The humoral response involves interaction of B cells with antigen (Ag) and their differentiation into antibody-secreting plasma cells. The secreted antibody (Ab) binds to the antigen and facilitates its clearance from the body.

The cell-mediated responses involve various subpopulations of T cells that recognize antigen presented on self-cells. Helper T cells respond to antigen by producing cytokines. Cytotoxic T cells respond to antigen by developing into cytotoxic T lymphocytes (CTLs), which mediate killing of altered self-cells (e.g., virus-infected cells).

The Immune Response

The MHC class I pathway

Antigen Presenting Cell

Proteasome

Antigen

Peptides

ER

MHC I TCD8+

T-cell epitope

Identifying of T-cell epitopes is important for development of peptide-based vaccines, evaluation of subunit vaccines, diagnostic development

The immunoglobulin fold

Common Structures - Both the antibodies of the humoral response and the molecules involved in the cellular response (antibody, TCR, most CD [cell surface molecules expressed on various cell types in the immune system]) contain elements of common structure.

The domains in these molecules are built on a common motif, called the immunoglobulin fold, in which two anti-parallel sheets lie face to face. This structure probably represents the primitive structural element in the evolution of the immune response. The immunoglobulin fold is also found in a number of other proteins.

Epitope, or antigenic determinant, is defined as the site of an antigen recognized by immune response

molecules (antibodies, MHC, TCR)

T cell epitope – a short linear peptide or

other chemical entity (native or

denatured antigen) that binds MHC

(class I binds 8-10 ac peptides; class II

binds 11-25 ac peptides) and may be

recognized by T-cell receptor (TCR).

T cell recognition of antigen involves

tertiary complex “antigen-TCR-MHC”. MHC class I

T-Cell

Receptor

VV

Xenoreactive Complex AHIII 12.2 TCR bound to P1049 (ALWGFFPVLS) /HLA-A2.1

-2-Microglobulin

Complex Of A Human TCR, Influenza HA Antigen Peptide (PKYVKQNTLKLAT) and MHC Class II

T-Cell

Receptor

V V

MHC

class II

MHC

class II

1fyt 1lp9

Igg2A Intact Mouse Antibody - Mab231 (PDB ID 1igt)

Fc fragment

Fab fragment

Fv fragmentVH

VL

CH

CL

Heavy chain

Light chain

Igg2A Intact Mouse Antibody - Mab231 (PDB ID 1igt)

Fc fragment

Fab fragment

Fv fragmentVH

VL

CH

CL

Heavy chain

Light chain

B cell epitope – a site on B cell epitope – a site on the surface of the the surface of the antigen structure that antigen structure that binds antibody binds antibody molecule.molecule. Protein antigens usually Protein antigens usually contain both sequential (or contain both sequential (or continues, continues, they could work as they could work as epitopes even when a protein epitopes even when a protein is denaturedis denatured) and ) and nonsequential (discontinues nonsequential (discontinues or conformational) epitopes. or conformational) epitopes.

B cell recognition of antigen B cell recognition of antigen involves binary complex involves binary complex “native antigen-membrane “native antigen-membrane immunoglobulin”.immunoglobulin”.

Different antibody recognize Different antibody recognize different epitopes.different epitopes.

Most of the surface of a Most of the surface of a globular protein is potentially globular protein is potentially antigenic.antigenic.

Sperm whale myoglobin (Sperm whale myoglobin (1vxg1vxg) contains ) contains five sequential epitopes (five sequential epitopes (redred, , green,green,

magenta,magenta, blue,blue, orangeorange) and two ) and two conformational epitopes (conformational epitopes (yellowyellow, , pinkpink).).

HIV-1 envelope protein gp120 core complexed with CD4 and a neutralizing human antibody 17b

The entry of HIV into cells requires the sequential interaction of the viral exterior envelope glycoprotein, gp120, with the CD4 glycoprotein and a chemokine receptor on the cell surface. These interactions initiate a fusion of the viral and cellular membranes. Although gp120 can elicit virus-neutralizing antibodies, HIV eludes the immune system. Antibody 17b

(Fab fragment)

HIV-1 envelope protein gp120 (core fragment)

CD4 (N-terminal two domain fragment)

17b epitope

PDB: 1gc1

17b epitope is comprised of four discontinuous -strands.

