Protein sequencing and mass spectrometry
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Protein sequencing and Mass Spectrometry. Enzymatic Digestion (Trypsin) +. Fractionation. Sample Preparation. Single Stage MS. Mass Spectrometry. LC-MS: 1 MS spectrum / second. Tandem MS. Secondary Fragmentation. Ionized parent peptide. The peptide backbone.

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Sample preparation

Enzymatic Digestion

(Trypsin)

+

Fractionation

Sample Preparation


Single stage ms
Single Stage MS

Mass

Spectrometry

LC-MS: 1 MS spectrum / second


Tandem ms
Tandem MS

Secondary Fragmentation

Ionized parent peptide


The peptide backbone
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

C-terminus

N-terminus

AA residuei-1

AA residuei+1

AA residuei


Ionization
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

C-terminus

N-terminus

AA residuei-1

AA residuei+1

AA residuei

Ionized parent peptide


Fragment ion generation
Fragment ion generation

The peptide backbone breaks to form

fragments with characteristic masses.

H+

H...-HN-CH-CONH-CH-CO-NH-CH-CO-…OH

Ri-1

Ri

Ri+1

C-terminus

N-terminus

AA residuei-1

AA residuei

AA residuei+1

Ionized peptide fragment


Tandem ms for peptide id
Tandem MS for Peptide ID

88

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+

0

250

500

750

1000

m/z


Peak assignment
Peak Assignment

88

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

y6

100

Peak assignment implies

Sequence (Residue tag)

Reconstruction!

y7

% Intensity

[M+2H]2+

y5

b3

b4

y2

y3

b5

y4

y8

b8

b9

b6

b7

y9

0

250

500

750

1000

m/z


Database searching for peptide id
Database Searching for peptide ID

  • 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


Spectra the real story
Spectra: the real story

  • Noise Peaks

  • Ions, not prefixes & suffixes

  • Mass to charge ratio, and not mass

    • Multiply charged ions

  • Isotope patterns, not single peaks


Peptide fragmentation possibilities ion types

xn-i

yn-i

yn-i-1

vn-i

wn-i

zn-i

-HN-CH-CO-NH-CH-CO-NH-

CH-R’

Ri

i+1

ai

R”

i+1

bi

bi+1

ci

di+1

low energy fragments

high energy fragments

Peptide fragmentation possibilities(ion types)


Ion types and offsets
Ion types, and offsets

  • P = prefix residue mass

  • S = Suffix residue mass

  • b-ions = P+1

  • y-ions = S+19

  • a-ions = P-27


Mass charge ratio
Mass-Charge ratio

  • The X-axis is (M+Z)/Z

    • Z=1 implies that peak is at M+1

    • Z=2 implies that peak is at (M+2)/2

      • M=1000, Z=2, peak position is at 501

    • Suppose you see a peak at 501. Is the mass 500, or is it 1000?


Spectral graph
Spectral Graph

  • Each prefix residue mass (PRM) corresponds to a node.

  • Two nodes are connected by an edge if the mass difference is a residue mass.

  • A path in the graph is a de novo interpretation of the spectrum

87

G

144


Spectral graph1

0

273

332

401

87

144

146

275

100

200

300

S

G

E

K

Spectral Graph

  • 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


Re defining de novo interpretation

0

273

332

401

87

144

146

275

100

200

300

S

G

E

K

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

87

G

144


Two problems

0

273

332

401

87

144

146

275

100

200

300

S

G

E

K

Two problems

  • Too many nodes.

    • Only a small fraction are correspond to b/y ions (leading to true PRMs) (learning problem)

    • Even if the b/y ions were correctly predicted, each peak generates multiple possibilities, only one of which is correct. We need to find a path that uses each peak only once (algorithmic problem).

    • In general, the forbidden pairs problem is NP-hard


However
However,..

  • The b,y ions have a special non-interleaving property

  • Consider pairs (b1,y1), (b2,y2)

    • If (b1 < b2), then y1 > y2


Non intersecting forbidden pairs

100

0

400

200

Non-Intersecting Forbidden pairs

332

300

87

S

  • 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.

G

E

K


The forbidden pairs method
The forbidden pairs method

  • There may be many paths that avoid forbidden pairs.

  • We choose a path that maximizes an objective function,

    • EX: the number of peaks interpreted


The forbidden pairs method1

332

100

300

0

400

200

87

The forbidden pairs method

  • Sort the PRMs according to increasing mass values.

  • For each node u, f(u) represents the forbidden pair

  • Let m(u) denote the mass value of the PRM.

f(u)

u


D p for forbidden pairs
D.P. for forbidden pairs

  • Consider all pairs u,v

    • m[u] <= M/2, m[v] >M/2

  • Define S(u,v) as the best score of a forbidden pair path from 0->u, v->M

  • Is it sufficient to compute S(u,v) for all u,v?

332

100

300

0

400

200

87

u

v


D p for forbidden pairs1
D.P. for forbidden pairs

  • Note that the best interpretation is given by

332

100

300

0

400

200

87

u

v


D p for forbidden pairs2
D.P. for forbidden pairs

  • Note that we have one of two cases.

    • Either u < f(v) (and f(u) > v)

    • Or, u > f(v) (and f(u) < v)

  • Case 1.

    • Extend u, do not touch f(v)

100

300

0

f(u)

400

200

u

v


The complete algorithm
The complete algorithm

for all u /*increasing mass values from 0 to M/2 */

for all v /*decreasing mass values from M to M/2 */

if (u > f[v])

else if (u < f[v])

If (u,v)E

/*maxI is the score of the best interpretation*/

maxI = max {maxI,S[u,v]}


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