1. Proteomics February 15, 2017 Dr. ir. Perry Moerland
Bioinformatics Laboratory
Academic Medical Center
Graduate School ‘Bioinformatics’

2. Mass spectrometry: MALDI-TOF
need only femtomole of
peptide material
ionized peptides are accelerated
time of flight spectrum
From Lodish et al: Ion source + mass analyzer
MALDI –TOF MS: Matrix-Assisted Laser Desorption Ionization Time Of Flight Mass Spectrometry
mass spectrum
intensity
mass 2

3. MS ID: peptide mass fingerprint
wet lab:
gel protein peptides spectrum
digest
in silico:
protein in
database theoretical spectrum
digest
compare
> 59,000,000 entries
score
From Graves & Haystead: K=lysine, R=arginine
We will come back to how to obtain the sticks representation of a spectrum later in this lecture 3

4. Identification: peptide mass fingerprint
Why a fingerprint and not the intact protein mass?
Pre-sequences
Post translational modifications
Alternative splicing
Errors in databases: 1 in bases
(i.e amino acids)
Measurement error:
10 ppm error on 100kDa = 1 Da
Pre-sequence: signal peptide 4

5. Intact mass and protein identity
Variable/unknown modifications
Intron/exon
boundaries
N terminal processing Pi X Ac
GlcNAc
Sequencing errors & polymorphisms
C terminal processing
Known/fixed modifications
Only a few modified peptides
Most peptides will have the predicted mass 5

6. Identification: how to score?
Simply counting the number of matches is not enough
The limited mass accuracy of MS can lead to both false positives and false negatives 6

7. Identification: how to score?
Simply counting the number of matches is not enough
False positive, because
MS has only limited mass accuracy
Bias towards ‘heavy’ proteins …
False negatives, because
Peptides can be post-translationally modified
Sometimes digestion is not perfect: missed cleavages
The limited mass accuracy of MS can lead to both false positives and false negatives 7 7

8. Importance of mass accuracy1H
12C 12
14N
16O
31P
32S 8

9. Peptide mass fingerprinting: Mascot
Trade-off between false positives (specificity) and false negatives (sensitivity) 9

10. Peptide mass fingerprinting: missed cleavages
Higher sensitivity, lower specificity 10

11. Scoring scheme: MASCOT
Score, Expect? 11

12. Scoring scheme: MOWSE/MASCOT (I)
# of matching proteins
For protein A:
Mowse score = 50/(NH)
50 : average protein of 50 kDa
H : molecular weight of protein A
N : product of match scores, each match score M (0≤M ≤ 1) weighted inversely proportionally to
peptide mass
accumulate statistics on the size distribution of peptide masses as a function of protein mass
Note: this implicitly incorporates the number of matches
Warning: many scoring schemes (and there are tens of them)
are very ad-hoc and prone to false positives 12

13. Scoring scheme: MOWSE/MASCOT (II)
Probabilistic: transformation of the Mowse score in probability P that match is a random event
Score: correct for size of database
P = 1 / (20 x 5000)
S = -10LogP = 50
Expect (E-value): number of times you could expect to get this score or better by chance
E-value≥1: completely random 13

14. LC-MS/MS in 30 sec. LC chromatogram MS mass spectrum fragmentation
From Peng and Gygi 14

15. MS/MS fragmentation Breaks a selected peptide in smaller parts
fragmentation spectrum:
identifies individual amino acids 15

16. MS/MS peptide identification
Sequence searching
Sequence tag
De novo 16

17. MS/MS ID: sequence searching
dBase
shortlist
Predict
MSMS
spectra
 tubulin
Compare
MATCH
Very dominant fragmentation pattern from some daughter ions:
-good chemical reasons for this behaviour.
No good contiguous sequence can be established to a high degree of
confidence. In these cases raw MSMS searching is much more apt in identifying parent.
RAW MSMS searching will be the future of fragmentation - db searching.
Computer generates a theoretical fragmentation spectrum: Mascot 17

