1. MS-MS: Applications to Proteomics
David Wishart
June 2005
MS-MS: Applications to Proteomics
David Wishart
University of Alberta
Edmonton, AB
(c) CGDN 2005

2. MS-MS Methods 2D-GE + MALDI-MS 2D-GE + MS-MS
Peptide Mass Fingerprinting (PMF)
2D-GE + MS-MS
Sequence Tag/Fragment Ion Searching
Multidimensional LC + MS-MS
ICAT Methods (isotope labelling)
MudPIT methods
1D-GE + LC + MS-MS
De Novo Peptide Sequencing (MS-MS)
Lecture 2.3

3. 2D-GE + MS-MS
Trypsin
+ Gel punch
p53
Lecture 2.3

4. MudPIT
IEX-HPLC
RP-HPLC
Trypsin
+ proteins
p53
Lecture 2.3

5. ICAT (Isotope Coded Affinity Tag)
Lecture 2.3

6. Some Interesting Examples

7. The E. coli Interactome
Butland et al., Nature, 433(7025): (2005)
Lecture 2.3

8. E. coli Interactome
Created C-terminal, affinity-tagged constructs of 1,000 open reading frames (approximately 23% of the genome)
A total of 857 proteins, including 198 of the most highly conserved, soluble non-ribosomal proteins were tagged successfully
648 could be purified to homogeneity and their interacting protein partners identified by mass spectrometry
Lecture 2.3

9. SPA or TAP Tagging E. coli Proteins
Lecture 2.3

10. Bait-Prey Selection & MS
Lecture 2.3

11. Gel Analysis (Silver Stain)
Lecture 2.3

12. LC-MS/MS (MudPIT)
Lecture 2.3

13. The E. coli Interactome
Lecture 2.3

14. Organellar Proteomics
Lecture 2.3

15. Organellar Proteomics
Taylor SW, Fahy E, Ghosh SS. Trends Biotechnol Feb;21(2):82-8.
Lecture 2.3

16. MS-MS for Protein ID
Proteins are isolated (from gel or HPLC) and subjected to tryptic digestion
Peptides are sent through ionizer and into a collision cell where the doubly charged ions are selected and fragmented through collision induced decay (CID)
The resulting singly charged ions (daughter ions) are analyzed to determine the sequence or to ID the parent peptide
Lecture 2.3

17. Why Trypsin for MS-MS?
CID of peptides less than 2-3 kD is most reliable for MS-MS studies – The frequency of tryptic cleavage guarantees that most peptides will be of this size
Trypsin cleaves on the C-terminal side of arginine and lysine. By putting the basic residues at the C-terminus, peptides fragment in a more predictable manner throughout the length of the peptide
Lecture 2.3

18. Why Double Charges?
Easiest spectra to interpret are those obtained from doubly-charged peptide precursors, where the resulting fragment ions are mostly singly-charged
Doubly-charged precursors also fragment such that most of the peptide bonds break with comparable frequency, such that one is more likely to derive a complete sequence
Lecture 2.3

19. MS-MS & Peptide Fragments
When peptides are proteins are admitted to a collision cell the peptide usually fragments at the weakest bond (the peptide bond, but some CH-NH and CH-CO breakage also occurs)
Collision conditions have to be optimized for each peptide
Two main types of daughter ions are produced -- “b” ions and “y” ions
Lecture 2.3

20. MS-MS Peptide Fragmentation
yn-1
yn-2 y1 R1 R2 R3 Rn
H2N-CH-CO-NH-CH-CO-NH-CH-CO…CO-NH-CH-CO2H b1 b2
bn-1
b1 y1 b2 y2 b3 y b4 y4 b5 y5
signal
Lecture 2.3

21. MS-MS Peptide Fragmentation
Ala-Gly-His-Leu-….Phe-Glu-Cys-Tyr
b1 y1 b2 y2 b3 y b4 y4 b5 y5
signal
Lecture 2.3

22. Tandem MS of BSA
Lecture 2.3

23. MS-MS of Fibrinogen Relative Abundance m/z 400 600 800 1000 1200 1400
1600
m/z 20 40 60 80
100
Relative Abundance E A X F D G S
YADSGEGDFLAEGGGVR
Lecture 2.3

24. Amino Acid Residue Masses
Monoisotopic Mass
Glycine
Alanine
Serine
Proline
Valine
Threonine
Cysteine
Isoleucine
Leucine
Asparagine
Aspartic acid
Glutamine
Lysine
Glutamic acid
Methionine
Histidine
Phenylalanine
Arginine
Tyrosine
Tryptophan
Lecture 2.3

