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Pernille Haste Andersen,
Ph.d. student
Immunological Bioinformatics
CBS, DTU
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS TECHNICAL UNIVERSITY OF DENMARK DTU
B cell epitopes and predictions
• What is a B-cell epitope?
• How can you predict B-cell epitopes?
• B-cell epitopes in rational vaccine
design.
– Case story: Using B-cell epitopes in
rational vaccine design against HIV.
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Outline
B-cell epitopes


Antibody Fab
fragment
Accessible structural
feature of a pathogen
molecule.
Antibodies are developed
to bind the epitope
specifically using the
complementary
determining regions
(CDRs).
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What is a B-cell epitope?
Binding strength




Salt bridges
Hydrogen bonds
Hydrophobic interactions
Van der Waals forces
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The binding interactions
 Many of the charged groups and hydrogen
bonding partners are present on highly flexible
amino acid side chains.
 Most crystal structures of epitopes and
antibodies in free and complexed forms have
shown conformational rearrangements upon
binding.
 “Induced fit” model of interactions.
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B-cell epitopes are dynamic
B-cell epitope – structural feature of a molecule or
pathogen, accessible and recognizable by B-cells
Linear epitopes
One segment of the
amino acid chain
Discontinuous epitope
(with linear determinant)
Discontinuous epitope
Several small segments brought
into proximity by the protein fold
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B-cell epitope classification
Antibody FAB fragment
complexed with Guinea
Fowl Lysozyme (1FBI).
Black: Light chain, Blue:
Heavy chain, Yellow:
Residues with atoms
distanced < 5Å from FAB
antibody fragments.
Guinea Fowl Lysozyme
KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNFNSQNRNTDGS
DYGVLNSRWWCNDGRTPGSRNLCNIPCSALQSSDITATANCAKKIVSDG
GMNAWVAWRKCKGTDVRVWIKGCRL
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Binding of a discontinuous epitope
• Linear epitopes:
– Chop sequence into small pieces and measure
binding to antibody
• Discontinuous epitopes:
– Measure binding of whole protein to antibody
• The best annotation method : X-ray crystal
structure of the antibody-epitope complex
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B-cell epitope annotation
• Databases: AntiJen, IEDB, BciPep, Los
Alamos HIV database, Protein Data
Bank
• Large amount of data available for
linear epitopes
• Few data available for discontinuous
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B-cell epitope data bases
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B cell epitope prediction
 Protein hydrophobicity – hydrophilicity algorithms
Parker, 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, Pellequer
 Protein “antigenicity” prediction :
Hopp and Woods, Welling
TSQDLSVFPLASCCKDNIASTSVTLGCLVTG
YLPMSTTVTWDTGSLNKNVTTFPTTFHETY
GLHSIVSQVTASGKWAKQRFTCSVAHAEST
AINKTFSACALNFIPPTVKLFHSSCNPVGDT
HTTIQLLCLISGYVPGDMEVIWLVDGQKATN
IFPYTAPGTKEGNVTSTHSELNITQGEWVSQ
KTYTCQVTYQGFTFKDEARKCSESDPRGVT
SYLSPPSPL
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Sequence-based methods for
prediction of linear epitopes
• The Parker
hydrophilicity scale
• Derived from
experimental data
D
E
N
S
Q
G
K
T
R
P
H
C
A
Y
V
M
I
F
L
W
2.46
1.86
1.64
1.50
1.37
1.28
1.26
1.15
0.87
0.30
0.30
0.11
0.03
-0.78
-1.27
-1.41
-2.45
-2.78
-2.87
-3.00
Hydrophilicity
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Propensity scales: The principle
….LISTFVDEKRPGSDIVEDLILKDENKTTVI….
(-2.78 + -1.27 + 2.46 +1.86 + 1.26 + 0.87 + 0.3)/7 = 0.39
Prediction scores:
0.38 0.1 0.6 0.9 1.0 1.2 2.6 1.0 0.9 0.5 -0.5
Epitope
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Propensity scales: The principle
• A Receiver
Operator Curve
(ROC) is useful
for finding a good
threshold and
rank methods
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Evaluation of performance
• Pellequer found that 50% of the epitopes in a
data set of 11 proteins were located in turns
•Turn propensity
scales for each
position in the turn
were used for
epitope prediction.
1
4
2
Pellequer et al.,
Immunology letters, 1993
3
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Turn prediction and B-cell epitopes
• Extensive evaluation of propensity
scales for epitope prediction
• Conclusion:
– Basically all the classical scales perform
close to random!
– Other methods must be used for epitope
prediction
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Blythe and Flower 2005
• Parker hydrophilicity scale
• Hidden Markow model
• Markow model based on linear epitopes
extracted from the AntiJen database
• Combination of the Parker prediction scores
and Markow model leads to prediction score
• Tested on the Pellequer dataset and
epitopes in the HIV Los Alamos database
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BepiPred: CBS in-house tool
A
C
D ………… G S T V W
Pos 1 7.28
Pos 2 9.39
Pos 3 0.3
Pos 4 5.2
Pos 5 7.9
….LISTFVDEKRPGSDIVEDLILKDENKTTVI….
2.46+1.86+1.26+0.87+0.3 = 6.75 Prediction value
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Hidden Markow Model
Evaluation
on HIV Los
Alamos data
set
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ROC evaluation
• Pellequer data set:
– Levitt
– Parker
– BepiPred
AROC = 0.66
AROC = 0.65
AROC = 0.68
• HIV Los Alamos data set
– Levitt
– Parker
– BepiPred
AROC = 0.57
AROC = 0.59
AROC = 0.60
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BepiPred performance
• BepiPred conclusion:
– On both of the evaluation data sets,
Bepipred was shown to perform better
– Still the AROC value is low compared to Tcell epitope prediction tools!
– Bepipred is available as a webserver:
www.cbs.dtu.dk/services/BepiPred
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BepiPred
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
Con
only ~10% of epitopes can
be classified as “linear”
weakly immunogenic in
most cases
most epitope peptides do

