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Selection of T Cell Epitopes Using an Integrative Approach

Selection of T Cell Epitopes Using an Integrative Approach. Mette Voldby Larsen cand. 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:

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Selection of T Cell Epitopes Using an Integrative Approach

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  1. Selection of T Cell Epitopes Using an Integrative Approach Mette Voldby Larsen cand. scient. in biology ph.d. student

  2. 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?

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

  4. CTL response Virus- infected cell Healthy cell MHC-I

  5. CTL response Virus- infected cell Healthy cell MHC-I

  6. 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.

  7. 0.56-0.09+1.80+0.94 = 3.21 2.73 2.8 -0.38 SLYNTVATL RSLYNTVATL LRSLYNTVATL ELRSLYNTVATL SLYNTVATL 2.09 Predicting TAPtransport efficiency ...… The score for a given peptide is an average over the 9mer, 10mer, 11mer and 12mer: Peters et al, 2003

  8. PredictingMHC 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.

  9. In theory, integrating all three steps should lead to improved identification of peptides capable of eliciting CTL responses Integration? ?How should we do it?

  10. 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

  11. Collecting and combining the parameters Hypothetical protein: MTSSAKRKMSPDNPDEGPSSKV

  12. Best performing combination: 1*MHC-I + 0.05*TAP + 0.1*C-term cleavage

  13. Performance measure – Roc curve

  14. AUC = 0.5 AUC = 1.0

  15. Results

  16. Results

  17. AUC-values

  18. 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)

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