CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU B cell epitopes and predictions Pernille Haste Andersen, Ph.d. student Immunological Bioinformatics CBS, DTU

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU What is a B cell epitope? Antibody Fab fragment 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)

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Computational Rational Vaccine Design >PATHOGEN PROTEIN KVFGRCELAAAMKRHGLDNYR GYSLGNWVCAAKFESNF Rational Vaccine Design

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Discontinuous B cell epitopes SLDEKNS V SV DLP GEMK VL VSKEK NKDGK Y DLIATVD KLELKGTSD KNN GSGVLEGV KADKCKVKLTIS DDLG QTTLE VFKEDGKTLVSKKVTS KDK S STEEKFNEKGEVSEKIITRADG TRLEYTGIKSDGSGKAKEVLK G..\Discotope\1OSP_epitope\1OSP_epitope.psw An example: The epitope of the outer surface protein A from Borrelia Burgdorferi (1OSP)

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU The binding interactions  Salt bridges  Hydrogen bonds  Hydrophobic interactions  Van der Waals forces Binding strength

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU B-cell epitopes are dynamic  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.

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU B-cell epitope classification 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 B-cell epitope – structural feature of a molecule or pathogen, accessible and recognizable by B-cells

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU B-cell epitope data bases Databases: AntiJen, IEDB, BciPep, Los Alamos HIV database, Protein Data Bank Large amount of data available for linear epitopes Few data available for discontinuous

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU B cell epitope prediction

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Propensity scales: The principle The Parker hydrophilicity scale Derived from experimental data D2.46 E1.86 N1.64 S1.50 Q1.37 G1.28 K1.26 T1.15 R0.87 P0.30 H0.30 C0.11 A0.03 Y-0.78 V-1.27 M-1.41 I-2.45 F-2.78 L-2.87 W-3.00 Hydrophilicity

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Propensity scales: The principle ….LISTFVDEKRPGSDIVEDLILKDENKTTVI…. ( )/7 = 0.39 Prediction scores: Epitope

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Evaluation of performance A Receiver Operator Curve (ROC) is useful for finding a good threshold and rank methods

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Turn prediction and B-cell epitopes 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. Pellequer et al., Immunology letters,

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU BepiPred: CBS in-house tool Parker hydrophilicity scale Hidden Markov model Markov model based on linear epitopes extracted from the AntiJen database Combination of the Parker prediction scores and Markov model leads to prediction score Tested on the Pellequer dataset and epitopes in the HIV Los Alamos database

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU ROC evaluation Evaluation on HIV Los Alamos data set

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU BepiPred performance Pellequer data set: –LevittAROC = 0.66 –ParkerAROC = 0.65 –BepiPredAROC = 0.68 HIV Los Alamos data set –LevittAROC = 0.57 –ParkerAROC = 0.59 –BepiPredAROC = 0.60

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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:

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU How can we get information about the three-dimensional structure? 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Protein structure prediction methods 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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 AUC Epitope log–odds AUC

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 3D information: Contact numbers Surface exposure and structural protrusion can be measured by residue contact numbers The predictive performance: Parker AUC Epitope log–odds AUC Contact numbers AUC

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU DiscoTope : Prediction of Discontinuous epitopes using 3D structures A combination of: –Sequentially averaged epitope log- odds values of residues in spatial proximity –Contact numbers Contact number : K 10 DiscoTope prediction value Sum of log-odds values.LIST..FVDEKRPGSDIVED……ALILKDENKTTVI.

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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 AUC Epitope log–odds AUC Contact numbers AUC DiscoTope0.711 AUC

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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 is available as web server:

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Presentation of the web server

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Presentation of the web server output

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Use the CEP server Conformational epitope server Uses protein structure as input Finds stretches in sequences which are surface exposed

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Use the 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Rational vaccine design >PATHOGEN PROTEIN KVFGRCELAAAMKRHGLDNYR GYSLGNWVCAAKFESNF Rational Vaccine Design

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Rational B-cell epitope design Protein target choice Structural analysis of antigen 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 Model

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Rational B-cell epitope design Surface accessibility Protrusion index Conserved sequence Glycosylation status Protein target choice Structural annotation Epitope prediction and ranking

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU 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

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Multi-epitope 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-cell epitope T-cell epitope

CENTER FOR BIOLOGICAL SEQUENCE ANALYSISTECHNICAL UNIVERSITY OF DENMARK DTU Conclusions 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