Epitope prediction and its algorithms

Epitope prediction and its algorithms

1. S.Prasanth Kumar, S.Prasanth Kumar,
BioinformaticianBioinformatician
Immunoinformatics
Epitope Prediction and its AlgorithmsEpitope Prediction and its Algorithms
S.Prasanth Kumar, S.Prasanth Kumar,
BioinformaticianBioinformatician
S.Prasanth Kumar
Dept. of Bioinformatics
Applied Botany Centre (ABC)
Gujarat University, Ahmedabad, INDIA www.facebook.com/Prasanth Sivakumar
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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. 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
Epitope’s Properties and its
Types
Sequential / Continuous Epitopes
Antibody Recognized by T
H
cells
Linear peptide fragments
Amphipathic helical 9-12
mer

4. Conformational / Discontinuous Epitopes
Antibody
Recognized by both T
H
& B cells
Non-linear discrete amino acid
sequences, come together due to
folding
Exposed 15-22 mer
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
CytosolER
*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
Hydrophilicit
y
Janin’s scale
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 acid2
nd
amino acid3
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

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 !!!