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Darren R Flower. QSAR and the Prediction of T cell Epitopes. http://www.jenner.ac.uk/res-bio darren.flower@jenner.ac.uk. Growth in sales Vaccines: 12% yr -1 Drugs: 5% yr -1. $ 15 B. $ 5 B. $ 1.75 B. 1990. 2000. 2010. VACCINE MARKET. VACCINES. 1% $5 Billion. DRUGS. 99%

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slide1

Darren R Flower

QSAR and the Prediction

of T cell Epitopes

http://www.jenner.ac.uk/res-bio

darren.flower@jenner.ac.uk

slide2

Growth in sales

Vaccines: 12% yr-1

Drugs: 5% yr-1

$ 15 B

$ 5 B

$ 1.75 B

1990

2000

2010

VACCINE MARKET

VACCINES

1%

$5 Billion

DRUGS

99%

$350 Billion

slide3

# of Biotech

companies

HUMAN VACCINES

ARE MOVING FROM A MARGINAL

TO A MAJOR R & D DRIVEN SECTOR

AIDS

ANTIBIOTIC

RESISTANCE

BIOTERRORISM

A limited number

of vaccines targeting

major diseases

A few innovative

vaccines with

blockbuster potential

R &D pipeline:

100s of new vaccines

1980

2000

10

150

slide4

IMMUNOVACCINOLOGY

WHOLE

ORGANISM

SUBUNIT

VACCINE

EPITOPE

VACCINE

attenuated

Vaccines induce protective immunity.

Protective immunity is an enhanced

adaptive immune response to re-infection.

Delivered as recombinant protein / vector

or as naked DNA

+ adjuvants &/or “danger signals”

slide5

Types of Peptide Epitope

Antibody

or “B cell”

Epitope

Conformational

Linear

B cell Epitope

T cell Epitope

Non-

Conformational

Class I MHCs

all cells

Foreign and self proteins

8-10 amino acids

Class II MHCs

Professional Antigen

Presenting cells

Foreign proteins

8-20 amino acids

slide6

T cell response

TCR

CD8

PEPTIDE

Class I

slide7

AFFINITY

MEASURE

“PEPTIDE”

FUNNEL

NARROWEST

POINT

IC50

IC50

KD

Half maximal

lysis or

qualitative

measure

(thymidine

incoporation,

cell killing,

etc

KD

BL50

C50

SC50

CLEAVAGE

PATTERNS

t1/2

RESPONSE

MEASURE

etc.

Class I

PROTEIN

T cell

response

PROTEA

SOME

MHC

TAP

TCR

slide8

PREDICTING EPITOPES

Traditional Motifs:

X{Y/F}XXPXXWS

Frequency Matrices, Profiles

“AI” Solutions:

Neural Networks, HMMs, etc

3D-QSAR:

ComFA / CoMSIA

2D-QSAR:

Free Wilson Analysis

Epitope prediction is a chemical problem.

We have taken a quantitative approach.

Molecular Dynamics

Quantitative Structure

Activity Relationships

slide9

Compile data

RELATIONAL

DATABASE

JenPep

Extract data for

particular Allele

CoMSIA / CoMFA

ADDITIVE METHOD

QSAR

TESTABLE PREDICTIONS

QUANTITITATIVE MEASURES

OF PEPTIDE-MHC AFFINITY

FROM THE LITERATURE

slide10

EDWARD JENNER INSTITUTE

FOR VACCINE RESEARCH

JENPEP

Helen McSparron

Martin Blythe

Christianna Zygouri

slide11

JENPEP

VERSION 1.0

2061 T-Cell Epitopes

5848 MHC Binding data (IC50, BL50, t1/2, etc)

432 TAP Binding data

ACCESS Relational Database

GUI using HTML and ASP

on our website: www.jenner.ac.uk/JenPep

MJ Blythe, IA Doytchinova, and DR Flower.

JenPep: a database of quantitative functional peptide data for immunology.

Bionformatics 2002 18 434-439

slide12

JENPEP

“VERSION 2.0”

3018 T cell epitopes

12210 MHC Binding data (IC50, BL50, t1/2, Kd, etc)

441 TAP Binding data

1656 B cell epitopes

300 pMHC-TCR Binding data

bespoke postgreSQL relational database

GUI using perl and HTML

on our website: www.jenner.ac.uk/JenPep

H McSparron, C Zygouri, D Taylor, MJ Blythe, IA Doytchinova, and DR Flower.

JenPep+: Novel developments in quantitative immunological databases

Nucleic Acids Research, commissioned

slide13

Develop database system further:

extend existing databases

(T cell, MHC, TAP, B cell, pMHC-TCR)

with new data and further retrospective analysis

add new database sections:

non-natural peptides and non-natural MHC mutants

antibody binding

whole protein antigens

Host - Superantigen / Virulence Factor Binding Data

Co-receptor Binding Data

etc.

binding affinity of peptides vs host immunogenicity
Binding Affinity of peptides vs. Host Immunogenicity

MHCs: hundreds of alleles.

Each with a different peptide binding selectivity.

T cell epitopes bind well to MHCs.

95% of all known T cell epitopes bind to

MHC with an IC50< 500nM.

Exact T cell response is dependent on the T cell repertoire.

