1. De novo interpretation of peptide mass spectra
Vineet Bafna, UCSD
(joint work with
Nathan Edwards, ABI and
Noah Zaitlen, UCSD)

2. Nobel Citation 2002

3. Talk Outline Tandem MS for Peptide Identification Earlier work
Description of algorithm
Results and applications

4. Tandem MS Secondary Fragmentation Ionized parent peptide
Tandem mass spectrometry selects one of the intense peaks observed in the single stage mass spectrum and further fragments all peptides with the selected mass to charge ratio. The tandem mass spectrum typically contains mass to charge ratio information about fragments of a a single peptide.
Secondary Fragmentation
Ionized parent peptide

5. The peptide backbone The peptide backbone breaks to form
fragments with characteristic masses.
H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri
Ri+1
N-terminus
C-terminus
Tandem MS can be used to determine the amino-acid sequence of a peptide because proteins are made up of amino-acid chains. During secondary fragmentation, the peptide backbone breaks forming fragments with characteristic masses.
AA residuei-1
AA residuei
AA residuei+1

6. Ionization The peptide backbone breaks to form
fragments with characteristic masses. H+
H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri
Ri+1
N-terminus
C-terminus
The parent peptide, when ionized, has at least one additional proton attached.
AA residuei-1
AA residuei
AA residuei+1
Ionized parent peptide

7. Fragment ion generation
The peptide backbone breaks to form
fragments with characteristic masses. H+
H...-HN-CH-CO NH-CH-CO-NH-CH-CO-…OH
Ri-1 Ri
Ri+1
N-terminus
C-terminus
When the peptide backbone breaks, the ionizing protons are retained on some of the fragments, which can then have their mass to charge ratio measured. Shown here is a suffix fragment, where the ionizing proton is retained on the C-terminus side of the backbone cleavage site. Also possible is a prefix fragment, where the ionizing proton is retained on the N-terminus side.
AA residuei-1
AA residuei
AA residuei+1
Ionized peptide fragment

8. Tandem MS for Peptide ID88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions
100
% Intensity
[M+2H]2+
An assignment of the peptide fragments to the spectral peaks. You can see there is an excellent correspondence between the fragments that are expected and the observed spectral peaks. Note too the presence of the doubly charged, unfragmented parent ion.
250
500
750
1000
m/z

9. Peak Assignment 88 145 292 405 534 663 778 907 1020 1166 b ions S G FL E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions y6
100
Peak assignment implies
Sequence (Residue tag)
Reconstruction! y7
% Intensity
[M+2H]2+
An assignment of the peptide fragments to the spectral peaks. You can see there is an excellent correspondence between the fragments that are expected and the observed spectral peaks. Note too the presence of the doubly charged, unfragmented parent ion. y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b9 b7 y9
250
500
750
1000
m/z

10. Database Searching For every peptide from a database
Generate a hypothetical spectrum
Compute a correlation between observed and experimental spectra
Choose the best
Database searching is very powerful and is the de facto standard for MS.
Sequest, Mascot, and many others

11. A case for de novo sequencing
With current technology, only about 30% of spectra yield a meaningful hit in a database
Multiple Reasons:
Incomplete databases (ex: pathogens)
Modifications/Mutations
Poor quality of fragmentation
Can we say anything about such ‘Orphan’ Spectra?
In the absence of a complete interpretation, can we have confidence in peak assignments?

12. De Novo Interpretation: Example
b-ions
S G E K
y-ions
Ion Offsets
b=P+1
y=S+19=M-P+19 y 2
100
500
400
300
200 y 1 b 1 b 2
M/Z

13. Putative Prefix Masses
Dancik et al., Recomb 98
Prefix Mass
M=401 b y
S G E K

14. Earlier Work: Spectral Graph
Dancik et al., Recomb 98
Each peak, when assigned to a prefix/suffix ion type generates a unique prefix residue mass.
Spectral graph:
Each node u defines a putative prefix residue M(u).
(u,v) in E if M(v)-M(u) is the residue mass of an a.a. (tag) or 0.
Paths in the spectral graph correspond to a interpretation
273
332 87
144
146
275
401
100
200
300 S G E K

15. Re-defining de novo interpretation
Find a subset of nodes in spectral graph s.t.
0, M are included
Each peak contributes at most one node (interpretation)(*)
Each adjacent pair (when sorted by mass) is connected by an edge (valid residue mass)
An appropriate objective function (ex: the number of peaks interpreted) is maximized
(*)In general, finding paths using forbidden pairs is NP-hard
100
400
200 S G E K

16. Earlier Work: Non-Intersecting Forbidden pairs
Chen et al. , SODA 2000
100
400
200
300
332 87 S G E K
If we consider only b,y ions, ‘forbidden’ node pairs are non-intersecting,
The de novo problem can be solved efficiently using a dynamic programming technique.

