proteomics informatics workshop part i: protein identification david fenyö february 4, 2011...

Proteomics Informatics WorkshopPart I: Protein Identification

David Fenyö

February 4, 2011

• Introduction to proteomics• Introduction to mass spectrometry• Analysis of mass spectra• Database searching• Spectrum library searching• de novo sequencing• Significance testing

Why Proteomics?

Geiger et al., “Proteomic changes resulting from gene copy number variations in cancer cells”, PLoS Genet. 2010 Sep 2;6(9). pii: e1001090.

MSMS/MS

Biological System

Samples

Information about each sample

Information about the biological system

Measurements

What does the sample contain?

How much?

Proteomics Informatics

ExperimentalDesign

Data Analysis

InformationIntegration

SamplePreparation


How much?

Data Analysis

Biological System

Information about each sample

Information about the biological system


How much?

Sample Preparation

ExperimentalDesign

Data Analysis

InformationIntegration

MSMS/MS

Samples

Measurements

SamplePreparation


How much?

EnrichmentSeparation etc

Digestion

Topdown

Bottomup

Mass Spectrometry (MS)

Ion Source

Mass Analyzer

Detector

MALDIESI

QuadrupoleIon Trap (3D, linear)

Time-of-FlightOrbitrapFTICR

mass/charge

inte

nsi

ty

Mass Spectrometry – MALDI-TOF

Ion Source

Mass Analyzer

Detector

MALDI Time-of-Flight

Laser

Detector

Detec

tor

Ion mirror

HV

Tandem Mass Spectrometry (MS/MS)

Mass Analyzer 1

Frag-mentation

Detector

mass/chargein

ten

sity

Ion Source

Mass Analyzer 2

CAD – Collision Activated

Dissociation

Quadrupole Quadrupole Quadrupole

time

m/z

time

m/z

time

m/z

time

m/z

time

m/z

time

m/z

NO

YES

time

m/z

time

m/z YESm/z

timeDm/z is constant

Dissociation Techniques

CAD: Collision Activated Dissociation (b, y ions)

increase of internal energy through collisions

ETD: Electron Transfer Dissociation (c, z ions)

radical driven fragmentation

Dissociation Techniques: CAD versus ETD

CAD

Low charge

Short peptides

Weakest bonds break first

Preferred cleavage N-terminal to proline

ETD

High charge

Up to intact proteins

More uniform fragmentation

No cleavage N-terminal to proline

Liquid Chromatography (LC)-MS/MS

Mass Analyzer 1

Frag-mentation

Detector

inte

ns

ity

mass/charge

Ion Source

Mass Analyzer 2

LC

inte

ns

ity

mass/chargeinte

ns

ity

mass/charge

inte

ns

ity

mass/chargeinte

ns

ity

mass/chargeinte

ns

ity

mass/charge

Time

inte

ns

ity

mass/chargeinte

ns

ity

mass/chargeinte

ns

ity

mass/charge

inte

ns

ity

mass/chargeinte

ns

ity

mass/chargeinte

ns

ity

mass/charge

inte

ns

ity

mass/chargeinte

ns

ity

mass/chargeinte

ns

ity

mass/charge

Data Independent Acquisistion

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

1. MS2. MS/MS 13. MS/MS 24. MS/MS 35. MS6. MS/MS 17. MS/MS 28. MS/MS 39. MS10. MS/MS 111. MS/MS 212. MS/MS 313. MS14. MS/MS 115. MS/MS 216. MS/MS 317. MS18. MS/MS 119. MS/MS 220. MS/MS 321. MS22. MS/MS 123. MS/MS 224. MS/MS 3…

