Immunological feature predictions and databases on the web

Ole LundCenter for Biological Sequence AnalysisBioCentrum-DTUTechnical University of Denmarklund@cbs.dtu.dk

Effect of vaccines

Vaccines have been made for 36 of >400 human pathogensImmunological Bioinformatics, The MIT press.+HPV & Rotavirus

Deaths from infectious diseases in the world in 2002www.who.int/entity/whr/2004/annex/topic/en/annex_2_en.pdf

Pathogenic VirusesData 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 humansRisk group 2 (RG2). Agents that are associated with human disease which is rarely serious and for which preventive or therapeutic interventions are often availableRisk 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 AC, 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 technologiesThe classical way of making vaccines have in many cases been tried for the pathogens for which no vaccines existNeed for new ways for making vaccines

Databases Used for Vaccine DesignSequence databasesGeneralSequences of proteins of the immune systemEpitope databasesPathogen centered databasesHIVmTBMalaria

Sequence DatabasesUsed to study sequence variability of microbesSequence conservationPositive/negative selectionExamplesSwissprot http://expasy.org/sprot/ GenBank http://www.ncbi.nlm.nih.gov/Genbank/

MHC Class I pathwayFigure 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 Mlgaard, 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 regionhttp://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 - alleleshttp://www.anthonynolan.com/HIG/index.html

HLA variabilityhttp://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 systemUsed to study variability of the human genomeIMmunoGeneTics HLA (IMGT/HLA) databaseSequences of HLA, antibody and other molecules http://imgt.cines.fr/ dbMHCClinical data and sequences related to the immune systemhttp://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init Anthony Nolan Databasehttp://www.anthonynolan.com/HIG/

Epitope DatabasesUsed to find regions that can be recognized by the immune systemGeneral Epitope DatabasesIEDB General epitope databasehttp://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 DatabasesSYFPEITHINatural ligands: sequences of peptides eluded from MHC molecules on the surface of cellshttp://www.syfpeithi.de/ MHCBN: Immune related databases and predictorshttp://www.imtech.res.in/raghava/mhcbn/ http://bioinformatics.uams.edu/mirror/mhcbn/HLA Ligand/Motif Database: DiscontinuedMHCPep: Static since 1998, replaced by FIMM

Prediction of HLA bindingMany 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=loadLinksRecent benchmark:http://mhcbindingpredictions.immuneepitope.org/internal_allele.html

B cell Epitope DatabasesLinearIEDB, Bcipep, Jenner, FIMM, BepiPredHIV specific databasehttp://www.hiv.lanl.gov/content/immunology/ab_searchConformationalCED: Conformational B cell epitopeshttp://web.kuicr.kyoto-u.ac.jp/~ced/

MHC class II pathwayFigure by Eric A.J. Reits

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

MHC Class II bindingVirtual matricesTEPITOPE: Hammer, J., Current Opinion in Immunology 7, 263-269, 1995, PROPRED: Singh H, Raghava GP Bioinformatics 2001 Dec;17(12):1236-7Web interface http://www.imtech.res.in/raghava/propred

MHC class II Supertypes5 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 PredictorsContinuous (Linear) epitopesIEDBhttp://tools.immuneepitope.org/tools/bcell/iedb_inputBcepredwww.imtech.res.in/raghava/btxpred/link.htmlBepipredhttp://www.cbs.dtu.dk/services/BepiPred/ Recent Benchmarking PublicationsBenchmarking B cell epitope prediction: Underperformance of existing methods. Blythe MJ, Flower DR. Protein Sci. 2005 14:246-24Improved method for predicting linear B-cell epitopes Jens Erik Pontoppidan Larsen, Ole Lund and Morten Nielsen Immunome Research 2:2, 2006Greenbaum 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 PredictorsDiscontinuous (conformational) epitopesDiscoTopehttp://www.cbs.dtu.dk/services/DiscoTope/ BenchmarkingPrediction 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 DatabasesHIVhttp://www.hiv.lanl.gov/content/indexInfluenzahttp://www.flu.lanl.gov/ Tuberculosishttp://www.sanger.ac.uk/Projects/M_tuberculosis/POXhttp://www.poxvirus.org/

