Immunological feature predictions and databases on the web

Ole LundCenter for Biological Sequence Analysis

BioCentrum-DTUTechnical University of Denmark

lund@cbs.dtu.dk

Effect of vaccines

Vaccines have been made for 36 of >400 human pathogens

Immunological Bioinformatics, The MIT press.

+HPV & Rotavirus

Deaths from infectious diseases in the world in 2002

www.who.int/entity/whr/2004/annex/topic/en/annex_2_en.pdf

Pathogenic Viruses

Data derived from /www.cbs.dtu.dk/databases/Dodo.

1st column: log10 of the number of deaths caused by the pathogen per year

2nd column: DNA Advisory Committee (RAC) classificationDNA Advisory Committee guidelines [RAC, 2002] which includes those biological agents known to infect humans, as well as selected animal agents that may pose theoretical risks if inoculated into humans. RAC divides pathogens intofour classes.Risk group 1 (RG1). Agents that are not associated with disease in healthy adult humans

Risk group 2 (RG2). Agents that are associated with human disease which is rarely serious and for which preventive or therapeutic interventions are often available

Risk group 3 (RG3). Agents that are associated with serious or lethal human disease for which preventive or therapeutic interventions may be available (high individual risk but low community risk)

Risk group 4 (RG4). Agents that are likely to cause serious or lethal human disease for which preventive or therapeutic interventions are not usually available (high individual risk and high community risk)

3rd column: CDC/NIAID bioterror classificationclassification of the pathogens according to the Centers for Disease Control and Prevention (CDC) bioterror categories A–C, where category A pathogens are considered the worst bioterror threats

4th column: Vaccines available A letter indicating the type of vaccine if one is available (A: acellular/adsorbet; C: conjugate; I: inactivated; L: live; P: polysaccharide; R: recombinant; S staphage lysate; T: toxoid). Lower case indicates that the vaccine is released as an investigational new drug (IND)).

5th column: G: Complete genome is sequenced

Need for new vaccine technologies

• The classical way of making vaccines have in many cases been tried for the pathogens for which no vaccines exist

• Need for new ways for making vaccines

Databases Used for Vaccine Design

• Sequence databases

• General

• Sequences of proteins of the immune system

• Epitope databases

• Pathogen centered databases

• HIV

• mTB

• Malaria

Sequence Databases

• Used to study sequence variability of microbes

• Sequence conservation

• Positive/negative selection

• Examples

• Swissprot http://expasy.org/sprot/

• GenBank http://www.ncbi.nlm.nih.gov/Genbank/

MHC Class I pathway

Figure by Eric A.J. Reits

The binding of an immunodominant 9-mer Vaccinia CTL epitope, HRP2 (KVDDTFYYV) to HLA-A*0201. Position 2 and 9 of the epitopes are buried deeply in the HLA class I molecule.

Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004).

Expression of HLA is codominant

Polymorphism and polygeny

The MHC gene region

http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init&user_id=0&probe_id=0&source_id=0&locus_id=0&locus_group=0&proto_id=0&banner=1&kit_id=0&graphview=0

Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles

http://www.anthonynolan.com/HIG/index.html

HLA variability

http://rheumb.bham.ac.uk/teaching/immunology/tutorials/mhc%20polymorphism.jpg

 

Logos of HLA-A alleles

O Lund et al., Immunogenetics. 2004 55:797-810

 

Clustering of HLA alleles

O Lund et al., Immunogenetics. 2004 55:797-810

Databases of Sequences of Proteins of Immune system

• Used to study variability of the human genome

• IMmunoGeneTics HLA (IMGT/HLA) database

• Sequences of HLA, antibody and other molecules

• http://imgt.cines.fr/

• dbMHC

• Clinical data and sequences related to the immune system

• http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init

• Anthony Nolan Database

• http://www.anthonynolan.com/HIG/

Epitope Databases

• Used to find regions that can be recognized by the immune system

• General Epitope Databases

• IEDB General epitope database

• http://immuneepitope.org/home.do

• AntiJen (MHC Ligand, TCR-MHC Complexes, T Cell Epitope, TAP , B Cell Epitope molecules and immunological Protein-Protein interactions)

