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iPRG 2012:

A Study on Detecting Modified Peptides in a Complex Mixture

ABRF 2012, Orlando, FL3/17-20/2012

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IPRG2012 STUDY:DESIGN

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Study Goals

• Primary: Evaluate the ability of participants to identify modified peptides present in a complex mixture

• Secondary: Find out why result sets might differ between participants

• Tertiary: Produce a benchmark dataset, along with an analysis resource

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Study Design

• Use a common, rich dataset• Use a common sequence database• Allow participants to use the bioinformatic tools and

methods of their choosing• Use a common reporting template• Report results at an estimated 1% FDR (at the

spectrum level)• Ignore protein inference

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Sample

• Tryptic digest of yeast (RM8323 – NIST), spiked with 69 synthetic modified peptides (tryptic peptides from 6 different proteins – sPRG)– Phospho (STY)– Sulfo (Y)– Mono-, di-, trimethyl (K)– Mono-, dimethyl (R)– Acetyl (K)– Nitro (Y)

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Supplied Study Materials

• 5600 TripleTOF dataset (i.e. WIFF file)– WIFF, mzML, dta, MGF (de-isotoped);–

conversions by MS Data Converter 1.1.0– MGF (not de-isotoped – conversion by Mascot

Distiller 2.4)• 1 fasta file (UniProtKB/SwissProt S. cerevisiae,

human, + 1 bovine protein + trypsin from Dec. 2011)

• 1 template (Excel)• 1 on-line survey (Survey Monkey)

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Instructions to Participants

1. Retrieve and analyze the data file in the format of your choosing, with the method(s) of your choosing

2. Report the peptide to spectrum matches in the provided template

3. Report measures of reliability for PTM site assignments (optional)

4. Fill out the survey

5. Attach a 1-2 page description of the methodology employed

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iPRG 2012 STUDY:

PARTICIPATION

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Study advertised on the ABRF website and listserv and by direct invitation from iPRG members

1. Email participation request to

‘iPRGxxxx@gmail.com’

2. Send official study letter with instructions

3. All further communication (e.g., questions, submission) through

‘iPRGxxx.anonymous@gmail.com’

iPRG membersParticipant

Questions / Answers

“Anonymizer”

Soliciting Participants and Logistics

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Participants (i) – overall numbers

• 24 submissions– One participant submitted two result sets

• 9 initialed iPRG member submissions (with appended ‘i’)

• 2 vendor submissions (identifiable by appended ‘v’)

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About the Participant

11

Bioinformatician/Software Developer

Mass Spectrometrist

Lab Scientist

Director/Manager

Post-Grad

ABRF MemberNonmember

I routinely analyze these sorts of dataI have worked with several data setsI have worked with a few data setsComplete Novice

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About the Participant’s Lab

12

AcademicManufacturer/VendorBiotech/Pharma/IndustryGovernmentOther

North AmericaEuropeAsiaAustralia/NZAfrica Core Only

Software devel-opment only (no research facility)

Conduct both core functions and non-core lab research

Non-core research lab

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Participation in sPRG Study

YESNO

•Only one participant indicated he used sPRG information to aid his analysis.

• This person was one of the least successful in identifying the spiked-in peptides!

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Search Engine Used

14

Mascot

OMSSA

X!Tandem

Peaks/PeaksD

BpFind

SpectraST

Protein Pilo

t

Byonics

MS-GFDB

MyriMatch

Protein Pro

specto

r

Sequest

Spectrum M

ill0

1

2

3

4

5

6

7

8

9

10

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Site Localization Software

Mascot

In-house

Peaks/PeaksD

B

OMSSApFind

Andromeda

A-Score

MyriMatch

Other

Protein Pilo

t

Protein Pro

specto

r

Proteome Disc

overer

Scaffold

Sequest

SpectraST

Spectrum M

ill0

1

2

3

4

5

6

7

8

•4 participants did not list using software for site localization.

