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Immunological feature predictions and
databases on the web
Ole Lund
Center for Biological Sequence Analysis
BioCentrum-DTU
Technical University of Denmark
[email protected]
Effect of vaccines
Vaccines have been
made for 36 of >400
human pathogens
+HPV & Rotavirus
Immunological Bioinformatics, The MIT press.
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: 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
Data derived from /www.cbs.dtu.dk/databases/Dodo.
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 http://expasy.org/sprot/
• GenBank http://www.ncbi.nlm.nih.gov/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, 7052-60 (2004).
Expression of HLA is codominant
Polymorphism and polygeny
The MHC gene region
http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init&user_id=0&probe_id=0&source_id=0&locus_id=0&locus_group=0&proto_id=0&banner=1&kit_id=0&graphview=0
Human Leukocyte antigen (HLA=MHC
in humans) polymorphism - alleles
http://www.anthonynolan.com/HIG/index.html
HLA variability
http://rheumb.bham.ac.uk/teaching/immunology/tutorials/mhc%20polymorphism.jpg
Logos of
HLA-A
alleles
O Lund et al., Immunogenetics. 2004 55:797-810
Clustering of HLA alleles
O Lund et al., Immunogenetics. 2004 55:797-810
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
• http://imgt.cines.fr/
• dbMHC
• Clinical data and sequences related to the immune
system
• http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init
• Anthony Nolan Database
• http://www.anthonynolan.com/HIG/
Epitope Databases
• Used to find regions that can be recognized by the
immune system
• General Epitope Databases
• IEDB General epitope database
• http://immuneepitope.org/home.do
• AntiJen (MHC Ligand, TCR-MHC Complexes, T Cell
Epitope, TAP , B Cell Epitope molecules and
immunological Protein-Protein interactions)
• http://www.jenner.ac.uk/AntiJen/
• FIMM (MHC, antigens, epitopes, and diseases)
• http://research.i2r.a-star.edu.sg/fimm/
More Epitope Databases
• SYFPEITHI
• Natural ligands: sequences of peptides eluded from
MHC molecules on the surface of cells
• http://www.syfpeithi.de/
• MHCBN: Immune related databases and predictors
• http://www.imtech.res.in/raghava/mhcbn/
• http://bioinformatics.uams.edu/mirror/mhcbn/
• 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:
• http://immuneepitope.org/hyperlinks.do?dispatch=load
Links
• Recent benchmark:
• http://mhcbindingpredictions.immuneepitope.org/intern
al_allele.html
B cell Epitope Databases
• Linear
• IEDB, Bcipep, Jenner, FIMM, BepiPred
• HIV specific database
• http://www.hiv.lanl.gov/content/immunology/ab_search
• Conformational
• CED: Conformational B cell epitopes
• http://web.kuicr.kyoto-u.ac.jp/~ced/
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, 263-269, 1995,
– PROPRED: Singh H, Raghava GP Bioinformatics 2001 Dec;17(12):1236-7
Web interface
http://www.imtech.res.in/raghava/propred
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. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
Linear B cell Epitope Predictors
•
Continuous (Linear) epitopes
•
IEDB
•
•
Bcepred
•
•
http://tools.immuneepitope.org/tools/bcell/iedb_input
www.imtech.res.in/raghava/btxpred/link.html
Bepipred
•
http://www.cbs.dtu.dk/services/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 (conformational) epitopes
• DiscoTope
• http://www.cbs.dtu.dk/services/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
• HIV
• http://www.hiv.lanl.gov/content/index
• Influenza
• http://www.flu.lanl.gov/
• Tuberculosis
• http://www.sanger.ac.uk/Projects/M_tuberculosis/
• POX
• http://www.poxvirus.org/
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)
• http://www.cbs.dtu.dk/researchgroups/immunology/
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
• http://immuneepitope.org/hyperlinks.do?dispatch=load
Links
Epitope Discovery
Pathogen
Bind ELISPOT
Influenza
X
X W Hildebrand
Variola major (smallpox) vaccine
X
X R Koup, S Joyce
Yersinia pestis
X
Francisella tularensis (tularemia)
X
LCM
X
Lassa Fever
X
(x) A Edelstein, J Botton
Hantaan virus (Korean hemorrhagic fever virus)
X
(x) A Edelstein, J Botton
Rift Valley Fever
X
Dengue
X
Ebola
X
Marburg
X
Multi-drug resistant TB (BCG vaccine)
X
X
Yellow fever
X
(X) T August
Typhus fever (Rickettsia prowazekii)
X
(x) S Miguel
West Nile Virus
X
(X) P Norris
(X) A Sjostedt
(X) E Marques
Determination of peptide-HLA binding
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
HLA Binding Results
KD\Pathogen
KD<50
50<KD<500
KD>500
in progress
Total
•
•
•
•
Influenza
42
63
87
9
201
Marburg
45
39
29
1
114
Pox
97
42
38
1
178
F. tularensis
45
21
6
4
76
Dengue
67
44
30
6
147
Hantaan
59
20
11
4
94
1215 peptides received
1114 tested for binding
827 (74%) bind with KD better than 500nM
484 (43%) bind with KD better han 50 nM
Søren Buus Lab
Lassa
27
21
22
12
82
West Nile
52
41
29
31
153
Yellow Fever
50
52
35
33
170
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
Peptide
Sequence
PB1591-599 VSDGGPNLY
HLA
Restriction
Elispot assay1
KD (nM) + peptide - peptide
Elispot assay2
+ peptide - peptide
HLA-A1
6
18 ± 2
3±3
12 ± 4
1±1
HLA-A1
7
34 ± 5
4±1
13 ± 4
0±0
PB1166-174 FLKDVMESM
HLA-A2
51
74 ± 10
11 ± 6
140 ± 36
20 ± 7
PB141-49
HLA-A26
6
40 ± 3
20 ± 7
38 ± 5
24 ± 3
PB1540-548 GPATAQMAL
HLA-B7
6
7±2
2±1
13 ± 2
6±1
NP225-233
ILKGKFQTA
HLA-B8
664
9±4
1±1
19 ± 7
2±2
PA601-609
SVKEKDMTK
HLA-B8
NB
23 ± 6
1±1
119 ± 8
2±1
PB1349-357 ARLGKGYMF
HLA-B27
246
10 ± 6
1±1
14 ± 4
1±1
NP383-391
SRYWAIRTR
HLA-B27
38
39 ± 6
1±1
40 ± 6
2±1
M1173-181
IRHENRMVL
HLA-B39
13
14 ± 3
3±1
84 ± 11
3±1
NP199-207
RGINDRNFW
HLA-B58
42
28 ± 5
1±1
15 ± 6
2±2
PB1347-355 KMARLGKGY HLA-B62
PB1566-574 TQIQTRRSF HLA-B62
178
77 ± 20
3±2
91 ± 8
10 ± 3
88
15 ± 5
2±2
21 ± 2
2±0
NP44-52
CTELKLSDY
DTVNRTHQY
Mingjun Wang et al., submitted
Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3: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
dI
kEI
dt  f b V T  I I  h  E  I

dV  b  I  cV
I
 dt

dE   E I   E
 dt km  E  I E
dT
    f b V T  T T
dt

Nicolas Rapin
f values from sequence
9
Sm   B Ai ,i 
i 1
Sm  p
fm 
Sw  p
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
From one to many virus strains
Simulation with many viruses
Nicolas Rapin
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 (www.cbs.dtu.dk)
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