mass spectrometry: protein identification strategies

04/11/2023 OISB: The ABC of Mass Spectrometry for Biology Workshop 1

Mass SpectrometryProtein Identification Strategies

Michel DumontierCarleton University


Typical MS experiment

Protein Identification


Protein identification strategies

• Mass Spectrometry– Peptide Mass Fingerprinting

• Tandem Mass Spectrometry– Spectral alignment– de novo sequencing


Peptide Mass Fingerprinting (PMF)


Matrix-Assisted Laser Desorption/Ionization (MALDI)


Electrospray Ionization (ESI)


Peptide Mass Fingerprinting

• Identify a protein from peptide signature– MALDI-TOF, ESI-TOF

• Approach– Compare observed with theoretical masses

• Requirements– Protease & cleavage pattern– Database of known sequences


Principles of Fingerprinting

>Protein Aacedfhsakdfqeasdfpkivtmeeewendadnfekqwfe

>Protein Bacekdfhsadfqeasdfpkivtmeeewenkdadnfeqwfe

>Protein Cacedfhsadfqekasdfpkivtmeeewendakdnfeqwfe

Sequence Mass (M+H) Tryptic Fragments

4842.05

4842.05

4842.05

acedfhsakdfgeasdfpkivtmeeewendadnfekgwfe

acekdfhsadfgeasdfpkivtmeeewenkdadnfeqwfe

acedfhsadfgekasdfpkivtmeeewendakdnfegwfe


Principles of Fingerprinting




Sequence Mass (M+H) Mass Spectrum

4842.05

4842.05

4842.05


Mass Calculation (Glycine)

NH2—CH2—COOH

R1—NH—CH2—CO—R3

free amino acid

amino acidresidue

Monoisotopic Mass1H = 1.00782512C = 12.0000014N = 14.0030716O = 15.99491

Glycine Free Amino Acid Mass5xH + 2xC + 2xO + 1xN= 75.032015 amuGlycine Residue Mass3xH + 2xC + 1xO + 1xN=57.021455 amu

Monoisotopic vs average mass

Monoisotopic mass is the mass determined using the masses of the most abundant isotopes

Average mass is the abundance weighted mass of all isotopic components


Amino Acid ResiduesMonoisotopic Masses

Glycine 57.02147Alanine 71.03712Serine 87.03203Proline 97.05277Valine 99.06842Threonine 101.04768Cysteine 103.00919Isoleucine 113.08407Leucine 113.08407Asparagine 114.04293

Aspartic acid 115.02695Glutamine 128.05858Lysine 128.09497Glutamic acid 129.0426Methionine 131.04049Histidine 137.05891Phenylalanine 147.06842Arginine 156.10112Tyrosine 163.06333Tryptophan 186.07932


Building a PMF Database

• Download protein sequence database – SwissProt or GenBank’s NR (non-redundant)

• Pick a protease, determine cleavage sites and identify resulting peptides for each protein entry

• Calculate the mass (M+H) for each peptide• Sort the mass list


Building A PMF Database




Sequence DB Calc. Tryptic Frags Mass List

acedfhsakdfgeasdfpkivtmeeewendadnfekgwfe



450.2017 (B-1) 538.2296 (A-4) 664.3300 (C-2) 1007.4251 (A-1)1112.4894 (A-2)1114.4416 (C-4)1300.5116 (B-4) 1407.6462 (B-3)1526.6211 (C-1)1593.7101 (C-3) 1740.7500 (B-2) 2098.8909 (A-3)


The Fingerprint (PMF) Approach• Take a mass spectrum of a protease-cleaved

protein (from gel or HPLC peak)• Identify as many peaks as possible in spectrum• Compare query peaks with database peaks and

calculate # of matches or matching score (based on length and mass difference)

• Rank hits and return top scoring entry (having the most matching peptides) – the protein of interest


Query (MALDI) Spectrum

500 1000 1500 2000 2500

698

2098

11991007

538

450

2211 (trypsin)

1940 (trypsin)


Query vs. DatabaseQuery Masses Database Mass List Results

450.2017 (B) 538.2296 (A) 664.3300 (C) 1007.4251 (A)1112.4894 (A)1114.4416 (C)1300.5116 (B) 1407.6462 (B)1526.6211 (C)1593.7101 (C) 1740.7501 (B) 2098.8909 (A)

