Immunological Bioinformatics

Ole LundCenter for Biological Sequence Analysis

BioCentrum-DTUTechnical University of Denmark

lund@cbs.dtu.dk

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

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

Deaths from infectious diseases in the world in 2002

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

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 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)

2nd columnclassification 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 columnA 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)).

Pathogenic Bacteria

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

Pathogenic Parasites

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

Pathogenic Fungi

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

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.

BiodefenceTargets

www2.niaid.nih.gov/Biodefense/bandc_priority.htm

How does the immune system “see” a virus?

 

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

MHC Class I pathway

Figure by Eric A.J. Reits

Genomes to vaccines

Lauemøller et al., 2000

Vaccination

•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

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.

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

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

Human Vaccines against pathogens

Immunological Bioinformatics, The MIT press.

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

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

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

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

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

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

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)

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

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])

MHC Class I pathway

Figure by Eric A.J. Reits

Weight matrices (Hidden Markov models)

YMNGTMSQVGILGFVFTLALWGFFPVVILKEPVHGVILGFVFTLTLLFGYPVYVGLSPTVWLSWLSLLVPFVFLPSDFFPSCVGGLLTMVFIAGNSAYE

A2 Logo

A

FC

G

Lauemøller et al., 2000

From Bill Paul, ”Fundamental Immunology”, 4th Ed

The MHC gene region

Human LeuHuman Leukkocyte antigen ocyte antigen (HLA=(HLA=MHC in humansMHC in humans) ) polymorphism - allelespolymorphism - alleles

A total of229 HLA-A464 HLA-B111 HLA-C

class I alleles have been named,a total of

2 HLA-DRA, 364 HLA-DRB22 HLA-DQA1, 48 HLA-DQB120 HLA-DPA1, 96 HLA-DPB1

class II sequences have also been assigned.As of October 2001 (http://www.anthonynolan.com/HIG/index.html)

HLA polymorphism HLA polymorphism - supertypes- 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

Supertypes Phenotype frequenciesCaucasian Black Japanese ChineseHispanicAverage

A2,A3, B27 83 % 86 % 88 % 88 % 86 % 86%

+A1, A24, B44 100 % 98 % 100 % 100 % 99 % 99 %

+B7, B58, B62 100 % 100 % 100 % 100 % 100 % 100 %

HLA polymorphism - frequencies

A Sette et al, Immunogenetics (1999) 50:201-212

 

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

 

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

 

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

 

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

 

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

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

Proteasomal cleavage

Polytope construction

NH2 COOH

Epitope

Linker

M

C-terminal cleavage

Cleavage within epitopesNew epitopes

cleavage

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.

Polytope starting configuration

Immunological Bioinformatics, The MIT press.

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]

Polytope final configuation

Immunological Bioinformatics, The MIT press.

World-wide Spread of SARSStatus as of July 11, 2003: 8437 Infected, 813  Dead

New corona viruses

1978 Porcine Epidemic diarrhea virus (PEDV)

Probably from humans1984 Porcine Respiratory Coronavirus1987 Porcine Reproductive and

Respiratory Syndrome (PRRS)1993 Bovine corona virus2003 SARS

 

Michael Buchmeier, Beijing June, 2003

Epitope predictions

Binding to MHC class IHigh probability for C-terminal

proteasomal cleavageNo sequence variation

Inside out:1. Position in RNA2. Translated regions (blue)3. Observed variable spots4. Predicted proteasomal cleavage5. Predicted A1 epitopes6. Predicted A*0204 epitopes7. Predicted A*1101 epitopes8. Predicted A24 epitopes9. Predicted B7 epitopes10. Predicted B27 epitopes11. Predicted B44 epitopes12. Predicted B58 epitopes13. Predicted B62 epitopes

DevelopmentDevelopment

22mmHeavy chainHeavy chain

peptidepeptide IncubationIncubationPeptide-MHC Peptide-MHC complexcomplex

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 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

SARS projectSARS projectWe 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

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

Prediction Results

Virtual matrices

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

MHC class II prediction

Complexity of problem– Peptides of different

length– Weak motif signal

Alignment crucialGibbs Monte Carlo

sampler

RFFGGDRGAPKRGYLDPLIRGLLARPAKLQVKPGQPPRLLIYDASNRATGIPAGSLFVYNITTNKYKAFLDKQSALLSSDITASVNCAKPKYVHQNTLKLATGFKGEQGPKGEPDVFKELKVHHANENISRYWAIRTRSGGITYSTNEIDLQLSQEDGQTIE

