De Novo Peptide Sequencing: Informatics and Pattern ...rose/790B/791_Presentation.pdf · Computer...

02/05/10CSCE 791

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De Novo Peptide Sequencing: Informatics and Pattern Recognition applied to

Proteomics

John R. RoseComputer Science and Engineering

University of South Carolina

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Overview

• Background

• Information Theoretic Scoring Function

• Test Data Set

• Comparison with Existing Methods

• Conclusions

• Future Work

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Background

• Analogy:– Genome Machine Code– Proteome Execution of Code

• Protein identification is important– For drug discovery research – For the identification microbes in environmental samples

• Approaches using tandem mass spectrometry data:– Database searching– De Novo Sequencing– Tagging

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Tandem MS Data

• A peptide is ionized and the peptide bonds are fragmented• Fragment ions form peaks in the spectrum corresponding to their

mass-charge ratio.

2117.187

1818.190

765.373

358.1171219.587880.468 1689.886244.089

1990.213666.267

526.191

1332.574 1593.3531009.58286.010

0

1000

2000

3000

Inte

ns. [

a.u.

]

0 250 500 750 1000 1250 1500 1750 2000 2250 2500m/z

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Tandem MS Data

• Fragment ions include a,b,c,x,y,z, ions.

• de Novo sequencing focuses on y and b ions.– y ions contain the carboxyl terminus– b ions containing the amino terminus

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Tandem MS Data

• A good quality spectrum consists of– a ladder of peaks of the y-ions and– a ladder of peaks of the b-ions

• Ex: b-ions y-ionsF GLSLVR

FG LSLVR

FGL SLVR

FGLS LVR

FGLSL VR

FGLSLV R

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Approaches to peptide identification

Frank et al. JPR. 2006.

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De Novo Sequencing

• Data: tandem MS spectrum• Goal: find the corresponding peptide• General approach:

– Identify y and/or b ions– propose candidate peptides– Score each candidate– Return highest ranking peptides

• Two key issues:– Model for candidate peptide generation– Scoring function to evaluate candidates

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Candidate Peptide Generation

• The peptide sequence can be derived by the mass differences of adjacent peaks in each of the two ladders

• Ex: b-ions y-ions

I YEVEGMR

IY EVEGMR

IYE VEGMR

IYEV EGMR

IYEVE GMR

IYEVEG MR

IYEVEGM R

• Complicating factors:– Missing peaks– Posttranslational modifications– Many-to-one equivalences, e.g.,

AG,GA,K,Q,E are similar in mass

IYEVEGMR

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Actual example of labeled y and b ion peaks

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The spectrum graph

Frank et al. JPR. 2006.

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Construction of the NC-spectrum GraphChen et. al JCB 2001

• Create a pair of nodes, Nj and Cj, for each ion Ij .• Create two auxiliary nodes N0 and C0. to represent the zero mass and parent

mass, respectively.• Let V = {N0 , N1 , …, Nk , C0 , C1 , …, Ck}.• Each node x is placed assigned coordinate cord(x) according to the total mass

of its amino acids, that is,

⎪⎪⎩

⎪⎪⎨

⎧

====

+−−−

=

j

j

j

j

CxNxCxNx

wWw

Wxcord 0

0

11

180

)(

0 429.22

N0 C0

174.11 273.11

C1 N1

87.10 360.12

N2C2

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Construction of the NC-spectrum Graph

0 429.22

N0 C0

274.112

361.121

Mass / Charge

Abu

ndan

c e (1

0 0%

)

W = 447.225

⎪⎪⎩

⎪⎪⎨

⎧

====

+−−−

=

j

j

j

j

CxNxCxNx

wWw

Wxcord 0

0

11

180

)(

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0 429.22

N0 C0

174.11 273.11

C1 N1

274.112

361.121

Mass / Charge

Abu

ndan

c e (1

0 0%

)

W = 447.225

⎪⎪⎩

⎪⎪⎨

⎧

====

+−−−

=

j

j

j

j

CxNxCxNx

wWw

Wxcord 0

0

11

180

)(

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0 429.22

N0 C0

174.11 273.11

C1 N1

87.10 360.12

N2C2

274.112

361.121

Mass / Charge

Abu

ndan

c e (1

0 0%

)

W = 447.225

⎪⎪⎩

⎪⎪⎨

⎧

====

+−−−

=

j

j

j

j

CxNxCxNx

wWw

Wxcord 0

0

11

180

)(

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0 429.22

N0 C0

174.11 273.11

C1 N1

87.10 360.12

N2C2

Mass(W) = 186.21W

S+WMass(S+W) = 273.29

Mass(S) = 87.08S R

Mass(R) = 156.19

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0 429.22

N0 C0

174.11 273.1187.10 360.12

C1 N1 C2N2


Each path from N0 to C0 represents a possible sequence for the peptideA feasible path is a path from N0 to C0 that goes through exactly one node for each pair (either Nj or Cj).