17. Peptide fragmentation possibilities
Multiple Ion Types
Peptide fragmentation possibilities
(ion types)
xn-i
yn-i
vn-i
yn-i-1
wn-i
zn-i
-HN-CH-CO-NH-CH-CO-NH- Ri
CH-R’
In reality, many more of the bonds between two amino-acids may break.
i+1 ai R”
i+1 bi
bi+1 ci
di+1
low energy fragments
high energy fragments

18. Spectra have Multiple Ion types
100
400
200
b-ions and y-ions often constitute a minority of interpretable ions. a-ions, and different neutral losses are very common. High energy spectra display a larger fraction of ions.
Multiple ions imply overlapping residue assignments and Chen et al. does not apply.

19. BE’2003
An efficient algorithm that handles all prefix/suffix fragmentations and their neutral losses using a different d.p. formulation
An algorithm to obtain the core interpretation of a spectrum, assignment of confidence values to peak assignments

20. Multiple Ion Types -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri R” xn-i yn-i
Residue
vn-i
yn-i-1
wn-i
zn-i
-HN-CH-CO-NH-CH-CO-NH- Ri
CH-R’
In reality, many more of the bonds between two amino-acids may break.
i+1 ai R”
i+1 bi
bi+1 ci
di+1
low energy fragments
high energy fragments

21. Simple Ion Lists
Peak i
Span
M/2
Prefix residue masses

22. Simple Ion Lists
Peak j
Peak i
l_i
r_i
100
200 S
400
Partition the putative residue masses for each peak around M/2
l_i (r_i) is the smallest (largest) mass for peak i
The span S of an ion-list is the maximum difference between the putative residue assignments on either LHS or RHS.
Define an Ion List as simple if
span <= minimum residue mass.
l_i < l_j implies r_i > r_j
Most natural ion-lists are simple. Ex: a,b,y,b-NH3,b-H20,y- NH3,y- H20

23. Simple Ion Lists and Spectral Peak orderingj ri rj
400
200
Order the peaks by increasing right-most putative residue
Lemma: If the left side residues ri is chosen from i, and rj is chosen from j >i , then ri >= rj

24. Ordering spectral peaks
i<j i j
Span <= aa

25. Forward algorithm
i-1 i
M[w]
M[v]
Goal: We only have peaks 1..i, and want to find the “best” path from node v (M[v]<= M/2) to node u (M[v]> M/2). Denote score S(i,v,w)
Best: Many notions of best.

26. Forward algorithm
i-1 i
M[w]
M[v]
M[u]
Since i is the outermost peak, a PRM from it is either connected to v, or to w, or NOT used ever.
One option is that none of the PRMs from peak i are used. In that case
S[i,v,w] = S[i-1,v,w]

27. Forward algorithm
i-1 i
M[w]
M[v]
M[u]
Otherwise, we choose a node u from one of the interpretations of i
Node u must have an edge from v or w. If it is from v
S(i,v,w) = S(i-1,u,w)

28. The Forward Algorithm
i-1 i
M[v]
M[u]
M[w]
Number the peaks in the order of increasing largest prefix residue mass
Define S[i,v,w] as the score of the best interpretation from M[v] to M[w] using peaks 1..i
S[i,v,w] = max
S[i-1,v,w] + f(i)
S[i-1,u,w] + g(i,M[u]), if valid
S[i-1,v,u] + g(i,M[u]), if valid

29. Forward Backward Pathsi
i+1
M[v]
M[w] M
S[i][v][w] is the best (M[v],M[w]) inner interpretation using peaks 1..i.
Let T[i][v][w] be the best (M[v],M[w]) outer-interpretation using peaks i..m.
It is the highest score of a path from 0 to v, and from w to M using peaks i, i+1,…,m

30. Forward Backward Scoring
M[v]
M[w] M
S[i][v][w] is the best (M[v],M[w]) inner interpretation using peaks 1..i.
Let T[i][v][w] be the best (M[v],M[w]) outer-interpretation using peaks i..m.
T[i+1,v,w] + f(i)
S[i+1,u,w] + g(i,M[u]), if valid
S[i+1,v,u] + g(i,M[u]), if valid
T[i,v,w] = max

31. Core Interpretations Do we care about this?
Suppose we can answer the following question:
What is the best scoring interpretation if we fix the interpretation of peak i to something (EX: b, y-H2O)?
This is equivalent to assigning peak i to one of its nodes
Do we care about this?