M Nielsen et al., Bioinformatics. 2004 20:1388-97

Class II binding motif

RFFGGDRGAPKRG YLDPLIRGLLARPAKLQVKPGQPPRLLIYDASNRATGIPA GSLFVYNITTNKYKAFLDKQ SALLSSDITASVNCAK PKYVHQNTLKLAT GFKGEQGPKGEP DVFKELKVHHANENI SRYWAIRTRSGGI TYSTNEIDLQLSQEDGQTI

Random ClustalW

Gibbs sampler

Alignment by Gibbs sampler

M Nielsen et al., Bioinformatics. 2004 20:1388-97

 

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

 

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

Prediction of Antibody epitopesLinear

– 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)

Secondary structure in epitopes

Sec struct: H T B E S G I .

Log odds ratio -0.19 0.30 0.21 -0.27 0.24 -0.04 0.00 0.17

H: Alpha-helix (hydrogen bond from residue i to residue i+4)G: 310-helix (hydrogen bond from residue i to residue i+3)I: Pi helix (hydrogen bond from residue i to residue i+5)E: Extended strandB: 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

Amino acids in epitopes

Amino Acid G A V L I M P F W S

e/E 0.09 0.07 0.05 0.08 0.04 0.02 0.06 0.03 0.01 0.08. 0.07 0.08 0.07 0.10 0.06 0.03 0.05 0.05 0.02 0.07Amino acid C T Q N H Y E D K R

e/E 0.03 0.08 0.04 0.04 0.02 0.04 0.06 0.07 0.07 0.04. 0.03 0.06 0.04 0.05 0.02 0.03 0.04 0.04 0.05 0.04

Dihedral angles in epitopes

Z-scores for number of dihedral angle combinations in epitopes vs. non epitopes

Phi\Psi 1 2 3 4 5 6 7 8 9 10 11 121 -0.47 0.44 -0.58 0.45 0.46 0.00 0.00 -0.73 -0.79 0.00 -0.83 1.422 -0.01 -0.12 -1.82 0.52 1.75 0.00 0.00 0.00 1.42 -0.82 0.00 0.003 1.82 -2.26 -1.57 0.48 0.10 0.00 -0.77 0.45 1.77 0.00 -0.82 0.994 1.76 1.15 -0.34 0.75 0.00 0.00 0.97 0.16 0.38 1.03 0.00 0.005 -0.85 0.45 -1.09 0.57 0.00 0.00 0.00 0.13 1.52 0.00 1.02 -0.796 0.60 1.28 1.30 1.73 0.00 0.00 0.00 0.00 1.32 -0.89 -0.76 0.007 0.27 -0.91 1.67 -0.51 0.00 0.00 0.00 0.00 -1.02 -1.09 0.00 0.008 0.93 1.21 -0.23 -3.63 0.49 0.00 0.00 0.00 0.00 -0.19 0.31 -0.829 0.00 0.28 -0.67 0.33 0.01 -0.83 0.00 0.00 0.87 0.23 0.00 0.0010 0.00 0.95 1.71 -0.70 0.00 0.00 0.00 1.29 1.08 0.00 1.00 0.0011 0.00 0.00 1.02 0.00 0.00 0.00 0.00 0.86 -0.75 0.00 0.00 0.0012 0.42 0.83 0.28 1.68 0.00 0.00 0.00 0.00 1.03 -0.21 -0.79 0.93

Immunological bioinformatics

Classical experimental research– Few data points– Data recorded by pencil and

paper/spreadsheetNew experimental methods

– Sequencing– DNA arrays– Proteomics

Need to develop new methods for handling these large data sets

• Immunological Bioinformatics/Immunoinformatics

 

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.pdf

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

Morten NielsenHLA binding

Claus LundegaardData bases, HLA

bindingAnne Mølgaard

MHC bindingMette Voldby Larsen

CTL predictionPernille Haste Andersen

B cell epitopesSune Frankild

DatabasesJens Pontoppidan

Linear B cell epitopes

CollaboratorsIMMI, University of Copenhagen

Søren Buus MHC bindingMogens H Claesson CTL

La Jolla Institute of Allergy and Infectious Diseases

Allesandro Sette Epitope DBLeiden University Medical Center

Tom Ottenhoff TuberculosisMichel Klein

GanymedUgur Sahin Genetic library

University of TubingenStefan Stevanovic MHC ligands

INSERMPeter van Endert Tap

University of MainzHansjörg Schild Proteasome

Schafer-NielsenClaus Schafer-Nielsen

Peptides