CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
B cell epitopes and predictions
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Outline
•What is a B-cell epitope?
•How can you predict B-cell epitopes?
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
What is a B-cell epitope?
Antibody Fabfragment
• B-cell epitopes:
• Accessible structural feature of a pathogen molecule.
• Antibodies are developed to bind the epitope specifically using the complementary determining regions (CDRs).
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interactions
• Salt bridges
• Hydrogen bonds
• Hydrophobic interactions
• Van der Waals forces
Binding strength
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interactions
• Salt bridges
• Hydrogen bonds
• Hydrophobic interactions
• Van der Waals forces
Binding strength
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interactionHowever spatial composition matters!!!
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interaction
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
B-cell epitope classification
Linear epitopesOne segment of the amino acid
chain
Discontinuous epitope (with linear determinant)
Discontinuous epitopeSeveral small segments brought into proximity by the protein fold
B-cell epitope: structural feature of a molecule or pathogen, accessible and recognizable by B-cell
receptors and antibodies
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
10%
90%
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
B-cell epitope annotation
•No epitope is purely linear
• Epitopes contains linear determinants of 5 or more residues
Longest linear stretch in epitope
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
B-cell epitope data bases
•Databases:
• IEDB, Los Alamos HIV database, Protein Data Bank,
AntiJen, BciPep
•Large amount of data available for linear epitopes
•Few data available for discontinuous
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
B cell epitope prediction
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
The immune system – prediction tools
•Cytotoxic T cell epitope: (AROC ~ 0.9)• Will a given peptide bind to a given MHC class I molecule
•Helper T cell Epitope (AROC ~ 0.85)• Will a part of a peptide bind to a given MHC II molecule
•B cell epitope (AROC ~ 0.74)• Will a given part of a protein bind to one of
the billions of different B Cell receptors
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
B-cell – prediction tools•Sequence based prediction tools– Predominantly predicts linear
epitopes•Structure based epitopes– Predicts Conformational epitopes
CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
Watch out for
overfitting
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Sequence-based methods for prediction of linear
epitopes
TSQDLSVFPLASCCKDNIASTSVTLGCLVTGYLPMSTTVTWDTGSLNKNVTTFPTTFHETYGLHSIVSQVTASGKWAKQRFTCSVAHAESTAINKTFSACALNFIPPTVKLFHSSCNPVGDTHTTIQLLCLISGYVPGDMEVIWLVDGQKATNIFPYTAPGTKEGNVTSTHSELNITQGEWVSQKTYTCQVTYQGFTFKDEARKCSESDPRGVTSYLSPPSPL
Input
TSQDLSVFPLASCCKDNIASTSVTLGCLVTGYLPMSTTVTWDTGSLNKNVTTFPTTFHETYGLHSIVSQVTASGKWAKQRFTCSVAHAESTAINKTFSACALNFIPPTVKLFHSSCNPVGDTHTTIQLLCLISGYVPGDMEVIWLVDGQKATNIFPYTAPGTKEGNVTSTHSELNITQGEWVSQKTYTCQVTYQGFTFKDEARKCSESDPRGVTSYLSPPSPL
Output
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
• PsiPred (D. Jones)
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
• ….LISTFVDEKRPGSDIVEDLILKDENKTTVI….
(-2.78 + -1.27 + 2.46 +1.86 + 1.26 + 0.87 + 0.3)/7 = 0.39
Prediction scores:
0.39 0.1 0.6 0.9 1.0 1.2 2.6 1.0 0.9 0.5 -0.5
Epitope
Propensity scales: The principle
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
1
2
3
4
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
BepiPred
• Parker hydrophilicity scale
• PSSM
• PSSM based on linear epitopes extracted from the AntiJen database
• Combination of the Parker prediction scores and PSSM leads to prediction score
• Tested on the Pellequer dataset and epitopes in the HIV Los Alamos database
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
PSSM
• ….LISTFVDEKRPGSDIVEDLILKDENKTTVI….
2.46+1.86+1.26+0.87+0.3 = 6.75 Prediction value
A R N D C Q E G H I L K M F P S T W Y V S I1 0.10 0.06 0.01 0.02 0.01 0.02 2.46 0.30 0.01 0.07 0.11 0.06 0.04 0.08 0.01 0.11 0.03 0.01 0.05 0.08 3.96 0.372 0.07 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.00 0.08 0.59 1.86 0.07 0.01 0.00 0.01 0.06 0.00 0.01 0.08 2.16 2.163 0.08 1.26 0.05 0.10 0.02 0.02 0.01 0.12 0.02 0.03 0.12 0.01 0.03 0.05 0.06 0.06 0.04 0.04 0.04 0.07 4.06 0.264 0.07 0.04 0.02 0.11 0.01 0.04 0.08 0.15 0.01 0.10 0.04 0.03 0.01 0.02 0.87 0.07 0.04 0.02 0.00 0.05 3.87 0.455 0.04 0.04 0.04 0.04 0.01 0.04 0.05 0.30 0.04 0.02 0.08 0.04 0.01 0.06 0.10 0.02 0.06 0.02 0.05 0.09 4.04 0.28
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
ROC evaluation
Evaluation on HIV Los Alamos data
set
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Prediction of linear epitopes
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
•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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Sources of three-dimensional structures
•Experimental 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
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Protein structure prediction methods
• Homology/comparative modeling • >25% sequence identity (seq 2 seq alignment)
• Fold-recognition• <25% sequence identity (Psi-blast search/ PSSM
2 seq alignment)
• Ab initio structure prediction• 0% sequence identity
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
What does antibodies recognize in a protein?