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Summary of Submitted Results71

755v

5828

8v

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

1000

2000

3000

4000

5000

6000

7000

# spectra Id Yes

# unique Peptides UC ID Yes

Only reported modified peptides

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Summary of IDs and Localizations71 58

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

1000

2000

3000

4000

5000

6000

7000# No Mods# Common Mods (^q,^c,m,n,q)# Nterm Mods# AA Mutation Mods# Interesting Mods

#

Sp

ectr

a

71 58

3356

4 93

1121

1

5840

9 87 94 97 42 77

2306

8 40 87

9265

3 34

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

100

200

300

400

500

600

700# Interesting Mod Loc Certainty N# Interesting Mod Loc Certainty Y

#

Sp

ectr

a

Peptide Identificationin all Spectra

Site Localization in SpectraWith Interesting Modifications

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Overlap of spectrum identifications

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

2000

4000

6000

8000

10000

12000

# Participants Agreeing

Cu

mm

ula

tive

# S

pec

tra 7840 agreed on by 3 or more participants

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An Andromeda/MaxQuant MG MS-GFDB pF pFind Sc Scaffold

AS A-Score MM MyriMatch Pk PEAKS SM Spectrum Mill

By Byonics MO MODa PkDB PEAKSDB Sq Sequest

IH In-house software O OMSSA PPi Protein Pilot ST SpectraST

IP IDPicker Ot Other PPr Protein Prospector TPP TransProteomic Pipeline

M Mascot P/PP Pep/Prot Prophet PR PhosphoRS XL Excel

MDe Mascot Delta Score PD ProteomeDiscoverer PW ProteoWizard XT X!Tandem

MDi Mascot Distiller

7175

5v

5828

8v

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

1000

2000

3000

4000

5000

6000

7000 #ND No Id, Diff from Consensus#Y<3 P Id Yes#YD Yes Id, Diff from Consensus#NS No Id, Same as Consensus#YS Yes Id, Same as Consensus

# S

pec

tra

Room for improvement in thresholding?

71755v 58288v 33564 93128i 11211 58409 87133i 94158i 97053i 42424i 77777i 23068 40104i 87048i 92653 34284i 23117 74564 14151 52781 47603 14152 45511 11821Peaklist mgf mzML mzML mgf mgf mgf mzML mgf_nd mgf mgf_nd mgf mzML mzML mgf_nd mgf mgf mzML mgf mgf_nd mgf mgf mgf_nd mgf mgf_nd

          mzML       mzML mgf_nd                                            WIFF                            

Spectral Pre-Processing Pk Pk PPi MDi Ot SM pF Pk MDi PDPkDB PW PkDB Sq

Peptide Identification By Pk Pk M PPi M O PPr pF M MG M P/PP SM pF M Pk M PPi M O M M PDPkDB PkDB O ST MM O TPP PkDB Sq

ST XT IH XT XTXTOt

Discovery of Unexpected Mods By Pk Pk M PPi ST PPr pF IH MO SM pF M Pk PPi O M

PkDB PkDBSite Localization Pk Pk MDe PPi M PPr pF M IH M SM pF M Pk M AS IH An MDe PD

PkDB O MM O PkDB Sc Ot SqST IH

Results Filtering By Pk Pk IH PPi P/PP P/PP PPr pF IP IH XL IH XL pF M Pk M XL Sc M PRPkDB XL Ot TPP TPP XL PkDB

NTT 2 1 1 2 1 ? 1 1 1 2 1 ? 1 2 2 2 2 ? 1 2 2 2 ? 2

Experience5-10 years

5-10 years

5-10 years

>10 years < 1 year 3-4 years

5-10 years

>10 years

5-10 years

5-10 years

5-10 years 3-4 years

5-10 years

>10 years 1-2 years

>10 years 1-2 years

>10 years

>10 years 1-2 years

>10 years

>10 years 1-2 years

>10 years

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ESR and FDRExtraordinary Skill Rate or High False Discovery Rate? ESR + FDR = 100* (Y<3P+YD)/total ids Y 24 participants

3 for consensus

7175

5v

5828

8v

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

2

4

6

8

10

12

14

16

18

20

22

Y<3 P percent

YD percent

%

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Characteristics of consensus spectra7840 spectra >=3 participants agreeing on sequence

Consensus requires agreement onSequence, but not modification localization

Nterm-Acetyl

Nterm-Carbamyl

Nterm-Other

PyroGlu Q

PyroGlu E

PyroCar-bamidomethylCys

m: Oxidation

n: Deamidation

q: Deamidation

c

d

e

w: Oxidation

p

k

r

s

t

y

No Variable Mods

1 10 100 1000 10000

447

3

3

70

11

6

94

310

183

6

94

107

5

3

165

45

294

137

132

6117

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Peak lists

• Two types of peak lists were supplied– Deisotoped and non deisotoped

• Can only tell fragment charge state from non-deisotoped

• Requires search engine to be able to de-isotope spectrum

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Peaklists

•Number of spectra with undefined precursor charge stateDeisotoped 1031 (304 in consensus results)Non-deisotoped 6094 (1140 in consensus results)

•For 1013 out of 7840 consensus spectra the precursor m/z differ by greater than 0.02 Da between deisotoped and non-deisotoped peak list.