B cells and T cells recognize different epitopes of the same protein antigen

T cell epitope

Denatured antigen

Linear peptide 8-30 ac

Internal (often)

Binding to T cell receptor:

Kd 10-5 – 10-7 M (low affinity)

Slow on-rate, slow off-rate (once bound, peptide may stay associated for hours to many days)

B cell epitope

Native or denatured (rare) antigen

Sequential or conformationalSequential or conformational

Accessible, hydrophilic, mobile, usually on the surface or could be exposed as a result of physicochemical change

Binding to antibody:

Kd 10-7 – 10-11 M (high affinity)

Rapid on-rate, variable off-rate

Types of protein-protein interactions (PPI)

Obligate PPI

usually permanent

the protomers are not found as stable structures on their

own in vivo

Non-obligate PPI

Obligate heterodimer

Human cathepsin D

Non-obligate transient homodimer, Sperm lysin (interaction is broken and

formed continuously)

Permanent

(most enzyme-inhibitor complexes)

dissociation constant Kd = [A] [B] / [AB]

10-7 ÷ 10-13 M

Transient

Weak

(electron transport

complexes)

Kd mM-M

Non-obligate permanent

heterodimer

Thrombin and rodniin inhibitor

Intermediate

(antibody-antigen, TCR-MHC-peptide, signal transduction PPI), Kd M-nM

Strong

(require a molecular trigger to shift the

oligomeric equilibrium)

Kd nM-fM

Bovine G protein dissociates into G and G subunits upon GTP, but forms a stable trimer upon GDP

B cell (magenta, orange) and T cell epitopes (blue, green, red) of hen egg-white lysozyme

PDB: 1dpx

Immune Epitope Database and Analysis Resource

IEDB, the newly developed public database by the LIAI together with the SAIC, UCSD, and Denmark University and sponsored by the NIH, maintains experimental data on immune epitopes (the sites on foreign molecules that are recognized by the immune system) curated from literature and submitted from the research community and provides analytical tools for epitope data analysis and their prediction in proteomes. 

Agenda

• Introduction to basic concepts of immunological

bioinformatics.

• Overview of the Immune Epitope Database (IEDB).

• Case study #1. Prediction of peptide-MHC binding (Peters et al. A

community resource benchmarking predictions of peptide binding to MHC-I

molecules. PLoS Comput Biol. 2006 Jun 9;2(6):e65): data compilation and

prediction methods evaluation.

• Case study #2. 3D structure based prediction of antibody

binding sites in proteins: data compilation and prediction

methods evaluation.

The MHC class I pathway

Antigen Presenting Cell

Proteasome

Antigen

Peptides

ER

MHC I TCD8+

T-cell epitope

Performance measures for prediction methods

TP

FP

FN

TN

threshold

sensitivity = TP / (TP + FN) = 6/7= 0.86

specificity = TN / (TN + FP) = 6/8 = 0.75

ROC curve

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False positive rate, FP / (FP + TN)

Tru

e p

osit

ive r

ate

, T

P /

(T

P +

F

N)

AROC

Prediction of MHC class I epitopes

• Gibbs sampling

• Sequence motifs, matrices• Sequence weighted matrices: performance of the method

(measured as AROC) depends on the number of training peptides (”Immunological Bioinformatics” O. Lund, 2005)

• Hidden Markov Models

• Artificial Neural Networks

For T-cell epitopes the most selective requirement is the ability to bind an MHC with high affinity.

ALAKAAAAM

ALAKAAAAN

ALAKAAAAV

ALAKAAAAT

GMNERPILT

GILGFVFTM

TLNAWVKVV

KLNEPVLLL

AVVPFIVSV

Peptides known to bind to the HLA-A*0201 molecule.

0.65

0.7

0.75

0.8

0.85

0.9

0.95

2 10 20 100 200 500

Number of training peptides

Aro

c

Assembling the dataset of measured peptide affinities to MHC class I molecules

(Peters et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol. 2006 Jun 9;2(6):e65)

• Data: pairs {peptide – affinity value in terms of IC50 nM} for a given MHC

allele

• 48 different mouse, human, macaque, and chimpanzee MHC class I alleles.