1. MS2. MS/MS 13. MS/MS 24. MS/MS 35. MS/MS 46. MS/MS 57. MS/MS 68. MS/MS 79. MS/MS 810. MS/MS 911. MS/MS 10

12. MS13. MS/MS 114. MS/MS 215. MS/MS 316. MS/MS 417. MS/MS 518. MS/MS 619. MS/MS 720. MS/MS 821. MS/MS 922. MS/MS 10…

Data Dependent Acquisistion

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty

Mass Spectrometry – ESI-LC-MS/MS

Mass Analyzer 1

Frag-mentation

Detector

Ion Source

Mass Analyzer 2

ESI Linear Ion Trap

Orbitrap

CADETD

Olsen J V et al. Mol Cell Proteomics 2009;8:2759-2769

Frag-mentation

HCD

Detector

Charge-State Distributions

mass/charge

inte

nsi

tyMALDI ESI

mass/charge

inte

nsi

ty

1+

1+ 2+

3+

4+

Peptide

Protein

2+

nnHM

zm M - molecular mass

n - number of chargesH – mass of a proton

mass/charge

inte

nsi

ty

mass/charge

inte

nsi

ty 1+ 27+2+

3+

4+

MALDI ESI

5+

31+

m = 1035 Da m = 1878 Da m = 2234 Da

Isotope Distributions

m/z m/z m/z

Inte

nsi

ty

0.015% 2H1.11% 13C 0.366% 15N0.038% 17O, 0.200% 18O, 0.75% 33S, 4.21% 34S, 0.02% 36S

Only 12C and 13C:p=0.0111n is the number of C in the peptidem is the number of 13C in the peptideTm is the relative intensity of the peptide m 13C

𝑇𝑚=( 𝑛𝑚)𝑝𝑚(1−𝑝)𝑛−𝑚

12C14N16O1H32S

+1Da

+2Da

+3Da

Isotope distributions

Peptide mass

Inte

nsi

ty r

atio

Peptide mass

Inte

nsi

ty r

atio

m/z

monoisotopicmass

GFP 29kDa

Noise

m/z

Inte

ns

ity

Peak Finding

m/z

Inte

ns

ity

2/||

)()(wlk

kIlSFind maxima of

The centroid m/z of a peak

2

2

/||

/||

)(

)()(

wlk

wlk

kI

kzmkI

The signal in a peak can beestimated with the RMSD

22

2

//||

))((w

wlkIkI

and the signal-to-noise ratio of a peak can be estimated by dividing the signal with the RMSD of the background

Isotope Clusters and Charge State

m/z

Inte

ns

ity

Possible to Determine Charge?

Yes

Yes

Maybe

No

1+1

1

1

2+0.5

0.5

0.5

3+0.33

0.33

0.33

Mass spectrometry

LysisFractionation

Identification – Peptide Mass Fingerprinting

MS

Digestion

Identified Proteins

Example data – Peptide Mapping by MALDI-TOF

m/z1000 4500

Inte

nsity

1800

0

D:\Users\Fenyo\Desktop\ATP.txt (15:42 02/03/11)Description: none available m/z2280 2400

Inte

nsi

ty

700

0

D:\Users\Fenyo\Desktop\ATP.txt (15:46 02/03/11)Description: none available

m/z1300 1460In

ten

sity

45

0


m/z1444.0 1458.0

Inte

nsi

ty

35

0


m/z2378.0 2394.0

Inte

nsi

ty

700

0


S. cerevisiae

Human

Information Content in a Single Mass Measurement

Tryptic peptide mass [Da]

1000 2000 3000

Tryptic peptide mass [Da]

1000 2000 3000

Av

g.

#o

f m

atc

hin

g p

ep

tid

es

#of matching peptides 1 2 3 4 6 8 10

10

8

6

432

1

Av

g.