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interaction
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
The binding interactionHowever spatial composition matters!!!
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
What does antibodies recognize in a protein?
probe
Antibody Fabfragment
Protrusion index
Surface Exposed
Hydrophobic region
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Structure base
prediction methods
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
DiscoTope
•Prediction of residues in discontinuous B cell epitopes using protein 3D structures
Pernille Haste Andersen, Morten Nielsen and Ole
Lund, Protein Science 2006
17.04.2008Præsentationens navn41 DTU Systembiologi, Danmarks Tekniske Universitet
Predicting B-cell epitopes
17.04.2008Præsentationens navn42 DTU Systembiologi, Danmarks Tekniske Universitet
The DiscoTope method
17.04.2008Præsentationens navn43 DTU Systembiologi, Danmarks Tekniske Universitet
•Some amino acids are preferred and disliked in the epitope
The DiscoTope method
Epitope amino acid preferences
Kringelum et al, 2011, manuscript in preparation
17.04.2008Præsentationens navn44 DTU Systembiologi, Danmarks Tekniske Universitet
•Some amino acids are preferred and disliked in the epitope•Epitopes reside on the surface of the protein
The DiscoTope method
Kringelum et al, 2011, manuscript in preparation
17.04.2008Præsentationens navn45 DTU Systembiologi, Danmarks Tekniske Universitet
•Predictions score for each residue are calculated by summing the epitope likelihood (propensity) of surrounding residues and subtracting the neighbor count
The DiscoTope method
Andersen et al., 2006
17.04.2008Præsentationens navn46 DTU Systembiologi, Danmarks Tekniske Universitet
•Predictions score for each residue are calculated by summing the epitope likelihood (propensity) of surrounding residues and subtracting the neighbor count
The DiscoTope method
Performance: Aroc = 0.700
Andersen et al., 2006
On a dataset of 75 antigen-antibody complexes divided in 25 proteins
17.04.2008Præsentationens navn47 DTU Systembiologi, Danmarks Tekniske Universitet
•Predictions score for each residue are calculated by summing the epitope likelihood (propensity) of surrounding residues and subtracting the neighbor count
The DiscoTope method
Performance: Aroc = 0.700
Andersen et al., 2006
On a dataset of 75 antigen-antibody complexes divided in 25 proteins
17.04.2008Præsentationens navn48 DTU Systembiologi, Danmarks Tekniske Universitet
•Uneven spatial distribution of amino acid in epitopes
However position matters
Kringelum et al, 2012, manuscript in review
17/04/2008Presentation name49 DTU Sytems Biology, Technical University of Denmark
Propensity score function
Amino acid log-odds score
Weight factor
17/04/2008Presentation name50 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
17/04/2008Presentation name51 DTU Sytems Biology, Technical University of Denmark
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
PS(THR256)
Propensity score function
Radius = 21.6Å
17/04/2008Presentation name52 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
Radius = 21.6Å
17/04/2008Presentation name53 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
2)Calculate summed propensity score: ls(THR) = -0.23
β256= 0.8*(1-0.0/21.6)+0.2 = 1.0
ls(THR)*β256 = -0.23*1.0 = -0.23
d256= 0.0Å
17/04/2008Presentation name54 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
2)Calculate summed propensity score:
d255= 3.8Å
ls(THR) = -0.23
β256= 0.8*(1-0.0/21.6)+0.2 = 1.0
ls(THR)*β256 = -0.23*1.0 = -0.23
ls(ASP) = 2.5
β255=0.8*(1-3.8/21.6)+0.2 = 0.86
ls(THR)*β255 = 2.5*0.86 = 2.15
17/04/2008Presentation name55 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
2)Calculate summed propensity score: ls(THR) = -0.23
β256= 0.8*(1-0.0/21.6)+0.2 = 1.0
ls(THR)*β256 = -0.23*1.0 = -0.23
ls(ASP) = 2.5
β255=0.8*(1-3.8/21.6)+0.2 = 0.86
ls(ASP)*β255 = 2.5*0.86 = 2.15
ls(THR) = -0.23
β254=0.8*(1-6.1/21.6)+0.2 = 0.77
ls(THR)*β254 = -0.23*0.77 = -0.18 d254= 6.1Å
17/04/2008Presentation name56 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
2)Calculate summed propensity score: ls(THR) = -0.23
β256= 0.8*(1-0.0/21.6)+0.2 = 1.0
ls(THR)*β256 = -0.23*1.0 = -0.23
ls(ASP) = 2.5
β255=0.8*(1-3.8/21.6)+0.2 = 0.86
ls(ASP)*β255 = 2.5*0.86 = 2.15
ls(THR) = -0.23
β254=0.8*(1-6.1/21.6)+0.2 = 0.77
ls(THR)*β254 = -0.23*0.77 = -0.18
…........
