ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUNPROTEOMICS USING PEPTIDE

DETECTABILITY

PEDRO ALVES

Advisor: Predrag Radivojac

School of Informatics BLOOMINGTON

Overview

• Shotgun Proteomics

• Protein Inference Problem

• Protein Identification Using Peptide Detectability

• Results

• Limitations and Improvements

Degenerate Peptides

Rat Sample/Rat IPI Database60%

Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

Protein Inference Problem

Solution 1 * (A, E)*

Solution 2 *

(B, C, D)

*

*

* *

Minimum Protein Set

11 Possible Solutions

Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

Identified Peptides

1

2

3

4

5

6

7

8

9

10

Proteins

1 2 3 4 2 6

1 2 35 4 1 7 8

10 9 6

6 9

10

GMPSA

5

3 3

1 3

2

3

2 2

2

0

0

1

Greedy Minimum Protein Set Algorithm

Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

Resolving Ambiguity

detectability of a peptide

– the probability that the peptide will be observed in a standard sample analyzed by a standard proteomics routine

Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488

Factors affecting Peptide Detection

Four classes of factors

1) Chemical properties of the peptide (and parent protein)

2) Limitations of peptide identification protocol

3) Abundance of the peptide in the sample

4) Presence of other peptides that compete for detection

Mean Accuracy : 71%

Mean AUC : 78%

Synthetic : ~30% of peptides identified

Real : ~10% of peptides identified

Peptide Detectability Prediction

Identified Peptides

1

2

3

4

5

6

7

8

9

10

Proteins

Minimum Missed Peptides

12

45

4

2

6

1

4

5

7

8

6

9

27

10

24

53

23

17

55

6

9

10

14

1

17

2

3

1

2

15

3

24

01

Missed peptide

MDAP

Identified Peptides

1

2

3

4

5

6

7

8

9

10

ProteinsLDFA

12

45

4

2

6

1

4

5

7

8

6

9

27

10

24

53

23

17

55

6

9

10

1

4

5

7

6

2

9

8

10

3

14

1

17

2

3

1

2

15

3

24

2

1

0 2

10

RESULTS

GMPSA LDFA

Synthetic Sample

with 12 Proteins

7 correct proteins 10 correct

proteins

5 tied proteins

1 tied protein1 incorrect tied protein

GMPSA vs LDFAin a R. norvegicus sample

GMPSA LDFA

Rat Sample/Rat IPI Database

2346 94

Indistinguishable pairs

GMPSA vs LDFA

GMPSA LDFA

247 275

Total proteins identified

62% 81%

Percent of proteins assigned with no ties

153 224

Total assignments with no ties

149

Proteins assigned due to unique peptides

4 75

Total unambiguous assignments excluding the proteins with unique peptides

Identified Proteins

Unambiguously Identified Proteins

Limitations and Improvements

• Include missed-cleavage peptides

• Include lower scoring peptides to aid in the differentiation of tied proteins

• Include peptides identified with charges +1 and +3

• Train on other analytical platforms

• Study the effects of detectability prediction on algorithm results

Publications

• PSB 2007– Alves, P. , Arnold, R. , Novotny, M. , Radivojac, P. , Reilly, J. ,

Tang, H. (2007). Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability. Pac. Symp. Biocomput., (2007) 12: 409-420

• ISMB 2006– Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E.,

Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488.

Acknowledgements

• Predrag Radivojac

• Haixu Tang

• Randy Arnold

• IU School of Informatics

• IU Chemistry Dept.