25. MS/MS – The Movie (Kathleen Binns)
Lecture 2.3

26. Protein ID by MS-MS
Peptide fragments from target protein are sequenced by MS-MS using a variety of algorithms (SEQUEST, Mascot) or via manual methods
The peptide fragment sequences are sent to BLAST to be queried against a protein sequence database
The protein having the highest number of sequence matches is ID’d as the target
Lecture 2.3

27. SEQUEST
Algorithm developed for MS-MS fragment ion identification by J. Eng (1994) in John Yates Lab (Scripps, U Wash)
Compares predicted MS-MS spectra against observed daughter ion spectra to identify and rank matches (no “sequencing” per se)
Lecture 2.3

28. SEQUEST and 2D-GE
Lecture 2.3

29. SEQUEST Algorithm
SEQUEST correlates uninterpreted tandem mass (MS-MS) spectra of peptides with amino acid sequences from protein and nucleotide databases
SEQUEST will determine the amino acid sequence and thus the protein(s) and organism(s) that correspond to the mass spectrum being analyzed
SEQUEST is distributed by Finnigan Corp.
Lecture 2.3

30. SEQUEST Algorithm Sequence DB Calc. Tryptic Frags Calc. MS-MS Spec.
acedfhsakdfqea
sdfpkivtmeeewe
ndadnfekgpfna
>P21234
acekdfhsadfqea
nkdadnfeqwfe
>P89212
acedfhsadfqeka
ndakdnfeqwfe
acedfhsak
dfgeasdfpk
ivtmeeewendadnfek
gpfna
acek
dfhsadfgeasdfpk
ivtmeeewenk
dadnfeqwfe
acedfhsadfgek
asdfpk
ivtmeeewendak
dnfegwfe
Lecture 2.3

31. Creating a Synthetic MS-MS Spectrum for GPFNA
b ions y ions G 57 P 97 F
147 N
114 A 71 A 71 N
114 F
147 P 97 G 57
combine
Lecture 2.3

32. SEQUEST Algorithm Query Spectrum Spectral Database Result acedfhsak
mtlsyk
giqwemncyk
nmqtydr
Score = 128
Accession P12345
Protein = p53
Org. Homo sapiens
giqwemncyk
Lecture 2.3

33. Alternatives to SEQUEST
Web software and servers using algorithms based on manual methods
Sending your data to friends who have a SEQUEST license
Manual analysis of MS-MS spectra
This is still the most reliable method for interpreting MS-MS spectra
Also allows for de-novo sequencing
Lecture 2.3

34. MS-MS on the Web PepSea (disabled) ProteinProspector
ProteinProspector
PeptideSearch (limited)
Mascot (probably the best)
Lecture 2.3

35. Mascot MS-MS Form
Lecture 2.3

36. Mascot MS-MS Input Format
COM=10 pmol digest of Sample X15
ITOL=1
ITOLU=Da
MODS=Met Ox,Cys B propionamide
MASS=Monoisotopic
USERNAME=Lou Scene
CHARGE=2+ and 3+
BEGIN IONS
TITLE=Peak 1
PEPMASS=983.6
Parent ion
Mass (2+)
Daughter ion
mass
intensity
Lecture 2.3

37. Mascot MS-MS Output
Lecture 2.3

38. Mascot MS-MS Output
Lecture 2.3

39. A Real Example
Lecture 2.3

40. Lecture 2.3

41. Lecture 2.3

42. Protocols for MS-MS Sequencing
Usually can’t tell a “b” ion from a “y” ion
Assume the lowest mass visible in the spectrum is a lysine or arginine (this is the y1 ion) this is because trypsin cuts after a lysine or arginine
This y1 mass should be for lysine or for arginine {The y1 ion is calculated by adding u (three hydrogens and one oxygen) to the residue masses of lysine and arginine}
Lecture 2.3

43. MS-MS Sequencing
Using the mass tables, look to the right of y1 and see if you can find another prominent peak that is equal to y1 + AA where AA is the residue mass for any of the 20 amino acids. This is the y2 ion
Proceed in a rightward direction, identifying other yn ions that differ by an AA residue mass (don’t expect to find all)
The yn series produces a “reverse” sequence
Watch for possible dipeptide peaks that may fool you
Lecture 2.3

