Ole Lund

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Information about Ole Lund

Published on October 23, 2007

Author: Lassie



Immunological feature predictions and databases on the web:  Immunological feature predictions and databases on the web Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark Slide2:  Effect of vaccines Vaccines have been made for 36 of >400 human pathogens:  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:  Deaths from infectious diseases in the world in 2002 Pathogenic Viruses:  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 Slide7:  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:  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:  Sequence Databases Used to study sequence variability of microbes Sequence conservation Positive/negative selection Examples Swissprot GenBank MHC Class I pathway:  MHC Class I pathway Figure by Eric A.J. Reits Slide11:  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). Slide12:  Expression of HLA is codominant Slide13:  Polymorphism and polygeny Slide14:  The MHC gene region Slide15:  Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles HLA variability:  HLA variability Logos of HLA-A alleles:    Logos of HLA-A alleles O Lund et al., Immunogenetics. 2004 55:797-810 Clustering of HLA alleles:    Clustering of HLA alleles O Lund et al., Immunogenetics. 2004 55:797-810 Databases of Sequences of Proteins of Immune system:  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:  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:  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:  Prediction of HLA binding Many methods available, including: bimas, syfpeithi, Hlaligand, libscore, mapppB, mapppS,mhcpred, netmhc, pepdist, predbalbc, predep, rankpep, svmhc See links at: Recent benchmark: B cell Epitope Databases:  B cell Epitope Databases Linear IEDB, Bcipep, Jenner, FIMM, BepiPred HIV specific database Conformational CED: Conformational B cell epitopes MHC class II pathway:  MHC class II pathway Figure by Eric A.J. Reits Virtual matrices:  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:  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 MHC class II Supertypes:  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:    Logos of HLA-DR alleles O Lund et al., Immunogenetics. 2004 55:797-810 Slide29:    O Lund et al., Immunogenetics. 2004 55:797-810 Linear B cell Epitope Predictors:  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. 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 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, 15:2558-2567, 2006 Pathogen Centered Databases:  Pathogen Centered Databases HIV Influenza Tuberculosis POX Reviews:  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) Other Resources:  Other Resources Gene expression data Localization prediction SignalP Other BioTools at CBS:  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:  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:  Links to links IEDB’s Links Slide38:  Epitope Discovery Slide39:  b2m Heavy chain peptide 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. 2002 59:251-8 HLA Binding Results:  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 Fever KD<50 42 45 97 45 67 59 27 52 50 50<KD<500 63 39 42 21 44 20 21 41 52 KD>500 87 29 38 6 30 11 22 29 35 in progress 9 1 1 4 6 4 12 31 33 Total 201 114 178 76 147 94 82 153 170 Søren Buus Lab ELISPOT assay:  ELISPOT assay Measure number of white blood cells that in vitro produce interferon-g 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 SLFNTVATL SLFNTVATL Two spots Influenza Peptides positive in ELISPOT:  Influenza Peptides positive in ELISPOT Mingjun Wang et al., submitted Slide43:  Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3:e91. Genome Projects -> Systems Biology:  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:  Simulation of the Immune system Example:  Example CTL escape mutant dynamics during HIV infection Ilka Hoof and Nicolas Rapin Slide47:  Flowchart - interactions Nicolas Rapin et al., Journal of Biological Physics, In press Mathematical model:  Mathematical model Nicolas Rapin f values from sequence:  f values from sequence Sequence f value -------------------- SLYNTVATL 1 SAYNTVATL 0.95283 SAYNTVATC 0.90566 SAFNTVATC 0.86792 SAINTVATC 0.83019 VAINTVATC 0.77358 VAINTHATC 0.70755 VAINEHATC 0.65094 VAICEHATC 0.56604 VAICEPATC 0.57547 Slide50:  From one to many virus strains Slide51:  Nicolas Rapin Simulation with many viruses HIV evolution tree. Initial virus is SLYNTVATL, that give rise to 6 functional mutants able to replicate. :  HIV evolution tree. Initial virus is SLYNTVATL, that give rise to 6 functional mutants able to replicate. Slide53:  Eleonora Kulberkyte Acknowledgements:  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 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

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