• http://www.jenner.ac.uk/AntiJen/

• FIMM (MHC, antigens, epitopes, and diseases)

• http://research.i2r.a-star.edu.sg/fimm/

More Epitope Databases

• SYFPEITHI

• Natural ligands: sequences of peptides eluded from MHC molecules on the surface of cells

• http://www.syfpeithi.de/

• MHCBN: Immune related databases and predictors

• http://www.imtech.res.in/raghava/mhcbn/

• http://bioinformatics.uams.edu/mirror/mhcbn/

• HLA Ligand/Motif Database: Discontinued

• MHCPep: Static since 1998, replaced by FIMM

Prediction of HLA binding

• Many methods available, including:

• bimas, syfpeithi, Hlaligand, libscore, mapppB, mapppS,mhcpred, netmhc, pepdist, predbalbc, predep, rankpep, svmhc

• See links at:

• http://immuneepitope.org/hyperlinks.do?dispatch=loadLinks

• Recent benchmark:

• http://mhcbindingpredictions.immuneepitope.org/internal_allele.html

B cell Epitope Databases

• Linear

• IEDB, Bcipep, Jenner, FIMM, BepiPred

• HIV specific database

• http://www.hiv.lanl.gov/content/immunology/ab_search

• Conformational

• CED: Conformational B cell epitopes

• http://web.kuicr.kyoto-u.ac.jp/~ced/

MHC class II pathway

Figure by Eric A.J. Reits

Virtual matrices

HLA-DR molecules sharing the same pocket amino acid pattern, are assumed to have identical amino acid binding preferences.

MHC Class II binding

Virtual matrices– TEPITOPE: Hammer, J., Current Opinion in Immunology 7, 263-269, 1995, – PROPRED: Singh H, Raghava GP Bioinformatics 2001 Dec;17(12):1236-7

Web interface http://www.imtech.res.in/raghava/propred

MHC class II Supertypes

•5 alleles from the DQ locus (DQ1, DQ2, DQ3, DQ4, DQ5) cover 95% of most populations [Gulukota and DeLisi, 1996]

•A number of HLA-DR types share overlapping peptide-binding repertoires [Southwood et al., 1998]

 

Logos of HLA-DR alleles

O Lund et al., Immunogenetics. 2004 55:797-810

 

O Lund et al., Immunogenetics. 2004 55:797-810

Linear B cell Epitope Predictors

• Continuous (Linear) epitopes

• IEDB

• http://tools.immuneepitope.org/tools/bcell/iedb_input

• Bcepred

• www.imtech.res.in/raghava/btxpred/link.html

• Bepipred

• http://www.cbs.dtu.dk/services/BepiPred/

• Recent Benchmarking Publications• Benchmarking B cell epitope prediction: Underperformance of existing methods. Blythe MJ,

Flower DR. Protein Sci. 2005 14:246-24

• Improved method for predicting linear B-cell epitopes Jens Erik Pontoppidan Larsen, Ole Lund and Morten Nielsen Immunome Research 2:2, 2006

• Greenbaum JA, Andersen PH, Blythe M, Bui HH, Cachau RE, Crowe J, Davies M, Kolaskar AS, Lund O, Morrison S, Mumey B, Ofran Y, Pellequer JL, Pinilla C, Ponomarenko JV, Raghava GP, van Regenmortel MH, Roggen EL, Sette A, Schlessinger A, Sollner J, Zand M, Peters B. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. J Mol Recognit. 2007 Jan 5

Discontinuous B cell Epitope Predictors

• Discontinuous (conformational) epitopes

• DiscoTope

• http://www.cbs.dtu.dk/services/DiscoTope/

• Benchmarking• Prediction of residues in discontinuous B cell epitopes using

protein 3D structures, Pernille Haste Andersen, Morten Nielsen and Ole Lund, Protein Science, 15:2558-2567, 2006

