Protein Identification by Sequence Database Search Nathan Edwards Department of Biochemistry and...

Protein Identification by

Sequence Database Search

Protein Identification by

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

Outline

• Proteomics

• Mass Spectrometry

• Protein Identification• Peptide Mass Fingerprint• Tandem Mass Spectrometry

Proteomics

• Proteins are the machines that drive much of biology• Genes are merely the recipe

• The direct characterization of a sample’s proteins en masse. • What proteins are present?• How much of each protein is present?

2D Gel-Electrophoresis

• Protein separation• Molecular weight (MW)• Isoelectric point (pI)

• Staining

• Birds-eye view of protein abundance

2D Gel-Electrophoresis

Bécamel et al., Biol. Proced. Online 2002;4:94-104.

Paradigm Shift

• Traditional protein chemistry assay methods struggle to establish identity.

• Identity requires:• Specificity of measurement (Precision)

• Mass spectrometry• A reference for comparison

(Measurement → Identity)• Protein sequence databases

Mass Spectrometer

Ionizer

Sample

Mass Analyzer Detector

• MALDI• Electro-Spray

Ionization (ESI)

• Time-Of-Flight (TOF)• Quadrapole• Ion-Trap

• ElectronMultiplier(EM)

Mass Spectrometer (MALDI-TOF)

Source

Length = s

Field-free drift zone

Length = D

Ed = 0

Microchannel plate detector

Backing plate(grounded) Extraction grid

(source voltage -Vs)

UV (337 nm)

Detector grid -Vs

Pulse voltage

Analyte/matrix

Mass Spectrum

Mass is fundamental

Peptide Mass Fingerprint

Cut out2D-Gel

Trypsin Digest

• 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

Protein Sequence

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

Protein Sequence

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

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

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!

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

Sample Preparation for Tandem Mass Spectrometry

Enzymatic Digestand

Fractionation

Single Stage MS

Tandem Mass Spectrometry(MS/MS)

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.

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

RiRi+1

yn-iyn-i-1

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

RiCH-R’

yn-iyn-i-1

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

0250 500 750 1000

S88 b ions

147260389504633762875102210801166 y ions

S88 b ions

0250 500 750 1000

147260389504633762875102210801166 y ions

y2 y3 y4

S88 b ions

0250 500 750 1000

147260389504633762875102210801166 y ions

y2 y3 y4

b5 b6 b7b8 b9

Peptide Identification

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

Output:• The amino-acid sequence of the

peptide

Two paradigms:

• De novo interpretation

• Sequence database search

De Novo Interpretation

0250 500 750 1000

E D E L

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

…from Lu and Chen (2003), JCB 10:1

• Find good paths in spectrum graph• Can’t use same peak twice

• Forbidden pairs: NP-hard• “Nested” forbidden pairs: Dynamic Prog.

• Simple peptide fragmentation model• Usually many apparently good

solutions• Needs better fragmentation model• Needs better path scoring

• Amino-acids have duplicate masses!• Incomplete ladders create ambiguity.• Noise peaks and unmodeled fragments

create ambiguity• “Best” de novo interpretation may have no

biological relevance• Current algorithms cannot model many

aspects of peptide fragmentation• Identifies relatively few peptides in high-

throughput workflows

• Compares peptides from a protein sequence database with spectra

• Filter peptide candidates by• Precursor mass• Digest motif

• Score each peptide against spectrum• Generate all possible peptide fragments• Match putative fragments with peaks• Score and rank

0250 500 750 1000

KLEDEELFGS

0250 500 750 1000

S88 b ions

147260389504633762875102210801166 y ions

S88 b ions

0250 500 750 1000

147260389504633762875102210801166 y ions

y2 y3 y4

b5 b6 b7b8 b9

• 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!

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

Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

No missed cleavage sitesMKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

Peptide Candidate Filtering>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

One missed cleavage siteMKWVTFISLLFLFSSAYSRWVTFISLLFLFSSAYSRGVFRGVFRRRDAHKDAHKSEVAHRSEVAHRFKFKDLGEENFKDLGEENFKALVLIAFAQYLQQCPFEDHVKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK…

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

Peptide Molecular Weight

Same peptide,i = # of C13 isotope

i=3i=4

Same peptide,i = # of C13 isotope

i=3i=4

…from “Isotopes” – An IonSource.Com Tutorial

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

WVTFISLLFLFSSAYSR 2018.06

… …

- 7 Molecular Weights- 64 “Peptides”

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

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

• Spectra may not contain enough information to be interpreted correctly• …fading in and out on a cell phone

• Spectra may contain too many peaks• …static or background noise

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

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

• Rank the best peptide identifications

• Is the top ranked peptide correct?

• 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?

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

Mascot MS/MS Ions Search

Mascot MS/MS Search Results

Sequence Database SearchTraps and Pitfalls

Search options may eliminate the correct peptide• Precursor mass 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

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

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?

• Incorrect instrument settings or fragment tolerance can render scores non-specific.

• 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

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

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

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.

Protein Identification by Sequence Database Search Nathan Edwards Department of Biochemistry and...

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