Protein Identification by Sequence Database Search

Protein Identification by

Sequence Database Search

Protein Identification by


Nathan EdwardsDepartment of Biochemistry and Mol. & Cell. BiologyGeorgetown University Medical Center

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Peptide Mass Fingerprint

Cut out2D-Gel

Spot

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Trypsin Digest

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MS

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• Trypsin: digestion enzyme• Highly specific• Cuts after K & R except if followed by P

• Protein sequence from sequence database• In silico digest• Mass computation

• For each protein sequence in turn:• Compare computer generated masses with

observed spectrum

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Protein Sequence

• Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

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Protein Sequence

• Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

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Amino-Acid Masses

Amino-Acid Residual MW Amino-Acid Residual MW

A Alanine 71.03712 M Methionine 131.04049

C Cysteine 103.00919 N Asparagine 114.04293

D Aspartic acid 115.02695 P Proline 97.05277

E Glutamic acid 129.04260 Q Glutamine 128.05858

F Phenylalanine 147.06842 R Arginine 156.10112

G Glycine 57.02147 S Serine 87.03203

H Histidine 137.05891 T Threonine 101.04768

I Isoleucine 113.08407 V Valine 99.06842

K Lysine 128.09497 W Tryptophan 186.07932

L Leucine 113.08407 Y Tyrosine 163.06333

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Peptide Mass & m/z

• Peptide Molecular Weight:N-terminal-mass (0.00) + Sum (AA masses) +C-terminal-mass (18.010560)

• Observed Peptide m/z:(Peptide Molecular Weight + z * Proton-mass (1.007825)) / z

• Monoisotopic mass values!

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Peptide Masses

1811.90 GLSDGEWQQVLNVWGK 1606.85 VEADIAGHGQEVLIR 1271.66 LFTGHPETLEK 1378.83 HGTVVLTALGGILK 1982.05 KGHHEAELKPLAQSHATK 1853.95 GHHEAELKPLAQSHATK 1884.01 YLEFISDAIIHVLHSK 1502.66 HPGDFGADAQGAMTK 748.43 ALELFR

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GL

SD

GE

WQ

QV

LN

VW

GK

VE

AD

IAG

HG

QE

VL

IR

LF

TG

HP

ET

LE

K

HG

TV

VL

TA

LG

GIL

K

KG

HH

EA

EL

KP

LA

QS

HA

TK

GH

HE

AE

LK

PL

AQ

SH

AT

KY

LE

FIS

DA

IIH

VL

HS

K

HP

GD

FG

AD

AQ

GA

MT

K

AL

EL

FR

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Sample Preparation for Tandem Mass Spectrometry

Enzymatic Digestand

Fractionation

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Single Stage MS

MS

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Tandem Mass Spectrometry(MS/MS)

MS/MS

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Peptide Fragmentation

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

Peptides consist of amino-acids arranged in a linear backbone.

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

RiRi+1

bi

yn-iyn-i-1

bi+1

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

RiCH-R’

bi

yn-iyn-i-1

bi+1

R”

i+1

i+1ai

xn-i

ci

zn-i

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Peptide: S-G-F-L-E-E-D-E-L-KMW ion ion MW

88 b1 S GFLEEDELK y9 1080

145 b2 SG FLEEDELK y8 1022

292 b3 SGF LEEDELK y7 875

405 b4 SGFL EEDELK y6 762

534 b5 SGFLE EDELK y5 633

663 b6 SGFLEE DELK y4 504

778 b7 SGFLEED ELK y3 389

907 b8 SGFLEEDE LK y2 260

1020 b9 SGFLEEDEL K y1 147

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K1166

L1020

E907

D778

E663

E534

L405

F292

G145

S88 b ions

100

0250 500 750 1000

m/z

% I

nte

nsit

y

147260389504633762875102210801166 y ions

y6

y7

y2 y3 y4

y5

y8 y9

b3

b5 b6 b7b8 b9

b4

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Peptide Identification

Given:• The mass of the precursor ion, and• The MS/MS spectrum

Output:• The amino-acid sequence of the

peptide

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100

0250 500 750 1000

m/z

% I

nte

nsit

y

KLEDEELFGS

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100

0250 500 750 1000

m/z

% I

nte

nsit

y

K1166

L1020

E907

D778

E663

E534

L405

F292

G145

S88 b ions

147260389504633762875102210801166 y ions

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K1166

L1020

E907

D778

E663

E534

L405

F292

G145

S88 b ions

100

0250 500 750 1000

m/z

% I

nte

nsit

y

147260389504633762875102210801166 y ions

y6

y7

y2 y3 y4

y5

y8 y9

b3

b5 b6 b7b8 b9

b4

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• No need for complete ladders• Possible to model all known peptide

fragments• Sequence permutations eliminated• All candidates have some biological

relevance• Practical for high-throughput peptide

identification• Correct peptide might be missing from

database!

