B cell epitopes and B cell epitope predictions

1. B cell epitopes andB cell epitope predictions Morten Nielsen, CBS, BioCentrum, DTU (mostly copied from Vsevolod Katritch’s, Siga presentation 2002)

2. Algorithms for epitope prediction and selection Antibody Fab fragment B-cell epitopes • Most epitope are structural epitopes • sequence based methods are limited • requires structure-based approach

3. B-cell epitope classification B-cell epitope – structural feature of a molecule or pathogen, accessible and recognizable by B-cells linear epitopes “Discontinuous epitope (with linear determinant) Discontinuous epitope

4. Sequence-based methods TSQDLSVFPLASCCKDNIASTSVTLGCLVTGYLPMSTTVTWDTGSLNKNVTTFPTTFHETYGLHSIVSQVTASGKWAKQRFTCSVAHAESTAINKTFSACALNFIPPTVKLFHSSCNPVGDTHTTIQLLCLISGYVPGDMEVIWLVDGQKATNIFPYTAPGTKEGNVTSTHSELNITQGEWVSQKTYTCQVTYQGFTFKDEARKCSESDPRGVTSYLSPPSPL • Protein hydrophobicity – hydrophilicity algorithms Fauchere, Janin, Kyte and Doolittle, Manavalan Sweet and Eisenberg, Goldman, Engelman and Steitz (GES), von Heijne • Protein flexibility prediction algorithm Karplus and Schulz • Protein secondary structure prediction algorithms GOR II method (Garnier and Robson), Chou and Fasman • Protein “antigenicity” prediction : Hopp and Woods, Parker, Protrusion Index (Thornton), Welling • Same as protein surface accessibility • Predict “linear” epitopes only

5. Linear Epitopes (flexible loops) Con • only ~5% of epitopes can be classified as “linear” • weakly immunogenic in most cases • most epitope peptides does not provide antigen-neutralizing immunity • in many cases represent hypervariable regions (HIV, HCV etc.) Pro • easily predicted computationally • easily identified experimentally • immunodominant epitopes in many cases • do not need 3D structural information • easy to produce and check binding activity experimentally

6. Prediction of linear epitopes

7. Q: What can antibodies recognize in a protein? probe Antibody Fab fragment Protrusion index A: Everything accessible to a 10 Å probe on a protein surface Novotny J. A static accessibility model of protein antigenicity. Int Rev Immunol 1987 Jul;2(4):379-89

8. Model Known 3D structure Rational B-cell epitope design • Protein target choice • Structural analysis of antigen • X-ray structure or homology model • Precise domain structure • Physical annotation (flexibility, electrostatics, hydrophobicity) • Functional annotation (sequence variations, active sites, binding sites, glycosylation sites, etc.)

9. Rational B-cell epitope design • Protein target choice • Structural annotation • Epitope prediction and ranking • Surface accessibility • Protrusion index • Conserved sequence • Glycosylation status

10. Rational B-cell epitope design • Protein target choice • Structural annotation • Epitope prediction and ranking • Optimal Epitope presentation • Fold minimization, or • Design of structural mimics • Choice of carrier (conjugates, DNA plasmids, virus like particles, ) • Multiple chain protein engineering

11. HIV gp120-CD4 epitope Binding of CD4 receptor Conformational changes in gp120 Opens chemokine-receptor binding site New highly concerved epitope Kwong et al.(1998) Nature393, 648-658

12. HIV gp120-CD4 epitope First efforts to design single-chain analogue • Elicit broadly cross-reactive neutralizing antibodies in rhesus macaques. • This conjugate is too large(~400 aa) and still contains a number of irrelevant loops • Fouts et al. (2000) Journal ofVirology, 74, 11427-11436 • Fouts et al. (2002) Proc Natl Acad Sci U S A. 99, 11842-7.

13. HIV gp120-CD4 epitope Further optimization of the epitope: • reduce to minimal stable fold (iterative) • optimize linker length • find alternative scaffold to present epitope (miniprotein mimic) Martin & Vita, Current Prot. An Pept. Science, 1: 403-430. Vita et al.(1999) PNAS 96:13091-6

14. Homology modeling Alignment BLAB._ 0 : EKLKDNLYVYTTYNTFNGTKY-AANAVYLVTDKGVVVIDCPWGEDKFKSFTDEIYKKHGKKVIMNIATHS1A8T.A 13 : TQLSDKVYTYVSLAEIEGWGMVPSNGMIVINNHQAALLDTPINDAQTEMLVNWVTDSLHAKVTTFIPNHWBLAB._ 69 : HDDRAGGLEYFGKIGAKTYSTKMTDSILAKENKPRAQYTFDNNKSFKVGKSEFQVYYPGKGHTADNVVVW1A8T.A 83 : HGDCIGGLGYLQRKGVQSYANQMTIDLAKEKGLPVPEHGFTDSLTVSLDGMPLQCYYLGGGHATDNIVVWBLAB._ 139 : FPKEKVLVGGCIIKSADSKDLGYIGEAYVNDWTQSVHNIQQKFSGAQYVVAGHDDWKDQRSIQHTLDLIN1A8T.A 153 : LPTENILFGGCMLKDNQTTSIGNISDADVTAWPKTLDKVKAKFPSARYVVPGHGNYGGTELIEHTKQIVNBLAB._ 209 : EYQQKQK1A8T.A 223 : QYIESTS Sequence identity 27%

15. Homology Modeling Blue: Template Red: Model

16. Homology Modeling Protein sequence Known 3D template(s) • Threading (seq-str. align.) • Side chain prediction • Loop building • Local reliability prediction Model Model by homology Known 3D structure

17. Protein Model Health Evaluation Model • High energy strain Lower energy strain • Local alignment strength • Local Energy strain Maiorov, Abagyan, Proteins 1998 Cardozo, Abagyan, 2000 Known 3D structure

18. Annotation of protein surface • Contour-buildup algorithm (J.Str.Biol, 116, 138, 1996). Requires 3D structure • Surface prediction using propensity scales (linear effects) • Surface prediction using Neural networks (higher order effects)

19. Multichain protein design B-cell epitope Rational optimization of epitope-VLP chimeric proteins: • Design a library of possible linkers (<10 aa) • Perform global energy optimization in VLP (virus-like particle) context • Rank according to estimated energy strain T-cell epitope

20. Conclusions • Rational vaccines can be designed to induce strong and epitope-specific B-cell response • Selection of protective B-cell epitope involves structural, functional and immunogenic analysis of the pathogenic proteins • Structural modeling tools are critical in design of epitope mimics and optimal epitope presentation