………….
………….
ls(PRO) = 1.2
β254=0.8*(1-20.6/21.6)+0.2 = 0.24
ls(PRO)*β254 = 1.2*0.24 = 0.29
d247= 20.6Å
17/04/2008Presentation name57 DTU Sytems Biology, Technical University of Denmark
PS(THR256)
Propensity score function
1)Identify Naighbor residues within 21.6Å (Cα-Cα)
2)Calculate summed propensity score: ls(THR) = -0.23
β256= 0.8*(1-0.0/21.6)+0.2 = 1.0
ls(THR)*β256 = -0.23*1.0 = -0.23
ls(ASP) = 2.5
β255=0.8*(1-3.8/21.6)+0.2 = 0.86
ls(ASP)*β255 = 2.5*0.86 = 2.15
ls(THR) = -0.23
β254=0.8*(1-6.1/21.6)+0.2 = 0.77
ls(THR)*β254 = -0.23*0.77 = -0.18
…........
………….
………….
ls(PRO) = 1.2
β254=0.8*(1-20.6/21.6)+0.2 = 0.24
ls(PRO)*β254 = 1.2*0.24 = 0.29
Summation of scores
PS(THR256) = 0.39
17/04/2008Presentation name58 DTU Sytems Biology, Technical University of Denmark
HS(THR256)
Half-sphere exposure
1)Create the upper half-sphere, which is the half-sphere where the residue side-chain is pointing
17/04/2008Presentation name59 DTU Sytems Biology, Technical University of Denmark
HS(THR256)
Half-sphere exposure
1)Create the upper half-sphere, which is the half-sphere where the residue side-chain is pointing2)Count neighbor residues within the half-sphere (nr of Cα-atoms)
3)As high counts means highly buried the counts are multiplied by -1
= 5
HS(THR256) = -5
17/04/2008Presentation name60 DTU Sytems Biology, Technical University of Denmark
1)Calculate Propensity score2)Calculate half-sphere exposure3)The final score is a weighted sum of the Propensity score and half-sphere exposure PS(THR256) = 0.39 HS(THR256) = -5
α = 0.115
DS = (1-α)*PS + α*HS
DS(THR256) = 0.885*0.39 + 0.115*(-5)
DS(THR256) = -0.23
DS(THR256)
The DiscoTope 2.0 Score
17.04.2008Præsentationens navn61 DTU Systembiologi, Danmarks Tekniske Universitet
Performance and limitations
Average AUC = 0.741
17.04.2008Præsentationens navn62 DTU Systembiologi, Danmarks Tekniske Universitet
Performance and limitations
Glycosylated proteins
17.04.2008Præsentationens navn63 DTU Systembiologi, Danmarks Tekniske Universitet
Performance and limitations
Gp120 Hemagglutinine
•Glycosylation effects predictions
17.04.2008Præsentationens navn64 DTU Systembiologi, Danmarks Tekniske Universitet
Performance and limitations
Small fragments (<120 residues) of larger biological units
17.04.2008Præsentationens navn65 DTU Systembiologi, Danmarks Tekniske Universitet
• Inclusion of biological units enhance performance
Performance and limitations
Potassium Channel
17.04.2008Præsentationens navn66 DTU Systembiologi, Danmarks Tekniske Universitet
• External Benchmark Dataset• 52 antigen:antibody structures• 33 homology groups
• Performance: 0.731 AUC
Performance and limitations
17.04.2008Præsentationens navn67 DTU Systembiologi, Danmarks Tekniske Universitet
Conclusions• DiscoTope V2.0 outperforms similar methods
• High performance on 15/25• Medium performance on 7/25• Fail on 3/25
• Inclusion of surface measures does only slightly enhance predictions
• Use the entire biological unit, when possible• Small fragments (< 120 residues) have lower performance
• Glycosylation might course the prediction to fail• Check for clash between predicted epitopes and glycosylation sites
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Rational vaccine design
>PATHOGEN PROTEINKVFGRCELAAAMKRHGLDNYRGYSLGN
WVCAAKFESNF
Rational Vaccine Design
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Rational B-cell epitope design
• Protein target choice
• Structural analysis of antigenKnown structure or homology modelPrecise domain structurePhysical annotation (flexibility, electrostatics, hydrophobicity)Functional annotation (sequence variations, active sites, binding sites, glycosylation sites, etc.)Known 3D structure
Model
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
• Protein target choice
• Structural annotation
• Epitope prediction and ranking
Rational B-cell epitope design
Surface accessibilityProtrusion indexConserved sequenceGlycosylation status
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
Rational B-cell epitope design
• Protein target choice
• Structural annotation
• Epitope prediction and ranking
• Optimal Epitope presentation
Fold minimization, orDesign of structural mimics Choice of carrier (conjugates, DNA plasmids, virus like particles)Multiple chain protein engineering
Technical University of Denmark - DTUTechnical University of Denmark - DTUDepartment of systems biologyDepartment of systems biology
ECCB/ISMB-2009 - Immunological Bioinformatics Tutorial
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