Epitope prediction algorithms

Download Report

Transcript Epitope prediction algorithms

Epitope prediction algorithms

Urmila Kulkarni-Kale Bioinformatics Centre University of Pune

October 2K5

Vaccine development In Post-genomic era: Reverse Vaccinology Approach.

• Rappuoli R. (2000). Reverse vaccinology.

Curr Opin Microbiol. 3:445-450.

Genome Sequence Proteomics Technologies

In silico

analysis High throughput Cloning and expression In vitro and in vivo assays for Vaccine candidate identification

DNA microarrays

In Silico

Analysis

Peptide Multiepitope vaccines VACCINOME

October 2K5

Candidate Epitope DB Disease related protein DB

Epitope prediction 4

What Are Epitopes?

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. © Bioinformatics Centre, UoP October 2K5 5

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 October 2K5 6

Properties of Epitopes

• They occur on the and are more protein.

surface flexible

of the protein than the rest of the • They have high degree of

solvent

exposure to the

• The amino acids making the epitope are usually

charged

and

hydrophilic.

Methods to identify epitopes 1.

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.

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 © Bioinformatics Centre, UoP October 2K5 9

Antigen-Antibody complex

October 2K5 © Bioinformatics Centre, UoP 10

Ab-binding sites:

Sequential & Conformational Epitopes

!

Paratope October 2K5 Sequential Conformational © Bioinformatics Centre, UoP Ab-binding sites 11

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

October 2K5 © Bioinformatics Centre, UoP : 12

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 October 2K5 13

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

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 October 2K5 15

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 October 2K5 16

CEP Server

• Predicts the conformational epitopes from X-ray crystals of Ag-Ab complexes.

• uses percent accessible surface area and distance as criteria October 2K5 © Bioinformatics Centre, UoP 17

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

October 2K5 © Bioinformatics Centre, UoP 19

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?

Different Abs (HyHEL10 & D1.3) have over-lapping binding sites • Limitation: – Limited by the availability of 3D structure data of antigens © Bioinformatics Centre, UoP October 2K5 20

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 October 2K5 21

CEP: Conformational Epitope Prediction Server http://bioinfo.ernet.in/cep.htm

October 2K5 © Bioinformatics Centre, UoP 22

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.

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

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.

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, transportation is high.

storage and © Bioinformatics Centre, UoP October 2K5 26

Predicted structure of JEVS Mutations: JEVN/

JEVS

October 2K5 © Bioinformatics Centre, UoP 27

October 2K5 © Bioinformatics Centre, UoP 28

CE of JEVN Egp

October 2K5 © Bioinformatics Centre, UoP 29

Species and Strain specific properties: TBEV/ JEVN/JEVS

• Loop1 in TBEV:

LA EE H QGGT

• Loop1 in JEVN:

H N E KR A D SS

• Loop1 in JEVS:

H N KKR A D SS

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.

October 2K5 © Bioinformatics Centre, UoP 30

Multiple alignment of Predicted T 426 H -cell epitope in the JE_Egp with corresponding epitopes in Egps of other Flaviviruses 457 JE MVE DF DF

G G

S S

I V

GG GG

VFN VFN

S S

I I

GK GK

AV AV

H H

Q Q

V V

F F

G G

GAFRTL GAFRTL

F F

G G

G G

MS MS

WNE KUN DF

VFT

GAFRSL

VFT

GAFRSL

SLE DF

VFN

GAFRTL

DEN2 DF

VFT

AIYGAA

YF DF

FFT

SAFQGL

TBE DF

FLS

GAFNSI

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 SENHGN YSAQVGASQ AAKFTITPNAPSITLKLG MVE STSHGNYSTQIGANQAVRFTISPNAPAITAKMG WNE VESHG----KIGATQAGRFSITPSAPSYTLKLG KUN VESHGNYFTQTGAAQAGRFSITPAAPSYTLKLG SLE STSHGNYSEQIGKNQAARFTISPQAPSFTANMG DEN2 HAVGNDTG-----KHGKEIKITPQSSTTEAELT YF QENWN--------TDIKTLKFDALSGSQEVEFI

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

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

• • • • • •

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), 163-169

Predicting antigenic sites on proteins

, TIBTECH, 9, 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

Epitope prediction algorithms

Transcript Epitope prediction algorithms

Epitope prediction algorithms

What Are Epitopes?

Antigen-Antibody (Ag-Ab) complexes

Ab-binding sites:

Sequential & Conformational Epitopes

!

CE: Beyond validation

CE: Features

We Have Chosen JE Virus, Because

We Have Chosen JE Virus, Because

CE of JEVN Egp

Species and Strain specific properties: TBEV/ JEVN/JEVS

Peptide Modeling

Relevant Publications & Patent

Directory