#o

f m

atc

hin

g p

ep

tid

es

10

8

6

432

1

#of matching peptides 1 2 3 4 6 8 10

Mass spectrometry

LysisFractionation


MS

Digestion

Identified Proteins

Peak Finding Charge determination

De-isotopingSearching

MS


MS

Digestion

All Peptide Masses

Pick Protein

Compare, Score, Test Significance

Rep

eat for each

pro

teinSequence

DB

Identified Proteins

ProFound – Search Parameters

http://prowl.rockefeller.edu/

ProFound Results

Example data – ESI-LC-MS/MS

Time

m/z

m/z

% R

ela

tive

Ab

un

da

nce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

9071020663 778 1080

1022

MS/MS

Peptide FragmentationMass

Analyzer 1Frag-

mentationDetector

Ion Source

Mass Analyzer 2

b

y

Identification – Tandem MS

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

Tandem MS – Sequence Confirmation

KLEDEELFGS

K1166

L1020

E907

D778

E663

E534

L405

F292

G145

S88 b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

KLEDEELFGS


147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

KLEDEELFGS


147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

907 1020663 778 1080

1022

KLEDEELFGS


147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

907 1020663 778 1080

1022

113

KLEDEELFGS

113


147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

907 1020663 778 1080

1022

129

129

KLEDEELFGS


KLEDEELFGS

147K

1166L

260

1020E

389

907D

504

778E

633

663E

762

534L

875

405F

1022

292G

1080

145S

1166

88

y ions

b ions

m/z

% R

elat

ive

Abu

ndan

ce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

907 1020663 778 1080

1022


Tandem MS – de novo Sequencing

m/z

% R

ela

tive

Ab

un

da

nce

100

0250 500 750 1000

[M+2H]2+

762

260 389 504

633

875

292405 534

9071020663 778 1080

1022

Mass Differences

1-letter code

3-letter code

Chemical formula

Monoisotopic

Average

A Ala C3H5ON 71.0371 71.0788

R Arg C6H12ON4 156.101 156.188

N Asn C4H6O2N2 114.043 114.104

D Asp C4H5O3N 115.027 115.089

C Cys C3H5ONS 103.009 103.139

E Glu C5H7O3N 129.043 129.116

Q Gln C5H8O2N2 128.059 128.131

G Gly C2H3ON 57.0215 57.0519

H His C6H7ON3 137.059 137.141

I Ile C6H11ON 113.084 113.159

L Leu C6H11ON 113.084 113.159

K Lys C6H12ON2 128.095 128.174

M Met C5H9ONS 131.04 131.193

F Phe C9H9ON 147.068 147.177

P Pro C5H7ON 97.0528 97.1167

S Ser C3H5O2N 87.032 87.0782

T Thr C4H7O2N 101.048 101.105

W Trp C11H10ON2 186.079 186.213

Y Tyr C9H9O2N 163.063 163.176

V Val C5H9ON 99.0684 99.1326

Amino acid masses

Sequences consistent

with spectrum

Tandem MS – de novo Sequencing260 292 389 405 504 534 633 663 762 778 875 907 1020 1022 1079

260 32 129 145 244 274 373 403 502 518 615 647 760 762 819

292 97 113 212 242 341 371 470 486 583 615 728 730 787

389 16 115 145 244 274 373 389 486 518 631 633 690

405 99 129 228 258 357 373 470 502 615 617 674

504 30 129 159 258 274 371 403 516 518 575

534 99 129 228 244 341 373 486 488 545

633 30 129 145 242 274 387 389 446

663 99 115 212 244 357 359 416

762 16 113 145 258 260 317

778 97 129 242 244 301

875 32 145 147 204

907 113 115 172

1020 2 59

1022 57

260 292 389 405 504 534 633 663 762 778 875 907 1020 1022 1079

260 32 E 145 244 274 373 403 502 518 615 647 760 762 819

292 P I/L 212 242 341 371 470 486 583 615 728 730 787

389 16 D 145 244 274 373 389 486 518 631 633 690

405 V E 228 258 357 373 470 502 615 617 674

504 30 E 159 258 274 371 403 516 518 575

534 V E 228 244 341 373 486 488 545

633 30 E 145 242 274 387 389 446

663 V D 212 244 357 359 416

762 16 I/L 145 258 260 317

778 P E 242 244 301

875 32 145 F 204

907 I/L D 172

1020 2 59

1022 G


X

X

X

X

X

X

…GF(I/L)EEDE(I/L)……(I/L)EDEE(I/L)FG……GF(I/L)EEDE(I/L)……(I/L)EDEE(I/L)FG…

Peptide M+H = 11661166 -1079 = 87 => S

SGF(I/L)EEDE(I/L)…

SGF(I/L)EEDE(I/L)…

1166 – 1020 – 18 = 128Þ K or Q

SGF(I/L)EEDE(I/L)(K/Q)