Therefore, prediction of MHC binding

is “best” option for predicting T cell epitopes.

slide15

EDWARD JENNER INSTITUTE

FOR VACCINE RESEARCH

PREDICTING

Tcell

EPITOPES

Irini Doytchinova

Christianna Zygouri

PingPing Guan

slide17

CAVEAT

our peptide sets are larger than

is typical in the pharmaceutical literature

the peptides themselves are physically large

physical properties of peptides are extreme:

multiple charges, zwitterions, huge range in hydrophobicity, etc.

Sequence & thus properties

are heavily biased in our peptide sets

Affinity data is “poor”:

multiple measurements of same peptide with orders

of magnitude differences, some values are clearly wrong, mix of

different standard peptides in radioligand competition assays, etc.

performing a “meta-analysis”:

probably many different binding modes

forced into one QSAR model

slide18

Predicting T cell Epitopes Using QSAR

CoMFA / CoMSIA

Towards the quantitative prediction of T-cell epitopes:

CoMFA and CoMSIA studies of peptides with

affinity to class I MHC molecule HLA-A*0201.

Doytchinova, I.A and Flower, D. R.

J. Med. Chem. 2001, 44, 3572-3581.

Physicochemical Explanation of Peptide Binding to HLA-A*0201

Major Histocompatibility Complex. A Three – Dimensional Quantitative

Structure – Activity Relationship Study.

Doytchinova, I.A and Flower, D. R.

Proteins, in press.

HLA-A*0201

most common

allele in Caucasian

population: 40%

~5x more binding

data than for any

other allele

FREE WILSON ANALYSIS

An Additive Method for the Prediction of

Protein-Peptide Binding Affinity.

Application to the MHC Class I Molecule HLA-A*0201

Irini A. Doytchinova*, Martin J. Blythe and Darren R. Flower

J. Proteome Research 2002, 1, 263-272.

comparison of comfa comsia for hla a 0201

152 peptides

152 peptides

with affinity to

with affinity to

the HLA-A2.1

the HLA-A2.1

Training set

Training set

Test set

Test set

102

102

50

50

peptides

peptides

peptides

peptides

Comparison of CoMFA & CoMSIA for HLA-A*0201

r2pred < 0.5

NC = 6 q2 =0.480 r2= 0.911

r2pred = 0.679

NC = 5 q2 = 0.542 r2 = 0.870

steric map

Full CoMSIA Analysis of HLA-A*0201

Hydrogen Bond Map

Steric Map

Electrostatic Map

Hydrophobic Map

NC = 7 q2 = 0.683 r2 = 0.891 n = 236

a dditive m ethod for b inding a ffinity p rediction

P

2

P4

P

6

P

8

H

O

H

O

H

O

H

O

H

O

H

N

N

N

N

N

H

N

N

N

N

O

H

O

H

O

H

O

H

O

P

1

P

3

P

5

P

7

P

9

ADDITIVE METHODFORBINDINGAFFINITY PREDICTION

HLA-A*0201: NC = 5 q2 = 0.337 r2 = 0.898 n = 340

amino acids contributions
Amino acids contributions

1-2 Interactions

1-3 interactions

how does the additive method work
How does the additive method work?

YLSPGPVTV with pIC

exp = 7.642

50

pIC

=

+ 1Y + 2L + 3S + 4P + 5G + 6P +7V + 8T + 9V

const

50

+ 1Y2L + 2L3S + 3S4P + 4P5G + 5G6P + 6P7V + 7V8T + 8T9V

+ 1Y3S + 2L4P + 3S5G + 4P6P + 5G7V + 6P8T + 7V9V

pIC

= 6.213 +0.304 + 0.219 – 0.164 + 0.135 + 0.013 - 0.008 + 0.096 + 0.035 + 0.263

50

+ 0.240 – 0.015 + 0 + 0 + 0.101 + 0.075 + 0.059 + 0.102

+ 0.031 + 0.044 – 0.107 + 0.046 + 0.011 + 0.008 - 0.001

= 7.700

slide24

CoMSIA & ADDITIVE METHOD

ARE COMPLEMENTARY

CoMSIA is “slow” but is “better” at extrapolating.

ADDITIVE is very fast

(analyze whole microbial genome in a few minutes)

but is worse at extrapolating to peptide sequences

very different to training data (missing values)

Our models are not perfect but

our results are at least as good as anyone else

working in predicting MHC binding

Trying to develop a range of “universal” models

each covering a different allele

slide26

MHCPred: an on-line server for

peptide MHC binding prediction

Models:

A*0101, A*0201,

A*0202, A*0203,

A*0206, A*0301,

A*1101, A*3301,

A*6801, A*6802,

B*3501

www.jenner.ac.uk/MHCPred

P Guan, IA Doytchinova, C Zygouri, and DR Flower.

MHCPred: bringing a quantitative dimension to online prediction of MHC Binding.

To be submitted

slide27

In

Progress

Develop Additive Method to be descriptor based

Develop “better” QSAR models using “clean”

thermodynamic data from ITC

and designed peptides

Planned

FUTURE DEVELOPMENTS

OF THIS WORK

Make “true” predictions - design new peptides

and test them experimentally

Develop models for uncharacterized MHC alleles

using peptides generated with Experimental Design