Computer-aided design of Tcell epitope vaccines
Pedro Antonio Reche Gallardo, PhD
INDEX
1. T-cell immunology background
•
•
•
•
Adaptive immune system
T cell immune recognition
Thymic selection
Processing
2. Prediction of T-cell epitopes
1. Methods
2. Problems
3. Applications to HIV
The Adaptive immune system
Lymph node
DC
CD8
Skin/Mucosa
CD8
CD8
CD8
CD8
CD8
CD8
DC
B
CD4
cellular immunity
Bonne marrow
IgM
IgG
IgE
Humoral response
B
B
B
B
B
CD4
CD4
IgA
CD4
CD4
CD4
AFC
CD4
CD8
B
CD4
B
T-cell immune biology
 T cells are activated by activated by Dendritic cells
 There are two types of T cells: CD4 T cells and CD8 T cells
 CD8 T cells are able to kill cells invaded by pathogens (viruses) and tumoral
cells. Important for controlling viral infection and tumors
 CD4 T cells (T-helper cells) provide help to both CD8-T-cells and B-cells through
the production of cytokines. They are important for controlling bacterial infections
 What are the molecular basis for T cell recognition/activation?
Background: T-cell immune responses
 T-cells immune responses are triggered by the recognition of foreign peptides in the
context of MHC molecules. The recognition is mediated via the T-cell Receptor (TCR) of the
T-cell
 CD8 and CD4 T cells recognize peptides in the context of two distinct classes of MHC
molecules
CD8 T-cell
TCR
CD8
MHCI
Antigen Presenting Cell (APC)
CD4 T-cell
TCR
CD4
MHCII
APC
Structural recognition of peptide-MHCI by TCR
CD8+ T-CELL
CD4 T-CELL
TCR
TCR
CD4
pMHC
CD8
pMHCI
b2m
APC
CD8 T-cell
TCR
MHCI
APC
CD4 T-cell
TCR
CD8
MHCII
APC
APC
CD4
Structural features of TCR Recognition of pMHCI
complexes
TCR
F
Y
K
A
T
Gp33-41
Db
1
Gp33-41
4
6 7 8
TCR CONTACTS
KAVYNFATC
General pMHCI/TCR interactions
TCR
1
2
3
4
5
6
MHCI
7
8
9
Structural recognition of peptide-MHC
MHCI
MHCII
C
N
TCR
1
2
3
4
5
6
MHCI
7
8
9
1
2
3
T
C
R
4
5
6
7
M
H
C
I
8
9
MHCI- and MHCII- peptide binding modes
MHC I
MHC II
• Peptide extend beyond the limits of the MHCII
binding groove. Variable peptide length (9-22).
a
• Only a core of nine residues bind to the MHCII
grove.
b2m
a
• Peptide is pinned into the MHCI binding groove by
their N- and C-terminal ends.
• Peptide length is fixed (8-11).
• Peptide usually have between 4 anchor residues.
Position is the most important.
• Major contribution to the binding energy comes
from peptide backbone.
• Peptide repertoire is larger than that of MHCI
molecules.
• Peptide usually have between 3 and 4 anchor
residues.
• Peptide anchor residues have a major contribution
to the total binding energy.
• Peptide repertoire is restricted and motifs are easy
to identify.
a
T cells: Priming versus effector function
Pathogen
CD8+
CD4+
Dendritic cell
Antigen Presenting Cell
T-cell activation is based on the recognition of foreign peptides in the context of MHC molecules
Cytotoxic T Lymphocytes (CTL)
CTL effectors function is predicated in
the recognition of pMHC/TCR
Killing
T helper Lymphocytes (Th1/2)
The effector function is
predicated on the production of
cytokines
TCR-pMHC fit and self tolerance of T-cells is adquired
during Thymic selection
Thymocyte selection is mediated by
peptide/MHC/TCR interactions
1.
APC
2.
APC
3.
APC
MHC
Ø
+++
+
Interact.
TCR
Nature Reviews Immunology 4; 278-289 (2004);
T
T
Death
Neglect
Death
– selection
T
Survival
+ selection
ANTIGEN PRESENTATION: OVERVIEW
ER
ER
Golgi
Nucleus
TAP
Nucleus
Ii
Proteasome
Cytosol
ANY CELL
CLASS I MHC
Golgi
Cytosol
DC, MACROPHAGES, B CELL
CLASS II MHC
T cell epitopes
 T cell epitopes are peptides derived from the processing of proteins that are able to
activate T cells in a detectable manner
APC
PEPTIDE
1.
