Prediction of B cell epitopes

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Prediction of B cell epitopes
Pernille Haste Andersen Immunological
Bioinformatics CBS, DTU pan_at_cbs.dtu.dk
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B cells and antibodies

• Antibodies are produced by B lymphocytes (B
  cells)
• Antibodies circulate in the blood
• They are referred to as the first line of
  defense against infection
• Antibodies play a central role in immunity by
  attaching to pathogens and recruiting effector
  systems that kill the invader

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What is a B cell epitope?

• B cell epitopes
• Accessible and recognizable structural feature of
  a pathogen molecule (antigen)
• Antibodies are developed to bind the epitope with
  high affinity by using the complementarity
  determining regions (CDRs)

Antibody Fab fragment
B cell epitope
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Motivations for prediction of B cell epitopes

• Prediction of B cell epitopes can potentially
  guide experimental epitope mapping
• Predictions of antigenicity in proteins can be
  used for selecting subunits in rational vaccine
  design
• Predictions of B cell epitopes may also be
  valuable for interpretation of results from
  experiments based on antibody affinity binding
  such as ELISA, RIA and western blotting

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Computational Rational Vaccine Design
gtPATHOGEN PROTEIN KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAA
KFESNF
Rational Vaccine Design
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B cell epitopes, linear or discontinuous?

• Classified into linear (10) and discontinuous
  epitopes (90)
• Databases AntiJen, IEDB, BciPep, Los Alamos HIV
  database, Protein Data Bank
• Large amount of data available for linear
  epitopes
• Few data available for discontinuous epitopes
• In general, B cell epitope prediction methods
  have relatively low performances

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Discontinuous B cell epitopes
An example The epitope of the outer surface
protein A from Borrelia Burgdorferi (1OSP)

• SLDEKNSVSVDLPGEMKVLVSKEKNKDGKYDLIATVDKLELKGTSDKNN
  GSGVLEGVKADKCKVKLTISDDLGQTTLEVFKEDGKTLVSKKVTSKDKSS
  TEEKFNEKGEVSEKIITRADGTRLEYTGIKSDGSGKAKEVLKG
• ..\Discotope\1OSP_epitope\1OSP_epitope.psw

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A data set of 3D discontinuous epitopes

• A data set of 75 discontinuous epitopes was
  compiled from structures of antibodies/protein
  antigen complexes in the PDB
• The data set has been used for developing a
  method for predictions of discontinuous B cell
  epitopes
• Since about 30 of the PDB entries represented
  Lysozyme, I have used homology grouping (25
  groups of non-homologous antigens) and 5 fold
  cross-validation for training of the method
• Performance was measured using ROC curves on a
  per antigen basis, and by weighted averaging of
  AUC values

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Epitope log-odds ratios

• Frequencies of amino acids in epitopes compared
  to frequencies of non-epitopes
• Several discrepancies compared to the Parker
  hydrophilicity scale which is often used for
  epitope prediction
• Both methods are used for predictions using a
  sequential average of scores
• Predictive performance of B cell epitopes
• Parker 0.614 AUC
• Epitope logodds 0.634 AUC

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3D information Contact numbers

• Surface exposure and
• structural protrusion can
• be measured by residue
• contact numbers

The predictive performance Parker 0.614
AUC Epitope logodds 0.634 AUC Contact numbers
0.647 AUC
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DiscoTope Prediction of Discontinuous epiTopes
using 3D structures

• A combination of
• Sequentially averaged epitope log-odds values of
  residues in spatial proximity
• Contact numbers

.LIST..FVDEKRPGSDIVEDALILKDENKTTVI.
-0.145
0.6910.3461.1361.1801.164
1.136
0.346
Sum of log-odds values
Contact number K 10
DiscoTope prediction value
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DiscoTope Prediction of Discontinuous epiTopes

• Improved prediction of residues in discontinuous
  B cell epitopes in the data set
• The predictive performance on B cell epitopes
• Parker 0.614 AUC
• Epitope logodds 0.634 AUC
• Contact numbers 0.647 AUC
• DiscoTope 0.711 AUC

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Evaluation example AMA1

• Apical membrane antigen 1 from Plasmodium
  falciparum (not used for training/testing)
• Two epitopes were identified using phage-display,
  point-mutation (black side chains) and sequence
  variance analysis (side chains of polyvalent
  residues in yellow)
• Most residues identified as epitopes were
  successfully predicted by DiscoTope (green
  backbone)

..\Discotope\1Z40_epitope\1Z40_movie.mov
DiscoTope is available as web server
http//www.cbs.dtu.dk/services/DiscoTope/
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Future improvements

• Add epitope predictions for protein-protein
  complexes
• Visualization of epitopes integrated in web
  server
• Testing a score for sequence variability fx based
  on entropy of positions in the antigens
• Combination with glycosylation site predictions
• Combination with predictions of trans-membrane
  regions
• Assembling predicted residues into whole epitopes

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Presentation of the web server
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Presentation of the web server output
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Acknowledgements

• DiscoTope
• Ole Lund
• Ideas, supervision and support
• Morten Nielsen
• Ideas, development of method and web server
• Nicholas Gauthier
• Improving the method, improving the web server