1. Immunological Bioinformatics Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark lund@cbs.dtu.dk

2. Infectious Diseases More than 400 microbial agents are associated with disease in healthy adult humans There are only licensed vaccines in the United states for 22 microbial agents (vaccines for 34 pathogens have been developed) Immunological Bioinformatics may be used to Identify immunogenic regions in pathogen These regions may be used as in rational vaccine design Which pathogens to focus on? Infectious diseases may be ranked based on Impact on health Dangerousness Economic impact

3. Infectious Diseases in the World 11 million (19%) of the 57 million people who died in the world in 2002 were killed by infectious or parasitic infection [WHO, 2004] The three main single infectious diseases are HIV/AIDS, tuberculosis, and malaria, each of which causes more than 1 million deaths

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

5. Pathogenic Viruses Adapted from Immunological Bioinformatics, The MIT press. Data derived from /www.cbs.dtu.dk/databases/Dodo. 1st column (and color of name) 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) 2nd column 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 3rd column 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)).

6. Pathogenic Bacteria Adapted from Immunological Bioinformatics, The MIT press. Data derived from www.cbs.dtu.dk/databases/Dodo.

7. Pathogenic Parasites Adapted from Immunological Bioinformatics, The MIT press. Data derived from www.cbs.dtu.dk/databases/Dodo.

8. Pathogenic Fungi Adapted from Immunological Bioinformatics, The MIT press. Data derived from www.cbs.dtu.dk/databases/Dodo.

9. Vaccines Market The vaccine market has increased fivefold from 1990 to 2000 Annual sales of 6 billion euros Less than 2% of the total pharma market. Major producers (85% of the market) GlaxoSmithKline (GSK), Merck, Aventis Pasteur, Wyeth, Chiron Main products (>50% of the market) Hepatitis B, flu, MMR (measles, mumps, and rubella) and DTP (diphtheria, tetanus, pertussis) 40% are produced in the United States and the rest is evenly split between Europe and the rest of the world [Gréco, 2002] It currently costs between 200 and 500 million US dollars to bring a new vaccine from the concept stage to market [André, 2002] Figure by Thomas Blicher.

10. Biodefence Targets www2.niaid.nih.gov/Biodefense/ bandc_priority.htm

11. How does the immune system “see” a virus?

12. The immune system The innate immune system –Found in animals and plants –Fast response –Complement, Toll like receptors The adaptive Immune system –Found in vertebrates –Stronger response 2nd time –B lymphocytes Produce antibodies (Abs) recognizes 3D shapes Neutralize virus/bacteria outside cells –T lymphocytes Cytotoxic T lymphocytes (CTLs) - MHC class I –Recognize foreign protein sequences in infected cells –Kill infected cells Helper T lymphocytes (HTLs) - MHC class II –Recognize foreign protein sequences presented by immune cells –Activates cells

13. MHC Class I pathway Figure by Eric A.J. Reits

14. Genomes to vaccines Lauemøller et al., 2000

15. Vaccination Administration of a substance to a person with the purpose of preventing a disease Traditionally composed of a killed or weakened microorganism Vaccination works by creating a type of immune response that enables the memory cells to later respond to a similar organism before it can cause disease

16. Early History of Vaccination Pioneered India and China in the 17th century The tradition of vaccination may have originated in India in AD 1000 Powdered scabs from people infected with smallpox was used to protect against the disease Smallpox was responsible for 8 to 20% of all deaths in several European countries in the 18th century In 1721 Lady Mary Wortley Montagu brought the knowledge of these techniques from Constantinople (now Istanbul) to England Two to three percent of the smallpox vaccinees, however, died from the vaccination itself Benjamin Jesty and, later, Edward Jenner could show that vaccination with the less dangerous cowpox could protect against infection with smallpox The word vaccination, which is derived from vacca, the Latin word for cow.

