Mass spectrometry in proteomics

Transcription

1 I519 Introduction to Bioinformatics, Fall, 2013 Mass spectrometry in proteomics Haixu Tang School of Informatics and Computing Indiana University, Bloomington Modified from:

2 Outline Proteomics & Mass spectrometry Application of MS/MS in proteomics Protein sequencing and identification by mass spectrometry Protein Identification via Database Search (SPC & spectral alignment) De Novo Peptide Sequencing (Spectrum graph) Hybrid Identifying Post Translationally Modified (PTM) Peptides (Quantitative proteomics) identifying proteins that are differentially abundant

3 Entering the era of human omics Genomic and other omic approach will become a part of routine healthcare practice Genomics: revealing static genetic information in somatic cells; may alter in tumor cells Omics approaches: high-throughput monitoring of cellular systems at the genome-scale Transcriptomics: dynamic alteration of gene transcription Proteomics: dynamic alteration of the forms and abundances of proteins Glycomics: : dynamic alteration of glycan synthesis Epigenomics: dynamic alteration of chromatin (on which the genome is physically located) status Metabolomics: dynamic alteration of metabolic reactions Immunomics: dynamic alteration of immune response

4 Mike Snyder (Stanford): integrated Personal Omics Profiling (ipop) An analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period; Every half a month to two months Uncover extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions; Revealed various medical risks, including type II diabetes Can be used to interpret healthy and disease states by connecting genomic information with additional dynamic omics activity; Cell, Volume 148, Issue 6, , 2012.

5 The Dynamic Nature of the Proteome The proteome of the cell is changing Various extra-cellular, and other signals activate pathways of proteins. A key mechanism of protein activation is post-translational modification (PTM) These pathways may lead to other genes being switched on or off Mass spectrometry is key to probing the proteome and detecting PTMs

6 Dynamic proteomic profiling Identify proteins in a complex sample (e.g., human blood samples) Shotgun approach (bottom-up); Top-down approach Quantify these proteins in samples from various conditions (e.g., disease vs. healthy, dynamic sampling from multiple time points of the same individual) Identify post-translation modifications (PTMs) of proteins (phosphrylations, acetylations, glycosylations, etc) Different PTMs are dynamic with different time scales

7 Mass Spectrometry (MS) An analytical technique for the determination of the elemental composition of a sample or molecule Ion source: ESI (electrospray ionization), MALDI (matrixassisted laser desorption/ionization) Mass analyzer: separate the ions according to their mass-tocharge ratio, e.g., TOF (time-of-flight)

8 Proteomics Approaches Digest Gel/AC WetLab Operation Proteins Pure Protein Peptides Pure Peptide Topdown Shotgun Bottomup LC ESI Molecular Ions MS Operation Single m/z Ions Fragments 1 st MS Dissociation 2 nd MS MS Spectra Protein ID Protein Quantiftn Computing Operation PTM sites Other Software Tools Search Engine Taken from:

9 Shotgun proteomics Protein identification Inferring which proteins are present in the sample A probabilistic point of view: the likelihood of each protein (in the database) being in the sample Peptide-spectrum matchings (PSMs) Peptide search engines (>20): Sequest, X!tandem cross-correlation Mascot, OMSSA probabilistic scoring InSpect spectra alignment scoring Matching score: 8.7

10 PSMs Peptide Identification: Assessing PSMs To determine the correct PSMs among all PSMs Each experimental spectrum is compared against all peptides in the database with the matched precursor mass; only the 1 st ranked PSM is considered to be correct. there is 1 correct PSM for each spectrum. Industrial standard: to report identified peptides by controlled false discovery rate (FDR) -- the forward-decoy strategy Search both the target and a decoy database (e.g. the reverse protein database) Use the peptide-spectrum matches (PSMs) in decoy database to estimate the false PSMs in the target database FDR = (# decoy PSMs) / (# target PSMs) Can be used for any search engine or scoring model

11 S e q u e n c e Protein Identification by Tandem Mass Spectrometry MS/MS instrument Database search Sequest 0 de Novo interpretation Sherenga S#: 1708 RT: AV: 1 NL: 5.27E6 T: + c d Full ms [ ] Relative Abundance m/z Ref: Mass spectrometry-based proteomics, Nature 2003, 422:198

