1 / 36

Epitope prediction algorithms

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.

huong
Download Presentation

Epitope prediction algorithms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  3. 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 © Bioinformatics Centre, UoP

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

  5. © Bioinformatics Centre, UoP

  6. 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 © Bioinformatics Centre, UoP

  7. 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. © Bioinformatics Centre, UoP

  8. 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. © Bioinformatics Centre, UoP

  9. 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 © Bioinformatics Centre, UoP

  10. Antigen-Antibody complex © Bioinformatics Centre, UoP

  11. Sequential Conformational Ab-binding sites Ab-binding sites:Sequential & Conformational Epitopes! Paratope © Bioinformatics Centre, UoP

  12. 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 © Bioinformatics Centre, UoP

  13. 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 © Bioinformatics Centre, UoP

  14. 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. © Bioinformatics Centre, UoP

  15. 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 © Bioinformatics Centre, UoP

  16. 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 © Bioinformatics Centre, UoP

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

  18. An algorithm to map sequential and conformational epitopes of protein antigens of known structure © Bioinformatics Centre, UoP

  19. © Bioinformatics Centre, UoP

  20. 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 © Bioinformatics Centre, UoP

  21. 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 © Bioinformatics Centre, UoP

  22. CEP: Conformational Epitope Prediction Serverhttp://bioinfo.ernet.in/cep.htm © Bioinformatics Centre, UoP

  23. 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. © Bioinformatics Centre, UoP

  24. Case study: Design & development of peptide vaccine against Japanese encephalitis virus © Bioinformatics Centre, UoP

  25. 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. © Bioinformatics Centre, UoP

  26. 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. © Bioinformatics Centre, UoP

  27. Predicted structure of JEVSMutations: JEVN/JEVS © Bioinformatics Centre, UoP

  28. © Bioinformatics Centre, UoP

  29. CE of JEVN Egp © Bioinformatics Centre, UoP

  30. 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. © Bioinformatics Centre, UoP

  31. Multiple alignment of Predicted TH-cell epitope in the JE_Egp with corresponding epitopes in Egps of other Flaviviruses • 426457 • JE DFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS • MVE DFGSVGGVFNSIGKAVHQVFGGAFRTLFGGMS • WNE DFGSVGGVFTSVGKAIHQVFGGAFRSLFGGMS • KUN DFGSVGGVFTSVGKAVHQVFGGAFRSLFGGMS • SLEDFGSIGGVFNSIGKAVHQVFGGAFRTLFGGMS • DEN2DFGSLGGVFTSIGKALHQVFGAIYGAAFSGVS • YFDFSSAGGFFTSVGKGIHTVFGSAFQGLFGGLN • TBEDFGSAGGFLSSIGKAVHTVLGGAFNSIFGGVG • 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 © Bioinformatics Centre, UoP

  32. 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: • SENHGNYSAQVGASQ • NHGNYSAQVGASQ • YSAQVGASQ • YSAQVGASQAAKFT • NHGNYSAQVGASQAAKFT • SENHGNYSAQVGASQAAKFT • 149 168

  33. © Bioinformatics Centre, UoP

  34. © Bioinformatics Centre, UoP

  35. 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 © Bioinformatics Centre, UoP

  36. 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

More Related