Pathogen Centered Databases

• HIV

• http://www.hiv.lanl.gov/content/index

• Influenza

• http://www.flu.lanl.gov/

• Tuberculosis

• http://www.sanger.ac.uk/Projects/M_tuberculosis/

• POX

• http://www.poxvirus.org/

Reviews

• Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform. 2006 Oct 31

• Web based Tools for Vaccine Design (Lund et al, 2002)

• http://www.cbs.dtu.dk/researchgroups/immunology/webreview.html

Other Resources

• Gene expression data

• Localization prediction

• SignalP

Other BioTools at CBS

• Mapping of epitopes from multiple strains on one reference sequence

• Training matrix and neural network methods

• Training of Gibbs sampler

Future challenges

• Consensus on benchmarks

• Like Rost-Sander set in secondary structure prediction

• …but more complicated

• Different types of epitopes

• B cell , T cell (Class I and II)

• Different validation experiments

• HLA binders, natural ligands, epitopes

• Linear and conformational B cell epitopes

• Many alleles

Links to links

• IEDB’s Links

• http://immuneepitope.org/hyperlinks.do?dispatch=loadLinks

Pathogen Bind ELISPOT

Influenza X X W Hildebrand

Variola major (smallpox) vaccine X X R Koup, S Joyce

Yersinia pestis X

Francisella tularensis (tularemia) X (X) A Sjostedt

LCM X

Lassa Fever X (x) A Edelstein, J Botton

Hantaan virus (Korean hemorrhagic fever virus) X (x) A Edelstein, J Botton

Rift Valley Fever X

Dengue X (X) E Marques

Ebola X

Marburg X

Multi-drug resistant TB (BCG vaccine) X X

Yellow fever X (X) T August

Typhus fever (Rickettsia prowazekii) X (x) S Miguel

West Nile Virus X (X) P Norris

Epitope Discovery

DevelopmentDevelopment

22mmHeavy chainHeavy chain

peptidepeptide IncubationIncubationPeptide-MHC Peptide-MHC complexcomplex

Determination of peptide-HLA binding

Step I: Folding of MHC class I molecules in solution

Step II: Detection of Step II: Detection of de novode novo folded MHC class I molecules by ELISA folded MHC class I molecules by ELISA

C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8

HLA Binding Results

• 1215 peptides received• 1114 tested for binding • 827 (74%) bind with KD better than 500nM• 484 (43%) bind with KD better han 50 nM

KD\Pathogen Influenza Marburg Pox F. tularensis Dengue Hantaan Lassa West Nile Yellow FeverKD<50 42 45 97 45 67 59 27 52 5050<KD<500 63 39 42 21 44 20 21 41 52KD>500 87 29 38 6 30 11 22 29 35in progress 9 1 1 4 6 4 12 31 33Total 201 114 178 76 147 94 82 153 170

Søren Buus Lab

ELISPOT assay

•Measure number of white blood cells that in vitro produce interferon- in response to a peptide

•A positive result means that the immune system has earlier reacted to the peptide (during a response to a vaccine/natural infection)