12 Breaking Protein into Peptides and Peptides into Fragment Ions 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.

13 Breaking Proteins into Peptides MPSERGTDIMRPAKID... protein GTDIMR PAKID MPSER peptides HPLC To MS/MS

14 Tandem Mass Spectrometry

15 RT: Relative Abundance LC Tandem Mass Spectrometry Time (min) NL: 1.52 E 8 Base Peak 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 [ ] Ion Source MS-1 collision cell MS-2 Relative Abundance MS/MS Scan m/z

16 Tandem Mass Spectrum Tandem Mass Spectrometry (MS/MS): mainly generates partial N- and C-terminal peptides Chemical noise often complicates the spectrum. Represented in 2-D: mass/charge axis vs. intensity axis

17 Protein Identification with MS/MS G V D L K MS/MS Peptide Identification: Intensity 0 mass

18 N- and C-terminal Peptides

19 N- and C-terminal Peptides

20 Peptide Shotgun Sequencing Reconstruct peptide from the set of masses of fragment ions (mass-spectrum)

21 (Ideal) Theoretical Spectrum

22 Issue 1: Spectrum Consists of Different Ion Types b 2 -H 2 O b 3 - NH 3 a 2 b 2 a 3 b 3 HO NH + 3 R 1 O R 2 O R 3 O R 4 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 because peptides can be broken in several places.

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

24 S#: 1708 RT: AV: 1 NL: 5.27E6 T: + c d Full ms [ ] m/z De Novo vs. Database Search Database Search Relative Abundance De Novo Database of known peptides MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT, HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE, ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA, EKLNKAATYIN.. Mass, Score W Database of all peptides = 20 n A V AAAAAAAA,AAAAAAAC,AAAAAAAD,AAAAAAAE, L G T AAAAAAAG,AAAAAAAF,AAAAAAAH,AAAAAAI, E P C L K AVGELTI, AVGELTK W, AVGELTL, AVGELTM, D T YYYYYYYS,YYYYYYYT,YYYYYYYV,YYYYYYYY R AVGELTK

25 Peptide Identification Problem (Database Search) Goal: Find a peptide from the database with maximal match between an experimental and theoretical spectrum. Input: S: experimental spectrum database of peptides Δ: set of possible ion types m: parent mass Output: A peptide of mass m from the database whose theoretical spectrum matches the experimental S spectrum the best

26 Peptide Identification by Database Search Compare experimental spectrum with theoretical spectra of database peptides to find the best fit 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 To find the peptide with theoretic spectrum that is most similar to the real spectrum

27 Peptide Sequencing Problem (De Novo) Goal: Find a peptide with maximal match between an experimental and theoretical spectrum. Input: S: experimental spectrum Δ: set of possible ion types m: parent mass Output: A peptide with mass m, whose theoretical spectrum matches the experimental S spectrum the best

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

29 Building Spectrum Graph How to create vertices (from masses) How to create edges (from mass differences) How to score paths How to find best path

30 a is an ion type shift in b Intensity y Mass/Charge (M/Z)

31 MS/MS Spectrum (Ion Types Unknown & With Noise) Intensity Mass/Charge (M/z)

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

33 Knowing Ion Types Some masses correspond to fragment ions, others are just random noise Knowing ion types Δ={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.

34 Vertices of Spectrum Graph Masses of potential N-terminal peptides Vertices are generated by reverse shifts corresponding to ion types Δ={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 1, s+δ 2,, s+δ k } corresponding to potential N-terminal peptides Vertices of the spectrum graph: {initial vertex} V(s 1 ) V(s 2 )... V(s m ) {terminal vertex}

35 Reverse Shifts b/b-h 2 O+H 2 O Intensity b-h 2 O b+h 2 O Red: Mass Spectrum Blue: shift (+H 2 O) Mass/Charge (M/Z) Two peaks b-h 2 O and b are given by the Mass Spectrum With a +H 2 O shift, if two peaks coincide that is a possible vertex.

36 Reverse Shifts Shift in H 2 O Shift in H 2 O+NH 3

37 Edges of Spectrum Graph 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

38 Paths 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

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

40 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) = maxp p(p,s) Peptides = paths in the spectrum graph P = the optimal path in the spectrum graph

41 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. But De novo sequencing is still not very accurate!

