peptide identification via tandem mass spectrometry sorin istrail

Peptide Identification via Tandem Mass Spectrometry

Sorin Istrail

Sample Preparation for MS

Enzymatic Digestion (Trypsin)

+Fractionation

Single Stage MS

MassSpectrometry

LC-MS: 1 MS spectrum / second

Tandem MS

Secondary Fragmentation

LC-MS/MS: 2-3 spectra / second

Tandem MS for Peptide ID

H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH

Ri-1 Ri Ri+1

AA residuei-1 AA residuei AA residuei+1

N-terminus C-terminus

The peptide backbone breaks to formfragments with characteristic masses.

Tandem MS for Peptide ID

m/z

KLEDEELFGS147260389504633762875102210801166

1166102090777866353440529214588

% R

elat

ive

Abu

nda

nce

100

0250 500 750 1000

Tandem MS for Peptide ID

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

RiCH-R’

ai

bici

xn-iyn-i

zn-i

yn-i-1

bi+1

R”

di+1

vn-i wn-i

i+1

i+1

low energy fragments high energy fragments

Peptide fragmentation possibilities

Tandem MS Spectrum Interpretation

Peptide sequenceOutput:

Mass of parent peptide,Tandem MS spectrum

Input:

• De novo

• Putative fragment comparison

- Combinatorial enumeration

- Sequence database

De novo Spectrum Interpretation

m/z

% R

elat

ive

Abu

nda

nce

100

0250 500 750 1000

E L F

KL

SGF G

E DE

L E

E D E L

De novo Spectrum Interpretation

• Works best for spectra with simple, well formed fragment ladders.

• Missing fragments create ambiguity.

• Noise or unexpected fragments create ambiguity.

• Many fragment types create ambiguity.

• “Best” de novo interpretation may have no biological relevance.

Putative Fragment Comparison

m/z

KLEDEELFGS147260389504633762875102210801166 y ions

1166102090777866353440529214588 b ions

% R

elat

ive

Abu

nda

nce

100

0250 500 750 1000

y2 y3 y4

y5

y6

y7

b3b4 b5 b8 b9

[M+2H]2+

b6 b7 y9

y8

y1y2y3y4y5y6y7y8y9M+H

b9b7 b8b5 b6b2 b4b3b1 M+H

Putative Fragment Comparison

Candidate peptide sequenceOutput:

Peptide mass, tryptic digestion properties, compositional information…

Input:

Generating candidate peptide sequences

• Combinatorial enumeration• Sequence database

Putative Fragment Comparison

Combinatorial enumeration• All possible sequences can be checked• Too many candidates• Many candidates are equally plausible. • “Best” candidate may have no biological relevance

Sequence database• Sequences with no biological relevance are eliminated• Few candidates to evaluate• Sequence permutations eliminated• Correct candidate might be missing from database• All candidates have some biological relevance

Candidate Peptide Evaluation

Score functions for candidate peptide evaluation

• Shared peak count

• Correlation

• Pr [ spectrum | peptide ]

By itself, the score of a peptide candidate is meaningless!

Candidate Peptide Evaluation

1 83.5 TCVADESAENCDK ALBU_HUMAN,ALBU_MACMU,ALBU_PIG

2 109.4 KCAADESAENCDK ALBU_HORSE

3 115.3 FKKCDGDTVWDK SRB9_YEAST

4 121.7 SGKAPILIATDVASR DD17_HUMAN

5 124.1 MGFINLSLFDVDK RRPO_RCNMV

6 126.4 QSDEDCVEIYIK LEM2_BOVIN

7 127.8 MLDQSTDFEERK SMOO_HUMAN

8 128.1 NFEMDTLTLLSSK DHAS_BACSU

9 129.3 DNIAKEYENKFK HPAA_HELNE

10 129.6 VEHVAFGLVLGDDK SYR_CAEEL

11 129.6 LVEVSHDAEDEQK DYHC_NEUCR

12 129.9 KTGYAHFFSRER HIS2_THEMA

13 130.2 DYTLFALQEGDVK RK27_PLECA,RK27_PLEHA

14 130.3 FNVTISLTDFITK SYK_CAEEL

15 130.4 ENCQTLDNYVSR GS27_CAEEL

Candidate Peptide Evaluation