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0 429.22

N0 C0

174.11 273.1187.10 360.12

C1 N1 C2N2

This is not a feasible path: misses ion I2

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0 429.22

N0 C0

174.11 273.1187.10 360.12

C1 N1 C2N2

This is a feasible path

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Problem Reformulation

• Input: an NC-spectrum graph G.• Output: a feasible path from N0 to C0.

• Difficulty:– A longest path does not always go through exactly

one of each pair of nodes.

– This is an NP-hard problem if the graph is a general directed graph.

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Renaming Nodes

Rename the nodes from left to right as X0 ,…, Xk ,Yk ,…,Y0

0 429.22

X0 Y0

174.11 273.1187.10 360.12

X2 Y2 Y1X1

0 429.22

N0 C0

174.11 273.1187.10 360.12

C1 N1 C2N2

Xi and Yi form a complementary pair of nodes for ion i.

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X0 Y0Xk Yk Y1X1 … …

• Let M(i, j) be a two-dimensional matrix with 0 ≤ i, j ≤ k. • Let M(i, j)=1 if

– there exists a path L from X0 to Xi and a path R from Yj to Y0, such that L and R together contain exactly one of Xp and Yp for each P in [0, max{i, j}].

X0 Y0YjXi

L R

Yi Y1X1 X2 Y2……

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X0 Y0Xk

L Re

Yj

• There is a feasible path if and only if– for some i and k, there is an edge e from Xi to Yk and M(i, k) = 1,

or– for some k and j, there is an edge e from Xk to Yj and M(k, j) = 1

X0 Y0YkXi

L Re

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• Complicating factors:– Posttranslational modifications– Many-to-one equivalences, e.g., AG,GA,K,Q,E are

similar in mass– Noise Peaks– Missing peaks

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– Missing peaks• Now a many-to-many combinatorial problem• Ex: ATEEQLK• If b4 ion is missing then b3 represents ATE and b5 represents

ATEEQ• Then the mass difference for EQ is unresolved.• Recall that AG,GA,K,Q,E are similar in mass• Thus EQ, QE, AGQ, GAQ, AGE, GAE,….. have similar mass

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Candidate Peptide Evaluation

• Model for candidate generation– Traditional focus on fragmentation model

• Increasing fragmentation model sophistication• Better posttranslational modification models• No model of peptide amino acid content

– QuasiNovo approach• Unsophisticated fragmentation model• No posttranslational modification model• Uses information theory to model peptide amino acid content

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Modeling Peptide Amino Acid Content

• Basic Idea: Examine actual proteins to characterize likely combinations of amino acids

• Underlying hypothesis: amino acid content is not random

• Analogy: model letter combinations in a language– examine documents in that language

– compile profiles of letter combinations

– predict missing letters from partial data

• Motivation:– Ability to distinguish between mass-equivalent combinations

– Ability to deal with missing peaks

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Amino Acid Distribution Data

Tabulation of amino acid distributions:Let <a1a2…an> be a contiguous sequence of n amino acids.

– There are n amino acids:<a1>, < a2>,…,<an>

– There are n-1 ordered amino acid pairs:<a1a2>, < a2a3 >,…,< an-1an>

– etc.QuasiNovo has been evaluated with 3-,4-,5-, and 6-tuples

Tuple frequencies are then normalized.

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Amino Acid Distribution Data

Three amino acid profiles used:1. Gammaproteobacteria:

– 206 complete genomes– 23,882,564 tryptic peptides

2. Actinobacteria:– 58 complete genomes– 7,380,927 tryptic peptides generated

3. Mammalia:– 4 complete genomes: Bovine, Human, Mouse, Rat– 9,835,585 tryptic peptides generated

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QuasiNovo’s Use of Tuple-Profiles

• Score candidate peptides

score(FGLSLVR) = p(SLVR)p(L|SLVR)p(G|LSLV)p(F|GLSL)

• Discard poor scoring candidates

• Handle missing peaks

– Find set of ai that maximize P(ai|ai-4ai-3ai-2ai-1)