not provide antigenneutralizing immunity
in many cases represent
hypervariable regions
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Prediction of linear epitopes




Linear methods for prediction of B cell epitopes have low
performances
The problem is analogous to the problems of representing the
surface of the earth on a two-dimensional map
Reduction of the dimensions leads to distortions of scales,
directions, distances
The world of B-cell epitopes is 3 dimensional and therefore more
sophisticated methods must be developed
Regenmortel 1996,
Meth. of Enzym. 9.
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Sequence based prediction methods
• Use of the three dimensional structure of the
pathogen protein
• Analyze the structure to find surface exposed
regions
• Additional use of information about
conformational changes, glycosylation and
trans-membrane helices
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So what is more sophisticated?
Structural
determination
• X-ray crystallography
• NMR spectroscopy
Both methods are time
consuming and not
easily done in a larger
scale
Structure prediction
• Homology modeling
• Fold recognition
Less time consuming,
but there is a
possibility of incorrect
predictions, specially
in loop regions
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How can we get information about the
three-dimensional structure?
• Homology/comparative modeling
>25% sequence identity (seq 2 seq alignment)
• Fold-recognition/threading
<25% sequence identity (Psi-blast search/ seq 2 str
alignment)
• Ab initio structure prediction
0% sequence identity
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Protein structure prediction methods
•Look at the surface of
your protein
•Analyse for turns and
loops
•Analyse for amino acid
composition
•Find exposed Bepipred
epitopes
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Annotation of protein surface
probe
Antibody Fab
fragment
Protrusion index
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Q: What can antibodies recognize in a protein?
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
• Conformational epitope server
http://202.41.70.74:8080/cgi-bin/cep.pl
• Uses protein structure as input
• Finds stretches in sequences which are
surface exposed
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Use the CEP server
• CBS server for prediction of discontinuous
epitopes
• Uses protein structure as input
• Combines propensity scale values of amino
acids in discontinuous epitopes with surface
exposure
• Will be available soon (not published yet)
• Contact me for more information
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Use the DiscoTope server
>PATHOGEN PROTEIN
KVFGRCELAAAMKRHGLDNYR
GYSLGNWVCAAKFESNF
Rational Vaccine
Design
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Rational vaccine design
• Protein target choice
• Structural analysis of antigen


Model

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Known 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
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Rational B-cell epitope design
• Protein target choice
• Structural annotation
• Epitope prediction and ranking
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Surface accessibility
Protrusion index
Conserved sequence
Glycosylation status
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Rational B-cell epitope design
•
•
•
•
Protein target choice
Structural annotation
Epitope prediction and ranking
Optimal Epitope presentation
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Fold minimization, or
Design of structural mimics
Choice of carrier (conjugates, DNA
plasmids, virus like particles)
Multiple chain protein engineering
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Rational B-cell epitope design
B-cell
epitope
T-cell
epitope
Rational optimization of
epitope-VLP chimeric
proteins:
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
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Design a library of possible
linkers (<10 aa)
Perform global energy
optimization in VLP (virus-like
particle) context
Rank according to estimated
energy strain
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Multi-epitope protein design
Using B-cell epitopes in the rational
vaccine design against HIV
The gp120-CD4 epitope
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Case story
Binding of CD4 receptor
Conformational changes in
gp120
Opens chemokine-receptor
binding site
New highly conserved
epitopes
Kwong et al.(1998) Nature 393, 648-658
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HIV gp120-CD4 epitope
SIV gp120 no ligands
Human gp120 complex with CD4
and 17b antibody
Chen et al. Nature 2005
Kwong et al. Nature 1998
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Conformational changes in gp120
Efforts to design a epitope fusion protein


Elicit broadly crossreactive 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.
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HIV gp120-CD4 epitope
Further optimization of the epitope:


reduce to minimal
stable fold (iterative)
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
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HIV gp120-CD4 epitope
• Rational vaccines can be designed to induce strong
and epitope-specific B-cell responses
• Selection of protective B-cell epitopes involves
structural, functional and immunogenic analysis of the
pathogenic proteins
• When you can: Use protein structure for prediction
• Structural modeling tools are helpful in prediction of
epitopes, design of epitope mimics and optimal epitope
presentation
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Conclusions