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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

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b cell epitopes and b cell epitope predictions

B cell epitopes andB cell epitope predictions

Morten Nielsen,

CBS, BioCentrum,

DTU

(mostly copied from Vsevolod Katritch’s, Siga presentation 2002)

algorithms for epitope prediction and selection
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
b cell epitope classification
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

sequence based methods
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
linear epitopes flexible loops
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
q what can antibodies recognize in a protein
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

rational b cell epitope design

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.)
rational b cell epitope design1
Rational B-cell epitope design
  • Protein target choice
  • Structural annotation
  • Epitope prediction and ranking
  • Surface accessibility
  • Protrusion index
  • Conserved sequence
  • Glycosylation status
rational b cell epitope design2
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
hiv gp120 cd4 epitope
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

hiv gp120 cd4 epitope1
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.
hiv gp120 cd4 epitope2
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

slide14

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%

slide15

Homology Modeling

Blue: Template

Red: Model

homology modeling
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

protein model health evaluation
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

slide18

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)
multichain protein design
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

conclusions
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