processing_netctl_ep
TRANSCRIPT
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Department of Systems Biology
Technical University of Denmark
Immunological Bioinformatics
Processing, combined predictions, and rational
epitope selection
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Department of Systems Biology
Technical University of Denmark
Cellular Immunity
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Department of Systems Biology
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Proteasome specificity
Low polymorphism– Constitutive & Immuno-
proteasome
Evolutionary conserved Stochastic and low specificity
– Only 70-80% of the cleavage sites are reproduced in repeated experiments
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Department of Systems Biology
Technical University of Denmark
Proteasome evolution (1 unit)
Constitutive
Immuno
Human (Hs) - HumanDrosophila (Dm) - Fly
Bos Taurus (Bota) - CowOncorhynchus mykiss (Om) - Fish
…
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Department of Systems Biology
Technical University of Denmark
Immuno- and Constitutive proteasome specificity
...LVGPTPVNIIGRNMLTQL..
P1 P1’
Immuno Constitutive
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Department of Systems Biology
Technical University of Denmark
NetChop– Neural network based method
PaProc– Weight matrix based method
FragPredict– Based on a statistical analysis of cleavage-
determining amino acid motifs present around the scissile bond• i.e. also weight matrix like
Predicting proteasomal cleavage
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Department of Systems Biology
Technical University of Denmark
NetChop 3.0 Cterm (MHC ligands)
LDFVRFMGVMSSCNNPA LVQEKYLEYRQVPDSDP RTQDENPVVHFFKNIVT TPLIPLTIFVGENTGVP LVPVEPDKVEEATEGEN YMLDLQPETTDLYCYEQ PVESMETTMRSPVFTDN ISEYRHYCYSLYGTTLE AAVDAGMAMAGQSPVLR QPKKVKRRLFETRELTD LGEFYNQMMVKAGLNDD GYGGRASDYKSAHKGLK KTKDIVNGLRSVQTFAD LVGFLLLKYRAREPVTK SVDPKNYPKKKMEKRFV SSSSTPLLYPSLALPAP FLYGALLLAEGFYTTGA
NetChop-3.0 C-term– Trained on class I
epitopes– Most epitopes are
generated by the immuno proteasome
– Predicts the immuno proteasome specificity
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Department of Systems Biology
Technical University of Denmark
NetChop20S-3.0In vitro digest data from the constitutive proteasome
Toes et al., J.exp.med. 2001
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Department of Systems Biology
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Prediction performance
€
Sens =TP
AP
Spec =TN
AN
CC =TP ⋅TN − FN ⋅FPPP ⋅AN ⋅AP ⋅PN
TPFP
APAN
Aroc=0.5
Aroc=0.8
1 - spec
Sen
s
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Department of Systems Biology
Technical University of Denmark
Predicting proteasomal cleavage
-0.4-0.2
00.20.40.60.8
1
Performance
FragPredictPAProCI Netchop2.0NetChop3.0
Sens Spec CC
0
0.5
1
Performance
CC PCC Aroc
CC 0.12 0.1 0.41 0.48
PCC 0.13 0.48 0.55
Aroc 0.56 0.82 0.85
FragPredict PAProCI Netchop20S NetChop20S-3.0
NetChop20S-3.0
NetChop-3.0
• Relative poor predictive performance–For MHC prediction CC~0.92 and AUC~0.95
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Department of Systems Biology
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Proteasome specificity
NetChop is one of the best available cleavage method– www.cbs.dtu.dk/services/NetChop-3.0
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Department of Systems Biology
Technical University of Denmark
Cellular Immunity
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Department of Systems Biology
Technical University of Denmark
What does TAP do?
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Department of Systems Biology
Technical University of Denmark
TAP affinity prediction
Transporter Associated with antigen Processing Binds peptides 9-18 long Binding determined mostly by N1-3 and C terminal amino acids
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Department of Systems Biology
Technical University of Denmark
A low matrix entry corresponds to an amino acid well suited for TAP binding
TAP binding motif matrix
Peters et el., 2003. JI, 171: 1741.
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Department of Systems Biology
Technical University of Denmark
TAP affinity prediction
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Department of Systems Biology
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Predicting TAP affinity
9 meric peptides >9 meric
Peters et el., 2003. JI, 171: 1741.
ILRGTSFVYV-0.11 + 0.09 - 0.42 - 0.3 = -0.74
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Department of Systems Biology
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Integrating all three steps (protesaomal cleavage, TAP transport and MHC binding) should lead to improved identification of peptides capable of eliciting CTL responses
Integration?