32. Core interpretations
If global score after assigning peak i to a node u is Much higher than a score due to any other interpretation
Then, the interpretation of peak I is likely to be correct even if the global interpretation is not.
This allows us to interpret peaks with incomplete fragmentation

33. Core Interpretations
Define H[i,u] as the highest scoring interpretation in which peak i is assigned to g[u]
If H[i,u] = S[m,0,n] for some u, and H[i,v] << S[m,0,n] then M[u] is probably the correct interpretation for I
H[i,u] = g(i,M[u]) +
Max_v (S[i-1,v,u] + T[i+1,v,u]) v<u
Max_w (S[i-1,u,w] + T[i+1,u,w]) u<w
max

34. More theory Reduce dimensionality of the recurrence.
Generate sub-optimal paths

35. Some results
How good is de novo?

36. Simulation test data set
Given a peptide, generate artificial tandem MS with differing intensities (as in SEQUEST)
b,y with intensity 1.0,
a with intensity 0.5,
appropriate neutral losses with intensity 0.2
Parameters g,e are chosen (fragmentation and error probability respectively).
Each fragment is generated with probability min{ g i,1}, where i is the intensity. An error offset is chosen uniformly at random from [-e,e].

37. Results: %Positions predicted
Ions/Position

38. Results:Peptide Id
Ions/Position

39. Results: % TIC explained
Ions/Position

40. Results: Core Interpretation
% Score Difference

41. Performance on real data sets
Zufar et al. (~150 spectra)
ISB (~500 spectra)
Performance
Best spectral interpretation was chosen for each spectra.
For tags of different lengths, the number of spectra with a correctly predicted sequence tag was reported.

42. De novo Performance on real data sets

43. Parameter optimization
Peak selection parameters
Scoring parameters (for different interpretations)
A simulated annealing step is used to optimize parameters on a learning data set.

44. De novo performance (optimization)

45. De novo analysis ~50% of spectra have a correct 5-mer prediction
Worthwhile if databases are incomplete.
Very useful as filters.
Mann & Wilm, MS Blast, GutenTag all use generated tags as filters

46. De novo interpretation as a filter
Only tags from high scoring paths are used. Fewer tags implies fewer candidate peptides
Experiment:
All substrings of lengths 3-6 were chosen as tags
The Uniprot database (143K proteins, 100Mb) was searched with these tag filters.
A peptide is a candidate if the tag and flanking masses are consistent with the interpretation M1 M2
IVLSDFYLDEERVADCVLL

47. Tags as effective filters

48. Tags as filters Pros: Cons
Eliminate a large portion of the database from consideration
Allow for efficient search using keyword trees (tries). This dictionary search is independent of the size of the tag space
Allow Searching with Post-translational modifications
Cons
Filter out some true hits. Dependent on interpretation. Suboptimal paths must be considered as well
General filters based on interpretations can be built

49. Mass spectra with PT modification
Most database search software allow PT modifications
Search based on generation of modified candidate peptides
A combinatorial expansion occurs.
Searching with PT modifications is computationally intensive.

50. Efficient handling of PT modifications
Consider the peptide SDFTYLDER
S,T,Y can all be phosphorylated (or not) giving 8 possibilities
Parent mass consideration reduces this, but there are still a large number of possibilities.
With an increase in the type of PT modifications, this number can become very large
De novo interpretation complexity is unchanged in the presence of PT modifications

51. PT modifications

52. Conclusions
Computational methods for de novo analysis are continually improving.
Things should get much better with technology improvement
Great potential as filters
Searching large genomic databases
Searching in the presence of PT modifications
Filtering without tags
De novo interpretation should be revived from the backwaters of MS analysis.

53. Acknowledgments Nathan Edwards, ABI UCSD:
Nuno Bandeira, Ari Frank, Qian Peng, Pavel A. Pevzner, Noah Zaitlen