SLFNTVATL

SLFNTVATL

SLFNTVATL

SLFNTVATL SLFNTVATLSLFNTVATL

Two spots

Influenza Peptides positive in ELISPOT

HLAPeptide Sequence Restriction KD (nM) + peptide - peptide + peptide - peptide

PB1591-599 VSDGGPNLY HLA-A1 6 18 ± 2 3 ± 3 12 ± 4 1 ± 1

NP44-52 CTELKLSDY HLA-A1 7 34 ± 5 4 ± 1 13 ± 4 0 ± 0

PB1166-174 FLKDVMESM HLA-A2 51 74 ± 10 11 ± 6 140 ± 36 20 ± 7

PB141-49 DTVNRTHQY HLA-A26 6 40 ± 3 20 ± 7 38 ± 5 24 ± 3

PB1540-548 GPATAQMAL HLA-B7 6 7 ± 2 2 ± 1 13 ± 2 6 ± 1

NP225-233 ILKGKFQTA HLA-B8 664 9 ± 4 1 ± 1 19 ± 7 2 ± 2

PA601-609 SVKEKDMTK HLA-B8 NB 23 ± 6 1 ± 1 119 ± 8 2 ± 1

PB1349-357 ARLGKGYMF HLA-B27 246 10 ± 6 1 ± 1 14 ± 4 1 ± 1

NP383-391 SRYWAIRTR HLA-B27 38 39 ± 6 1 ± 1 40 ± 6 2 ± 1

M1173-181 IRHENRMVL HLA-B39 13 14 ± 3 3 ± 1 84 ± 11 3 ± 1

NP199-207 RGINDRNFW HLA-B58 42 28 ± 5 1 ± 1 15 ± 6 2 ± 2

PB1347-355 KMARLGKGY HLA-B62 178 77 ± 20 3 ± 2 91 ± 8 10 ± 3

PB1566-574 TQIQTRRSF HLA-B62 88 15 ± 5 2 ± 2 21 ± 2 2 ± 0

Elispot assay1 Elispot assay2

Mingjun Wang et al., submitted

Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3:e91.

Genome Projects -> Systems Biology

•Genome projects

•Create list of components

•Sequence genomes

•Find genes

•Systems Biology

•Find out how these components play together

•Networks of interactions

•Simulation of systems

•Over time

•In 3D space

Simulation of the Immune system

Example

•CTL escape mutant dynamics during HIV infection

Ilka Hoof and Nicolas Rapin

Flowchart - interactions

Nicolas Rapin et al., Journal of Biological Physics, In press

Mathematical model

dI

dt f V T I I

k E I

h E IdV

dtbI I cV

dE

dt E Ikm E I

EE

dT

dt f V T T T

Nicolas Rapin

f values from sequence

Sequence f value--------------------SLYNTVATL 1SAYNTVATL 0.95283SAYNTVATC 0.90566SAFNTVATC 0.86792SAINTVATC 0.83019VAINTVATC 0.77358VAINTHATC 0.70755VAINEHATC 0.65094VAICEHATC 0.56604VAICEPATC 0.57547

Sm B Ai ,i i1

9

fm Sm pSw p

From one to many virus strains

Nicolas Rapin

Simulation with many viruses

HIV evolution tree.

Initial virus is SLYNTVATL, that give rise to 6 functional mutants able to replicate.

Eleonora Kulberkyte

AcknowledgementsImmunological Bioinformatics group, CBS, Technical University of Denmark (www.cbs.dtu.dk)

Claus Lundegaard

Data bases, HLA binding

Morten Nielsen

HLA binding

Jean Vennestrøm

2D proteomics

Thomas Blicher (50%)

MHC structure

Mette Voldby Larsen

Phd student - CTL prediction

Pernille Haste Andersen

PhD student – Structure

Sune Frankild

PhD student - Databases

Sheila Tuyet Tang

Pox/TB

Thomas Rask (50%)

Evolution

Ilka Hoof and Nicolas Rapin

Simulation of the immune system

Hao Zhang

Protein potentials

Collaborators

IMMI, University of Copenhagen

Søren Buus MHC binding

Mogens H Claesson Elispot Assay

La Jolla Institute of Allergy and Infectious Diseases

Allesandro Sette Epitope database

Bjoern Peters

Leiden University Medical Center

Tom Ottenhoff Tuberculosis

Michel Klein

Ganymed

Ugur Sahin Genetic library

University of Tubingen

Stefan Stevanovic MHC ligands

INSERM

Peter van Endert Tap binding

University of Mainz

Hansjörg Schild Proteasome

Schafer-Nielsen

Claus Schafer-Nielsen Peptide synthesis

ImmunoGrid

Elda Rossi & Simulation of the

Partners Immune system

University of Utrectht

Can Kesmir Ideas