Challenges in de novo sequencing

Neutral loss (-H2O, -NH3)

Modifications

Background peaks

Incomplete information

Challenges in de novo sequencing

Neutral loss (-H2O, -NH3)

Modifications

Background peaks

Incomplete information

MS/MS

LysisFractionation

Tandem MS – Database Search

MS/MS

Digestion

SequenceDB

All FragmentMasses

Pick Protein


Rep

eat for all p

rotein

s

Pick PeptideLC-MS

Rep

eat for

all pep

tides

Tandem MS – Database Search

X! Tandem - Search Parameters

http://www.thegpm.org/

X! Tandem - Search Parameters

sequences

sequences

spectra

Multi-stage searching

Trypticcleavage

Modifications #1

Modifications #2

Point mutation

X! Tandem

Search Results

How many fragment masses are needed for identification?

1

0

0.5

5 10 15Number of Matching Fragments

Pro

bab

ilit

y o

f Id

enti

fica

tio

n

Critical # ofMatching Fragments

16

0

8

A parameter

Cri

tica

l #

of

Mat

chin

g F

rag

men

ts

Critical # ofMatching Fragments

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n

Number of fragment ions

1000 Da1500 Da2000 Da2500 Da

Small peptides are slightly more difficult to identify

Dmprecursor = 1 DaDmfragment = 0.5 DaNo modification

mprecursor

0

2

4

6

8

10

12

14

16

500 1000 1500 2000 2500 3000C

riti

ca

l #

of

Fra

gm

en

tsPrecursor Mass [Da]

A lower precursor mass error requires fewer fragment masses for identification of unmodified peptides

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n


0.01 Da

1 Da

10 Da

mprecursor = 2000 DaDmfragment = 0.5 DaNo modification

0

2

4

6

8

10

12

14

16

0.001 0.01 0.1 1 10C

riti

ca

l #

of

Fra

gm

en

tsPrecursor Mass Error [Da]

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n


0.01 Da0.5 Da1 Da2 Da

The dependence on the fragment mass error is weak below a threshold for identification

of unmodified peptides

Dmfragment

mprecursor = 2000 DaDmprecursor = 1 DaNo modification

0

2

4

6

8

10

12

14

16

0.001 0.01 0.1 1 10C

riti

ca

l #

of

Fra

gm

en

tsFragment Mass Error [Da]

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n


0%

50%

80%

A moderate number of background peaks can be tolerated when identifying

unmodified peptides

mprecursor = 2000 DaDmprecursor = 1 DaDmfragment = 0.5 DaNo modification

Background

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100C

riti

ca

l #

of

Fra

gm

en

ts

Background [%]

A large number of background peaks can be tolerated if the fragment mass is accurate

mprecursor = 2000 DaDmprecursor = 1 DaDmfragment = 0.01 DaNo modification

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n


0%

50%

80%

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100C

riti

ca

l #

of

Fra

gm

en

ts

Background [%]

Background

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Pro

ba

bili

ty o

f Id

en

tifi

ca

tio

n


Phosphorylated

Unmodified

Identification of phosphopeptides is only slightly more difficult

mprecursor = 2000 DaDmprecursor = 1 DaDmfragment = 0.5 Da

LysisFractionation

DigestionLC-MS/MS

Identification – Spectrum Library Search

MS/MS

Spectrum Library

PickSpectrum


Rep

eat for

all spectra

Identified Proteins

0

2

4

6

8

10

0 10 20 30 40 50

peptide length

fract

ion o

f lib

rary

(%

)Spectrum Library Characteristics – Peptide Length

0

10

20

30

40

50

10 30 50 70 90 110 130 150 170 190

protein Mr (kDa)

