De Novo Sequencing and Homology Searching with De Novo Sequence Tags

1. De Novo Sequencing and Homology Searching with De Novo Sequence Tags

2. Inexact protein DB protein DB Possible Ways to Interpret MS/MS Data MS/MS Spectra 2 de novo sequencing peptides homology search database search 1 3 peptides homologous peptides

3. Why Bother? • De novo sequencing derives the sequence without looking into a database. • De novosequencing is useful for • unsequenced genomes (no protein database) • novel peptides (unmatched spectra after database search) • single amino acid polymorphism • unexpected PTM • database error • validate a database match

4. Outline • Basics • Manual De Novo Sequencing • De novo Sequencing Algorithm (PEAKS) • Homology Search with De Novo Tags

5. O [N-term]-NH-CHR-C---NH2+-[C-term]H+ Sequence-specific fragment ions (M+2H)+2 [N-term]-NH-CHR-CO+ + NH2-[C-term] H+ b-ion y-ion a – NH3 or H2O b – NH3 or H2O y – NH3 or H2O [N-term]-NH=CHR+ + CO a-ion

6. Non-sequence-specific fragmentations

7. Why does everyone analyze positively-charged tryptic peptides? • Usually better sensitivity from positively-charged peptide ions. • “Mobile protons” protonate peptide bonds and promote b/y fragmentation • Arg sequesters protons in gas phase • Tryptic peptides typically have 0 -1 Arg • Tryptic peptide ions typically have two protons • Therefore, tryptic peptides usually have b/y ions • Placing Arg’s at the C-terminus makes it more likely that a complete series of y-ions will be observed.

8. MS/MS spectrum of doubly-charged tryptic peptide (one Arg and two protons) Y y6 y5 y4 y3 y2 y1 Y L Y E I A R b2 a2 y5 b2 L y2 y1 y4 y3 y6

9. MS/MS spectrum of a doubly-charged non-tryptic peptide(two Arg’s and two protons) (M+2H)+2 y2 Y S R R H P E a5-17 (b6+18)+2 Relative Ab. (%) b5-17 Y y4-17 (b5+18)+2 b4 H y4 y3 P y6-17 y1

10. 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 m/z 0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 CID in traps vsquadrupoles b13 IPIGFAGAQGGFDTR Ion trap y9 y14+2 y2 y10 Relative Abundance y12 y6 y5 b6 b9 y3 y7 y8 b10 y4 b7 b8 b12 y13 b4 b5 y11 b3 b11 y2 IPIGFAGAQGGFDTR Qtof b2 (M + 2H) +2 Relative Abundance b6 b4 b5 y3 y5 y14+2 y1 y9 y12 y4 y10 y6 b3 y7 y8 y13 y11 b13 m/z

11. Annoying things to remember when sequencing peptides by MS/MS • Leucine and isoleucine have the same mass • Glutamine and lysine differ by 0.036 u • Phenylalanine and oxidized methionine differ by 0.033 u • Cleavages do not occur at every bond (more often than not, there is no cleavage between the first and second residues) • Certain amino acids have the same mass as pairs of other amino acids: G + G = N, A + G = Q, G + V ~ R, A + D ~ W, • S + V ~ W • However: mass accuracy resolves many of these ambiguities

12. Outline • Basics • Manual De Novo Sequencing • De novo Sequencing Algorithm (PEAKS) • Homology Search with De Novo Tags

13. Two approaches to manually sequencing peptides from MS/MS spectra • Finding a series of ions in the middle of the peptide, and working out towards the termini (illustrated using ion trap data) • Finding the C-terminus and working towards the N-terminus (illustrated using qtof data)

14. Sequencing from the middle: look for ion series in the region above the precursor ion (m/z 615)

15. An obvious series is the one that involves the more abundant fragment ions (m/z 575, 688, 775, 888, and 987) L S L V

16. -18 -18 -18 -18 -18 Another ion series contains pairs separated by 18 Da (water losses) L V E S

