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Epitope prediction algorithms. Urmila Kulkarni-Kale Bioinformatics Centre University of Pune. Vaccine development In Post-genomic era: Reverse Vaccinology Approach. Rappuoli R. (2000). Reverse vaccinology. Curr Opin Microbiol. 3:445-450. . Genome Sequence. Proteomics Technologies.

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epitope prediction algorithms

Epitope prediction algorithms

Urmila Kulkarni-Kale

Bioinformatics Centre

University of Pune

Vaccine development

In Post-genomic era:

Reverse Vaccinology


  • Rappuoli R. (2000). Reverse vaccinology. Curr Opin Microbiol. 3:445-450.

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Genome Sequence



In silico




High throughput

Cloning and expression

In vitro and in vivo assays for

Vaccine candidate identification

Global genomic approach to identify new vaccine candidates

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in silico analysis
In Silico Analysis





Candidate Epitope DB

Epitope prediction

Disease related protein DB

Gene/Protein Sequence Database

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Types of Epitopes

  • Sequential / Continuous epitopes:
      • recognized by Th cells
      • linear peptide fragments
      • amphipathic helical 9-12 mer
  • Conformational / Discontinuous epitopes:
      • recognized by both Th & B cells
      • non-linear discrete amino acid sequences, come together due to folding
      • exposed 15-22 mer

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properties of epitopes
Properties of Epitopes
  • They occur on the surface of the protein and are more flexible than the rest of the protein.
  • They have high degree of exposure to the solvent.
  • The amino acids making the epitope are usually charged and hydrophilic.

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methods to identify epitopes
Methods to identify epitopes
  • Immunochemical methods
        • ELISA : Enzyme linked immunosorbent assay
        • Immunoflurorescence
        • Radioimmunoassay
  • X-ray crystallography: Ag-Ab complex is crystallized and the structure is scanned for contact residues between Ag and Ab. The contact residues on the Ag are considered as the epitope.
  • Prediction methods: Based on the X-ray crystal data available for Ag-Ab complexes, the propensity of an amino acid to lie in an epitope is calculated.

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antigen a ntibody ag ab complexes
Antigen-Antibody (Ag-Ab) complexes
  • Non-obligatory heterocomplexes that are made and broken according to the environment
  • Involve proteins (Ag & Ab) that must also exist independently
  • Remarkable feature:
    • high affinity and strict specificity of antibodies for their antigens.
  • Ab recognize the unique conformations and spatial locations on the surface of Ag
  • Epitopes & paratopesare relational entities

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Antigen-Antibody complex

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ab binding sites sequential conformational epitopes



Ab-binding sites

Ab-binding sites:Sequential & Conformational Epitopes!


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b cell epitope prediction algorithms
B cell epitope prediction algorithms :
  • Hopp and Woods –1981
  • Welling et al –1985
  • Parker & Hodges -1986
  • Kolaskar & Tongaonkar – 1990
  • Kolaskar & Urmila Kulkarni - 1999

T cell epitope prediction algorithms :

  • Margalit, Spouge et al - 1987
  • Rothbard & Taylor – 1988
  • Stille et al –1987
  • Tepitope -1999

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hopp woods method
Hopp & Woods method
  • Pioneering work
  • Based on the fact that only the hydrophilic nature of amino acids is essential for an sequence to be an antigenic determinant
  • Local hydrophilicity values are assigned to each amino acid by the method of repetitive averaging using a window of six
  • Not very accurate

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welling s method
Welling’s method
  • Based on the % of each aa present in known epitopes compared with the % of aa in the avg. composition of a protein.
  • assigns an antigenicity value for each amino acid from the relative occurrence of the amino acid in an antigenic determinant site.
  • regions of 7 aa with relatively high antigenicity are extended to 11-13 aa depending on the antigenicity values of neighboring residues.

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parker hodges method
Parker & Hodges method
  • Utilizes 3 parameters :
        • Hydrophilicity : HPLC
        • Accessibility : Janin’s scale
        • Flexibility : Karplus & Schultz
  • Hydrophilicity parameter was calculated using HPLC from retention co-efficients of model synthetic peptides.
  • Surface profile was determined by summing the parameters for each residue of a seven-residue segment and assigning the sum to the fourth residue.
  • One of the most useful prediction algorithms

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kolaskar tongaonkar s method
Kolaskar & Tongaonkar’s method
  • Semi-empirical method which uses physiological properties of amino acid residues
  • frequencies of occurrence of amino acids in experimentally known epitopes.
  • Data of 169 epitopes from 34 different proteins was collected of which 156 which have less than 20 aa per determinant were used.
  • Antigen: EMBOSS

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cep server
CEP Server
  • Predicts the conformational epitopes from X-ray crystals of Ag-Ab complexes.
  • uses percent accessible surface area and distance as criteria

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an algorithm to map sequential and conformational epitopes of protein antigens of known structure
An algorithm to map sequential and conformational epitopes of protein antigens of known structure

