1. Epitope prediction algorithms Urmila Kulkarni-Kale Bioinformatics Centre University of Pune

2. October 2K5© Bioinformatics Centre, UoP2 Vaccine development In Post-genomic era: Reverse Vaccinology Approach. Rappuoli R. (2000). Reverse vaccinology. Curr Opin Microbiol. 3:445-450.

3. October 2K5© Bioinformatics Centre, UoP3 Genome Sequence Proteomics Technologies In silico analysis DNA microarrays High throughput Cloning and expression In vitro and in vivo assays for Vaccine candidate identification Global genomic approach to identify new vaccine candidates

4. October 2K5© Bioinformatics Centre, UoP4 In Silico Analysis Gene/Protein Sequence Database Disease related protein DB Candidate Epitope DB VACCINOME Peptide Multiepitope vaccines Epitope prediction

5. October 2K5© Bioinformatics Centre, UoP5

6. October 2K5© Bioinformatics Centre, UoP6 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

7. October 2K5© Bioinformatics Centre, UoP7 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.

8. October 2K5© Bioinformatics Centre, UoP8 Methods to identify epitopes 1.Immunochemical methods ELISA : Enzyme linked immunosorbent assay Immunoflurorescence Radioimmunoassay 2.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. 3.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.

9. October 2K5© Bioinformatics Centre, UoP9 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 & paratopes are relational entities

10. October 2K5© Bioinformatics Centre, UoP10 Antigen-Antibody complex

11. October 2K5© Bioinformatics Centre, UoP11 Ab-binding sites: Sequential & Conformational Epitopes ! Sequential Conformational Ab-binding sites Paratope

12. October 2K5© Bioinformatics Centre, UoP12 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

13. October 2K5© Bioinformatics Centre, UoP13 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

14. October 2K5© Bioinformatics Centre, UoP14 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.

15. October 2K5© Bioinformatics Centre, UoP15 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

16. October 2K5© Bioinformatics Centre, UoP16 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

17. October 2K5© Bioinformatics Centre, UoP17 CEP Server Predicts the conformational epitopes from X-ray crystals of Ag-Ab complexes. uses percent accessible surface area and distance as criteria

18. October 2K5© Bioinformatics Centre, UoP18 An algorithm to map sequential and conformational epitopes of protein antigens of known structure

19. October 2K5© Bioinformatics Centre, UoP19

20. October 2K5© Bioinformatics Centre, UoP20 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

21. October 2K5© Bioinformatics Centre, UoP21 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

22. October 2K5© Bioinformatics Centre, UoP22 CEP: Conformational Epitope Prediction Server http://bioinfo.ernet.in/cep.htm

23. October 2K5© Bioinformatics Centre, UoP23 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.

24. October 2K5© Bioinformatics Centre, UoP24 Case study: Design & development of peptide vaccine against Japanese encephalitis virus

25. October 2K5© Bioinformatics Centre, UoP25 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.

26. October 2K5© Bioinformatics Centre, UoP26 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.

27. October 2K5© Bioinformatics Centre, UoP27 Predicted structure of JEVS Mutations: JEVN/JEVS

28. October 2K5© Bioinformatics Centre, UoP28

29. October 2K5© Bioinformatics Centre, UoP29 CE of JEVN Egp

30. October 2K5© Bioinformatics Centre, UoP30 Loop1 in TBEV: LA EEH QGGT Loop1 in JEVN: HN EKR ADSS Loop1 in JEVS: HN KKR ADSS Species and Strain specific properties: TBEV/ JEVN/JEVS 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.

31. October 2K5© Bioinformatics Centre, UoP31 Multiple alignment of Predicted T H -cell epitope in the JE_Egp with corresponding epitopes in Egps of other Flaviviruses 426457 JE DFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS MVE DFGSVGGVFNSIGKAVHQVFGGAFRTLFGGMS WNE DFGSVGGVFTSVGKAIHQVFGGAFRSLFGGMS KUN DFGSVGGVFTSVGKAVHQVFGGAFRSLFGGMS SLE DFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS DEN2 DFGSLGGVFTSIGKALHQVFGAIYGAAFSGVS YF DFSSAGGFFTSVGKGIHTVFGSAFQGLFGGLN TBE DFGSAGGFLSSIGKAVHTVLGGAFNSIFGGVG 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 JE SENHGNYSAQVGASQAAKFTITPNAPSITLKLG MVE STSHGNYSTQIGANQAVRFTISPNAPAITAKMG WNE VESHG ‑‑‑‑ KIGATQAGRFSITPSAPSYTLKLG KUN VESHGNYFTQTGAAQAGRFSITPAAPSYTLKLG SLE STSHGNYSEQIGKNQAARFTISPQAPSFTANMG DEN2 HAVGNDTG ‑‑‑‑‑ KHGKEIKITPQSSTTEAELT YF QENWN ‑‑‑‑‑‑‑‑ TDIKTLKFDALSGSQEVEFI TBE VAANETHS ‑‑‑‑ GRKTASFTIS ‑‑ SEKTILTMG

32. Peptide Modeling Initial random conformation Force field: Amber Distance dependent dielectric constant 4r ij Geometry optimization: Steepest descents & Conjugate gradients Molecular dynamics at 400 K for 1ns Peptides are: SENHGNYSAQVGASQ NHGNYSAQVGASQ YSAQVGASQ YSAQVGASQAAKFT NHGNYSAQVGASQAAKFT SENHGNYSAQVGASQAAKFT 149 168

33. October 2K5© Bioinformatics Centre, UoP33

34. October 2K5© Bioinformatics Centre, UoP34

35. October 2K5© Bioinformatics Centre, UoP35 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

36. October 2K5© Bioinformatics Centre, UoP36 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