17. Two ion series have been identified in the region above the precursor ion Problem: Two ion series defining partial sequences LSLV and LVES have been identified, but it is not known if these are y- or b-ions (i.e., the sequence direction is unknown). Solution: Since ion trap data often exhibits high mass b-ions, check to see if the highest mass ion in either series corresponds to a loss of either Arg or Lys (the usual tryptic C-terminus). If not, check to see if the mass difference corresponds to a dipeptide containing Lys or Arg (it is possible that the b-ion defining the C-terminus is absent). Calculation: Peptide MW – 17 – fragment ion = C-terminal residue mass

18. For the first ion series: 1228 – 17 – 987 = 224 Da L S L V 224 – 128 = 96 224 – 156 = 68 Therefore this does not look like a b-ion series

19. -18 -18 -18 -18 -18 For the second ion series: 1228 – 17 – 1083 = 128 (the residue mass of Lys); this looks like a b-ion series and maybe the other one is a y-ion series L V E S K

20. The high mass b-series predicts the presence of some low mass y-ions; are they there? b-series: …LVESK y1: 147 No y2: 234 Yes y3: 363 Yes y4: 462 Yes y5: 575 Yes!! y-ions = residue mass plus 19 Da b-ions y-ions

21. The high mass y-series predicts the presence of some low mass b-ions; are they there? y-series: [242]VLSL… b2: 243 Yes b3: 342 Yes b4: 455 Yes b5: 542 Yes b6: 655!! Yes b-ions y-ions

22. Can I account for most of the remaining ions as neutral losses or internal fragments? [242]VLSLLVESK 242 = N+Q, N+K, L+E b-ions y-ions neutral loss

23. Two approaches to manually sequencing peptides from MS/MS spectra • Finding a series of ions in the middle of the peptide, and working out towards one of the termini (illustrated using ion trap data) • Finding the C-terminus and working towards the N-terminus (illustrated using qtof data)

32. Outline • Basics • Manual De Novo Sequencing • De novo Sequencing Algorithm (PEAKS) • Homology Search with De Novo Tags

33. Algorithm Design • The first thing for algorithm design is to define the property of the solution. • For the de novo sequencing problem, one wants to compute a peptide that “best matches” the given spectrum. • This “best match” is practically defined by a scoring function.

34. Peptide-Spectrum Match Score peptide suffix prefix • A fragment score can be computed for every two adjacent amino acids. This score depends on the presence of the corresponding b and y ions. • The peptide score is the sum of the fragment scores.

35. The Fragment Score for a Mass peptide suffix prefix • The fragment score calculation only requires the prefix mass but not the sequence • Note: suffix mass = total residue mass – prefix mass. • Thus it is possible to define score for each prefix mass value .

36. How to Define Statistically • Learn two probabilities from large training data • : Prob(a peak is observed at a y-ion m/z). • : Prob(a peak is observed at a random m/z). • Usually . • If an expected y-ion is observed, is added to . • is called the log-likelihood-ratio • If an expected y-ion is missing, , is added to . • Thus, matching ion is rewarded and missing ion is penalized. • Other fragment ion types can be considered similarly.

37. De Novo Sequencing • For a sequence with prefix masses the peptide score is defined as • De Novo Seuqenicng: Given scoring function and mass , computes a sequence P with total residue mass , and maximizing .

38. Algorithm Idea • : the maximized score that can be achieved by a prefix with mass . best sequence for • If is the best sequence for , then must be the best sequence for . • Thus, . • To compute , try 20 residues and use the one that maximizes the above formula.

39. Dynamic Programming • The algorithm initializes and all other cells to be . • Then computes for from 1 to by . • The best sequence can be retrieved by a backtracking process by repetitively computing the last amino acid . BestScore 0 3 1 2

40. A Note on PTM • Variable PTM does not cause major speed slow down for de novo sequencing algorithms. • Instead of trying 20 regular amino acids in the maximization, the algorithm simply tries all modified amino acids too. • The time complexity is increased by a constant factor. (Compare to the exponential growth in database search approach). • However, since the solution space is larger when many variable PTMs are allowed, the accuracy of the algorithm is reduced.