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Test Data Set

280 spectra of peptides selected by Frank & Pevzner (2005)– molecular mass of up to 1400 Da– peptides with 7-16 amino acids (average length of 10.5)– source: ISB protein mixture data set and Open Proteomics Database

Data set used to compare PepNovo with– Sherenga– Peaks– Lutefisk

Later used to compare NovoHMM with– PepNovo– Sherenga– Peaks– Lutefisk

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Results

• The contenders:– PepNovo v1.03– PepNovo+– NovoHMM– QuasiNovo– QuasiNovo Reranking

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Results

0

20

40

60

80

100

0 1 2 3

Number of Incorrect Residues

% C

orre

ct

PepNovo+

PepNovo v1.03

NovoHMM

Quasinovo

Quasinovo Reranking

Results for set of 280 MS-MS test spectra comparing PepNovo+, PepNovo, NovoHMM, with a QuasiNovo reranking and QuasiNovo.

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Results

Results for set of 76 MS-MS test spectra for E. coli peptides comparing PepNovo+, PepNovo, NovoHMM, with three QuasiNovo scoring functions based on amino acid distributions in Gammaproteobacteria, Actinobacteria, and Mammalia.

0

20

40

60

80

100

0 1 2 3

Number of Incorrect Residues

% C

orre

ct PepNovo+

PepNovo v1.03

NovoHMM

Gammaproteobacteria

Actinobacteria

Mammalia

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Results

0.8150.8130.716QuasinovoReranking

0.7350.7590.523NovoHMM

0.7020.6160.509PepNovo+

y2-ionb2-ion

Complete peptideTerminal ion pairAlgorithm

Comparison of Terminal Pair and Overall Accuracy

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Conclusions and Future Work

• The QuasiNovo peptide model– predicts peptide amino acid content– has limited understanding of fragmentation– outperforms the PepNovo+ and NovoHMM

• QuasiNovo reranking– reranks PepNovo+ and NovoHMM results– proof-of-concept for combining peptide & fragmentation

models– shows best overall performance

• Future: Combine QuasiNovo amino acid model with a sophisticated fragmentation model

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Acknowledgements

Rose Lab– Jimmy Cleveland– Achraf Elallali– Amadeo Bellotti

Fox Lab– Alvin Fox– Karen Fox– Jennifer Intelicato-Young

Support– Funding from Alfred P. Sloan Foundation– Experiments were conducted on a 128-core shared memory computer

funded by NSF (CNS 0708391).

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Gammaproteobacteria

• Cumulative results from 174 spectra• x = n number of correctly predicted amino acids• Note: a predicted amino acid is correct if it appears within 2.5 Da of its position in the

actual peptide

0.000.100.200.300.400.500.600.700.800.901.00

x = 3 x = 4 x = 5 x = 6 x = 7 x = 8 x = 9 x = 10 x = 11 x = 12

QuasiNovo MM Reranking NovoHMM PepNovo+

x = 3 x = 4 x = 5 x = 6 x = 7 x = 8 x = 9 x = 10 x = 11 x = 12QuasiNovo 0.95 0.90 0.82 0.74 0.68 0.64 0.63 0.60 0.58 0.50MM Reranking 0.97 0.93 0.90 0.86 0.84 0.81 0.71 0.63 0.51 0.46NovoHMM 0.97 0.94 0.87 0.75 0.61 0.45 0.27 0.23 0.13 0.02PepNovo+ 0.95 0.93 0.84 0.67 0.55 0.38 0.26 0.20 0.11 0.09

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Actinobacteria


actual peptide


0.000.100.200.300.400.500.600.700.800.901.00

x = 3 x = 4 x = 5 x = 6 x = 7 x = 8 x = 9 x = 10 x = 11 x = 12


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Results: Mammalia


actual peptide


0.000.100.200.300.400.500.600.700.800.901.00

x = 3 x = 4 x = 5 x = 6 x = 7 x = 8 x = 9 x = 10 x = 11 x = 12


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EF-Tu Protein

• DISTILLER/MASCOT identification: AIDKPFLLPIEDVFSISGR • QuasiNovo identification: DSDKPFMMPVEDVFSITGR

– Score(AIDKPFLLPIEDVFSISGR) = 1.83164551734336e-38– Score(DSDKPFMMPVEDVFSITGR) = 7.10172913187262e-36

• QuasiNovo result supported by microbiological data– Gram stain– physiological tests– visual comparison of spectra of environmental isolates versus known S. aureus

and interpretation of Distiller/Mascot sequence assignment• Note: Distiller results based on 18 peaks vs 12 peaks for QuasiNovo• Peptide displays loss of 3 water molecules

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