Outline

1
Outline

• Introduction
• What's it all about
• Course Mechanics
• From Genomics to Proteomics
• Protein Separation
• Protein Identification
• Mass Spectrometry
• Fundamentals
• Protein Chemistry
• Ionization Techniques
• Fragmentation techniques
• Mass Analyzers
• CID MS/MS Interpretation
• Peptide Fragmentation Chemistry
• Interpretation of Spectra

2
Complications

• Doubly charges ions
• Neutral losses
• Isotopes (especially 13C)
• Post Translational Modifications

3
Complications Doubly Charged Ions

• Single Charged IonPeak m/z ion mass 1 Da
  (Proton) /charge (1)m 1 m 1 1
• Double ChargedIon Peak m/z ion mass 2 Da(2
  protons) / charge (2)m 2 2
  Example for
  mass 378.2

4
Isotopes

• A Peak is monoisotopic peak

5
Isotopes
6
Isotopes

• 3 carbons per Alanine residue
• One Alanine
• Probability of no 13C
• .99 .99 .99 0.99 3 0.97
• 8 Alanines
• Probability of no 13C (A) (monoisotopic)
• 0.99 8 0.92
• Probability of one 13C (A1)
• 7 0.99 7 0.01 7 0.009 0.063
• Probability of two 13C (A2)
• 8! . 56 6! . 38
• 6! 2! 6! 2

7
Isotopes at Low Resolution
8
Isotope Calculator

• http//www.chem.shef.ac.uk/WebElements.cgiisot

9
Other Analyses
10
Ion Types

• Some peaks correspond to fragment ions, others
  are just random noise
• Knowing ion types ?d1, d2,, dk lets us
  distinguish fragment ions from noise
• We can learn ion types di and their probabilities
  qi by analyzing a large test sample of annotated
  spectra.

11
Example of Ion Type

• ?d1, d2,, dk
• Ion types
• b, b-NH3, b-H2O
• correspond to
• ?0, 17, 18
• Note In reality the d value of ion type b is -1
  but we will hide it for the sake of simplicity

12
Match between Spectra and Shared Peak Count

• 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

13
Peptide Sequencing Problem

• 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

14
Vertices

• Masses of potential N-terminal peptides
• Vertices are generated by reverse shift
• Every peak s in a spectrum generates vertices
• V(s) sd1, s d2, , s dk

15
Vertices (contd)

• Vertices of the spectrum graph
• vinit?V(s1) ?V(s2) ?... ?V(sm) ?vfin
• Where ?d1, d2,, dk are ion types.

16
Reverse Shifts
b/b-H2OH2O
Red Mass Spectrum Blue shift (H2O)
b-H2O
bH2O
Intensity
Mass/Charge (M/Z)

• Two peaks b-H2O and b are given by the Mass
  Spectrum
• With a H2O shift, if two peaks coincide that is
  a possible vertex.

17
Example of Reverse Shift
Shift in H2O
Shift in H2O and NH3
18
Edges

• 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

19
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

20
Path Score

• p(P,S) probability that peptide P produces
  spectrum S s1,s2,sq
• p(P, s) the probability that peptide S
  generates a peak s
• Scoring computing probabilities
• p(P,S) ps?S p(P, s)

21
Peak Score

• For a position t that represents ion type dj
• qj, if peak is generated
  at t
• p(P,st)
• 1-qj , otherwise

22
Peak Score (contd)

• For a position t that is not associated with an
  ion type
• qR , if peak is
  generated at t
• pR(P,st)
• 1-qR , otherwise
• qR the probability of a noisy peak that does
  not correspond to any ion type

23
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
• Peptides paths in the spectrum graph
• P the optimal path in the spectrum graph

24
Ions and Probabilities

• Tandem mass spectrometry is characterized by a
  set of ion types d1,d2,..,dk and their
  probabilities q1,...,qk
• di-ions of a partial peptide are produced
  independently with probabilities qi

25
Ions and Probabilities

• A peptide has all k peaks with probability
  
• and no peaks with probability
• A peptide also produces a random noise'' with
  uniform probability qR in any position.

26
Ratio Test Scoring for Partial Peptides

• Incorporates premiums for observed ions and
  penalties for missing ions.
• Example for k4, assume that for a partial
  peptide P we only see ions d1,d2,d4. The score
  is calculated as

27
Scoring Peptides

• T- set of all positions.
• Tit d1,, t d2,..., ,t dk,- set of positions
  that represent ions of partial peptides Pi.
• A peak at position tdj is generated with
  probability qj.
• RT- U Ti - set of positions that are not
  associated with any partial peptides (noise).

28
Probabilistic Model

• For a position t dj ? Ti the probability p(t,
  P,S) that peptide P produces a peak at position
  t.
• Similarly, for t?R, the probability that P
  produces a random noise peak at t is

29
Probabilistic Score

• For a peptide P with n amino acids, the score for
  the whole peptides is expressed by the following
  ratio test

30
Role of de novo Interpretation

• Interpreting MS/MS of novel peptides
• Automatic validation of MS/MS database matches.
• Leveraging homology matching across
• species

31
Protein Identification Problem

• Input A database of proteins, an experimental
  spectrum S, a set of ion types ?, and a parent
  mass m.
• Output A peptide of mass m from the database
  with the best match to spectrum S.

32
De novo Peptide Sequencing ProblemProtein
Identification Problem in the Database of ALL
Peptides

• Although de novo peptide sequencing
• problem seems to be more difficult that
• peptide identification problem, the algorithms
  for the former problem are actually much faster!

