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Immunological Bioinformatics
Ole Lund
Center for Biological Sequence Analysis
BioCentrum-DTU
Technical University of Denmark
[email protected]
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
•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)).
Adapted from Immunological Bioinformatics, The MIT press.
Data derived from /www.cbs.dtu.dk/databases/Dodo.
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.
Biodefence
Targets
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
Haemophilus influenzae
Streptococcus
pneumoniae
and
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)
YMNGTMSQV
GILGFVFTL
ALWGFFPVV
ILKEPVHGV
ILGFVFTLT
LLFGYPVYV
GLSPTVWLS
WLSLLVPFV
FLPSDFFPS
CVGGLLTMV
FIAGNSAYE
A2 Logo
G
F
C
A
Lauemøller et al., 2000
The MHC gene region
From Bill Paul, ”Fundamental Immunology”, 4th Ed
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)
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
HLA polymorphism - frequencies
Supertypes
Phenotype frequencies
Caucasian
Black
Japanese
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 %
A Sette et al, Immunogenetics (1999) 50:201-212
Chinese Hispanic
Average
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
Linker
NH2 M
Epitope
COOH
C-terminal cleavage
Cleavage within epitopes
cleavage
New epitopes
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 SARS
Status as of July 11, 2003: 8437 Infected, 813 Dead
New corona viruses
1978 Porcine Epidemic diarrhea virus (PEDV)
Probably from humans
1984 Porcine Respiratory Coronavirus
1987 Porcine Reproductive and Respiratory
Syndrome (PRRS)
1993 Bovine corona virus
2003 SARS
Michael Buchmeier, Beijing June, 2003
Epitope predictions
Binding to MHC class I
High probability for C-terminal proteasomal
cleavage
No sequence variation
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
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
b2m
Heavy chain
peptide
Incubation
Peptide-MHC
complex
Step II: Detection of de novo folded MHC class I molecules by ELISA
Development
C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8
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
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 crucial
Gibbs Monte Carlo
sampler
M Nielsen et al., Bioinformatics. 2004 20:1388-97
RFFGGDRGAPKRG
YLDPLIRGLLARPAKLQV
KPGQPPRLLIYDASNRATGIPA
GSLFVYNITTNKYKAFLDKQ
SALLSSDITASVNCAK
PKYVHQNTLKLAT
GFKGEQGPKGEP
DVFKELKVHHANENI
SRYWAIRTRSGGI
TYSTNEIDLQLSQEDGQTIE
Class II binding motif
Alignment by Gibbs sampler
Random
RFFGGDRGAPKRG
YLDPLIRGLLARPAKLQV
KPGQPPRLLIYDASNRATGIPA
GSLFVYNITTNKYKAFLDKQ
SALLSSDITASVNCAK
PKYVHQNTLKLAT
GFKGEQGPKGEP
DVFKELKVHHANENI
SRYWAIRTRSGGI
TYSTNEIDLQLSQEDGQTI
M Nielsen et al., Bioinformatics. 2004 20:1388-97
ClustalW
Gibbs sampler
O Lund et al., Immunogenetics. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
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)
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:
G:
I:
E:
B:
S:
T:
.:
Alpha-helix (hydrogen bond from residue i to residue i+4)
310-helix (hydrogen bond from residue i to residue i+3)
Pi helix (hydrogen bond from residue i to residue i+5)
Extended strand
Beta bridge (one residue short strand)
Bend (five-residue bend centered at residue i)
H-bonded turn (3-turn, 4-turn or 5-turn)
Coil
Amino acids in epitopes
Amino
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
0.07
Amino
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
12
1
-0.47
0.44
-0.58
0.45
0.46
0.00
0.00
-0.73
-0.79
0.00
-0.83
1.42
2
-0.01
-0.12
-1.82
0.52
1.75
0.00
0.00
0.00
1.42
-0.82
0.00
0.00
3
1.82
-2.26
-1.57
0.48
0.10
0.00
-0.77
0.45
1.77
0.00
-0.82
0.99
4
1.76
1.15
-0.34
0.75
0.00
0.00
0.97
0.16
0.38
1.03
0.00
0.00
5
-0.85
0.45
-1.09
0.57
0.00
0.00
0.00
0.13
1.52
0.00
1.02
-0.79
6
0.60
1.28
1.30
1.73
0.00
0.00
0.00
0.00
1.32
-0.89
-0.76
0.00
7
0.27
-0.91
1.67
-0.51
0.00
0.00
0.00
0.00
-1.02
-1.09
0.00
0.00
8
0.93
1.21
-0.23
-3.63
0.49
0.00
0.00
0.00
0.00
-0.19
0.31
-0.82
9
0.00
0.28
-0.67
0.33
0.01
-0.83
0.00
0.00
0.87
0.23
0.00
0.00
10
0.00
0.95
1.71
-0.70
0.00
0.00
0.00
1.29
1.08
0.00
1.00
0.00
11
0.00
0.00
1.02
0.00
0.00
0.00
0.00
0.86
-0.75
0.00
0.00
0.00
12
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/spreadsheet
New 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
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 Buus
MHC binding
Mogens H Claesson
CTL
La Jolla Institute of Allergy and Infectious
Diseases
Allesandro Sette
Epitope DB
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
University of Mainz
Hansjörg Schild
Proteasome
Schafer-Nielsen
Claus Schafer-Nielsen Peptides