Selection of T Cell Epitopes Using an Integrative Approach

Mette Voldby Larsencand. scient. in biology

ph.d. student

Outline

Summary of biological processes preceding a CTL response

Summary of the methods available for predicting the processes

Case study: -Obtaining data, generating method, evaluating the method (small exercise – how to make Roc curves)

- What can you use the method for?

MHC-I molecules present peptides on the surface of most cells

CTL response

Healthy cell

Virus-infectedcell

MHC-I

CTL response

Healthy cell

Virus-infectedcell

MHC-I

Predicting proteasomalcleavage

NetChop (Keşmir et al, 2002, Nielsen et al, 2005)

Artificial Neural Networks (ANN) trained on different kinds of data.

- NetChop 20S: Trained on in vitro data- NetChop C-term: Trained on 1110 MHC I ligands

SLYNTVATL

Output: All aa in a protein are assigned a value between 0 and 1. Low values correspond to low probability of cleavage, high values to high probability of cleavage.

N1 N2 N3 C

A 1,56 0,25 0,1 -0,55

C -0,05 0,01 0,02 0

D -1,37 -1,42 -1,83 -1,83

E -1,65 -0,02 -1,51 -1,58

F -1,03 0,45 1,05 2,52

G -0,28 -1,14 -1,70 -1,41

H -0,21 -0,33 0,23 -0,55

I 0,11 0,49 0,62 0,52

K 1,03 0,41 -0,09 0,45

L 0,50 -0,09 0,11 0,94

M 0,38 0,46 0,58 0,29

N 1,43 -0,69 -1,01 -1,33

P -1,43 -3,00 -0,22 0,09

Q -0,47 0,97 -0,39 -0,12

R 1,34 1,47 0,42 1,47

S 0,56 0,34 -0,11 -2,26

T 0,12 0,04 -0,43 -0,72

V 0,49 0,50 0,71 0,30

W -0,54 0,64 1,65 0,87

Y -0,50 0,67 1,80 2,91

Predicting TAP transport efficiency

...…

Peters et al, 2003

SLYNTVATL RSLYNTVATL

0.56-0.09+1.80+0.94 = 3.212.73

SLYNTVATL 2.97

The score for a given peptide is an average over the 9mer, 10mer

HLA-A HLA-BA1 B7

A2 B8

A3 B27

A24 B39

A26 B44

B58

B62

Predicting MHC class I binding

Different ANN predict binding affinity to different MHC class I supertypes

Output: Each peptide is assigned a value between 0 and 1. Low values correspond to low binding affinity, high values to high binding affinity.

In theory, integrating all three steps should lead to improved identification of peptides capable of eliciting CTL responses

Integration?

How should we do it?

Dataset

– 148 9meric epitopes collected from the SYFPEITHI Database

– 69 9meric epitopes collected from the Los Alamos HIV Database

-The epitopes were grouped according to which MHC class I they bind

- The complete aa sequence of each sourceprotein was found in Swiss-Prot

- All other 9mers in the proteins were considered to be nonepitopes

Collecting and combining the parameters

Hypothetical protein: MTSSAKRKMSPDNPDEGPSSKV

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ProteasomalcleavagePos1 Pos2 Pos3 Pos4 Pos5 Pos6 Pos7 Pos8 Pos9 TAP MHC-I Epi/nonepi

MTSSAKRKM 0,87 0,00 0,17 0,06 0,59 0,89 0,96 0,76 0,97 2,14 0,76 0

TSSAKRKMS 0,00 0,17 0,06 0,59 0,89 0,96 0,76 0,97 0,02 1,01 0,32 0

SSAKRKMSP 0,17 0,06 0,59 0,89 0,96 0,76 0,97 0,02 0,02 3,05 0,44 0

SAKRKMSPD 0,06 0,59 0,89 0,96 0,76 0,97 0,02 0,02 0,02 -0,02 0,21 0

AKRKMSPDN 0,59 0,89 0,96 0,76 0,97 0,02 0,02 0,02 0,00 2,22 0,54 0

KRKMSPDNP 0,89 0,96 0,76 0,97 0,02 0,02 0,02 0,00 0,01 -1,09 0,33 0

RKMSPDNPD 0,96 0,76 0,97 0,02 0,02 0,02 0,00 0,01 0,56 1,04 0,05 0

KMSPDNPDE 0,76 0,97 0,02 0,02 0,02 0,00 0,01 0,56 0,04 0,03 0,12 0

MSPDNPDEG 0,97 0,02 0,02 0,02 0,00 0,01 0,56 0,04 0,25 0,72 0,43 0

SPDNPDEGP 0,02 0,02 0,02 0,00 0,01 0,56 0,04 0,25 0,14 0,83 0,11 0

PDNPDEGPS 0,02 0,02 0,00 0,01 0,56 0,04 0,25 0,14 0,08 2,01 0,11 0

DNPDEGPSS 0,02 0,00 0,01 0,56 0,04 0,25 0,14 0,08 0,06 1,70 0,66 0

NPDEGPSSK 0,00 0,01 0,56 0,04 0,99 0,14 0,08 0,06 0,98 0,71 0,43 1

PDEGPSSKV 0,01 0,56 0,04 0,25 0,14 0,08 0,06 1,00 0,98 1,01 0,02 0

Best performing combination:

1*MHC-I + 0.05*TAP + 0.15*C-term cleavage

Performance measure – Roc curve

True positive

False positive

False negative

True negative

Threshold TP FN

TP/(TP+FN) FP TN

FP/(FP+TN)

>0,8  4  10  0,29  1  12  0,08

>0,6 8  6  0,57  3  10  0,23 

>0,4 11  3  0,79  6  7  0,46 

>0,2 13 1  0,93  9  4  0,69 

>0 14  0  1  13  0  1 

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

False positives rate

True positives rate

AUC = 0.5AUC = 1.0

Results

Results

NetChop 20s

NetChop C-term 2.0

TAP MHC-I Integrated method

0.643 0.789 0.786 0.933 0.948

AUC-values

Practical use of NetCTL -ongoing projects

Prediction of epitopes in:HIV (collaboration with Karolinska Institute in Sweden)

Influenza A (collaboration with Panum institute)

Tuberculosis (collaboration with Leiden University in the Netherlands)

West nile virus (collaboration with Panum institute)

Yellow fever virus (collaboration with Panum institute)

Rickettsia (collaboration with Argentina)

Lassa/Junin virus (collaboration with Panum and Instituto Malbran, Argentina)