17. Early History of Vaccination II In 1879 Louis Pasteur showed that chicken cholera weakened by growing it in the laboratory could protect against infection with more virulent strains 1881 he showed in a public experiment at Pouilly-Le-Fort that his anthrax vaccine was efficient in protecting sheep, a goat, and cows. In 1885 Pasteur developed a vaccine against rabies based on a live attenuated virus A year later Edmund Salmon and Theobald Smith developed a (heat) killed cholera vaccine. Over the next 20 years killed typhoid and plague vaccines were developed In 1927 the bacille Calmette-Guérin (BCG vaccine) against tuberculosis vere developed

18. Vaccination since WW II After the Second World War the ability to make cell cultures, i.e., the ability to grow cells from higher organisms such as vertebrates in the laboratory, made it easier to develop new vaccines, and the number of pathogens for which vaccines can be made have almost doubled. Many vaccines were grown in chicken embryo cells (from eggs), and even today many vaccines such as the influenza vaccine, are still produced in eggs Alternatives are being investigated

19. Human Vaccines against pathogens Immunological Bioinformatics, The MIT press.

20. Vaccination Today Vaccines have been made for only 34 of the more than 400 known pathogens that are harmful to man (<10%). Immunization saves the lives of 3 million children each year, but that 2 million more lives could be saved if existing vaccines were applied on a full-scale worldwide

21. Categories of Vaccines Live vaccines Are able to replicate in the host but are attenuated (weakened), i.e., they do not cause disease Subunit vaccines Part of organism Genetic Vaccines

22. Live Vaccines Characteristics Able to replicate in the host Attenuated (weakened) so they do not cause disease Advantages Induce a broad immune response (cellular and humoral) Low doses of vaccine are normally sufficient Long-lasting protection are often induced Disadvantages May cause adverse reactions May be transmitted from person to person

23. Subunit Vaccines Relatively easy to produce (not live) Induce little CTL (viral and bacterial proteins are not produced within cells) Classically produced by inactivating a whole virus or bacterium by heat or by chemicals The vaccine may be purified further by selecting one or a few proteins which confer protection This has been done for the Bordetella pertussis vaccine to create a better-tolerated vaccine that is free from whole microorganism cells

24. Subunit Vaccines: Polysaccharides Polysaccharides Many bacteria have polysaccharides in their outer membrane Basis of vaccines against Neisseria meningitidis and Streptococcus pneumoniae. Generate a T cell-independent response making them inefficient in children younger than 2 years old. Overcome by conjugating the polysaccharides to peptides Used in vaccines against Streptococcus pneumoniae and Haemophilus influenzae

25. Subunit Vaccines: Toxoids Toxins Responsible for the pathogenesis of many bacteria. Vaccines based on inactivated toxins (toxoids) have been developed for Bordetella pertussis, Clostridium tetani, and Corynebacterium diphtheriae Traditionally done by chemical means but now also by altering the DNA sequences important toxicity

26. Subunit Vaccines: Recombinant The hepatitis B virus (HBV) vaccine was originally based on the surface antigen purified from the blood of chronically infected individuals. Due to safety concerns, the HBV vaccine became the first to be produced using recombinant DNA technology It is now produced in bakers’ yeast (Saccharomyces cerevisiae) Recombinant technologies can also be used to produce viral proteins that self-assemble to viral-like particles (VLPs) with the same size as the native virus. VLP is the basis of a promising new vaccine against human papilloma virus (HPV)

27. Genetic Vaccines Introduce DNA or RNA into the host Injected (Naked) Coated on gold particles Carried by viruses such as vaccinia, adenovirus, or alphaviruses, and bacteria such as Salmonella typhi or Mycobacterium tuberculosis Advantages Easy to produce Induce cellular response Disadvantages Low response in 1st generation

28. Epitope based vaccines Advantages( Ishioka et al. [1999]) : Can be more potent Can be controlled better Can induce subdominant epitopes (e.g. against tumor antigens where there is tolerance against dominant epitopes) Can target multiple conserved epitopes in rapidly mutating pathogens like HIV and Hepatitis C virus (HCV) Can be designed to break tolerance Can overcome safety concerns associated with entire organisms or proteins Epitope-based vaccines have been shown to confer protection in animal models ([Snyder et al., 2004], Rodriguez et al. [1998] and Sette and Sidney [1999])

29. MHC Class I pathway Figure by Eric A.J. Reits

30. Weight matrices (Hidden Markov models) YMNGTMSQV GILGFVFTL ALWGFFPVV ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CVGGLLTMV FIAGNSAYE A2 Logo

31. A F C G Lauemøller et al., 2000

32. From Bill Paul, ”Fundamental Immunology”, 4th Ed The MHC gene region

33. Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles A total of 229 HLA-A 464 HLA-B 111 HLA-C class I alleles have been named, a total of 2 HLA-DRA, 364 HLA-DRB 22 HLA-DQA1, 48 HLA-DQB1 20 HLA-DPA1, 96 HLA-DPB1 class II sequences have also been assigned. As of October 2001 (http://www.anthonynolan.com/HIG/index.html)