% c

ove

rag

e

residues

peptides

Spectrum Library Characteristics – Protein Coverage

Spectrum Library Characteristics – Size

Species Spectra Peptides Redundancy

H. sapiens 1002326 270345 ×3.7P. troglodytes 889232 238688 ×3.7

M. mulata 754601 195701 ×3.9M. musculus 732382 199182 ×3.7R. norvegicus 637776 160439 ×4.0

B. taurus 592070 140063 ×4.2E. caballus 590514 139849 ×4.2

S. cerevisiae 201253 133166 ×1.5C. elegans 190952 90981 ×2.1

D. rerio 174049 46546 ×3.7T. rubripes 169551 36514 ×4.6

D. melanogaster 122353 71928 ×1.7A. thaliana 111689 62574 ×1.8

Library spectrum

Test spectrum

(5:25)

(5:25)

Results: 4 peaks selected, 1 peak missed


Matches Probability

1 0.452 0.153 0.0164 0.000395 0.0000037

Apply a hypergeometric probability model: - 25 possible m/z values; - 5 peaks in the library spectrum; and - 4 selected by the test spectrum.

How likely is this?


If you have 1000 possible m/z values and 20 peaks in test and library spectrum?

1.0E-14

1.0E-12

1.0E-10

1.0E-08

1.0E-06

1.0E-04

1.0E-02

1.0E+00

1 2 3 4 5 6 7 8 9 10

matches

p 1 matched: p = 0.6

5 matched: p = 0.0002

10 matched: p = 0.0000000000001


ExperimentalMass Spectrum

Library of AssignedMass Spectra

M/Z

Best search result


X! Hunter Result

Query Spectrum

Library Spectrum

Significance Testing

False protein identification is caused by random matching

An objective criterion for testing the significance of protein identification results is necessary.

The significance of protein identifications can be tested once the distribution of scores for false results is known.

Significance Testing - Expectation Values

The majority of sequences in a collection will give a score due to random matching.

Database Search

M/Z

List of Candidates

ExtrapolateAnd Calculate Expectation Values

List of Candidates With Expectation Values

Distribution of Scoresfor Random and False Identifications

Significance Testing - Expectation Values

Rho-diagrams: Overall Quality of a Data Set

)exp()( sse

iN

iNi

EE i

))}1exp(1{

)}1exp(1){exp(log()log()(

0

)}1exp(){exp()exp(

)1exp(

iiNNdeie

ieiE

Definition: Ei (i=0,-1,-2,…) is the number of spectra that has been assigned an expectation value between exp(i) and exp(i-1). For random matching:

Expectation values as a function of score for random matching:

-6

-5

-4

-3

-2

-1

0

-6 -5 -4 -3 -2 -1 0

log(e)

Rho-diagramRandom Matching

Rho-diagramData Quality

-10

-8

-6

-4

-2

0

-10 -8 -6 -4 -2 0

log(e)

Rho-diagramParameters

Summary

Protein identification strategies:- de Novo Sequencing- Searching Sequence Collections- Searching Spectrum Libraries

It is important to report the significance of the results

Google Group for Proteomics in NYC

Please join!

Proteomics Informatics WorkshopPart II: Protein Characterization

February 18, 2011

• Top-down/bottom-up proteomics• Post-translational modifications• Protein complexes• Cross-linking• The Global Proteome Machine Database

Proteomics Informatics WorkshopPart III: Protein Quantitation

February 25, 2011

• Metabolic labeling – SILAC• Chemical labeling• Label-free quantitation• Spectrum counting• Stoichiometry• Protein processing and degradation• Biomarker discovery and verification

Proteomics Informatics Workshop

Part I: Protein Identification, February 4, 2011

Part II: Protein Characterization, February 18, 2011

Part III: Protein Quantitation, February 25, 2011

proteomics informatics workshop part i: protein identification david fenyö february 4, 2011...

Documents

proline slide

sample information

data analysis slide

constant slide

linear time

biological system information

dissociation techniques

biological system measurements