450.2201538.2296 698.31001007.53911199.49162098.9909

2 Unknown masses1 hit on B3 hits on A

Conclude the queryprotein is A


What You Need To Do PMF

• A list of query masses (as many as possible)• Protease(s) used or cleavage reagents• Databases to search (SP, NR)• Estimated mass and pI of protein spot (opt) • Cysteine (or other) modifications• Minimum number of hits for significance• Mass tolerance (100 ppm = 1000.0 ± 0.1 Da)• A PMF website (Prowl, ProFound, Mascot, PepIdent)


Challenge 1:Overlap in combined masses

Gly + Gly = 114.043 -> Asn = 114.043 Ala + Gly = 128.059 -> Gln = 128.059

-> Lys = 128.095 Gly + Val = 156.090 -> Arg = 156.101Ala + Asp = Glu + Gly = 186.064

Trp = 186.079 Ser + Val = 186.100 -> Trp = 186.079 u Leu = Ile = 113.084


Challenge 2:Missed Cleavage


Sequence Tryptic Fragments (no missed cleavage)

acedfhsak (1007.4251) dfgeasdfpk (1183.5266) ivtmeeewendadnfek (2098.8909) gwfe (538.2296)

Tryptic Fragments (1 missed cleavage)

acedfhsak (1007.4251) dfgeasdfpk (1183.5266) ivtmeeewendadnfek 2098.8909) gwfe (609.2667)acedfhsakdfgeasdfpk (2171.9338)ivtmeeewendadnfekgwfe (2689.1398)dfgeasdfpkivtmeeewendadnfek (3263.2997)


Advantages of PMF• Uses a “robust” & inexpensive form of MS (MALDI)• Doesn’t require too much sample optimization• Can be done by a moderately skilled operator (don’t

need to be an MS expert) • Widely supported by web servers• Improves as DB’s get larger & instrumentation gets

better• Very amenable to high throughput robotics (up to 500

samples a day)


Limitations With PMF

• Requires that the protein of interest already be in a sequence database

• Not good for 3+ protein mixtures • Spurious or missing critical mass peaks always

lead to problems• Mass resolution/accuracy is critical, best to

have <20 ppm mass resolution• Generally found to only be about 40%

effective in positively identifying gel spots


Tandem Mass Spectrometry


MS-MS Peptide Fragmentation


S E Q U E N C E

b-ions (prefix or N-terminal ions)

Mass/Charge (M/Z)


a-ions = b-ions - CO = b-ions - 28

Mass/Charge (M/Z)

S E Q U E N C E


y-ions (suffix of C-terminal ions)

Mass/Charge (M/Z)

E C N E U Q E S


Mass/Charge (M/Z)

Inte

nsit

y


noise

Mass/Charge (M/Z)


MS/MS Spectrum

Mass/Charge (M/z)

Inte

nsit

y


Some Mass Differences between Peaks Correspond to Amino Acids

s

ss

e

ee

e

e

e

e

e

q

q

qu

u

u

n

n

n

e

cc

c


database search vs de novoS#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]

200 400 600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Re

lative

Ab

un

da

nce

850.3

687.3

588.1

851.4425.0

949.4

326.0524.9

589.2

1048.6397.1226.9

1049.6489.1

629.0

WR

A

C

VG

E

K

DW

LP

T

L T

WR

A

C

VG

EK

DW

LP

T

L T

de novo

AVGELTK

Database Search

Database ofknown peptides

MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,

HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,

ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA,

EKLNKAATYIN..


