1. Previous Lecture: Regression and Correlation

2. This Lecture Introduction to Biostatistics and Bioinformatics
Proteomics Informatics

3. Proteomics Informatics – Learning Objectives
Structure of mass spectrometry data
Protein identification
Protein quantitation

4. Protein Identification and Quantitation
by Mass Spectrometry
Samples
Peptides
Mass
Spectrometry
Quantity
intensity
m/z
Identity

5. Sample preparation for protein identification,
characterization and quantitation
Lysis
Fractionation
Digestion
Mass spectrometry

6. Overview of Mass spectrometry
Ion Source
Mass Analyzer
Detector
intensity
mass/charge

7. Mass Spectrometry (MS)

8. Example data – MALDI-TOF Peptide intensity vs m/z

9. Peptide Fragmentation
Mass Analyzer 1
Frag-mentation
Detector
Ion Source
Mass Analyzer 2 b y

10. Liquid Chromatography (LC)-MS/MS
Ion Source
Mass Analyzer 1
Frag-mentation
Mass Analyzer 2
Detector
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
intensity
mass/charge
Time

11. Example data – ESI-LC-MS/MS
Peptide intensity vs m/z vs time
m/z
m/z
% Relative Abundance
100
250
500
750
1000
[M+2H]2+
762
260
389
504
633
875
292
405
534
907
1020
663
778
1080
1022
MS/MS
Fragment intensity vs m/z
Time

12. Charge-State Distributions
MALDI
ESI 1+ 2+ 3+
Peptide
intensity
intensity 4+ 2+ 1+
mass/charge
mass/charge
M - molecular mass
n - number of charges
H – mass of a proton
MALDI
ESI 2+
27+ 3+ 1+
Protein
31+
intensity 4+
intensity 5+
mass/charge
mass/charge

13. Charge-State Example:
M - molecular mass
n - number of charges
H – mass of a proton
Example:
peptide of mass 898 carrying 1 H+ = ( ) / 1 = 899 m/z
carrying 2 H+ = ( ) / 2 = 450 m/z
carrying 3 H+ = ( ) / 3 = m/z

14. Isotope Distributions
12C
14N
16O 1H
32S
+1Da
Intensity
+2Da
+3Da
m/z
m/z
m/z
0.015% 2H
1.11% 13C
0.366% 15N
0.038% 17O, 0.200% 18O,
0.75% 33S, 4.21% 34S, 0.02% 36S
Only 12C and 13C:
p=0.0111
n is the number of C in the peptide
m is the number of 13C in the peptide
Tm is the relative intensity of
the peptide m 13C
𝑇 𝑚 = 𝑛 𝑚 𝑝 𝑚 (1−𝑝) 𝑛−𝑚

15. Isotope Clusters and Charge State1+ 1
Intensity
m/z 2+
0.5
Intensity
m/z 3+
0.33
Intensity
m/z

16. What is the Charge State?
between the isotopes is 0.5 Da
between the isotopes is 0.33 Da

17. Protein Identification
by Mass Spectrometry
Samples
Peptides
Mass
Spectrometry
intensity
m/z
Identity

18. Protein Identification - Exercise
1. Protein identification: NUP1 was genomically tagged protein A, affinity purified under two conditions, and the resulting protein mixture was analyzed with liquid chromatography mass spectrometry (LC-MS). Search the resulting spectra (NUP1-less-stringent-wash.mgf, NUP1-more-stringent-wash.mgf) using X! Tandem ( Change the taxon to “S. cerevisiae (budding yeast)” but otherwise keep the default parameter settings.
a. Look at the list of identified proteins and explain why they are found in this sample. More information is also available by selecting the “go”, “path”, “ppi”, “doms”, “string” tabs on top of the page.
b. Select the “mh” display on top right of the page, and zoom in to +/-100 ppm (the default setting for the mass accuracy that was used in the search). What precursor mass accuracy should we have used? Zoom in further and determine what precursor mass accuracy could have been used if the spectra were recalibrated (the error distribution centered at zero).

19. Identification – Tandem MS

20. Tandem MS – Sequence ConfirmationK L E D F G S
m/z
% Relative Abundance
100
250
500
750
1000

21. Tandem MS – Sequence ConfirmationK L E D F G S K
1166 L
1020 E
907 D
778
663
534
405 F
292 G
145 S 88
b ions
m/z
% Relative Abundance
100
250
500
750
1000

22. Tandem MS – Sequence ConfirmationK L E D F G S
147 K
1166 L
260
1020 E
389
907 D
504
778
633
663
762
534
875
405 F
1022
292 G
1080
145 S 88
y ions
b ions
m/z
% Relative Abundance
100
250
500
750
1000

23. Tandem MS – Sequence ConfirmationK L E D F G S
147 K
1166 L
260
1020 E
389
907 D
504
778
633
663
762
534
875
405 F
1022
292 G
1080
145 S 88
y ions
b ions
m/z
% Relative Abundance
100
250
500
750
1000
[M+2H]2+
762
260
389
504
633
875
292
405
534
907
1020
663
778
1080
1022

24. Tandem MS – Sequence ConfirmationK L E D F G S
147 K
1166 L
260
1020 E
389
907 D
504
778
633
663
762
534
875
405 F
1022
292 G
1080
145 S 88
y ions
b ions
m/z
% Relative Abundance
100
250
500
750
1000
[M+2H]2+
762
260
389
504
633
875
292
405
534
907
1020
663
778
1080
1022