42 Sequencing of Modified Peptides De novo peptide sequencing is invaluable for identification of unknown proteins: However, de novo algorithms are designed for working with high quality spectra with good fragmentation and without modifications. Another approach is to compare a spectrum against a set of known spectra in a database.

43 Protein identification Protein inference problem Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4,

44 Extreme case: multiple proteins sharing the same (single) identified peptide By T-COFFEE Version_5.05 [

45 Protein identification problem: a probabilistic formulation Protein i is present All PSMs of tryptic peptides j from protein i Peptide j is observed if protein i is present Prior probability of protein i being present in the sample P( p i =1 S ) ij P( S ij q j =1)P ( q j =1 p i =1)P p i =1 j P( S ij q j =1) P( q j =1 S ) ij : The probability of a given PSM S ij being true (Assessment of PSMs) ( ) = P p i =1 P q j =1 p i =1 ( ) " 1 if i is present in the database # $ 0 otherwise ( ) : the prior probability of peptide j being identified as a PSM in a proteomics protocol (peptide detectability d ij ) Challenges: 1) Is peptide detectability predictable? 2) How to assess PSMs? 3)How to model degenerate peptides?

46 Extending the probabilistic formulation to protein inference problem Protein configuration graph Include not only identified peptides, but also nonidentified peptides Edge weights represent the prior probabilities of observing a peptide in a shotgun proteomics platform (i.e. peptide detectabilities) Y. Li, et. al. 2008, RECOMB & J. Comp. Biol.

47 Basic Bayesian model Protein configuration: (x 1, x 2, x m ) P ( x,..., x y,..., y ) MAP solution: ( x x ) 1 m 1 n = j P( xi ) ( 1 xidij ) 1 ( 1 xidij ) j P( xi ) ( 1 x' i dij ) 1 ( 1 x' i dij ) ( ',..., x' ) m i x i j 1 ( x,..., x ) j Peptide configuration: (y 1, y 2, y n ) i Huge protein configuration space (2 m putative solutions)! i 1 y ( x,..., x y,..., y ) 1,..., m = arg max P MAP 1 m 1 1 m n 1 y i i y j detectability y j Y. Li, et. al. 2008, RECOMB & J. Comp. Biol.

48 Probabilistic approach to protein inference problem Incorporation of peptide detectability: the probability that the peptide will be observed in a standard sample analyzed by a standard proteomics routine Combinatorial formulation: minimum missed peptides 1 Probabilistic formulation: Bayesian models 2 1. P. Alves, R. J. Arnold, M. V. Novotny, P. Radivojac, J. P. Reilly and H. Tang (2007), Advancement in protein inference from shotgun proteomics using peptide detectability. Pacific Symposium on Biocomputing, 12: Y. Li, R. J. Arnold, Y. Li, P. Radivojac, Q. Sheng and H. Tang, A Bayesian approach to protein inference problem in shotgun proteomics, submitted to RECOMB 2008.

49 From protein identification to protein quantification Quantity (abundance) of protein i A ijz = r ijz q i +ε ijz Peak area of peptide j charged c from protein i Peptide response rate: only dependent on the peptide sequence Background noise Assumption: 1) considering only unique (non-redundant) peptides; q i = j,z 2) errors model, Log-normal/Poisson: σ 2 jz r2 jz ; Gaussian: σ2 jz r jz A ijz r jz C N i if r jz are known (i.e. can be predicted from peptide sequences) and errors follows Poisson distribution (consistent with previous studies of signal scanning of mass spectrometers)

50 Post-Translational Modifications Proteins are involved in cellular signaling and metabolic regulation. They are subject to a large number of biological modifications. Almost all protein sequences are posttranslationally modified and 200 types of modifications of amino acid residues are known.

51 Examples of Post- Translational Modification Post-translational modifications increase the number of letters in amino acid alphabet and lead to a combinatorial explosion in both database search and de novo approaches.

52 Identification of Peptides with Mutations: Challenge Very similar peptides may have very different spectra (so SPC won t work)! Goal: Define a notion of spectral similarity that correlates well with the sequence similarity. If peptides are a few mutations/modifications apart, the spectral similarity between their spectra should be high.