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Peptide Candidate Filtering

• Digestion Enzyme: Trypsin• Cuts just after K or R unless followed by a

P.• Basic residues (K & R) at C-terminal

attract ionizing charge, leading to strong y-ions

• “Average” peptide length about 10-15 amino-acids

• Must allow for “missed” cleavage sites

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Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

No missed cleavage sitesMKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

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Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

One missed cleavage siteMKWVTFISLLFLFSSAYSRWVTFISLLFLFSSAYSRGVFRGVFRRRDAHKDAHKSEVAHRSEVAHRFKFKDLGEENFKDLGEENFKALVLIAFAQYLQQCPFEDHVKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

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Peptide Candidate Filtering

• Peptide molecular weight• Only have m/z value

• Need to determine charge state• Ion selection tolerance• Mass for each amino-acid symbol?

• Monoisotopic vs. Average• “Default” residual mass• Depends on sample preparation protocol• Cysteine almost always modified

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Peptide Molecular Weight

Same peptide,i = # of C13 isotope

i=0

i=1

i=2

i=3i=4

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…from “Isotopes” – An IonSource.Com Tutorial

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• Peptide sequence WVTFISLLFLFSSAYSR• Potential phosphorylation? S,T,Y + 80 Da

WVTFISLLFLFSSAYSR 2018.06









… …


- 7 Molecular Weights- 64 “Peptides”

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Peptide Scoring

• Peptide fragments vary based on• The instrument• The peptide’s amino-acid sequence• The peptide’s charge state• Etc…

• Search engines model peptide fragmentation to various degrees. • Speed vs. sensitivity tradeoff• y-ions & b-ions occur most frequently

• The scores have no apriority “scale”

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• High-throughput workflows demand we analyze all spectra, all the time.

• Spectra may not contain enough information to be interpreted correctly• ...cell phone call drops in and out

• Spectra may contain too many irrelevant peaks• …bad static

• Peptides may not match our assumptions• …its all Greek to me

• “Don’t know” is an acceptable answer!

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• Rank the best peptide identifications

• Is the top ranked peptide correct?

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• Incorrect peptide has best score• Correct peptide is missing?• Potential for incorrect conclusion• What score ensures no incorrect

peptides?• Correct peptide has weak score

• Insufficient fragmentation, poor score• Potential for weakened conclusion• What score ensures we find all correct

peptides?

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Statistical Significance

• Can’t prove particular identifications are right or wrong...• ...need to know fragmentation in advance!

• A minimal standard for identification scores...• ...better than guessing.• p-value, E-value, statistical significance

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Random Peptide Models

• "Generate" random peptides• Real looking fragment masses• No theoretical model!• Must use empirical distribution• Usually require they have the correct

precursor mass

• Score function can model anything we like!

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Fenyo & Beavis, Anal. Chem., 2003

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Fenyo & Beavis, Anal. Chem., 2003

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• Truly random peptides don’t look much like real peptides• Just use (incorrect) peptides from the sequence

database!

• Caveats:• Correct peptide (non-random) may be included• Homologous incorrect peptides may be

included• (Incorrect) peptides are not independent

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Extrapolating from the Empirical Distribution

• Often, the empirical shape is consistent with a theoretical model

Geer et al., J. Proteome Research, 2004 Fenyo & Beavis, Anal. Chem., 2003

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False Positive Rate Estimation

• A form of statistical significance

• Search engine independent• Easy to implement

• Assumes a single threshold for all spectra• Best if E-value or similar is used to compute a

spectrum normalized score

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False Positive Rate Estimation

• Each spectrum is a chance to be right, wrong, or inconclusive.• At any given threshold, how many peptide

identifications are wrong?• Computed for an entire spectral dataset

• Given identification criteria:• SEQUEST Xcorr, E-value, Score, etc., plus...• ...threshold

• Use “decoy” sequences • random, reverse, cross-species• Identifications must be incorrect!

