Vaccine Design

Need for new vaccine technologies

• The classical way of making vaccines have in many cases been tried for the pathogens for which no vaccines exist

• Need for new ways for making vaccines

Categories of Vaccines

•Live vaccines

•Are able to replicate in the host

•Attenuated (weakened) so they do not cause disease

•Subunit vaccines

•Part of organism

•Genetic Vaccines

•Part of genes from organism

Polytope optimization

•Successful immunization can be obtained only if the epitopes encoded by the polytope are correctly processed and presented.

•Cleavage by the proteasome in the cytosol, translocation into the ER by the TAP complex, as well as binding to MHC class I should be taken into account in an integrative manner.

•The design of a polytope can be done in an effective way by modifying the sequential order of the different epitopes, and by inserting specific amino acids that will favor optimal cleavage and transport by the TAP complex, as linkers between the epitopes.

Polytope starting configuration

Immunological Bioinformatics, The MIT press.

Polytope optimization Algorithm

• Optimization of of four measures:

1. The number of poor C-terminal cleavage sites of epitopes (predicted cleavage < 0.9)

2. The number of internal cleavage sites (within epitope cleavages with a prediction larger than the predicted C-terminal cleavage)

3. The number of new epitopes (number of processed and presented epitopes in the fusing regions spanning the epitopes)

4. The length of the linker region inserted between epitopes.

• The optimization seeks to minimize the above four terms by use of Monte Carlo Metropolis simulations [Metropolis et al., 1953]

Polytope final configuation

Immunological Bioinformatics, The MIT press.

Prediction of antigens

• Protective antigens

• Functional definition (phenotype)

• Which antigens will be protective (genotype)?

• They must be recognized by the immune system

• Predict epitopes (include processing)

• CTL (MHC class I)

• http://www.cbs.dtu.dk/services/NetCTL/

• Helper (MHC class II)

• http://mail1.imtech.res.in/raghava/hlapred/index.html

• Antibody

• http://www.cbs.dtu.dk/services/BepiPred/

• http://www.cbs.dtu.dk/services/DiscoTope/ More Links: http://www.cbs.dtu.dk/researchgroups/immunology/webreview.html

Function and conservation

• Some of the epitopes must exist in the wild type

• Conservation

• http://www.ncbi.nlm.nih.gov/BLAST/

• Function

• When is it expressed?

• Where is it trafficked to?

• SecretomePNon-classical and leaderless secretion of eukaryotic proteins.SignalP Signal peptide and cleavage sites in gram+, gram-and eukaryotic amino acid sequences.TargetPSubcellular location of proteins: mitochondrial,chloroplastic, secretory pathway, or other.

• Expression level?

Selection of antigens

• Epitopes

• Polytope

• Proteins

• Helper epitopes

• Does it contain any

• Can they be added

• Hide epitopes

• Immunodominant and variable ones

Examples of antigen selections

The binding of an immunodominant 9-mer Vaccinia CTL epitope, HRP2 (KVDDTFYYV) to HLA-A*0201. Position 2 and 9 of the epitopes are buried deeply in the HLA class I molecule.

Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004).

 

Clustering of HLA alleles

Clustering in: O Lund et al., Immunogenetics. 2004 55:797-810

Study is being updated in the Buus project using data from Buus and Sette

Inside out:1. Position in RNA2. Translated regions (blue)3. Observed variable spots4. Predicted proteasomal cleavage5. Predicted A1 epitopes6. Predicted A*0204 epitopes7. Predicted A*1101 epitopes8. Predicted A24 epitopes9. Predicted B7 epitopes10. Predicted B27 epitopes11. Predicted B44 epitopes12. Predicted B58 epitopes13. Predicted B62 epitopes

DevelopmentDevelopment

22mmHeavy chainHeavy chain

peptidepeptide IncubationIncubationPeptide-MHC Peptide-MHC complexcomplex

Strategy for the quantitative ELISA assay C. Sylvester-Hvid, et al., Tissue antigens, 2002: 59:251

Step I: Folding of MHC class I molecules in solution

Step II: Detection of Step II: Detection of de novode novo folded MHC class I molecules by ELISA folded MHC class I molecules by ELISA

C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8

SARS projectSARS project

We scanned HLA supertypes and identified almost 100 potential vaccine candidates.