53 Similar Peptides with Different Spectra S(PRTEIN) = {98, 133, 246, 254, 355, 375, 476, 484, 597, 632} S(PRTEYN) = {98, 133, 254, 296, 355, 425, 484, 526, 647, 682} S(PGTEYN) = {98, 133, 155, 256, 296, 385, 425, 526, 548, 583} no mutations SPC=10 1 mutation SPC=5 2 mutations SPC=2 Problem: SPC diminishes very quickly as the number of mutations increases. (Only a small portion of correlations between the spectra is captured by SPC.)

54 Search for Modified Peptides: Virtual Database Approach Yates et al.,1995: an exhaustive search in a virtual database of all modified peptides. Exhaustive search leads to a large combinatorial problem, even for a small set of modifications types. Problem (Yates et al.,1995). Extend the virtual database approach to a large set of modifications.

55 Spectral Convolution S2 S1 = {s2 Number of pairs s ( S 1 s1:s1 S1,s2 S2 } S1, s2 S2with s2 S )( x) 2 1 s 1 = x : The shared peaks count ( S S )(0) 2 1 (SPC peak): convolution is a mathematical operation on two functions producing a third function that is typically viewed as a modified version of one of the original functions; cross-correlation

56 Elements of S 2 S 1 represented as elements of a difference matrix. The elements with multiplicity >2 are colored; the elements with multiplicity =2 are circled. The SPC takes into account only the red entries

57 Spectral Comparison: Difficult Case S = {10, 20, 30, 40, 50, 60, 70, 80, 90, 100} Which of the spectra S = {10, 20, 30, 40, 50, 55, 65, 75,85, 95} or S = {10, 15, 30, 35, 50, 55, 70, 75, 90, 95} fits the spectrum S the best? SPC: both S and S have 5 peaks in common with S. Spectral Convolution: reveals the peaks at 0 and 5.

58 Spectral Comparison: Difficult Case S S S S

59 Limitations of the Spectrum Convolutions Spectral convolution does not reveal that spectra S and S are similar, while spectra S and S are not. Clumps of shared peaks: the matching positions in S come in clumps while the matching positions in S don't. This important property was not captured by spectral convolution.

60 Shifts A = {a 1 < < a n } : an ordered set of natural numbers. A shift (i,δ) is characterized by two parameters, the position (i) and the length (Δ). The shift (i,δ) transforms {a 1,., a n } into {a 1,.,a i-1,a i +Δ,,a n + Δ }

61 Shifts: An Example The shift (i,δ) transforms {a 1,., a n } into {a 1,.,a i-1,a i +Δ,,a n + Δ } e.g shift (4, -5) shift (7,-3)

62 Spectral Alignment Problem Find a series of k shifts that make the sets A={a 1,., a n } and B={b 1,.,b n } as similar as possible. k-similarity between sets D(k) - the maximum number of elements in common between sets after k shifts.

63 Representing Spectra in 0-1 Alphabet Convert spectrum to a 0-1 string with 1s corresponding to the positions of the peaks.

64 Comparing Spectra=Comparing 0-1 Strings A modification with positive offset corresponds to inserting a block of 0s A modification with negative offset corresponds to deleting a block of 0s Comparison of theoretical and experimental spectra (represented as 0-1 strings) corresponds to a (somewhat unusual) edit distance/alignment problem where elementary edit operations are insertions/deletions of blocks of 0s Use sequence alignment algorithms!

65 Spectral Alignment vs. Sequence Alignment Manhattan-like graph with different alphabet and scoring. Movement can be diagonal (matching masses) or horizontal/vertical (insertions/deletions corresponding to PTMs). At most k horizontal/vertical moves.

66 Use of k-similarity SPC reveals only D(0)=3 matching peaks. Spectral Alignment reveals more hidden similarities between spectra: D(1)=5 and D(2)=8 and detects corresponding mutations.

67 Protein Identification We can detect peptides from mass spectra by database search or de novo approaches Homologous proteins

68 References Mass spectrometry-based proteomics, Nature 422:198, 2003 Applying mass spectrometry-based proteomics to genetics, genomics and network biology, Nature Reviews Genetics 10, , 2009