•For 238 consensus spectra the peak lists had different specified charge state

–193 consensus results only possible with deisotoped peak list–45 consensus results only possible with non-deisotoped peak list

–For 19 consensus results multiple people who searched the nd peak list agreed on a confident different answer–For 4 consensus results multiple people who searched the deisotoped peak list agreed on a confident different answer

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Mixed Spectra465.19 2+

464.59 3+

464.59 3+Non-deisotopedpeaklist

465.19 2+Deisotopedpeaklist

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DEGkASSAKFkDLGEENFK

GLFIIDDkGILRLkAEGSEIR

LkLVSELWDAGIKADEGISFrGLFIIDDK

AEGSEIrLAKLDELrDEGK

GILrQITVNDLPVGRGTrDYSPR

FPkAEFAEVSKEkLLDFIK

LkAEGSEIRLkAQLGPDESK

NGDTASPkEYTAGRAEGSEIrLAK

ESTLHLVLrLRLDELrDEGK

GILrQITVNDLPVGRGTrDYSPR

EkLLDFIKFPkAEFAEVSK

LkAEGSEIRLkAQLGPDESK

NGDTASPkEYTAGREkLLDFIK

FPkAEFAEVSKLkAEGSEIR

LkAQLGPDESKNGDTASPkEYTAGR

0 5 10 15 20 25

Synthetic Peptide ID by Peptide

0 5 10 15 20 25 30

ALAPEyAKDISLSDyK

SVSDyEGKTIAQDyGVLK

TLSDyNIQKADEGIsFRAEFAEVsKDISLsDyKDISLSDyKDIsLSDyKDIsLsDyK

DQGGELLsLREStLHLVLREsTLHLVLREstLHLVLR

IFsIVEQRLVNEVtEFAK

LVQAFQFtDKNVAVDELsR

SVsDYEGKtHILLFLPKsVsDyEGKTHILLFLPKsVSDYEGK

TITLEVEPsDtIENVKtItLEVEPsDtIENVK

tLSDYNIQKtLsDYNIQK

TLSDyNIQKtLsDyNIQK

tyEtTLEKVDAtEEsDLAQQyGVR

VPQVstPtLVEVSRVPQVstPtLVEVsR

WVtFIsLLFLFssAYSRyKPEsDELtAEK

ALAPEyAKDISLSDyK

SVSDyEGKTIAQDyGVLK

TVIDyNGER

Sulfo

Phospho

Nitro

Trimethyl

Methyl (R)

Dimethyl (R)

Acetyl

Methyl (K)

Dimethyl (K)

# participants # participants

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Synthetic Peptide ID by Participant

71755v

58288v

33564

93128i

11211

58409

87133i

94158i

97053i

42424i

77777i

23068

40104i

87048i

92653

34284i

23117

74564

14151

52781

47603

14152

45511

11821

Acetyl (K)1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Dimethyl (K)1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Dimethyl (R)1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Methyl (K)1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Methyl (R)1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Nitro (Y)1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

Phospho (STY)

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

Sulfo (Y)1 1

1 1

1 1 1

1 1 1 1

Trimethyl (K)1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

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Correct Localization of Modified Synthetic Peptides

70 synthetic modified peptides were spiked into sample.7 of these were confidently found by no participant

Correct localization & nameof modification reported

7175

5v

5828

8v

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

10

20

30

40

50

60

70

# Spiked Unique Peptides Correct Mod Loc, Name; Mod Loc Certainty N# Spiked Unique Peptides Correct Mod Loc, Name; Mod Loc Certainty Y

# U

niq

ue

Pep

tid

es C

orr

ect

Mo

d L

oca

liza

tio

n

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FLR of Modified Synthetic PeptidesFLR = 100% * # PSMs wrong localization of s,t,y,k,r # PSMs wrong + right localization of s,t,y,k,r

Ignored PSMs contain mods of residues other than s,t,y,k,r . Sample handling mods (n,q,d,e, etc).