© Bioinformatics Centre, UoP

ce beyond validation
CE: Beyond validation
  • High accuracy:
    • Limited data set to evaluate the algorithm
    • Non-availability of true negative data sets
  • Prediction of false positives?
    • Are they really false positives?
  • Limitation:
    • Limited by the availability of 3D structure data of antigens

Different Abs (HyHEL10 & D1.3) have over-lapping binding sites

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ce features
CE: Features
  • The first algorithm  for the prediction of conformational epitopes or antibody binding sites of protein antigens
  • Maps both: sequential & conformational epitopes
  • Prerequisite: 3D structure of an antigen

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cep conformational epitope prediction server http bioinfo ernet in cep htm
CEP: Conformational Epitope Prediction Serverhttp://bioinfo.ernet.in/cep.htm

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t cell epitope prediction algorithms
T-cell epitope prediction algorithms
  • Considers amphipathic helix segments, tetramer and pentamer motifs (charged residues or glycine) followed by 2-3 hydrophobic residues and then a polar residue.
  • Sequence motifs of immunodominant secondary structure capable of binding to MHC with high affinity.
  • Virtual matrices which are used for predicting MHC polymorphism and anchor residues.

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Case study: Design & development of peptide vaccine against Japanese encephalitis virus

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we have chosen je virus because
We Have Chosen JE Virus, Because
  • JE virus is endemic in South-east Asia including India.
  • JE virus causes encephalitis in children between 5-15 years of age with fatality rates between 21-44%.
  • Man is a "DEAD END" host.

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we have chosen je virus because1
We Have Chosen JE Virus, Because
  • Killed virus vaccine purified from mouse brain is used presently which requires storage at specific temperatures and hence not cost effective in tropical countries.
  • Protective prophylactic immunity is induced only after administration of 2-3 doses.
  • Cost of vaccination, storage and transportation is high.

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ce of jevn egp
CE of JEVN Egp

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Species and Strain specific properties:TBEV/ JEVN/JEVS

  • Loop1 in TBEV: LA EEH QGGT
  • Loop1 in JEVN: HN EKR ADSS
  • Loop1 in JEVS: HN KKR ADSS

Antibodies recognising TBEV and JEVN would require exactly opposite pattern of charges in their CDR regions.

Further, modification in CDR is required to recognise strain-specific region of JEVS.

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Multiple alignment of Predicted TH-cell epitope in the JE_Egp with corresponding epitopes in Egps of other Flaviviruses

        • 426457
        • COMM DF S GG S GK H V G F G
  • Multiple alignment of JE_Egp with Egps of other Flaviviruses in the YSAQVGASQ region.
        • 151 183

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peptide modeling

Peptide Modeling

  • Initial random conformation
  • Force field: Amber
  • Distance dependent dielectric constant 4rij
  • Geometry optimization: Steepest descents & Conjugate gradients
  • Molecular dynamics at 400 K for 1ns
  • Peptides are:
      • 149 168
relevant publications patent
Relevant Publications & Patent
  • Urmila Kulkarni-Kale, Shriram Bhosale, G. Sunitha Manjari, Ashok Kolaskar, (2004). VirGen: A comprehensive viral genome resource. Nucleic Acids Research 32:289-292.
  • Urmila Kulkarni-Kale & A. S. Kolaskar (2003). Prediction of 3D structure of envelope glycoprotein of Sri Lanka strain of Japanese encephalitis virus. In Yi-Ping Phoebe Chen (ed.), Conferences in research and practice in information technology. 19:87-96.
  • A. S. Kolaskar & Urmila Kulkarni-Kale (1999) Prediction of three-dimensional structure and mapping of conformational antigenic determinants of envelope glycoprotein of Japanese encephalitis virus. Virology. 261:31-42.

Patent: Chimeric T helper-B cell peptide as a vaccine for Flaviviruses.

Dr. M. M. Gore, Dr. S.S. Dewasthaly, Prof. A.S. Kolaskar, Urmila Kulkarni-Kale Sangeeta Sawant WO 02/053182 A1

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important references
Important references
  • Hopp, Woods, 1981, Prediction of protein antigenic determinants from amino acid sequences, PNAS U.S.A 78, 3824-3828
  • Parker, Hodges et al, 1986, New hydrophilicity scale derived from high performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray derived accessible sites, Biochemistry:25, 5425-32
  • Kolaskar, Tongaonkar, 1990, A semi empirical method for prediction of antigenic determinants on protein antigens, FEBS 276, 172-174
  • Men‚ndez-Arias, L. & Rodriguez, R. (1990), A BASIC microcomputer program forprediction of B and T cell epitopes in proteins, CABIOS, 6, 101-105
  • Peter S. Stern (1991), Predicting antigenic sites on proteins, TIBTECH, 9, 163-169
  • A.S. Kolaskar and Urmila Kulkarni-Kale, 1999 - Prediction of three-dimensional structure and mapping of conformational epitopes of envelope glycoprotein of Japanese encephalitis virus,Virology, 261, 31-42

© Bioinformatics Centre, UoP