41. Accounting for Other Ion Types • When internal cleavage ions are considered in the scoring function, it becomes difficult to design efficient algorithm to find the optimal sequence. • A compromise between efficiency and accuracy is to employ a two-stage approach. • First, compute many (e.g. 10,000) sequences using an efficient score function that uses only a few of the most important ions. • Then, evaluate these candidates using a more sophisticated scoring function additional ions. • This two-round approach is a tradeoff between the algorithm speed and accuracy.

42. Mass Segment Error • Most errors are due to incomplete ion ladders in the spectrum. • Thus, a segment of amino acids cannot be determined. • However, the total mass of the segment, is fixed. • E.g. [242]VLSLLVESK, where 242 = N+Q, N+K, or L+E • The first two or three residues often have low confidence, because of a lack of fragment ions. • Most de novo sequencing software uses the precursor mass as a constraint (thus the mass of the derived sequence is usually correct).

43. Outline • Basics • Manual De Novo Sequencing • De novo sequencing Algorithm • Homology Search with De Novo Tags

44. Why Homology Search with De Novo Sequence • Advantages: • Database may not contain the exact peptide sequence, but a homologous one is there. • De novo + homology search is great to use the database of one organism to study a similar organism. • Disadvantages: • De novo sequence can only provide partially correct sequence tags. • Conventional homology search may fail due to de novo sequencing errors.

45. Traditional Sequence Alignment • Two peptide sequences are aligned by inserting spaces to appropriate positions. E.g. FVEVTKL-TDLTK | || || ||||| FAEV-KLVTDLTK • The matching residues (including gaps, ‘-’) in each column has a similarity score that can be looked up in a pre-defined amino acid substitution matrix, such as BLOSUM or PAM. • The alignment score is equal to the sum of the column-wise scores. • There are algorithms to compute the optimal alignment that maximizes the alignment score.

46. Limitations of Conventional Homology Search • Conventional search ignores the possible errors in de novo sequencing. • Suppose a true sequence is SLCAFK, and the de novo sequence is LSCFAK, and the homolog is SLAAFK. (denovo) X: LSCFAK | (homolog) Z: SLAAFK (denovo) X: [LS]C[FA]K (real) Y: [SL]C[AF]K || || | (homolog) Z: [SL]A[AF]K Conventional search using evolutionary similarities to explain the mismatches results in a poor match. If de novo sequencing errors are considered, the match becomes more significant.

47. A Simple Approach • We can enumerate all possible combinations of a mass segment, and search all of them together. • MS BLAST will do this. • Difficulties: • Do not know which portion of the sequence is error. • Exponential growth of possibilities. LSCFAK SLCFAK TVCFAK VTCFAK LSCAFK SLCAFK TVCAFK VTCAFK [LS]C[FA]K

48. SPIDER Model (de novo) X: [LS]C[FA]K (real) Y: [SL]C[AF]K || || | (homolog) Z: [SL]A[AF]K • Given a de novo sequence X, and a database sequence Z. Try to reconstruct the real sequence Y. • The difference between X and Y is explained by de novo sequencing errors. • The difference between Y and Z is explained by homology mutations. • The real Y should minimize the de novo errors and the homology mutations needed in the above explanation.

49. Two exercises (denovo) X: LSCFAV (real) Y: SLCFAV (homolog) Z: SLCF-V • The swap of L and S is more likely a de novo error than a mutation. • The deletion of A is unlikely a de novo error (de novo does not change peptide mass). (denovo) X: LSCFV (real) Y: EACFV (homolog) Z: DACFV • Mutation and de novo error overlap. Hard for manual interpretation. Algorithm is needed. blosum62 m(LS)=m(EA)=200.1 Da

50. Conclusion • When the target peptides are not in a database. • De novo sequencing • When the homologous peptides are in database • Homology search with the de novo tags can find them • Some de novo errors can be corrected by combining the homolog information