44. Things To Remember Gly + Gly = 114.043 u and Asn = 114.043 u
Ala + Gly = u and Gln = u and Lys = u
Gly + Val = u and Arg = u
Ala + Asp = Glu + Gly = and Trp = u
Ser + Val = u and Trp = u
Leu = Ile = u
Lecture 2.3

45. MS-MS Sequencing
Use the remaining “unassigned” peaks to see if you can construct a “b” ion series
The highest mass peak corresponds to the parent ion or parent minus 147 (K) or 175 (R)
The “b” ions give the “normal” sequence
Both forward (b ion) and backward (y ion) sequences should be consistent
Use the resulting sequence tag to search the databases using BLAST (remember to use a high Expect value ~ 100) to see if the sequence matches something
Lecture 2.3

46. Tandem MS of BSA
Lecture 2.3

47. Different MS-MS Instruments Yield Different Spectra
A typical QTOF or triple quad MS-MS spectrum of a tryptic peptide contains a continuous series of y-type ions. The b-type ions are usually seen only at lower masses below the precursor m/z value
Ion trap CID data of tryptic peptides is different in that one often finds a continuous series of both b-type and y-type ions throughout the spectrum
Lecture 2.3

48. Post-Translational Modifications (PTM)
Lecture 2.3

49. PTM by MALDI (PMF) Trypsin MS 600 Da 840 Da 1044 Da 1236 Da 1730 Da
Database:
MKALSPVRGCYEAVCCLSERSLAIARGRGKSPSAEEPLSLLDDMNHCYSRLRELVPGVPRGTQLSQVEILQRVIDYILDLQVVLAEPAPGPPDGPHLPIQVREGARPGSSERAGWDAAGLPHRVLEYLG
AVAKVELRGTVQPASNFNDDSSQGLGTDEGSIVLTQRSNAQAVEGAGTDESTLIELMATRNNQEIAAINEAYSLEDDLSSDTSGHFRILVSLALGNRDEGPENLTQAVVAETLNKPAFFADRLLALXGGDD
MRWLTPFGMLFISGTYYGLIFFGLIMEVIHNALISLVLAFFVVFAWDLVLSLIYGLRFVKEGDYIALDWDGQFPDCYGLFASTCLSAVIWTYTDSLLLGLIVPVIIVFLGKQLMRGLYEKIKS MS
600 Da
840 Da
1044 Da
1236 Da
1730 Da
Trypsin
GTVQPASNFNDDSSQGLGTDEGSIVLTQR
Lecture 2.3

50. PTM by MS-MS Trypsin MS/MS spectra GTVQPASNFNDDSSQGLGTDEGSIVLTQR
AVAKVELRGTVQPASNFNDDSSQGLGTDEGSIVLTQRSNAQAVEGAGTDESTLIELMATRNNQEIAAINEAYSLEDDLSSDTSGHFRILVSLALGNRDEGPENLTQAVVAETLNKPAFFADRLLALXGGDDFKLMAAG
Lecture 2.3

51. Phosphoserine Detection
200
600
1000
1400
m/z 10 30 50
Relative Abundance
843.9
794.7
257.9
128.9
445.8
686.4
1331.2
174.1
559.0
1262.5
758.1
326.3
1061.6
961.4
629.9
1459.8
1175.2
1430.1
[M+2H] y8 y7 y6 y5 y4 y1 y9
y12
y13
y14-H3PO4
y14
y10 b1 b2
b3-phos
846.7
y11
y15-H3PO4
b-ions
NH- K E s* S N T D S A G A L G T L R -OH
y-ions
s* = phosphoserine
Lecture 2.3

52. De Novo Sequencing (MS-MS)
Done when sample is not amenable to Edman Degradation
Done when no sequence or PMF match seems to exist in databases
Requires a very high resolution mass analyzer (FT-ICR, QTOF or Qstar instrument) with <20 ppm resolution
Usually requires multi-enzyme digestion
Still a difficult process but possible to do at much lower amounts than Edman Deg.
Lecture 2.3

53. MS-MS & Proteomics Advantages Disadvantages
Provides precise sequence-specific data
More informative than PMF methods (>90%)
Can be used for de-novo sequencing (not entirely dependent on databases)
Can be used to ID post-trans. modifications
Requires more handling, refinement and sample manipulation
Requires more expensive and complicated equipment
Requires high level expertise
Slower, not generally high throughput
Lecture 2.3