• Length pf peptides 8 – 11 aa.

• If affinities for the same peptide to the same MHC molecule were recorded

in multiple assays, the geometric mean of the IC50 values was taken.

• 84% of peptides differ in at least two residues with every other peptide in

the dataset.

• 48,828 data points collected from two experimental groups.

An example of the problem of pooling experimental data from different sources

• There is a good agreement between the measured affinity values by two experimental groups (Sette and Buus) for intermediate- and low-affinity peptides, less for high-affinity peptides.

• For peptides with high affinity of 50 nM or better the Matthews correlation coefficient is below 0.37.

• Important message: Pooling experimental data from different sources requires additional validation.

High affinity Low affinity (IC50 500 nM – non-binder)

Peptide binding to MHC class I affinity prediction methods comparison (the same training and test data sets)

Correlation coefficients (ARB=0.55, SMM=0.62, ANN=0.69) are significantly different (p<0.05 using a t test).

Aroc values (ARB=0.934, SMM=0.952, ANN=0.957) are significantly different (p<0.05 using a paired t test on Aroc values generated by bootstrap).

Peptide binding to MHC class I affinity prediction methods comparison.

Prediction performance as a function of training set size.

• Ideally the comparison should be done using ‘blind’ test set excluding every peptide used for any method training. Otherwise the performance of a method can be overestimated.

• That was not done in the discussed work of Peters et al.

Peptide binding to MHC class I affinity prediction methods comparison

(external tools: different training data sets)

Agenda

• Introduction to basic concepts of immunological

bioinformatics.

• Overview of the Immune Epitope Database (IEDB).

• Case study #1. Prediction of peptide-MHC binding (Peters et al. A

community resource benchmarking predictions of peptide binding to MHC-I

molecules. PLoS Comput Biol. 2006 Jun 9;2(6):e65): data compilation and

prediction methods evaluation.

• Case study #2. 3D structure based prediction of antibody

binding sites in proteins: data compilation and prediction

methods evaluation.

HIV-1 envelope protein gp120 core complexed with CD4 and a neutralizing human antibody 17b

The entry of HIV into cells requires the sequential interaction of the viral exterior envelope glycoprotein, gp120, with the CD4 glycoprotein and a chemokine receptor on the cell surface. These interactions initiate a fusion of the viral and cellular membranes. Although gp120 can elicit virus-neutralizing antibodies, HIV eludes the immune system. Antibody 17b

(Fab fragment)

HIV-1 envelope protein gp120 (core fragment)

CD4 (N-terminal two domain fragment)

17b epitope

PDB: 1gc1

17b epitope is comprised of four discontinuous -strands.

Why is the knowledge of antibody epitopes is so important?

• Vaccine design (immunogenicity, i.e. ability of vaccine to elicit in the naïve individual the production of pathogen neutralizing antibodies, is required):

Purified antigen (subunit) vaccines:• Inactivated toxins “toxoids”: tetanus toxoid, diphteria toxoid

• Vaccines composed of bacterial polysaccharide antigens: flu, pneumococcus

Synthetic antigen vaccines: • hepatitus B (recombinant protein), herpes simplex virus

• Diagnostic design (antigenicity, i.e. ability of synthetic antigen to be recognized by the original antibody, is required):

• Autoimmune diseases: lupus, rheumatoid arthritis

• Allergic reactions

• Basic knowledge of antigenicity.

“HIV vaccine design and the neutralizing antibody problem” Nature Immun., 2004, 5, 233

For fusion with its target cells, HIV-1 uses a trimeric Env complex containing gp120 and gp41 subunits.

There are known four broadly reactive and neutralizing anti-HIV mAbs (NAbs):

b12 (epitope on gp120),

2G12 (epitope on gp120,

2F5 (epitope on gp41),

4E10 (epitope on gp41).

Other known mAbs, 447-52D and 58.2 (‘V3 loop Abs’), 17b, and X5 (‘CD4i Abs’) have limited activity.

