Prediction of CTL responses Mette Voldby Larsen cand. scient. in biology ph.d. student.

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Prediction of CTL responses Mette Voldby Larsen cand. 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 proteasomal cleavage 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.

N1N2N3C A1,560,250,1-0,55 C-0,050,010,020 D-1,37-1,42-1,83 E-1,65-0,02-1,51-1,58 F-1,030,451,052,52 G-0,28-1,14-1,70-1,41 H-0,21-0,330,23-0,55 I0,110,490,620,52 K1,030,41-0,090,45 L0,50-0,090,110,94 M0,380,460,580,29 N1,43-0,69-1,01-1,33 P-1,43-3,00-0,220,09 Q-0,470,97-0,39-0,12 R1,341,470,421,47 S0,560,34-0,11-2,26 T0,120,04-0,43-0,72 V0,490,500,710,30 W-0,540,641,650,87 Y-0,500,671,802,91 Predicting TAP transport efficiency...… Peters et al, 2003 SLYNTVATL RSLYNTVATL LRSLYNTVATL ELRSLYNTVATL = SLYNTVATL 2.09 The score for a given peptide is an average over the 9mer, 10mer, 11mer and 12mer:

HLA-AHLA-B A1B7 A2B8 A3B27 A24B39 A26B44 B58 B62 Predicting MHC 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 ? ? ? ? ? ? ? ? ? ? ? ? ? ? Proteasomal cleavage Pos1Pos2Pos3Pos4Pos5Pos6Pos7Pos8Pos9TAPMHC-IEpi/nonepi MTSSAKRKM0,870,000,170,060,590,890,960,760,972,140,760 TSSAKRKMS0,000,170,060,590,890,960,760,970,021,010,320 SSAKRKMSP0,170,060,590,890,960,760,970,02 3,050,440 SAKRKMSPD0,060,590,890,960,760,970,02 -0,020,210 AKRKMSPDN0,590,890,960,760,970,02 0,002,220,540 KRKMSPDNP0,890,960,760,970,02 0,000,01-1,090,330 RKMSPDNPD0,960,760,970,02 0,000,010,561,040,050 KMSPDNPDE0,760,970,02 0,000,010,560,040,030,120 MSPDNPDEG0,970,02 0,000,010,560,040,250,720,430 SPDNPDEGP0,02 0,000,010,560,040,250,140,830,110 PDNPDEGPS0,02 0,000,010,560,040,250,140,082,010,110 DNPDEGPSS0,020,000,010,560,040,250,140,080,061,700,660 NPDEGPSSK0,000,010,560,040,990,140,080,060,980,710,431 PDEGPSSKV0,010,560,040,250,140,080,061,000,981,010,020

Best performing combination: 1*MHC-I *TAP *C-term cleavage

Performance measure – ROC curve AUC = 0.5 AUC = 1.0

Results

Results

Comparison with other methods for CTL epitope prediction Why 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)