De novo sequencing and homology searching with de novo sequence tags
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De Novo Sequencing and Homology Searching with De Novo Sequence Tags. 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. h omologous peptides. Why Bother?.

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De Novo Sequencing and Homology Searching with De Novo Sequence Tags

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De novo sequencing and homology searching with de novo sequence tags

De Novo Sequencing and Homology Searching with De Novo Sequence Tags


Possible ways to interpret ms ms data

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


Why bother

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


Outline

Outline

  • Basics

  • Manual De Novo Sequencing

  • De novo Sequencing Algorithm (PEAKS)

  • Homology Search with De Novo Tags


Sequence specific fragment ions

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


Non sequence specific fragmentations

Non-sequence-specific fragmentations


Why does everyone analyze positively charged tryptic peptides

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.


Ms ms spectrum of doubly charged tryptic peptide one arg and two protons

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


Ms ms spectrum of a doubly charged non tryptic peptide two arg s and two protons

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


Cid in traps vs quadrupoles

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


Annoying things to remember when sequencing peptides by ms ms

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


Outline1

Outline

  • Basics

  • Manual De Novo Sequencing

  • De novo Sequencing Algorithm (PEAKS)

  • Homology Search with De Novo Tags


Two approaches to manually sequencing peptides from ms ms spectra

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)


Sequencing from the middle look for ion series in the region above the precursor ion m z 615

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


De novo sequencing and homology searching with de novo sequence tags

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


Another ion series contains pairs separated by 18 da water losses

-18

-18

-18

-18

-18

Another ion series contains pairs separated by 18 Da (water losses)

L

V

E

S


Two ion series have been identified in the region above the precursor ion

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


For the first ion series 1228 17 987 224 da

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


De novo sequencing and homology searching with de novo sequence tags

-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


The high mass b series predicts the presence of some low mass y ions are they there

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


The high mass y series predicts the presence of some low mass b ions are they there

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


Can i account for most of the remaining ions as neutral losses or internal fragments

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


Two approaches to manually sequencing peptides from ms ms spectra1

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)


Outline2

Outline

  • Basics

  • Manual De Novo Sequencing

  • De novo Sequencing Algorithm (PEAKS)

  • Homology Search with De Novo Tags


Algorithm design

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.


Peptide spectrum match score

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.


The fragment score for a mass

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 .


How to define statistically

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.


De novo sequencing

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 .


Algorithm idea

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.


Dynamic programming

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


A note on ptm

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.


Accounting for other ion types

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.


Mass segment error

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).


Outline3

Outline

  • Basics

  • Manual De Novo Sequencing

  • De novo sequencing Algorithm

  • Homology Search with De Novo Tags


Why homology search with de novo sequence

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.


Traditional sequence alignment

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.


Limitations of conventional homology search

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.


A simple approach

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


Spider model

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.


Two exercises

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


Conclusion

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


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