B cell epitopes and predictions
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CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS TECHNICAL UNIVERSITY OF DENMARK DTU B cell epitopes and predictions Pernille Haste Andersen, Ph.d. student Immunological Bioinformatics CBS, DTU
description
B cell epitopes and predictions. Pernille Haste Andersen, Ph.d. student Immunological Bioinformatics CBS, DTU. Outline. What is a B-cell epitope? How can you predict B-cell epitopes? B-cell epitopes in rational vaccine design. - PowerPoint PPT Presentation
Transcript of B cell epitopes and predictions
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B cell epitopes and predictions
Pernille Haste Andersen,
Ph.d. student
Immunological Bioinformatics
CBS, DTU
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Outline
• 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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What is a B-cell epitope?
B-cell epitopes Accessible structural
feature of a pathogen molecule.
Antibodies are developed to bind the epitope specifically using the complementary determining regions (CDRs).
Antibody Fabfragment
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The binding interactions
Salt bridges Hydrogen bonds Hydrophobic interactions Van der Waals forces
Binding strength
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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.
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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
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Binding of a discontinuous epitope
Guinea Fowl Lysozyme KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNFNSQNRNTDGSDYGVLNSRWWCNDGRTPGSRNLCNIPCSALQSSDITATANCAKKIVSDGGMNAWVAWRKCKGTDVRVWIKGCRL
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.
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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
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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
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B cell epitope prediction
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Sequence-based methods for prediction of linear epitopes
Protein hydrophobicity – hydrophilicity algorithms Parker, Fauchere, Janin, Kyte and Doolittle, ManavalanSweet 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
TSQDLSVFPLASCCKDNIASTSVTLGCLVTGYLPMSTTVTWDTGSLNKNVTTFPTTFHETYGLHSIVSQVTASGKWAKQRFTCSVAHAESTAINKTFSACALNFIPPTVKLFHSSCNPVGDTHTTIQLLCLISGYVPGDMEVIWLVDGQKATNIFPYTAPGTKEGNVTSTHSELNITQGEWVSQKTYTCQVTYQGFTFKDEARKCSESDPRGVTSYLSPPSPL
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Propensity scales: The principle
• The Parker hydrophilicity scale
• Derived from experimental data
D 2.46E 1.86N 1.64S 1.50Q 1.37G 1.28K 1.26T 1.15R 0.87P 0.30H 0.30C 0.11A 0.03Y -0.78V -1.27M -1.41I -2.45 F -2.78L -2.87W -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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Evaluation of performance
• A Receiver Operator Curve (ROC) is useful for finding a good threshold and rank methods
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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, 1993
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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
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BepiPred: CBS in-house tool
• 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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Hidden Markow Model
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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ROC evaluation
Evaluation on HIV Los Alamos data set
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BepiPred performance
• Pellequer data set:– Levitt AROC = 0.66– Parker AROC = 0.65 – BepiPred AROC = 0.68
• HIV Los Alamos data set – Levitt AROC = 0.57– Parker AROC = 0.59– BepiPred AROC = 0.60
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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
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Prediction of linear epitopes
Cononly ~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
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
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Sequence based prediction methods
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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So what is more sophisticated?
• 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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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
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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
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Annotation of protein surface
•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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Q: What can antibodies recognize in a protein?
A: Everything accessible to a 10 Å probe on a protein surfaceNovotny J. A static accessibility model of protein antigenicity.Int Rev Immunol 1987 Jul;2(4):379-89
probe
Antibody Fabfragment
Protrusion index
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Use the CEP server
• 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 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
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Rational vaccine design
>PATHOGEN PROTEINKVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNF
Rational Vaccine Design
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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
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Rational B-cell epitope design
Surface accessibility Protrusion index Conserved sequence Glycosylation status
• Protein target choice• Structural annotation• Epitope prediction and ranking
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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
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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-cellepitope
T-cellepitope
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Case story
Using B-cell epitopes in the rational vaccine design against HIV
The gp120-CD4 epitope
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HIV gp120-CD4 epitopeBinding 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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Conformational changes in gp120
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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HIV gp120-CD4 epitope
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.
Efforts to design a epitope fusion protein
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HIV gp120-CD4 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
Further optimization of the epitope:
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Conclusions
• 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
![Deep learning methods improve linear B-cell epitope prediction · 2020. 6. 25. · B-cell epitopes have been developed [1, 2]. However, due to the easy operation of linear epitopes](https://static.fdocuments.us/doc/165x107/60abee97c721a92a735206e3/deep-learning-methods-improve-linear-b-cell-epitope-prediction-2020-6-25-b-cell.jpg)