Reviews Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform. 2006 Oct 31Web based Tools for Vaccine Design (Lund et al, 2002)http://www.cbs.dtu.dk/researchgroups/immunology/webreview.html

Other Resources Gene expression dataLocalization predictionSignalP

Other BioTools at CBSMapping of epitopes from multiple strains on one reference sequenceTraining matrix and neural network methodsTraining of Gibbs sampler

Future challengesConsensus on benchmarksLike Rost-Sander set in secondary structure predictionbut more complicatedDifferent types of epitopesB cell , T cell (Class I and II)Different validation experimentsHLA binders, natural ligands, epitopesLinear and conformational B cell epitopesMany alleles

Links to linksIEDBs Linkshttp://immuneepitope.org/hyperlinks.do?dispatch=loadLinks

Epitope Discovery

PathogenBindELISPOTInfluenzaXX W HildebrandVariola major (smallpox) vaccineXX R Koup, S JoyceYersinia pestisXFrancisella tularensis (tularemia)X(X) A Sjostedt LCMXLassa FeverX(x) A Edelstein, J BottonHantaan virus (Korean hemorrhagic fever virus)X(x) A Edelstein, J BottonRift Valley FeverXDengueX(X) E MarquesEbolaXMarburgXMulti-drug resistant TB (BCG vaccine)XXYellow feverX(X) T AugustTyphus fever (Rickettsia prowazekii)X(x) S MiguelWest Nile VirusX(X) P Norris

b2mHeavy chainpeptideDetermination of peptide-HLA bindingStep I: Folding of MHC class I molecules in solutionStep II: Detection of de novo folded MHC class I molecules by ELISAC Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8

HLA Binding Results1215 peptides received1114 tested for binding 827 (74%) bind with KD better than 500nM484 (43%) bind with KD better han 50 nMKD\PathogenInfluenzaMarburgPoxF. tularensisDengueHantaanLassaWest NileYellow FeverKD

ELISPOT assayMeasure number of white blood cells that in vitro produce interferon-g in response to a peptideA positive result means that the immune system has earlier reacted to the peptide (during a response to a vaccine/natural infection)SLFNTVATLSLFNTVATLSLFNTVATLSLFNTVATLSLFNTVATLSLFNTVATLTwo spots

Influenza Peptides positive in ELISPOTMingjun Wang et al., submitted

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

Genome Projects -> Systems BiologyGenome projectsCreate list of componentsSequence genomesFind genesSystems BiologyFind out how these components play togetherNetworks of interactionsSimulation of systemsOver timeIn 3D space

Simulation of the Immune system

ExampleCTL escape mutant dynamics during HIV infectionIlka Hoof and Nicolas Rapin

Flowchart - interactionsNicolas Rapin et al., Journal of Biological Physics, In press

Mathematical modelNicolas Rapin

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

From one to many virus strains

Nicolas RapinSimulation 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 LundegaardData bases, HLA bindingMorten NielsenHLA bindingJean Vennestrm2D proteomicsThomas Blicher (50%)MHC structureMette Voldby LarsenPhd student - CTL predictionPernille Haste AndersenPhD student StructureSune FrankildPhD student - DatabasesSheila Tuyet TangPox/TBThomas Rask (50%)EvolutionIlka Hoof and Nicolas RapinSimulation of the immune systemHao ZhangProtein potentialsCollaboratorsIMMI, University of CopenhagenSren BuusMHC bindingMogens H ClaessonElispot AssayLa Jolla Institute of Allergy and Infectious DiseasesAllesandro SetteEpitope databaseBjoern PetersLeiden University Medical CenterTom OttenhoffTuberculosisMichel KleinGanymedUgur SahinGenetic libraryUniversity of TubingenStefan StevanovicMHC ligandsINSERMPeter van EndertTap bindingUniversity of MainzHansjrg SchildProteasomeSchafer-NielsenClaus Schafer-NielsenPeptide synthesisImmunoGridElda Rossi&Simulation of thePartnersImmune systemUniversity of UtrecthtCan KesmirIdeas

I = infected cells (CD4+ T cell)V = free HIV virusE= immune response cells (CD8+ Tcell) T = target cells (CD4+ t cells)

Competitive killing of I by E at rate kCompetitive activation of E by I at rate alpha