These should be further validated in SARS survivors and may be used for vaccine formulation.

Prediction method available: www.cbs.dtu.dk/services/NetMHC/

C Sylvester-Hvid et al., Tissue Antigens. 2004 63:395-400

NIH project

Develop improved methods to predict cytotoxic T cell (CTL) epitopes

Scan 15 different pathogens from the NIAID A-C list agents of bioterrorism

Test if cytotoxic T cells from preselected immune blood donors can react to the selected peptides for 3 selected pathogens: Influenza, Smallpox vaccine and tuberculosis vaccine (BCG)

Culture in vitro for 10 days + peptidePBMCs + Peptide

Flow Chart of ELISPOT Assay

+ peptideIncubating in anti IFN- pre-coated plate for 18-20 h

Washing off the cells

Adding Biotin-anti IFN-

Adding Streptavidin-HRP after washing the plate

Adding a substrate

•Coating Ab: Coating Ab: –Human IFN-Human IFN- MAb MAb (ENDOGEN, Pierce (ENDOGEN, Pierce Biotechnology, Inc)Biotechnology, Inc)

•Detection Ab:Detection Ab:–Human IFN-Human IFN- MAb, Biotin MAb, Biotin labeled labeled –(ENDOGEN, Pierce (ENDOGEN, Pierce Biotechnology, Inc)Biotechnology, Inc)

Automatical counting

- peptide

Pathogen HLA binding ELISPOT

Influenza X X

Variola major (smallpox) vaccine strain X X/VRC, NIH

Yersinia pestis X

Francisella tularensis (tularemia) X (X) A Sjostedt

LCM X

Lassa Fever X

Hantaan virus (Korean hemorrhagic fever virus) X

Rift Valley Fever X

Dengue X (X) T August

Ebola X

Marburg X

Multi-drug resistant TB (BCG vaccine) X X

Yellow fever X (X) T August

Typhus fever (Rickettsia prowazekii) X

West Nile Virus X (X) T August

Selected pathogens

Prediction of Class II epitopes

Eric A. J. Reits

Prediction of MHC Class II binding

Virtual matrices– TEPITOPE: Hammer, J., Current Opinion in Immunology 7, 263-269, 1995, – PROPRED: Singh H, Raghava GP Bioinformatics 2001 Dec;17(12):1236-7

Web interface http://www.imtech.res.in/raghava/propred

Prediction Results

Prediction of Antibody epitopes

Linear– Hydrophilicity scales (average in ~7 window)

• Hoop and Woods (1981)• Kyte and Doolittle (1982)• Parker et al. (1986)

– Other scales & combinations• Pellequer and van Regenmortel• Alix

– New improved method (Pontoppidan et al. in preparation)• http://www.cbs.dtu.dk/services/BepiPred/

Discontinuous– Protrusion (Novotny, Thornton, 1986)

Prediction of proteins structure

•Homology modeling•Secondary structure prediction

AcknowledgementsImmunological Bioinformatics group, CBS, Technical University of Denmark (www.cbs.dtu.dk)

Morten Nielsen

HLA binding

Claus Lundegaard

Data bases, HLA binding

Anne Mølgaard

MHC structure

Mette Voldby Larsen

Phd student - CTL prediction

Pernille Haste Andersen

PhD student – Structure

Sune Frankild

PhD student - Databases

Jens Pontoppidan

Linear B cell epitopes

Thomas Blicher

MHC structure

Sheila Tang

Pox/TB

Thomas Rask

Evolution

Nicolas Rapin/Ilka Hoff

Simulation of the immune system

Collaborators

IMMI, University of Copenhagen

Søren Buus MHC binding

Mogens H Claesson Elispot Assay

La Jolla Institute of Allergy and Infectious Diseases

Allesandro Sette Epitope database

Bjoern Peters

Leiden University Medical Center

Tom Ottenhoff Tuberculosis

Michel Klein

Fatima Kazi

Ganymed

Ugur Sahin Genetic library

University of Tubingen

Stefan Stevanovic MHC ligands

INSERM

Peter van Endert Tap binding

University of Mainz

Hansjörg Schild Proteasome

Schafer-Nielsen

Claus Schafer-Nielsen Peptide synthesis

University of Utrecht

Can Kesmir Bioinformatics