Your SlideShare is downloading. ×
0
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Epitope prediction and its algorithms
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Epitope prediction and its algorithms

1,585

Published on

Epitope prediction and its algorithms

Epitope prediction and its algorithms

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,585
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
63
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. S.Prasanth Kumar, Bioinformatician Immunoinformatics Epitope Prediction and its Algorithms S.Prasanth Kumar, Bioinformatician S.Prasanth Kumar Dept. of Bioinformatics Applied Botany Centre (ABC) Gujarat University, Ahmedabad, INDIA www.facebook.com/Prasanth Sivakumar FOLLOW ME ON ACCESS MY RESOURCES IN SLIDESHARE prasanthperceptron CONTACT ME [email_address]
  • 2. Epitope Antigenic determinants or Epitopes are the portions of the antigen molecules which are responsible for specificity of the antigens in antigen-antibody (Ag-Ab) reactions and that combine with the antigen binding site of Ab, to which they are complementary. Antibody Epitope
  • 3. <ul><li>They occur on the surface of the protein and are more flexible than </li></ul><ul><li>the rest of the protein </li></ul><ul><li>They have high degree of exposure to the solvent . </li></ul><ul><li>The amino acids making the epitope are usually charged and </li></ul><ul><li>hydrophilic </li></ul>Epitope’s Properties and its Types Sequential / Continuous Epitopes Antibody <ul><li>Recognized by T H cells </li></ul><ul><ul><ul><li>Linear peptide fragments </li></ul></ul></ul><ul><ul><ul><li>Amphipathic helical 9-12 mer </li></ul></ul></ul>
  • 4. Conformational / Discontinuous Epitopes Antibody <ul><ul><ul><li>Recognized by both T H & B cells </li></ul></ul></ul><ul><ul><ul><li>Non-linear discrete amino acid sequences, come together due to folding </li></ul></ul></ul><ul><ul><ul><li>Exposed 15-22 mer </li></ul></ul></ul>Paratope Paratope Sites of Antigen binding on Antibody molecule
  • 5. Immunological Processes Ag-Ab Complex MHC molecules function as antigen-recognition molecules Class I – Presents Ag so that T C cells recognize & kill. Requires CD8+ on T c Class II – Presents Ag so that T H cells recognize & kill. Requires CD4+ on T H
  • 6. Immunological Processes Endogenous pathway (class I MHC) Endogenous antigen Cytosol Proteasome Antigenic peptides peptidases N-terminally trimmed peptides TAP (transporter associated with antigen processing)
  • 7. Immunological Processes Cytosol ER *aminopeptidase associated with antigen processing (ERAAP) ERAAP N-terminally trimmed peptides MHC Class I GC Exogenous Antigen Class II Exogenous pathway (class II MHC) Cell Surface
  • 8. Immunological Processes Cytosol Cell Surface Where MHC are expressed ? Class-I all nucleated cells e.g. virus infected cells Class-II APCs (macrophages, B lymphocytes, and dendritic cells) T H cells expresses CD4,CD4 recognizes MHC class II molecules T C cells expresses CD8,CD8 recognizes MHC class I molecules T H Cell TCR CD4 MHC Class II Antigenic Peptide T C Cell CD8 MHC Class I Immuological Responses
  • 9. B-Cell Epitope Prediction Hopp & Woods method …… -Ser-Thr-Cys-Asn-Glu-…… ……-Ser-Thr-Val-Asn-Glu-….. e.g. ser-1, thr-2, cys-3,asn-4,glu-5, etc,…..x-10 alignment score = 22 Based on alignment score predict Antigenicity 1 + 2 + 10 + 4 + 5 = 22
  • 10. Database of Known Epitopes % of Epitope aa : % 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 epitope Welling’s method B-Cell Epitope Prediction High antigencity value Extend to 11-13 aa Report Probable Low antigencity value
  • 11. Karplus & Schultz Structural parameters Parker & Hodges method B-Cell Epitope Prediction HPLC from retention co-efficient of model synthetic peptides Hydrophilicity <ul><ul><ul><ul><li>Janin’s scale </li></ul></ul></ul></ul>Summing the ASA parameters for each residue of a seven-residue segment and assigning the sum to the fourth residue Surface profile Flexibility Predict Antigenicity
  • 12. B-Cell Epitope Prediction Semi-empirical method Kolaskar & Tongaonkar’s method Frequencies of occurrence of amino acids in experimentally known epitopes Physiological properties of amino acid residues Data of 169 epitopes from 34 different proteins was collected of which 156 which have less than 20 aa per determinant It is available as Antigen under ExPaSy and in EMBOSS Suite Predict Validate
  • 13. T-Cell Epitope Prediction Margalit, Spouge et al. method Considers Amphipathic helix segments (tetramer & pentamer motifs) A polar residue Charged residues &/ Glycine Hydrophobic residues 1 st amino acid 2 nd amino acid 3 rd amino acid &/ 4 th amino acid Predict Antigenicity
  • 14. Rothbard & Taylor method T-Cell Epitope Prediction Immunodominant secondary structure capable of binding to MHC with high affinity Database of Sequence motifs 3D Structures Sequence based search Predict Antigenicity
  • 15. Stille et al. method T-Cell Epitope Prediction Known MHC polymorphisms from HLA Identify anchor residues for different polymorphisms Construct Virtual matrices ……. ……. MHC polymorphism anchor residues Sequence based search with MHC polymorphism Predict Antigenicity
  • 16. T-Cell Epitope Prediction MHC binding is based on molecular dynamic simulation Darren R Flower et al. method Calculate the free energy of binding for a given molecular system No reliance on known binding data, but based on de novo prediction Required Experimentally determined structure, or a homology model, of a MHC peptide complex
  • 17.  
  • 18. Databases & Prediction Servers SYFPEITHI MHCPEP JenPep FIMM MHCBN HLALigand/Motif database HIV Molecular Immunology database EPIMHC Prediction of MHC binding BIMAS SYFPEITHI PREDEPP Epipredict Predict Propred MHCPred NetMHC MHC-binding peptides databases
  • 19. SYFPEITHI Epitope Prediction Server
  • 20. SYFPEITHI Epitope Prediction Server
  • 21. Thank You For Your Attention !!!

×