peaks Correspond to Amino Acids u q e s e u q e n c n e e s e q u e n c e s c e

38 Some masses correspond to fragment ions, others are just random noise Knowing ion types ={δ 1, δ 2,, δ k } lets us distinguish fragment ions from noise We can learn ion types δ i and their probabilities q i by analyzing a large test sample of annotated spectra.

39 ={δ 1, δ 2,, δ k } Ion types {b, b-nh 3, b-h 2 O} correspond to ={0, 17, 18} *Note: In reality the δ value of ion type b is -1 but we will hide it for the sake of simplicity

40 The match between two spectra is the number of masses (peaks) they share (Shared Peak Count or SPC) In practice mass-spectrometrists use the weighted SPC that reflects intensities of the peaks Match between experimental and theoretical spectra is defined similarly

41 Goal: Find a peptide with maximal match between an experimental and theoretical spectrum. Input: S: experimental spectrum : set of possible ion types m: parent mass Output: P: peptide with mass m, whose theoretical spectrum matches the experimental S spectrum the best

42 Masses of potential N-terminal peptides Vertices are generated by reverse shifts corresponding to ion types ={δ 1, δ 2,, δ k } Every N-terminal peptide can generate up to k ions m-δ 1, m-δ 2,, m-δ k Every mass s in an MS/MS spectrum generates k vertices V(s) = {s+δ, s+δ,, s+δ } 1 2 k corresponding to potential N-terminal peptides Vertices of the spectrum graph: {initial vertex} V(s ) V(s )... V(s ) {terminal vertex} 1 2 m

43 Shift in H 2 O Shift in H 2 O+NH 3

44 Two vertices with mass difference corresponding to an amino acid A: Connect with an edge labeled by A Gap edges for di- and tri-peptides

45 Path in the labeled graph spell out amino acid sequences There are many paths, how to find the correct one? We need scoring to evaluate paths

46 p(p,s) = probability that peptide P produces spectrum S= {s,s, s } 1 2 q p(p, s) = the probability that peptide P generates a peak s Scoring = computing probabilities p(p,s) = π sєs p(p, s)

47 For a position t that represents ion type d j : p(p,s t ) = q j, if peak is generated at t 1-q j, otherwise

48 (cont d) For a position t that is not associated with an ion type: q, if peak is generated at t R p (P,s ) = R t 1-q, otherwise R q = the probability of a noisy peak that does R not correspond to any ion type

49 Finding Optimal Paths in the Spectrum Graph For a given MS/MS spectrum S, find a peptide P maximizing p(p,s) over all possible peptides P: p(p',s) = max P p(p,s) Peptides = paths in the spectrum graph P = the optimal path in the spectrum graph

50 Tandem mass spectrometry is characterized by a set of ion types {δ,δ,..,δ } and their 1 2 k probabilities {q,...,q } 1 k δ -ions of a partial peptide are produced i independently with probabilities q i

51 A peptide has all k peaks with probability k i= 1 q i k and no peaks with probability i= 1 (1 q i ) A peptide also produces a ``random noise'' with uniform probability q R in any position.

52 Ratio Test Scoring for Partial Peptides Incorporates premiums for observed ions and penalties for missing ions. Example: for k=4, assume that for a partial peptide P we only see ions δ 1,δ 2,δ 4. The score is calculated as: q q (1 q ) q q q (1 q ) q R R R R

53 T- set of all positions. T i ={t δ1,, t δ2,...,,t δk, }- set of positions that represent ions of partial peptides P i. A peak at position t δj is generated with probability q j. R=T- U T i - set of positions that are not associated with any partial peptides (noise).

54 For a position t δj T i the probability p(t, P,S) that peptide P produces a peak at position t. P( t, P, S ) = q j if a peak is generatedat position t δ j 1 q j otherwise Similarly, for t R, the probability that P produces a random noise peak at t is: P R ( t ) = 1 q R q R if a peak is generated at position t otherwise

55 For a peptide P with n amino acids, the score for the whole peptides is expressed by the following ratio test: = = = n i k j i R i R j j t p S P t p S p S P p 1 1 ) ( ),, ( ) ( ), ( δ δ

56 How can we find the amino acid sequence that best explains the spectrum? F S N A M S D I a) Explains the largest number of peaks V SGQ L I D b) Explains the most intensity Exhaustive enumeration? 1. Generate every possible sequence with the same peptide mass 2. Match each sequence to the spectrum 3. Choose the sequence that explains the most intensity in the spectrum Takes too long!

57 Computer programs for de-novo interpretation of MS/MS spectra date as far back as 1966 when Biemman et al. proposed a prefix extension algorithm: Tries every possible prefix extension Eliminates solutions with missing peaks Outputs every peptide with a matching parent mass ALL prefix peaks must be present in the spectrum In an attempt to include interpretations with missing peaks, Sakurai et al. (1984) proposed: Exhaustive search over the space of all possible permutations of amino acid multisets where the total mass equals the parent mass of the MS/MS spectrum Score peptide/spectrum matches by counting prefix and suffix mass matches Naturally more sensitive but also very slow

58 The second half of the eighties saw a few better designed approaches to this problem, based on the same type of algorithm: Prefix extension, one amino acid at a time. Tolerate missing peaks. Include both prefix and suffix peaks in the score. User-specified maximum number of candidates in memory at any point of the execution. (sub-optimal) Ishikawa and Niwa 86, Siegel and Baumann 88, Johnson and Biemann 89, Zidarov et al. 90

59 In 1990 Bartels introduced a graph representation of an MS/MS spectrum Every peak in the spectrum defines a vertex Vertices connected by an edge if peak mass difference is an amino acid mass v 0 v M(S) Best peptide is defined as the best path between the two endpoint vertices: v to v 0 M(S) No detailed algorithm was given for finding the best peptide; interactive exploration tool was made available.

60 What is the DP recursion? Score(i) = intensity(i) + max( Score(j) ), for all j with mass(i)-mass(j) Amino acid masses Recovered de-novo sequence? ESESE