Strategies for design immunogens that elicit broadly neutralizing antibodies

(from “HIV vaccine design and the neutralizing antibody problem” Nature Immunology, 2004, 5, 233)

To produce molecules that mimic the mature trimer Env on the

virion surface. These molecules can be recombinant or expressed

on the surface of particles such as pseudovirions or

proteoliposomes.

To produce Env molecules engineered to better present NAb

epitopes than do “wild-type” molecules.

To generate stable intermidiates of the entry process with the goal

of exposing conserved epitopes to which antibodies could gain

access during entry.

To produce epitope mimics of the broadly NAbs determined from

structural studies of antibody-antigen complexes.

The best precision in identification of antibody epitopes is provided by X-ray crystallography.

Epitope identification

Other methods to predict structure and location of antibody epitopes include:

- mass spectrometry combined with immunoaffinity procedures;

- screening of combinatorial phage-display peptide libraries;

- mimitope approach: selection ligands from a library of random combinatorial ligands;

- alanine scan;

- etc.

Methods for antibody epitope prediction

• Sequence-based (suitable for linear epitopes only)• Amino acid scales: hydrophobicity, secondary structure (beta-turn),

polarity, flexibility, solvent accessibility etc.• The combination of scales and experimentation with several machine

learning algorithms showed little improvement over single scale-based methods.

• Maximum sensitivity is 59%.

• Structure-based (antibody binding site prediction for a protein of a given 3D structure):

• CEP• DiscoTope

• Epitope mapping using peptide libraries

• Sensitivity = TP / (TP + FN) - a proportion of correctly predicted epitope residues (TP) with respect to the total number of epitope residues (TP+FN).

• Specificity = 1- FP / (TN + FP) – a proportion of correctly predicted non-epitope residues (TN) with respect to the total number of non-epitope residues (TN+FP).

• Positive predictive value (PPV) = TP / (TP + FP) - a proportion of correctly predicted epitope residues (TP) with respect to the total number of predicted epitope residues (TP+FN).

Performance measures for patch prediction methodsPerformance measures for patch prediction methods

FN=5

220 aa

TP=15FP=35

sensitivity = 75%,

ppv = 30%

specificity = 82.5%

TN=165

FN=5

220 aa

TP=15

FP=85

TN=115

sensitivity = 75%, ppv = 15% specificity = 57.5%

FN=5

220 aa

TP=15

FP=10

TN=190sensitivity = 75%, ppv = 60% specificity = 95%

For a 80aa proteinSpecificity=83%

The tool purpose

220 aa

TP=20 FP=30

TN=170

Vaccine design:

High sensitivity

Diagnostic design:

High specificity and PPV

sensitivity = 100%

ppv = 40%

specificity = 85%

FN=0

220 aaTP=5

FP=0

TN=200

FN=15

sensitivity = 25%

ppv = 100%

specificity = 100%

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DiscoTope (score >-7.7)

CEP

ClusPro (DOT) (best modelof 10 first)

ClusPro (DOT) (1st model)

PatchDock (best model of 10first)

PatchDock (1st model)

PPI-PRED (best patch of 3)

PPI-PRED (1st patch)

ProMate

Epitope prediction methods (CEP and DiscoTope) have a tendency to be less specific than other methods.

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CEP

ClusPro (DOT) (best modelof 10 first)ClusPro (DOT) (1st model)

PatchDock (best model of 10first)PatchDock (1st model)

PPI-PRED (best patch of 3)

PPI-PRED (1st patch)

ProMate

Protein docking methods (DOT and PatchDock) in comparison with protein-protein binding site prediction methods (PPI-PRED and ProMate) give better PPV at the same level of specificity.

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ClusPro (DOT) (best modelof 10 first)ClusPro (DOT) (1st model)

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PPI-PRED (best patch of 3)

PPI-PRED (1st patch)

ProMate

Epitope prediction methods (CEP and DiscoTope) show worse correlation between sensitivity and ppv than other methods (e.g. linear correlation coefficient r for CEP is 0.48, for DiscoTope (-7.7) is 0.51, whereas r for PPI-PRED is 0.65, for CLusPro is 0,88 and PatchDock is 0.91).