33
MS/MS Database Search

• Database search in mass-spectrometry has been
  very successful in identification of already
  known proteins.
• Experimental spectrum can be compared with
  theoretical spectra database peptides to find
  the best fit.
• SEQUEST (Yates et al., 1995)
• But reliable algorithms for identification of
  modified peptides are not yet known.

34
Functional Proteomics

• Problem Given a large collection of
  uninterpreted spectra, find out which spectra
  correspond to similar peptides.
• A method that cross-correlates related spectra
  (e.g., from normal and diseased individuals)
  would be valuable in functional proteomics.

35
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
  post-translationally modified and 200 types of
  modifications of amino acid residues are known.

36
Examples of Post-Translational Modification
37
Difficulties in Finding Post-Translational
Modifications

• Currently post-translational modifications cannot
  be reliably inferred from DNA sequences.
• Finding post-translational modifications remains
  an open problem even after the human genome is
  completed.
• 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.

38
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.

39
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.

40
Peptide Identification Problem Revisited

• Input Experimental spectrum S
• Database of peptides
• A set of ion types ?
• Parent mass m
• Output a peptide of mass m with the best match
  to the spectrum S that is present in the database.

41
Modified Peptide Identification Problem

• Input Experimental spectrum S
• Database of peptides
• A set of ion types ?
• Parent mass m
• Parameter k ( of mutations/modificat
  ions)
• Output a peptide of mass m with the best match
  to the spectrum S that is
• at most k mutations/modifications apart
  from
• a database peptide.

42
Database Search Sequence Analysis vs. MS/MS
Analysis
43
Peptide Identification Problem Challenge

• Very similar peptides may have very different
  spectra!
• 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.

44
Deficiency of the Shared Peaks Count

• Shared peaks count (SPC) intuitive measure of
  spectral similarity.
• Problem SPC diminishes very quickly as the
  number of mutations increases.
• Only a small portion of correlations between the
  spectra of mutated peptides is captured by SPC.

45
SPC Diminishes Quickly
no mutations SPC10
1 mutation SPC5
2 mutations SPC2
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
46
Spectral Convolution
47
Elements of S2 S1 represented as elements
of a difference matrix. The elements with
multiplicity gt2 are colored the elements with
multiplicity 2 are circled. The SPC takes into
account only the red entries
48
Spectral Convolution An Example
49
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.

50
Spectral Comparison Difficult Case
51
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.

52
Shifts

• A a1 lt lt an an ordered set of natural
  numbers.
• A shift (i,?) is characterized by two parameters,
  
• the position (i) and the length (?).
• The shift (i,?) transforms
• a1, ., an
• into
• a1, .,ai-1,ai?,,an ?

53
Shifts An Example

• The shift (i,?) transforms a1, ., an
• into a1, .,ai-1,ai?,,an ?
  
• e.g.
• 10 20 30 40 50 60 70 80 90
• 10 20 30 35 45 55 65 75 85
• 10 20 30 35 45 55 62 72 82

54
Spectral Alignment Problem

• Find a series of k shifts that make the sets
• Aa1, ., an and Bb1,.,bn
• as similar as possible.
• k-similarity between sets
• D(k) - the maximum number of elements in common
  between sets after k shifts.

55
Representing Spectra in 0-1 Alphabet

• Convert spectrum to a 0-1 string with 1s
  corresponding to the positions of the peaks.

56
Spectral Alignment vs. Sequence Alignment

• Manhattan-like graph with different alphabet and
  scoring.
• Axes in the graph correspond to peaks in the two
  spectra.
• In this case, score is 1 if the diagonal line
  goes through a peak on both axes, 0 otherwise.
• Movement can be diagonal or perpendicular (but
  only k times total).

57
Spectral Product

• Aa1, ., an and Bb1,., bn
• Spectral product A?B two-dimensional matrix
  with nm 1s corresponding to all pairs of
• indices (ai,bj) and remaining
• elements being 0s.

SPC the number of 1s at the main
diagonal. ?-shifted SPC the number of 1s on the
diagonal (i,i ?)
58
Spectral Alignment k-similarity

• k-similarity between spectra the maximum number
  of 1s on a path through this graph that uses at
  most k1 diagonals.
• k-optimal spectral
• alignment a path.

The spectral alignment allows one to detect more
and more subtle similarities between spectra by
increasing k.
59
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.
60
Black lines represent the paths for k0 Red
lines represent the paths for k1 blue line in
Fig.(b) represents the path for k2
61
Spectral Convolution Limitation

• The spectral convolution considers diagonals
  separately without combining them into feasible
  mutation scenarios.

D(1) 10 shift function score 10
D(1) 6
62
Dynamic Programming for Spectral Alignment

• Dij(k) the maximum number of 1s on a path to
  (ai,bj) that uses at most k1 diagonals.
• Running time O(n4 k)

63
Edit Graph for Fast Spectral Alignment
diag(i,j) the position of previous 1 on the
same diagonal as (i,j)
64
Fast Spectral Alignment Algorithm
Running time O(n2 k)
65
Spectral Alignment Complications

• Spectra are combinations of an increasing
  (N-terminal ions) and a decreasing (C-terminal
  ions) number series.
• These series form two diagonals in the spectral
  product, the main diagonal and the perpendicular
  diagonal.
• The described algorithm deals with the main
  diagonal only.

66
Spectral Alignment Complications

• Simultaneous analysis of N- and C-terminal ions
• Taking into account the intensities and charges
• Analysis of minor ions
• Much more complicated!