SEQUEST Algorithm

• SEQUEST correlates uninterpreted tandem mass (MS-MS) spectra of peptides with amino acid sequences from protein and nucleotide databases


SEQUEST Algorithm

>Aacedfhsakdfqeasdfpkivtmeeewendadnfekgpfna

>Bacekdfhsadfqeasdfpkivtmeeewenkdadnfeqwfe

>Cacedfhsadfqekasdfpkivtmeeewendakdnfeqwfe

Sequence DB Calc. Tryptic Frags Calc. MS-MS Spec.

acedfhsakdfgeasdfpkivtmeeewendadnfekgpfna




Creating a Synthetic MS-MS Spectrum for GPFNA

57 154 301 415 486 71 185 332 429 486

G57

P97

F147

N114

A71

A71

N114

F147

P97

G57

b ions y ions

combine


SEQUEST Algorithm

acedfhsak

mtlsyk

nmqtydr

giqwemncyk

Query Spectrum Spectral Database Result

giqwemncyk

Score = 128Accession P12345Protein = p53Org. Homo sapiens


SEQUEST Xcorrhigher is better

CrossCorr

avg AutoCorr offset=-75 to 75

Cross Correlation(direct comparison)

Auto Correlation(background)

XCorr =Gentzel M. et al Proteomics 3 (2003) 1597-1610

Offset (AMU)

Corr

elati

on S

core


Accuracy Score Relative ScoreAl

tern

ate

Met

hod

Strong(XCorr)

Weak

Weak(DeltaCn)

Strong

SEQ

UES

T

Mascot and X! Tandem


Mascot

Mascot Score: 120 = 1x10-12

– Scoring based on peptide frequency distribution from a non-redundant database (MOWSE – Molecular Weight SEarch)

– The significance of that result depends on the size of the database being searched. Mascot shades in green the insignificant hits using a P=0.05 cutoff.

In this example, scores less than 74 are insignificant


9%

19% 7%

34%

5%

4%22%

Mascot

Each search engine identifies about the same number of spectra,

Each search engine identifies about the same number of spectra,

But the overlap is surprisingly small.

Different search engines match different spectra.

But the overlap is surprisingly small.

Different search engines match different spectra.

Each search engine scores differently

SEQUEST

X!tandem

Courtesy: Proteome Software Inc.


database search vs de novoS#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]

200 400 600 800 1000 1200 1400 1600 1800 2000

m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Re

lative

Ab

un

da

nce

850.3

687.3

588.1

851.4425.0

949.4

326.0524.9

589.2

1048.6397.1226.9

1049.6489.1

629.0

WR

A

C

VG

E

K

DW

LP

T

L T

WR

A

C

VG

EK

DW

LP

T

L T

de novo

AVGELTK

Database Search

Database ofknown peptides

MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT,

HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE,

ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA,

EKLNKAATYIN..


de novo vs Database Search: A Paradox

• A database search scans all peptides to find the best one.• de novo eliminates the need to scan all peptides by

modeling the problem as a graph search.• de novo algorithms are much faster, even though their

search space is much larger!• Done when no PMF or ms/ms spectral match

• Advantage:– Gets the sequences that are not necessarily in the

database.• Disadvantage:

– Requires higher quality spectra to be accurate.


de novo sequencing is not very accurate:

• Less than 30% of the peptides sequenced were completely correct!

Algorithm Amino Acid

Accuracy

Whole Peptide Accuracy

Lutefisk, 1997 0.566 0.189SHERENGA, 1999 0.690 0.289Peaks, 2003 0.673 0.246PepNovo, 2005 0.727 0.296


References

• SLIDES– Proteomics. 2005 Canadian Bioinformatics Workshops. David Wishart, Gary Van Domselaar.

http://bioinformatics.ca/workshop_pages/proteomics2005/index.html– Protein Sequencing and Identification by Mass Spectrometry. http://bioalgorithms.info– Interpreting MS/MS Proteomics Results. Brian C. Searle. Proteome Software Inc

• Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003 Mar 13;422(6928):198-207. Review.

• Mueller LN, Brusniak MY, Mani DR, Aebersold R. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res. 2008 Jan;7(1):51-61.

• MOWSE: Pappin DJC, Hojrup P, and Bleasby AJ (1993) Rapid identification of proteins by peptide-mass fingerprinting. Curr. Biol. 3:327-332

• MASCOT: Perkins DN, Pappin DJC, Creasy DM, and Cottrell JS (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551-3567.

http://bioinformatics.ca/workshop_pages/proteomics2005/index.html

http://bioalgorithms.info/

http://bioalgorithms.info/

http://www.ncbi.nlm.nih.gov/pubmed/12634793?ordinalpos=4&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum

http://www.ncbi.nlm.nih.gov/pubmed/12634793?ordinalpos=4&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum

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