34. HLA polymorphism - supertypes Each HLA molecule within a supertype essentially binds the same peptides Nine major HLA class I supertypes have been defined HLA-A1, A2, A3, A24,B7, B27, B44, B58, B62 Sette et al, Immunogenetics (1999) 50:201-212

35. SupertypesPhenotype frequencies CaucasianBlackJapaneseChineseHispanicAverage A2,A3, B2783 %86 %88 %88 %86 %86% +A1, A24, B44100 %98 %100 %100 %99 %99 % +B7, B58, B62100 %100 %100 %100 %100 %100 % HLA polymorphism - frequencies A Sette et al, Immunogenetics (1999) 50:201-212

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

41. Conclusions We suggest to –Split some of the alleles in the A1 supertype into a new A26 supertype –Split some of the alleles in the B27 supertype into a new B39 supertype. –The B8 alleles may define their own supertype –The specificities of the class II molecules can be clustered into nine classes, which only partly correspond to the serological classification O Lund et al., Immunogenetics. 2004 55:797-810

42. Proteasomal cleavage

44. Polytope construction NH2 COOH Epitope Linker M C-terminal cleavage Cleavage within epitopes New epitopes cleavage

45. Polytope optimization Successful immunization can be obtained only if the epitopes encoded by the polytope are correctly processed and presented. Cleavage by the proteasome in the cytosol, translocation into the ER by the TAP complex, as well as binding to MHC class I should be taken into account in an integrative manner. The design of a polytope can be done in an effective way by modifying the sequential order of the different epitopes, and by inserting specific amino acids that will favor optimal cleavage and transport by the TAP complex, as linkers between the epitopes.

46. Polytope starting configuration Immunological Bioinformatics, The MIT press.

47. Polytope optimization Algorithm Optimization of of four measures: 1.The number of poor C-terminal cleavage sites of epitopes (predicted cleavage < 0.9) 2.The number of internal cleavage sites (within epitope cleavages with a prediction larger than the predicted C-terminal cleavage) 3.The number of new epitopes (number of processed and presented epitopes in the fusing regions spanning the epitopes) 4.The length of the linker region inserted between epitopes. The optimization seeks to minimize the above four terms by use of Monte Carlo Metropolis simulations [Metropolis et al., 1953]

48. Polytope final configuation Immunological Bioinformatics, The MIT press.

49. World-wide Spread of SARS Status as of July 11, 2003: 8437 Infected, 813 Dead

50. New corona viruses 1978Porcine Epidemic diarrhea virus (PEDV) Probably from humans 1984Porcine Respiratory Coronavirus 1987Porcine Reproductive and Respiratory Syndrome (PRRS) 1993Bovine corona virus 2003SARS Michael Buchmeier, Beijing June, 2003

51. Epitope predictions Binding to MHC class I High probability for C-terminal proteasomal cleavage No sequence variation

53. Inside out: 1.Position in RNA 2.Translated regions (blue) 3.Observed variable spots 4.Predicted proteasomal cleavage 5.Predicted A1 epitopes 6.Predicted A*0204 epitopes 7.Predicted A*1101 epitopes 8.Predicted A24 epitopes 9.Predicted B7 epitopes 10.Predicted B27 epitopes 11.Predicted B44 epitopes 12.Predicted B58 epitopes 13.Predicted B62 epitopes

54. Development 2m2m2m2m Heavy chain peptide Incubation Peptide-MHC complex Strategy for the quantitative ELISA assay C. Sylvester-Hvid, et al., Tissue antigens, 2002: 59:251 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

55. SARS project We scanned HLA supertypes and identified almost 100 potential vaccine candidates. These should be further validated in SARS survivors and may be used for vaccine formulation. Prediction method available: www.cbs.dtu.dk/services/NetMHC/ C Sylvester-Hvid et al., Tissue Antigens. 2004 63:395-400

56. 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 http://www.imtech.res.in/raghava/propred Prediction Results

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

58. MHC class II prediction Complexity of problem –Peptides of different length –Weak motif signal Alignment crucial Gibbs Monte Carlo sampler RFFGGDRGAPKRG YLDPLIRGLLARPAKLQV KPGQPPRLLIYDASNRATGIPA GSLFVYNITTNKYKAFLDKQ SALLSSDITASVNCAK PKYVHQNTLKLAT GFKGEQGPKGEP DVFKELKVHHANENI SRYWAIRTRSGGI TYSTNEIDLQLSQEDGQTIE M Nielsen et al., Bioinformatics. 2004 20:1388-97