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Decoy Search Strategies

• Concatenated target & decoy• “Competition” for best hit...• Masks good decoy scores due to spectral variation

• Separate searches• Cleaner estimation of false hit distribution• More conservative than concatenation

• Must ensure:• Decoy searches do not change target peptide scores• Single score distribution across dataset

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Decoy Search Strategies

• Reversed Decoys• Captures redundancy of peptide sequences• Susceptible to mass-shift anomalies• Bad choice for protein-level statistics

• Shuffled & Random Decoys• Multiple independent decoys can be created.• Better estimation of tail probabilities• More conservative than reversed decoys

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False Positive Rate Estimation: Concatenated Target & Decoy

1. Choose a threshold t.

2. Count # of (rank 1) target ids (Tt) with score ≥ t.3. Count # of (rank 1) decoy ids (Dt) with score ≥ t.

4. Compute FPR = ( 2 x Dt ) / ( Tt + Dt )

Principle:• Decoy peptides equally likely as false hits at rank 1

Issues:• What to do with decoy hits?• Change in database size may affect scores

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False Positive Rate Estimation: Separate Decoy Search

1. Choose a threshold t.

2. Count # of (rank 1) target ids (Tt) with score ≥ t.3. Count # of (rank 1) decoy ids (Dt) with score ≥ t.

4. Compute FPR = Dt / Tt

Principle:• Find the distribution of false hit scores, apply to target

Issues:• Can choose to merge after the fact...• Decoy search cannot change target scores• A few good decoy scores can inflate small FDR values

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Peptide Prophet

• Re-analysis of SEQUEST results• Spectrum dependant scores (XCorr) + • Additional features form discriminant

score

• Assumes that many of the spectra are not correctly identified• These identifications act like decoy hits

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Peptide Prophet

Distribution of spectral scores in the results

Keller et al., Anal. Chem. 2002

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Peptides to Proteins

Nesvizhskii et al., Anal. Chem. 2003

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• A peptide sequence may occur in many different protein sequences• Variants, paralogues, protein families

• Separation, digestion and ionization is not well understood

• Proteins in sequence database are extremely non-random, and very dependent

• No great tools for assessing statistical confidence of protein identifications.

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Mascot MS/MS Ions Search

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Mascot MS/MS Search Results

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Sequence Database SearchTraps and Pitfalls

Search options may eliminate the correct peptide• Precursor mass tolerance too small• Fragment m/z tolerance too small• Incorrect precursor ion charge state• Non-tryptic or semi-tryptic peptide• Incorrect or unexpected modification• Sequence database too conservative• Unreliable taxonomy annotation

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Search options can cause infinite search times

• Variable modifications increase search times exponentially

• Non-tryptic search increases search time by two orders of magnitude

• Large sequence databases contain many irrelevant peptide candidates

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Best available peptide isn’t necessarily correct!

• Score statistics (e-values) are essential!• What is the chance a peptide could score this

well by chance alone?• The wrong peptide can look correct if the

right peptide is missing!• Need scores (or e-values) that are invariant

to spectrum quality and peptide properties

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Search engines often make incorrect assumptions about sample prep

• Proteins with lots of identified peptides are not more likely to be present

• Peptide identifications do not represent independent observations

• All proteins are not equally interesting to report

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Good spectral processing can make a big difference

• Poorly calibrated spectra require large m/z tolerances

• Poorly baselined spectra make small peaks hard to believe

• Poorly de-isotoped spectra have extra peaks and misleading charge state assignments

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Summary

• Protein identification from tandem mass spectra is a key proteomics technology.

• Protein identifications should be treated with healthy skepticism.• Look at all the evidence!

• Spectra remain unidentified for a variety of reasons.

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Further Reading

• Matrix Science (Mascot) Web Site• www.matrixscience.com

• Seattle Proteome Center (ISB)• www.proteomecenter.org

• Proteomic Mass Spectrometry Lab at The Scripps Research Institute • fields.scripps.edu

• UCSF ProteinProspector• prospector.ucsf.edu

http://www.matrixscience.com/

http://www.proteomecenter.org/

http://fields.scripps.edu/

http://prospector.ucsf.edu/

Date post:	27-Jan-2016
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Protein Identification by Sequence Database Search

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