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

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

3. Why Bother? De novo sequencing derives the sequence without looking into a database. De novo sequencing 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. Sequence-specific fragment ions [N-term]-NH-CHR-C---NH 2 + -[C-term]H + O [N-term]-NH-CHR-CO + + NH 2 -[C-term] H + [N-term]-NH=CHR + + CO a – NH 3 or H 2 O b – NH 3 or H 2 O y – NH 3 or H 2 O (M+2H) +2 b-iony-ion a-ion

6. 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 L Y E I A R y1y1 y2y2 y3y3 y4y4 y5y5 y6y6 y6y6 y5y5 y4y4 y3y3 y2y2 y1y1 L Y b2b2 a2a2 b2b2

9. MS/MS spectrum of a doubly-charged non- tryptic peptide(two Arg’s and two protons) Relative Ab. (%) Y S R R H P E y2y2 P H Y y1y1 (b 6 +18) +2 (b 5 +18) +2 y4y4 y3y3 y 4 -17 (M+2H) +2 b4b4 y 6 -17 b 5 -17 a 5 -17

10. CID in traps vs quadrupoles 300400500600700800900100011001200130014001500 Relative Abundance 100200300400500600700800900100011001200130014001500 0 m/z y 13 b 13 y 12 y 11 b 12 y 10 b 11 b 10 y9y9 y9y9 b9b9 y8y8 y8y8 y7y7 y7y7 (M + 2H) +2 b8b8 y 14 +2 b7b7 y6y6 y6y6 b6b6 y2y2 y2y2 y1y1 b2b2 y3y3 y4y4 y5y5 y3y3 y4y4 y5y5 Ion trap Qtof b3b3 b3b3 b4b4 b4b4 b5b5 b5b5 b6b6 IPIGFAGAQGGFDTR

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 1.Finding a series of ions in the middle of the peptide, and working out towards the termini (illustrated using ion trap data) 2.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) LSLV

16. Another ion series contains pairs separated by 18 Da (water losses) -18 LVES

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 LSLV 224 – 128 = 96 224 – 156 = 68 Therefore this does not look like a b-ion series

19. 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 -18 LVESK

20. The high mass b-series predicts the presence of some low mass y-ions; are they there? b-series: …LVESK y 1 : 147 No y 2 : 234 Yes y 3 : 363 Yes y 4 : 462 Yes y 5 : 575 Yes!! b-ions y-ions y-ions = residue mass plus 19 Da

21. The high mass y-series predicts the presence of some low mass b-ions; are they there? b-ions y-ions y-series: [242]VLSL… b 2 : 243 Yes b 3 : 342 Yes b 4 : 455 Yes b 5 : 542 Yes b 6 : 655!! Yes

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

23. Two approaches to manually sequencing peptides from MS/MS spectra 1.Finding a series of ions in the middle of the peptide, and working out towards one of the termini (illustrated using ion trap data) 2.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 prefix suffix 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. peptide prefix suffix

37. De Novo Sequencing

38. Algorithm Idea

39. Dynamic Programming BestScore 0 1 2 3

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. 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. Limitations of Conventional Homology Search (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. [LS]C[FA]K LSCFAK SLCFAK TVCFAK VTCFAK LSCAFK SLCAFK TVCAFK VTCAFK

48. SPIDER Model 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. (de novo) X: [LS]C[FA]K (real) Y: [SL]C[AF]K || || | (homolog) Z: [SL]A[AF]K

49. Two exercises (denovo) X: LSCFV (real) Y: EACFV (homolog) Z: DACFV m(LS)=m(EA)=200.1 Da (denovo) X: LSCFAV (real) Y: SLCFAV (homolog) Z: SLCF-V blosum62 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). Mutation and de novo error overlap. Hard for manual interpretation. Algorithm is needed.

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