1. Laxman Yetukuri T-61.6070: Modeling of Proteomics Data
PepHMM: A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search
Laxman Yetukuri
T : Modeling of Proteomics Data

2. Outline Motivation Basics: MS and MS/MS for Protein Identification
Computational Framework of Database Search
Scoring Algorithms
PepHMM
MOWSE
Results
Summary

3. Motivation Proteomics studies- dynamic and context sensitive
Speed and accuracy of omics-driven methods
High throughput MS-based approaches
Real analysis starts with protein identification
Protein identification is challenging
The heart of protein identification algorithm is scoring function

4. Protein Identification Is Challenging
Sample Contamination
Imperfect Fragmentation
Post translational Modifications
Low signal to noise ratio
Machine errors

5. Basics: MS and MS/MS for protein Identification
Trypsin Digest
Liquid Chromatography
Mass
Spectrometry
Precursor selection +
collision induced dissociation
(CID)
MS/MS

6. Computational Problem
Nesvizhskii and Aebersold, Drug Discovery Today, 2004, 9,

7. Peptide Fragmentation: b & y ionsbi
yn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH- Ri
CH-R’
i+1 R”
i+1
bi+1

8. Peptide Fragmentation: b & y ions …88
145
292
405
534
663
778
907
1020
1166
b ions S G F L E E D E L K
1166
1080
1022
875
762
633
504
389
260
147
y ions y6
100 y7
% Intensity y5 b3 b4 y2 y3 y4 b5 y8 b6 b8 b7 b9 y9
m/z
250
500
750
1000

9. Peptide Fragmentation with other ions
xn-i bi
yn-i ci
zn-i
yn-i-1
-HN-CH-CO-NH-CH-CO-NH- Ri
CH-R’
i+1 ai R”
i+1
bi+1

10. Peptide Identification
Two main methods for tandem MS:
De novo interpretation
Sequence database search

11. De Novo Interpretation
100
250
500
750
1000
m/z
% Intensity E L F KL
SGF G D

12. Sequence Database Search
Widely used approach
Compares peptides from a protein sequence database with experimental spectra
Scoring function summarise the comparison
Critical for any search engine
Score each peptide against spectrum
Cross correlation (SEQUEST)
MOWSE scoring and its extensions (MASCOT)
Probabilistic scoring systems (OMSSA, OLAV, ProbID…..)
PepHMM is HMM based probabilistic scoring function

13. Computational Framework for pepHMM
MSDB based peptide extraction
Hypothetical spectrum generation
b,y,y-H2O,b-H2O,b2+ and y2+
Computing probabilistic scores
Initial classification :Match, missing or noise
Compute pepHMM scores (discussed later)
Compute Z-score
Compute E-score

14. Contents of pepHMM Model
PepHMM combines the information on correlation among the ions, peak intensity and match tolerance
Input – sets of matches, missing and noise
Model is based on b and y ions
Each match is associated with observation (T,I)
Observation state = observed (T,I)
Hidden state =True assignement of the observations

15. Model Structure
Four possible assignments corresponding to four hidden states

16. Model Computation
Goal: Calculate highest score peptide in the database
Let a path in HMM be represents configuration of
states, probability of the path

17. Model Computation… Considering all possible paths
Forward algorithm: Probability of all possible Paths
from the first position to state v at postion i

18. Emmission Probabilities
Probability of observing (Tb,Ib) and (Ty, Iy) for the state 1 at position i
---Normal distribution
---Exponential distribution

19. MOWSE Scoring System MOWSE Algorithm is implemented in MASCOT software
Where
mi,j -elements of MOWSE frequence matrix

20. Data Sets
ISB data set:
A,B mixtures of 18 different proteins with modifications/relative amounts
Analysed using SEQUEST and other in-house Software
Data set is curated
Final data set with charge 2+ for trypsin digestion contains 857 spectra
5-fold cross validation by random selection
-Training set :687 spectra
-Testing set : 170 spectra
EM algorithm is used for estimating parameters

21. Results: Distributions of Ions
Noise
b and y ions
Match Tolerance
Parameter estimates

22. Comparative Studies
Dat set selection repeated 10 times to select both training
and test data set
For each group parameters are similar values
Prediction is considered correct if the peptide has highest score

23. Independent Data Set
A.Y’s Lab: The other independent data set for comparing with
other tools like SEQUEST and MASCOT
size of data set =20,980 spectra

24. False/True Positive Rates

25. Summary
Developed probabilistic scoring function called pepHMM for improving protein identifications
PepHMM outperform other tools like MASCOT with low false postive rate (always?)
Can this handle other type of ions other than b and y ions
Need to handle post translational modifications