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Department of Systems Biology
Technical University of Denmark
Identifying CTL epitopes
1 EBN3_EBV YQAYSSWMY 2.56 1.00 0.03 0.34 0.99 0.02 0.01 0.75 0.94 0.92 2.97 0 2.802 EBN3_EBV QSDETATSH 2.22 0.01 0.28 0.88 0.04 0.83 0.51 0.30 0.11 0.99 -0.80 0 2.283 EBN3_EBV PVSPAVNQY 1.55 0.01 0.97 0.01 0.22 0.21 1.00 0.02 0.04 1.00 2.63 0 1.784 EBN3_EBV AYSSWMYSY 1.31 0.34 0.99 0.02 0.01 0.75 0.94 0.92 0.09 1.00 3.28 1 1.585 EBN3_EBV LAAGWPMGY 1.02 1.00 0.97 0.22 0.01 0.18 0.01 0.06 0.01 1.00 3.01 0 1.276 EBN3_EBV IVQSCNPRY 0.99 0.10 0.97 0.50 0.05 0.01 0.01 0.01 0.02 0.93 3.19 0 1.247 EBN3_EBV FLQRTDLSY 0.94 0.46 0.99 0.02 0.82 0.07 0.01 0.63 0.01 0.96 2.79 0 1.188 EBN3_EBV YTDHQTTPT 1.15 1.00 0.01 0.42 0.02 0.04 0.01 0.02 0.54 0.14 -0.87 0 1.129 EBN3_EBV GTDVVQHQL 0.96 0.01 0.02 0.03 0.99 1.00 0.02 0.46 0.30 1.00 0.53 0 1.09...
HLA affinity
Proteasomal cleavage
TAP affinity
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Department of Systems Biology
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Department of Systems Biology
Technical University of Denmark
Large scale method validation
HIV A3 epitope predictions
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Pathogen and population coverage
How to hit them all in a few strokes
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Department of Systems Biology
Technical University of Denmark
HCV Genotypes
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Department of Systems Biology
Technical University of Denmark
Genotype Variation
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Department of Systems Biology
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Genotype variation
de Oliveira et al., Nature 444, 836-837(14 December 2006)
HIV-1 CRF02_AG (a), HCV genotype 4 (b) and HCV genotype 1 (c)
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Department of Systems Biology
Technical University of Denmark
Genotype 1
Top Scoring Peptides Top Scoring Peptides
Genotype 2
Genotype 3
Genotype 4
Genotype 5
Genotype 6
Select peptide with maximal coverage
Select peptide with maximal coverage
preferring uncovered strains
Select peptide with maximal coverage preferring lowest covered strains
Repeat until the Repeat until the desired number of desired number of
peptides is selectedpeptides is selected
GenoCover
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Department of Systems Biology
Technical University of Denmark
Genotype 1
Genotype 2
Genotype 3
Genotype 4
Genotype 5
Genotype 6
QPRGRRQPIQPRGRRQPI
PeptidePeptide Predicted Predicted affinity (nM)affinity (nM)
55
SPRGSRPSWSPRGSRPSW 4343
GenomeGenomeCoverageCoverage
55
44
DPRRRSRNLDPRRRSRNL** 336666
RARAVRAKLRARAVRAKL
PeptidesPeptides
66 33
TPAETTVRLTPAETTVRL** 3838 33
33
33
22
33
44
33
* Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database
HCV Results - B7
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Department of Systems Biology
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Population Diversity
http://static.howstuffworks.com/gif/population-six-billion-1.jpg
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Department of Systems Biology
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MHC-Cover
HLA-A*0101
Top Scoring Peptides Top Scoring Peptides
HLA-A*0201
HLA-A*0301
HLA-B*0702
HLA-B*2705
HLA-B*4402
Select peptide with maximal MHC
coverage
Select peptide with maximal MHC
coverage preferring uncovered MHCs
Select peptide with maximal MHC
coverage preferring lowest
covered HLAs
Repeat until the Repeat until the desired number of desired number of
peptides is selectedpeptides is selected
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Department of Systems Biology
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Population diversity
http://www.piperreport.com/archives/Images/Marketing%20to%20Diverse%20Medicare%20Population.jpg
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Department of Systems Biology
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MHC-Cover
HLA-A*0101
Top Scoring Peptides Top Scoring Peptides
HLA-A*0201
HLA-A*0301
HLA-B*0702
HLA-B*2705
HLA-B*4402
Select peptide with maximal
population coverage
Select peptide with maximal coverage
preferring uncovered MHCs
with highest population coverage
Select peptide with maximal coverage preferring lowest
covered HLAs with highest population
coverage
Repeat until the Repeat until the desired number of desired number of
peptides is selectedpeptides is selected
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Department of Systems Biology
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Epi-select
HLA-A*0101
HLA-A*0201
HLA-A*0301
HLA-B*0702
HLA-B*2705
HLA-B*4402
Select peptide with maximal
population coverage and
maximal genotype coverage
Select peptide with maximal coverage
preferring uncovered MHCs
with highest population
coverage and maximal genotype
coverage Select peptide with maximal coverage preferring lowest
covered HLAs with highest population
coverage and maximal genotype
coverage
Repeat until the Repeat until the desired number of desired number of
peptides is selectedpeptides is selected
Genotype 1
Genotype 2
Genotype 3
Genotype 4
Genotype 5
Genotype 6
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Department of Systems Biology
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Reaching optimal coverage
HCV Genotypes