59. Class II binding motif RFFGGDRGAPKRG YLDPLIRGLLARPAKLQV KPGQPPRLLIYDASNRATGIPA GSLFVYNITTNKYKAFLDKQ SALLSSDITASVNCAK PKYVHQNTLKLAT GFKGEQGPKGEP DVFKELKVHHANENI SRYWAIRTRSGGI TYSTNEIDLQLSQEDGQTI Random ClustalW Gibbs sampler Alignment by Gibbs sampler M Nielsen et al., Bioinformatics. 2004 20:1388-97

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

62. Prediction of Antibody epitopes Linear –Hydrophilicity scales (average in ~7 window) Hoop and Woods (1981) Kyte and Doolittle (1982) Parker et al. (1986) –Other scales & combinations Pellequer and van Regenmortel Alix –New improved method (Pontoppidan et al. in preparation) http://www.cbs.dtu.dk/services/BepiPred/ Discontinuous –Protrusion (Novotny, Thornton, 1986)

63. Secondary structure in epitopes Sec struct:HTBESGI. Log odds ratio-0.190.300.21-0.270.24-0.040.000.17 H: Alpha-helix (hydrogen bond from residue i to residue i+4) G: 3 10 -helix (hydrogen bond from residue i to residue i+3) I: Pi helix (hydrogen bond from residue i to residue i+5) E: Extended strand B: Beta bridge (one residue short strand) S:Bend (five-residue bend centered at residue i) T:H-bonded turn (3-turn, 4-turn or 5-turn). : Coil

64. Amino acids in epitopes Amino Acid GAVLIMPFWS e/E 0.09 0.070.050.080.040.020.060.030.010.08.0.070.080.070.100.060.030.05 0.020.07 Amino acid CTQNHYEDKR e/E0.030.080.04 0.020.040.060.07 0.04.0.030.060.040.050.020.030.04 0.050.04

65. Dihedral angles in epitopes Z-scores for number of dihedral angle combinations in epitopes vs. non epitopes Phi\Psi123456789101112 1-0.470.44-0.580.450.460.00 -0.73-0.790.00-0.831.42 2-0.01-0.12-1.820.521.750.00 1.42-0.820.00 31.82-2.26-1.570.480.100.00-0.770.451.770.00-0.820.99 41.761.15-0.340.750.00 0.970.160.381.030.00 5-0.850.45-1.090.570.00 0.131.520.001.02-0.79 60.601.281.301.730.00 1.32-0.89-0.760.00 70.27-0.911.67-0.510.00 -1.02-1.090.00 80.931.21-0.23-3.630.490.00 -0.190.31-0.82 90.000.28-0.670.330.01-0.830.00 0.870.230.00 100.000.951.71-0.700.00 1.291.080.001.000.00 110.00 1.020.00 0.86-0.750.00 120.420.830.281.680.00 1.03-0.21-0.790.93

66. Immunological bioinformatics Classical experimental research –Few data points –Data recorded by pencil and paper/spreadsheet New experimental methods –Sequencing –DNA arrays –Proteomics Need to develop new methods for handling these large data sets Immunological Bioinformatics/Immunoinformatics

67. Links Overview over web based tools for vaccine design HTML version http://www.cbs.dtu.dk/researchgroups/immunology/webreview.html PDF version http://hiv-web.lanl.gov/content/ immunology/pdf/2002/1/Lund2002.pdfhttp://hiv-web.lanl.gov/content/ immunology/pdf/2002/1/Lund2002.pdf

69. Acknowledgements Immunological Bioinformatics group, CBS, Technical University of Denmark (www.cbs.dtu.dk) Morten Nielsen HLA binding Claus Lundegaard Data bases, HLA binding Anne Mølgaard MHC binding Mette Voldby Larsen CTL prediction Pernille Haste Andersen B cell epitopes Sune Frankild Databases Jens Pontoppidan Linear B cell epitopes Collaborators IMMI, University of Copenhagen Søren BuusMHC binding Mogens H ClaessonCTL La Jolla Institute of Allergy and Infectious Diseases Allesandro SetteEpitope DB Leiden University Medical Center Tom Ottenhoff Tuberculosis Michel Klein Ganymed Ugur SahinGenetic library University of Tubingen Stefan Stevanovic MHC ligands INSERM Peter van EndertTap University of Mainz Hansjörg Schild Proteasome Schafer-Nielsen Claus Schafer-NielsenPeptides