prediction of ctl responses mette voldby larsen cand. scient. in biology ph.d. student
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Prediction of CTL responses
Mette Voldby Larsencand. scient. in biology
ph.d. student
Outline
- Short summary of the CTL response and the biological processes that precede it
- An integrated method for CTL epitope prediction: - existing methods for predicting the steps preceding a CTL response
- datasets
- evaluation and comparison to other methods for CTL epitope prediction
- what is the method used for?
CTL response
A CTL has identified a virus infected cell and
kills it
MHC class I molecules present peptides on the cell surface
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 TAPtransport efficiency
...…
Peters et al, 2003
SLYNTVATL RSLYNTVATL LRSLYNTVATL
ELRSLYNTVATL
0.56-0.09+1.80+0.94 = 3.212.732.8-0.38
SLYNTVATL 2.09
The score for a given peptide is an average over the 9mer, 10mer, 11mer and 12mer:
HLA-A HLA-BA1 B7
A2 B8
A3 B27
A24 B39
A26 B44
B58
B62
PredictingMHC class I binding
NetMHC: 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
– 863 nonameric epitopes collected from the SYFPEITHI Database
– 216 nonameric 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 nonamers in the proteins were considered to be nonepitopes
Collecting and combining the parameters
Hypothetical protein: MTSSAKRKMSPDNPDEGPSSKV
INT
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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
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0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
False positives rate
True
pos
itive
s ra
te
AUC = 0.5AUC = 1.0
Results
Results
Comparison with other methods for CTL epitope
predictionWhy ROC curves doesn’t work
Another performance measure
– Who ranks the epitope the highest?
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 Argentina)