Immunological feature predictions and databases on the web Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark

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

Pathogenic Viruses Data derived from / 1st column: log10 of the number of deaths caused by the pathogen per year 2nd column: DNA Advisory Committee (RAC) classification DNA 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 into four 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 classification classification 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 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, (2004).

Expression of HLA is codominant

Polymorphism and polygeny

The MHC gene region

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

HLA variability

Logos of HLA-A alleles O Lund et al., Immunogenetics :

Clustering of HLA alleles O Lund et al., Immunogenetics :

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 dbMHC Clinical data and sequences related to the immune system Anthony Nolan Database

Epitope Databases Used to find regions that can be recognized by the immune system General Epitope Databases IEDB General epitope database AntiJen (MHC Ligand, TCR-MHC Complexes, T Cell Epitope, TAP, B Cell Epitope molecules and immunological Protein-Protein interactions) FIMM (MHC, antigens, epitopes, and diseases)

More Epitope Databases SYFPEITHI Natural ligands: sequences of peptides eluded from MHC molecules on the surface of cells MHCBN: Immune related databases and predictors 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: Linkshttp://immuneepitope.org/hyperlinks.do?dispatch=load Links Recent benchmark: al_allele.htmlhttp://mhcbindingpredictions.immuneepitope.org/intern al_allele.html

B cell Epitope Databases Linear IEDB, Bcipep, Jenner, FIMM, BepiPred HIV specific database Conformational CED: Conformational B cell epitopes

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, , 1995, –PROPRED: Singh H, Raghava GP Bioinformatics 2001 Dec;17(12): Web interface

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 :

Linear B cell Epitope Predictors Continuous (Linear) epitopes IEDB Bcepred Bepipred Recent Benchmarking Publications Benchmarking B cell epitope prediction: Underperformance of existing methods. Blythe MJ, Flower DR. Protein Sci : 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 Jan 5

Discontinuous B cell Epitope Predictors Discontinuous (conformational) epitopes 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: , 2006

Pathogen Centered Databases HIV Influenza Tuberculosis POX

Reviews Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform Oct 31 Web based Tools for Vaccine Design (Lund et al, 2002) webreview.htmlhttp:// 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 Links

PathogenBindELISPOT InfluenzaXX W Hildebrand Variola major (smallpox) vaccineXX R Koup, S Joyce Yersinia pestisX Francisella tularensis (tularemia)X(X) A Sjostedt LCMX Lassa FeverX(x) A Edelstein, J Botton Hantaan virus (Korean hemorrhagic fever virus)X(x) A Edelstein, J Botton Rift Valley FeverX DengueX(X) E Marques EbolaX MarburgX Multi-drug resistant TB (BCG vaccine)XX Yellow feverX(X) T August Typhus fever (Rickettsia prowazekii)X(x) S Miguel West Nile VirusX(X) P Norris Epitope Discovery

Development 2m2m2m2m Heavy chain peptide Incubation Peptide-MHC complex Determination of peptide-HLA binding Step I: Folding of MHC class I molecules in solution Step II: Detection of de novo folded MHC class I molecules by ELISA C Sylvester-Hvid et al., Tissue Antigens :251-8

HLA Binding Results 1215 peptides received 1114 tested for binding 827 (74%) bind with K D better than 500nM 484 (43%) bind with K D better han 50 nM KD\PathogenInfluenzaMarburgPoxF. tularensisDengueHantaanLassaWest NileYellow Fever K D < <K D < K D > in progress Total 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 Two spots

Influenza Peptides positive in ELISPOT Mingjun Wang et al., submitted

Peters B, et al. Immunogenetics :326-36, PLoS Biol :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 Nicolas Rapin

f values from sequence Sequence f value SLYNTVATL 1 SAYNTVATL SAYNTVATC SAFNTVATC SAINTVATC VAINTVATC VAINTHATC VAINEHATC VAICEHATC VAICEPATC

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

Acknowledgements Immunological Bioinformatics group, CBS, Technical University of Denmark ( 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 BuusMHC binding Mogens H ClaessonElispot Assay La Jolla Institute of Allergy and Infectious Diseases Allesandro SetteEpitope database Bjoern Peters Leiden University Medical Center Tom OttenhoffTuberculosis Michel Klein Ganymed Ugur SahinGenetic library University of Tubingen Stefan StevanovicMHC ligands INSERM Peter van EndertTap binding University of Mainz Hansjörg SchildProteasome Schafer-Nielsen Claus Schafer-NielsenPeptide synthesis ImmunoGrid Elda Rossi&Simulation of the PartnersImmune system University of Utrectht Can KesmirIdeas