71 58

3356

4 93

1121

1

5840

9 87 94 97 42 77

2306

8 40 87

9265

3 34

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

50

100

150

200

250

300

350

400

450

500# Spiked PSMs Mod Loc Certainty N# Spiked PSMs Mod Loc Certainty Y, Ignored# Spiked PSMs Wrong Mod Loc; Mod Loc Certainty Y

# S

pik

ed

Pep

tid

e P

SM

s

71 58

3356

4

9312

8i

1121

1

5840

9

8713

3i

9415

8i

9705

3i

4242

4i

7777

7i

2306

8

4010

4i

8704

8i

9265

3

3428

4i

2311

7

7456

4

1415

1

5278

1

4760

3

1415

2

4551

1

1182

1

0

2

4

6

8

10

12

14

16

18

Sp

iked

Pep

tid

e P

SM

FL

R (

%)

5% 5% 1% 1-2% 1% <1 0.5 10% <30% 0.01 <5% <1%

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Incorrect Localization by Peptide

71755v

58288v

33564

93128i

11211

58409

87133i

94158i

97053i

42424i

77777i

23068

40104i

87048i

92653

34284i

23117

74564

14151

52781

47603

14152

45511

11821

EKLLDFIKAEGSEIRLAK 1VDATEESDLAQQYGVRTITLEVEPSDTIENVKESTLHLVLRAEFAEVSK 1LKLVSELWDAGIKDQGGELLSLRTYETTLEK 1NGDTASPKEYTAGR 1 4 4 1 4 1LKAEGSEIRTVIDYNGER 4 3 4 4 4 4 1 1 3 3ADEGISFRYKPESDELTAEKGTRDYSPR 1VPQVSTPTLVEVSRADEGISFRGLFIIDDKALAPEYAKTIAQDYGVLK 1 1 1 1 2 1 1 1 1THILLFLPKSVSDYEGK 5 3 6 8 1 1 2 4 1WVTFISLLFLFSSAYSRIFSIVEQRTLSDYNIQK 3 1GILRQITVNDLPVGRNVAVDELSRLDELRDEGKESTLHLVLRLR 1DEGKASSAKSVSDYEGK 1 1 1 1 1 1LVQAFQFTDKLVNEVTEFAK 1GLFIIDDKGILR 1FPKAEFAEVSKLKAQLGPDESKDISLSDYK 1 1 2 3 1 3 6FKDLGEENFK

Number of PSM’s with Incorrect Site Localization – Mod Loc Confidence Y• Present as sulfo-Tyr• Present as phospho S-10 often mislocalized as S-12 or Y-14• Present as mono, di, tri methyl K often mislocalized at R

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30

DISLSDY(Phospho)K

DISLSDY(Sulfo)K

Observe modified fragmentions.

Observe ‘unmodified’ fragment ions.Spectrum looks essentially identical to unmodified peptide spectrum

Phospho vs Sulfo

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Conclusions

• Reasonable number of participants from around the globe, mainly experienced users but a few first-timers

• Large spread in number of spectra identified

• False negatives (NS) are generally much higher than false positives, so there is generally room for improvement

• Peak list was a significant factor on performance

• Varied performance in detecting PTMs

• Most participants struggled with sulfation

• Multiply phosphorylated harder to find than singly

• Most common errors in site assignment were:

• Reporting sulfo(Y) as phospho(ST)

• Mis-assignment of site/s in multiply phosphorylated peptides

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What did the participants think?

22 out of 24 participants found the study useful

“Too many modifications at the same time. Manual validation is necessary and the right time necessary for this study is too demanding for this challenge.”

“The spiked proteins made it possible to game the study - look for the uncommon modifications only on the spikes. Of course we didn't do this. Overall I'd say this was a flawed but very interesting ABRF study.”

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3333

Very ConfidentConfidentNot Confident

Very ConfidentConfidentNot ConfidentNo Experience

Before After

Participant’s Confidence in Analyzing PTM Data

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0-8 8-16 16-24 24-32 32-40 40-48 >480

2

4

6

8

10

12

Time (hrs)

Too difficultChallengingJust right

How difficult do you think this study was?

What was your total analysis time for the entire project?

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35

Based on this study, would you consider participating in future ABRF studies?

YesNo

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36

Thank you! Questions?

iPRGNuno BandeiraRobert Chalkley(chair)Matt ChambersKarl ClauserJohn CottrellEric DeutschEugene KappHenry LamHayes McDonaldTom Neubert (EB liaison)Ruixiang Sun

Dataset CreationChris Colangelo

Anonymizer:Jeremy Carver, UCSD

THANK YOU TO ALLSTUDY PARTICIPANTS!