25. Tandem MS – Sequence ConfirmationK L E D F G S
147 K
1166 L
260
1020 E
389
907 D
504
778
633
663
762
534
875
405 F
1022
292 G
1080
145 S 88
y ions
b ions
m/z
% Relative Abundance
100
250
500
750
1000
[M+2H]2+
762
260
389
504
633
875
292
405
534
907
1020
663
778
1080
1022
113
113

26. Tandem MS – Sequence ConfirmationK L E D F G S
147 K
1166 L
260
1020 E
389
907 D
504
778
633
663
762
534
875
405 F
1022
292 G
1080
145 S 88
y ions
b ions
m/z
% Relative Abundance
100
250
500
750
1000
[M+2H]2+
762
260
389
504
633
875
292
405
534
907
1020
663
778
1080
1022
129
129

27. Tandem MS – de novo Sequencing
762
100
Amino acid masses
875
[M+2H]2+
% Relative Abundance
633
292
405
260
389
534
1022
504
663
778
907
1020
1080
250
500
750
1000
m/z
Mass Differences
Sequences
consistent
with spectrum

28. Tandem MS – de novo Sequencing

29. Tandem MS – de novo Sequencing

30. Tandem MS – de novo SequencingX X X
…GF(I/L)EEDE(I/L)…
…(I/L)EDEE(I/L)FG…
Peptide M+H = 1166
= 87 => S
SGF(I/L)EEDE(I/L)…
SGF(I/L)EEDE(I/L)…
1166 – 1020 – 18 = 128
K or Q
SGF(I/L)EEDE(I/L)(K/Q)
…GF(I/L)EEDE(I/L)…
…(I/L)EDEE(I/L)FG… X X X

31. Tandem MS – de novo Sequencing
Challenges in de novo sequencing
Neutral loss (-H2O, -NH3)
Modifications
Background peaks
Incomplete information
Challenges in de novo sequencing
Neutral loss (-H2O, -NH3)
Modifications
Background peaks
Incomplete information

32. Tandem MS – Database Search
Sequence DB
Lysis
Fractionation
Pick Protein
Digestion
LC-MS
Pick Peptide
Repeat for all proteins
MS/MS
All Fragment
Masses
all peptides
Repeat for
MS/MS
Compare, Score, Test Significance

33. Information Content in a Single Mass Measurement
Human 10 8 6
Avg. #of matching peptides 4 3 2 1
#of matching peptides
Tryptic peptide mass [Da]
S. cerevisiae 10 8 6
Avg. #of matching peptides 4 3 2 1
#of matching peptides
Tryptic peptide mass [Da]

34. Protein Identification and Quantitation
by Mass Spectrometry
Samples
Peptides
Mass
Spectrometry
Quantity
intensity
m/z

35. Protein Quantitation by Mass Spectrometry
Sample i
Protein j
Peptide k
Lysis
Fractionation
Digestion MS
LC-MS

36. Quantitation – Label-Free (MS)
Sample i
Protein j
Peptide k
Lysis
Assumption:
constant for all samples
Fractionation
Digestion
LC-MS MS MS

37. Quantitation – Metabolic Labeling
Light
Heavy
Lysis
Fractionation
Digestion
LC-MS
Sample i
Protein j
Peptide k MS H L
Oda et al. PNAS 96 (1999) 6591
Ong et al. MCP 1 (2002) 376

38. Quantitation – Labeled Synthetic Peptides
Assumption: All losses after mixing are identical for the heavy and light isotopes and
Lysis
Fractionation
Digestion
Synthetic
Peptides
(Heavy)
Light
Enrichment with
Peptide antibody
LC-MS
Anderson, N.L., et al.
Proteomics 3 (2004) MS H L
Gerber et al. PNAS 100 (2003) 6940

39. Estimating peptide quantity
Peak height
Peak height
Curve fitting
Curve fitting
Intensity
Peak area
m/z

40. What is the best way to estimate quantity?
Peak height - resistant to interference
- poor statistics
Peak area - better statistics
- more sensitive to interference
Curve fitting - better statistics
- needs to know the peak shape
- slow
Spectrum counting - resistant to interference
- easy to implement
- poor statistics for
low-abundance proteins

41. Proteomics Informatics - Summary
Structure of mass spectrometry data
Protein identification
Protein quantitation

42. Next Lecture: Gene Expression

43. Protein Quantitation - Exercise
2. Protein quantitation: Two breast tumor xenografts (one basal and one luminal) were analyzed in by LC-MS and the spectral counts for the identified peptides in the different analyses are listed in two-sample-three-replicate-comparison.txt.
a. Compare replicate one of Sample 1 with replicate one of Sample 2 using proteomics_no_replicate.py. Which differences are significant?
b. Compare replicate one and two of Sample 1 using proteomics_one_replicate.py. Compare to the distribution in 2a. Which differences are significant in 2a?
c. Compare the three replicates of Sample 1 with the three replicates of Sample 2 using proteomics_three_replicates.py. Which differences are significant?
d. In cases when a protein is not observed in one sample, how many spectra do we need to observe in the other sample to say that there is a significant difference?

44. Phosphorylation Exercise: an unmodified peptide
Theoretical fragment ions
You could give that as a help to see what changes etc.

45. Spectrum of the phosphorylated peptide
You could give that as a help to see what changes etc.

46. Spectrum of the peptide phosphorylated at a different site
You could give that as a help to see what changes etc.