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1 Web Site: Dr. G P S Raghava, Head Bioinformatics Centre Institute of Microbial Technology, Chandigarh, India Prediction.

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Presentation on theme: "1 Web Site: Dr. G P S Raghava, Head Bioinformatics Centre Institute of Microbial Technology, Chandigarh, India Prediction."— Presentation transcript:

1 1 Web Site: http://www.imtech.res.in/raghava/ Dr. G P S Raghava, Head Bioinformatics Centre Institute of Microbial Technology, Chandigarh, India Prediction of Linear and Conformational B-cell Epitopes

2 2 Whole Organism Type of Vaccines Subunit Vaccines Polysaccharides Toxoids Recombinant Genetic Vaccines Passive Immunization Non-Specific Immunotherapy Epitope based Vaccines Living vaccines Annutated Killed organism Vaccine Delivery Adjuvants Edible Vaccine

3 WHOLE ORGANISM Attenuated/Killed Epitopes (Subunit Vaccine) Purified Antigen B-cell, T-cell epitopes Protein/Oligosaccharide

4 4 Pathogens/Invaders Disease Causing Agents Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

5 5 Types of epitopes Linear B cell Epitope T cell Epitope B cell Epitope Conformational Non- Conformational Helper T cell epitope Class II MHCs Exogenous Antigen Processing CTL epitope Class I MHCs Endogenous Antigen Processing

6 Linear B-cell epitope prediction  Linear B-cell epitopes are as important or even more applicative than conformational epitopes.  Most of the existing methods for linear B-cell epitopes are based on old and very small dataset (~1000 epitopes).  Performance of all the methods are not better than 0.75 AUC.  None of the methods used experimentally verified negative datasets.

7 7 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh Saha et al.(2005) BMC Genomics 6:79. Saha et al. (2006) NAR (Online) BCIPEP: A database of B-cell epitopes.

8 8 BCEpred: Benchmarking of physico-cemical properties used in existing B-cell epitope prediction methods In 2003, we evaluate parmeters on 1029 non-redudant B-cell epitopes obtained from BCIpep and 1029 random peptide Saha and Raghava (2004) ICARIS 197-204.

9 9 ABCpred: ANN based method for B-cell epitope prediction Challenge in Developing Method 1.Machine learnning technique needs fixed length pattern where epitope have variable length 2.Classification methods need positive and negative dataset 3.There are experimentally proved B-cell epitopes (positive) dataset but not Non- epitopes (negative) Assumptions to fix the Problem 1.More than 90% epitope have less than 20 residue so we fix maximum length 20 2.We added residues at both end of small epitopes from source protein to make length of epitope 20 3. We generate random peptides from proteins and used them as non-epitopes

10 10 Creation of fixed pattern of 20 from epitopes

11 11 Prediction of B-Cell Epitopes BCEpred: Prediction of Continuous B-cell epitopes BCEpred: Benchmarking of existing methods Evaluation of Physico-chemical properties Poor performance slightly better than random Combine all properties and achieve accuracy around 58% Saha and Raghava (2004) ICARIS 197-204. ABCpred: ANN based method for B-cell epitope prediction ABCpred: Extract all epitopes from BCIPEP (around 2400) 700 non-redundant epitopes used for testing and training Recurrent neural network Accuracy 66% achieved Saha and Raghava (2006) Proteins,65:40-48 ALGpred: Mapping and Prediction of Allergenic Epitopes ALGpred: Allergenic proteins IgE epitope and mapping Saha and Raghava (2006) Nucleic Acids Research 34:W202-W209 Innate ImmunityVaccine Delivery Protective AntigensAdaptive Immunity Bioinformatics Centre IMTECH, Chandigarh

12 12 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

13 3D Structure Sequence??

14 Antigenic sequences with conformational B-cell epitope information Immune Epitope Database (365 sequences) Ponomarenko et al 2007 (161) 526 187 CD- HIT 40% 107414 antibody non- interacting residues 2261 antibody interacting residues + - Data collection and processing Feature Extraction Training and model generation

15 Polar and charged residues are preferred in antibody interaction

16 Propensities of surface-exposed and buried residues 2 sample Logo

17 Pattern Generation Composition profile Binary profile Physico- chemical profile

18 ValueProMate PSI- PRED best patch Patch Dock best model ClusPro (DOT) best model CEP DiscoTopeCBTOPE (This Study) Sen0.090.330.430.450.310.420.80 1-Spe0.080.140.110.070.220.210.15 PPV0.100.190.260.390.110.160.31 Acc0.840.820.850.890.740.750.84 AUC0.510.600.660.690.540.600.89 PPV - positive predictive value Comparison of structure based and CBTOPE algorithm on Benchmark dataset* *Ponomarenko et al 2007, BMC Structural Biology

19 http://www.imtech.res.in/raghava/cbtope/submit.php

20 B-cell epitope prediction related resources Rev. Med. Virol. 2009, 19: 77–96.

21

22 . Epitope Mapping Using Phage Display Peptides

23 Linear B-Cell Epitope Tools

24 Conformational B-Cell Epitope Tools

25 Thanks for your attention

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