18. MS/MS ID: sequence TAG Internal sequence dBase
Format: 409, T1A2G ,13
mass1 internal sequence mass2
Compare
mass
Internal sequence
tag
shortlist
Compare
fragments
Strech of contiguous high peaks easily found
Peak to peak distance corresponds with aminoacid mass
Predominantly Y” low energy CID
C&N masses correspond to a combination of aa’s (all permutations)
2+ precursor charge state:
Fragment masses> parent mass ensure these are products from a
non singly charged molecule.
match 18

19. MS/MS ID: de novo (I)
De novo: for peptides/proteins not yet in database or not identified by fingerprint techniques:
unsequenced species
modified peptides
Algorithms for extracting long stretches of peptide are needed: high-resolution & very good quality spectra 19

20. MS/MS ID: de novo (II)
Problem: MS/MS spectra display a mixture of C- and N-terminal fragment ion series (a, b, c, and x, y, z, respectively)
Solution: reduce complexity of MS/MS spectra
Protein digestion using metalloendopeptidase with Lys-N specificity: cleavage N-terminally of lysine
electron-transfer-induced dissociation MS/MS spectrum is dominated by c-ions
MS/MS spectrum has few gaps
In a gold standard dataset 42% of peptides identified via database search can be identified de novo
Van Breukelen et al, Proteomics (2010) 20

21. MS/MS ID: validation (I)
Many MS/MS search engines are threshold-based
Probabilistic approaches:
compute probability that a match is correct:
Mascot
PeptideProphet
Nesvizhskii et al, Nature Methods (2007) 21

22. MS/MS ID: validation (II)
Many MS/MS search engines are threshold-based
Target-decoy searching:
Database augmented with reversed or randomized sequences of DB
Filter using various score cutoffs (FDR)
Nesvizhskii et al, Nature Methods (2007) 23

23. Cellular changes in T-cell expression patterns upon infection with HIV
Analysis of human T cells upon HIV-1 infection
1921 protein spots detected with 2D DIGE
288 spots differentially expressed at peak infection
93 unique proteins identified via peptide mass fingerprinting
188 unidentified spots
Goal: try to find candidate proteins in silico
Nandal et al, 16:25, BMC Bioinformatics (2015) 24

24. Recapitulation: 2D gel electrophoresis
Iso-electric point
Horizontal axis: separation by pI
Vertical axis: separation by Mw, lower = lighter
Molecular weight
2D-DIGE: quantification of changes in protein expression using fluorescent labeling 25

25. In-depth mining of 2D-DIGE data26

26. Step 1: Calculation of pI and Mw 27

27. Step 2: Fitting calibration curves
Cubic smoothing splines 28 28 28

28. Step 3: Generation of candidate list
Properties & pI/Mw ranges of proteins sent to TagIdent -> get list of UNIPROT identifiers back of proteins that are close to predicted pI/Mw
Isoforms are disregarded, as STRING does not handle isoforms. 29 29 29

29. Step 4: Prioritization of candidates
STRING 30

30. STRING: functional protein association network
STRING is a database of known and predicted protein interactions, derived from different sources:
Interactions are visualized via graphs
v9.1: >5,200,000 proteins from 1,133 organisms
Different line colors represent the types of evidence for the association
STRING 10: database currently covers 9'643'763 proteins from 2'031 organisms. 31

31. STRING network: 2D-DIGE identified proteins
Visualisation of the STRING network. Associations are represented with lines. Different colours of lines code for different evidence categories.
New proteins have to fit in this network as well as possible.
377 observed interactions as compared to 66.2 expected interactions (P << ) 32

32. ? Step 5: gene expression-based filtering gene expression ~
protein expression ? 33

33. Properties of candidate lists
Post-translational modifications
Hydrophobic proteins 34

34. Results TPR: true-positive rate With optimal settings for
pI range and Mw range:
~44% of correct proteins in
top-5 35

35. Results: gene expression-based filtering 36

36. Further pointers Catalogues: http://www.humanproteomemap.org/
Galaxy:
Boekel et al, Nature Biotechnology, 33:2, 139 (2015) 37