Prediction of B cell epitopes
Ole Lund
Outline
• What is a B-cell epitope?
• How can you predict B-cell epitopes?
What is a B-cell epitope?
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).
B-cell epitope classification
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
The Antibody
• Two light and heavy chains
• High variability in the complementary determine regions
(CDR)
• ~2.5 * 107 different phenotypes
Binding of a discontinuous epitope
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
B-cell epitope annotation
• 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
B-cell epitope data bases
• Databases: IEDB, AntiJen, BciPep, Los
Alamos HIV database, Protein Data Bank
• Large amount of data available for linear
epitopes
• Few data available for discontinuous
epitopes
Sequence-based methods for
prediction of linear epitopes
 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
Propensity scales: The principle
• 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
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
Evaluation of performance
• A Receiver
Operator Curve
(ROC) is useful
for finding a good
threshold and
rank methods
Blythe and Flower 2005
• 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
BepiPred: CBS Web server
• 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
Data from:
J. L. Pellequer, E. Westhof, and Van M. H. Regenmortel.
Correlation between the loca- tion of antigenic sites and
the prediction of turns in proteins. Immunol. Lett., 36:
83–99, 1993.
Ole Lund, Morten Nielsen, Claus Lundegaard, Can
Kesmir and Søren Brunak. Immunological
Bioinformatics. MIT press, Cambridge, Massachusetts.
2005 312 pp.
ROC evaluation
Evaluation on
HIV Los
Alamos data
set
Linear epitope prediction performance
• 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
BepiPred
• BepiPred conclusion:
– On both of the evaluation data sets, Bepipred
was shown to perform better
– Still the AROC value is low compared to T-cell
epitope prediction tools!
– Bepipred is available as a webserver:
www.cbs.dtu.dk/services/BepiPred
Prediction of linear epitopes
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 antigen-neutralizing
immunity
in many cases represent
hypervariable regions
DiscoTope 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
DiscoTope
Andersen PH, Nielsen M, Lund O. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci. 2006 15:2558-67.
Larsen JE, Lund O, Nielsen M. Improved method for predicting linear B-cell epitopes. Immunome Res. 2006 2: 2.
Web server output
The Biological unit
Prediction of conformational epitopes
with DiscoTope:
Refining the benchmark
Characterization of the B-cell
epitope area
Data processing
• 224 resolved 3-dimensional
antigen-protein complexes
– Kindly provided by Søren Padkjær
• Complexes with antigens below 20
amino acids were removed
= 162
• Similar interactions were removed
= 109
• 2 complexes were identified as
outliers and removed
= 107
• 70 epitope annotated amino acids
were found outside the general
binding area and removed
Characterization of the B-cell
epitope area
Spatial distribution of amino acid in
epitopes
• Hydrofobic center
• Charged edge
RSA = 0.511
Characterization of the B-cell
epitope
area
Modeling the most likely epitope
• Shape: Flat, oblong, oval shaped area
• Direction: -30 to 60 degrees relative to the light to heavy chain
direction
• Amino acid distribution: Hydrofobic residues in the center and
charged residues on the edge
RSA = 0.511
Human proteome (106 peptides) on a chip
Schafer-Nielsen, Søren Buus, Massimo Andretta, FP6 PepChipOmics project
The parasite exports VAR2CSA to the RBC membrane which enable
adhesion of parasites to CSA in the placenta
IRBC
Causing:
Placental malaria
Structural envelope of VAR2CSA with 7 domains
Red blood cell
membrane
1. We expressed full length 350 kD VAR2CSA and immunized rats
2. We affinity purified rat antibodies on the recombinant protein (thereby
purifying IgG reacting with exposed epitopes)
3. Tested the affinity purified IgG on the PePtide array covering the entire
VAR2CSA protein.
Conclusion: Very few linear epitopes exposed in the VAR2CSA
protein, however a number of peptides were identified. These
were synthezised, coupled to KLH and used in immunization.
Note 1 : The IgG before affinity purification did not reveal many more epitopes
Note 2 : The same sera were tested on Pepscan array and were completely blank, ie
Schafern peptide array mimicks more epitopes.
Rat sera were then tested by flow cytometry to
test if IgG reacts with native VAR2CSA on
VAR2CSA expressing malaria parasites:
MFI
18
16
14
12
10
8
MFI
6
4
2
0
pep152 KLHconjugated
pep152 KLHconjugated
pep153 KLHconjugated
pep153 KLHconjugated
Control protein
Conclusion. Both peptides induce IgG that reacts with native
VAR2CSA.
To do: Denature VAR2CSA and induce antibodies against linear
epitopes and test on array
Prediction of epitopes
• Cytotoxic T cell epitope: (AROC ~ 0.9)
• Will a given peptide bind to a given MHC
class I molecule
• Helper T cell Epitope (AROC ~ 0.8)
• Will a part of a peptide bind to a given MHC II
molecule
• B cell epitope (AROC ~ 0.7)
• Will a given part of a protein bind to one of the
billions of different B Cell receptors