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Epitope Selection Rational Vaccine design. Why? Therapeutic vaccines Therapeutic vaccines Treatment of viral infections (e.g., HIV, HCV), and resistant.

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Presentation on theme: "Epitope Selection Rational Vaccine design. Why? Therapeutic vaccines Therapeutic vaccines Treatment of viral infections (e.g., HIV, HCV), and resistant."— Presentation transcript:

1 Epitope Selection Rational Vaccine design

2 Why? Therapeutic vaccines Therapeutic vaccines Treatment of viral infections (e.g., HIV, HCV), and resistant intracellular bacterial infections. Treatment of viral infections (e.g., HIV, HCV), and resistant intracellular bacterial infections. Treatment of cancer Treatment of cancer Protective vaccines Protective vaccines Bacterial infections (e.g., Tuberculosis) Bacterial infections (e.g., Tuberculosis) Parasitic infections (e.g., Malaria) Parasitic infections (e.g., Malaria) Viral infections, new and old (e.g., Influenza, SARS, papyloma, smallpox) Viral infections, new and old (e.g., Influenza, SARS, papyloma, smallpox)

3 MHC-Class I (T-cell) Epitope Vaccines Analytical Methods Analytical Methods Predictive Methods Predictive Methods Methods of Evaluation Methods of Evaluation Case Stories Case Stories

4 Levels of Consideration Organism detection Organism detection Protein selection Protein selection Epitope selection Epitope selection Vaccine design Vaccine design

5 Identification of Pathogen Isolation of a viral/bacterial/parasitic agent unique to patients with certain symptoms Isolation of a viral/bacterial/parasitic agent unique to patients with certain symptoms Genome sequencing of the pathogenic agent Genome sequencing of the pathogenic agent

6 Concerns of Protein Selection General General Substantial free protein in the cytoplasm Substantial free protein in the cytoplasm Targets Specific Targets Specific Cancer Cancer Bacterial Infections Bacterial Infections Parasitic Infections Parasitic Infections Viral Infections Viral Infections

7 Cancer Schultze & Vonderheide 2001

8 Bacteria and parasites Surface proteins Surface proteins Detection or prediction of signal and anchor peptides Detection or prediction of signal and anchor peptides Exported proteins Exported proteins Detection or prediction of signal peptides Detection or prediction of signal peptides Escape from vesicles Escape from vesicles ???? ???? Prediction Server

9 Viral Infections Protein selection Protein selection Early Expression Early Expression High Levels of Expression High Levels of Expression Micro array expression analysis Micro array expression analysis Use all proteins as potential candidates Use all proteins as potential candidates

10 Select proteins with specific function(s) Assignment by homology Assignment by homology Sequence -> Structure prediction Sequence -> Structure prediction Structure -> Function prediction Structure -> Function prediction Sequence -> Function prediction (ProtFun) Sequence -> Function prediction (ProtFun) Secondary structure Secondary structure Signal peptide Signal peptide Trans membrane Trans membrane Phosphorylation Phosphorylation Glycosylation Glycosylation

11 Epitope Selection Proteasome Processing Proteasome Processing TAP recognition TAP recognition MHC affinity MHC affinity T-cell receptor affinity T-cell receptor affinity Dominance Dominance Variability Variability

12 How to combine predictions Step by step filtering Step by step filtering Setting a hard cutoff for each prediction Setting a hard cutoff for each prediction Determine a scoring function Determine a scoring function Give each prediction a different weight to obtain a final score (beware of over fitting!) Give each prediction a different weight to obtain a final score (beware of over fitting!) Neural Networks Neural Networks Use the output scores from other predictions to train a NN. Use the output scores from other predictions to train a NN.

13 Evaluating Methods Proteins Proteins Detection of presence in the (infected or malfunctioning) cell Detection of presence in the (infected or malfunctioning) cell Antibodies raised against whole or part of the protein Antibodies raised against whole or part of the protein Biochemical verification of function Biochemical verification of function Different assays Different assays Detection of location Detection of location Fluorescence labeled antibodies Fluorescence labeled antibodies

14 Epitopes Proteasome cleavage Proteasome cleavage In vitro cleavage In vitro cleavage Detection of selected peptides in cytoplasm? Detection of selected peptides in cytoplasm? TAP binding TAP binding Binding assays Binding assays MHC binding MHC binding Binding assays Binding assays TCR recognition TCR recognition Transgenic mice Transgenic mice EliSpot (  -interferon release) EliSpot (  -interferon release) Tetramer analysis Tetramer analysis

15 Vaccine design Sylvie Corbet Thursday

16 Case Stories HIV and TB approaches

17 Tuberculosis Vaccine, Cho et al. PNAS 97 (2000) Goal Goal Identify epitopes in selected proteins giving rise to a CTL response Identify epitopes in selected proteins giving rise to a CTL response Prediction Method Prediction Method MHC - binding (Sette) MHC - binding (Sette)

18 Evaluation In vitro binding assay In vitro binding assay 85 binders/222 predicted binders (38%) 85 binders/222 predicted binders (38%) CTL recall CTL recall 3 peptides tested 3 peptides tested A respons in 1/2 A respons in 1/2 B respons in 1/2 B respons in 1/2 C respons in 2/3 C respons in 2/3

19 World clade HIV vaccine, De- Groot et al., Vaccine 19 (2001) Goal Goal To identify high affinity epitopes with low variability present in most subtypes. To identify high affinity epitopes with low variability present in most subtypes. Prediction methods Prediction methods MHC binding (matrix method) MHC binding (matrix method) Variability studies Variability studies

20 Evaluation

21 Optimisation and immune recognition, Corbet et al., J Gen Virol. 84 (2003) Goal Goal Identify conserved high affinity HIV-1 epitopes Identify conserved high affinity HIV-1 epitopes Optimize potential binding peptides by anchor residue improvement Optimize potential binding peptides by anchor residue improvement Selection Methods Selection Methods Variability studies Variability studies MHC binding predictions (Neural Networks) MHC binding predictions (Neural Networks)

22 Evaluation MHC-binding MHC-binding 36/52 (69%) predicted peptides bind with an IC 50 < 500 nM 36/52 (69%) predicted peptides bind with an IC 50 < 500 nM

23 Evaluation

24 Present work Combination of predictions Combination of predictions Proteasome processing + TAP binding + MHC binding Proteasome processing + TAP binding + MHC binding Prediction of CTL reponse Prediction of CTL reponse


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