b cell epitopes and b cell epitope predictions
DESCRIPTION
B cell epitopes and B cell epitope predictions. Morten Nielsen, CBS, BioCentrum, DTU ( mostly copied from Vsevolod Katritch’s, Siga presentation 2002 ). Algorithms for epitope prediction and selection. Antibody Fab fragment. B-cell epitopes Most epitope are structural epitopes - PowerPoint PPT PresentationTRANSCRIPT
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B cell epitopes andB cell epitope predictions
Morten Nielsen, CBS, BioCentrum,
DTU(mostly copied from Vsevolod
Katritch’s, Siga presentation 2002)
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Algorithms for epitope prediction and selection
B-cell epitopes Most epitope are
structural epitopes sequence based
methods are limited requires structure-
based approach
Antibody Fabfragment
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B-cell epitope classification
linear epitopes
“Discontinuous epitope (with linear determinant)
Discontinuous epitope
B-cell epitope – structural feature of a molecule or pathogen, accessible and recognizable by B-cells
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Sequence-based methods• Protein hydrophobicity –
hydrophilicity algorithms
Fauchere, Janin, Kyte and Doolittle, ManavalanSweet 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
TSQDLSVFPLASCCKDNIASTSVTLGCLVTGYLPMSTTVTWDTGSLNKNVTTFPTTFHETYGLHSIVSQVTASGKWAKQRFTCSVAHAESTAINKTFSACALNFIPPTVKLFHSSCNPVGDTHTTIQLLCLISGYVPGDMEVIWLVDGQKATNIFPYTAPGTKEGNVTSTHSELNITQGEWVSQKTYTCQVTYQGFTFKDEARKCSESDPRGVTSYLSPPSPL
Same as protein surface accessibility
Predict “linear” epitopes only
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Linear Epitopes (flexible loops)
Cononly ~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
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Prediction of linear epitopes
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Q: What can antibodies recognize in a protein?
A: Everything accessible to a 10 Å probe on a protein surfaceNovotny J. A static accessibility model of protein antigenicity.Int Rev Immunol 1987 Jul;2(4):379-89
probe
Antibody Fabfragment
Protrusion index
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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.)
Known 3D structure
Model
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Rational B-cell epitope design
Surface accessibility Protrusion index Conserved sequence Glycosylation status
• Protein target choice• Structural annotation• Epitope prediction and ranking
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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
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HIV gp120-CD4 epitopeBinding of CD4 receptor
Conformational changes in gp120
Opens chemokine-receptor binding site
New highly concerved epitope
Kwong et al.(1998) Nature 393, 648-658
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HIV gp120-CD4 epitope
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.
First efforts to design single-chain analogue
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HIV gp120-CD4 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
Further optimization of the epitope:
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Homology modelingAlignment
BLAB._ 0 : EKLKDNLYVYTTYNTFNGTKY-AANAVYLVTDKGVVVIDCPWGEDKFKSFTDEIYKKHGKKVIMNIATHS1A8T.A 13 : TQLSDKVYTYVSLAEIEGWGMVPSNGMIVINNHQAALLDTPINDAQTEMLVNWVTDSLHAKVTTFIPNHW
BLAB._ 69 : HDDRAGGLEYFGKIGAKTYSTKMTDSILAKENKPRAQYTFDNNKSFKVGKSEFQVYYPGKGHTADNVVVW1A8T.A 83 : HGDCIGGLGYLQRKGVQSYANQMTIDLAKEKGLPVPEHGFTDSLTVSLDGMPLQCYYLGGGHATDNIVVW
BLAB._ 139 : FPKEKVLVGGCIIKSADSKDLGYIGEAYVNDWTQSVHNIQQKFSGAQYVVAGHDDWKDQRSIQHTLDLIN1A8T.A 153 : LPTENILFGGCMLKDNQTTSIGNISDADVTAWPKTLDKVKAKFPSARYVVPGHGNYGGTELIEHTKQIVN
BLAB._ 209 : EYQQKQK1A8T.A 223 : QYIESTS
Sequence identity 27%
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Homology Modeling
Blue: TemplateRed: Model
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Homology Modeling
Protein sequence
Known 3D structure
Known 3D template(s)
Model by homology
• Threading (seq-str. align.)• Side chain prediction• Loop building• Local reliability prediction
Model
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Protein Model Health Evaluation
Known 3D structureMaiorov, Abagyan, Proteins 1998Cardozo, Abagyan, 2000
Model
High energy strain Lower energy strain Local alignment strength Local Energy strain
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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)
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Multichain protein design
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
B-cellepitope
T-cellepitope
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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