Processed (released) from a protein in vivo
2.
is presented by the MHC molecule in the cell
surface of the APC
3.
peptide-MHC is recognized by T cell, and
recogniztion result in activation of the T cell in a
manner that can be detected
P
MHC
P
MHC-p-TCR
TCR
T CELL
 Identification of T-cell epitopes is important for understanding disease pathogenesis
and for vaccine design
Experimental Identification of T-cell epitopes
is very expensive
 Overlapping peptides: Full length amino acid sequence of a protein is covered by
making 20 mer peptides overlapping 10 residues, and T response is determined
NH3+
P1
20 aa
10 aa
P2
APC
COO-
 Prediction of T-cell epitopes
T-CELL
ACTIVATION
READ OUT
MHCI and MHCII peptide binding
A
B
N
1
2
C
3
T
C
R
4
5
6
7
M
H
C
I
8
C
N
9
1
2
3
T
C
R
4
5
6
7
M
H
C
I
8
9
MHCI and MHCII impose constraints on the type of
peptides they bind
Prediction of MHC-peptide binding using sequence patterns
 Most widely used method: simple ( [AS]-X(2)-[W]-[FTS] )
 Losses information. Pattern matching algorithms are quite rigid
 Generation of motif is manual
Prediction of peptide-MHC-binding using PSSMs
 Peptides that bind to the same MHC are related by sequence similarity and thereby a Position
Specific Scoring Matrix (PSSM) or profile can be applied to the prediction of MCH binding
Db PEPTIDES
MUS10165
MUS1117A
24mdm2
MUS1195E
29Der
MUS117A9
9iavnp
MUS1005C
FQPQNGQFI
FQPQNGCFI
GRPKNGCIV
VNIRNCCYI
FGISNYCQI
YSNENMDAM
ASNENMDAM
ASNENMETM
wij = ln ( fobs / fexp )
PSSM
i +1
i
A V W Y D D V W Y E T E T T
PROTEIN
P1
P2
P3
P4
P5
P6
P7
P8
P9
A
C
D
…
T
1.941 5.635 -4.904 … -1.657
3.065 1.813 -4.667 … -1.106
-1.012 -4.061 -5.640 … -4.712
-2.624 4.497 -0.749 … -0.490
-7.366 -7.593 -4.054 … -1.876
1.209 3.057 -0.825 … 0.054
0.899 6.183 4.968 … 1.723
-0.799 1.723 -5.113 … 2.248
-1.289 1.062 -6.286 … -2.107
V
W
Y
-2.563 5.905 -1.073
-1.006 -5.868 -0.989
1.676 0.703 -0.690
-0.429 4.873 1.732
-8.438 -9.188 -7.853
0.140 2.529 0.895
-0.396 2.951 -4.715
1.433 4.143 4.361
1.069 -3.953 -3.338
Si = 1.941+ -1.006 + 0.703 + 1.732 + -4.054 + -0.825 + -0.396 + 4.143 + -3.338
SCORE <=> SIMILARITY TO ALIGNMENT OF BINDERS <=> FUNCTION (MHC BINDING)
Defining profile motifs from peptides binding to MHC
molecules
EPIMHC
http://bio.dfci.harvard.edu/epimhc/
MHC II
MHC I
MHC sub-setting
MHCIIx
MHCIX
Peptide length
sub-setting
ALN
MHCIX (l)
l (peptide length)
ALN
MHCIIX (9)
Create PSSM
PSSM
MHCIX (l)
PSSM
MHCIIX (9)
meme
EM algorithm
Motif size: 9
Predictions Tests: Results
PSSM are able to predict known T-cell epitopes within their protein
sources among the top scoring peptides.
MHC II
Predicted epitopes (%)
Predicted epitopes (%)
MHCI
120
120
100
100
80
80
60
60
40
40
20
20
0
0.5%
1%
2%
3%
5%
10%
20%
0
0.5%
1%
Threshold (%)
2%
3%
5%
Threshold (%)
Performance on cross-validation
SE (3%)
SP (3%)
AUC
MHCI
89 % ± 8
92% ± 7
0.85 ± 0.09
MHCII
80% ± 9
89% ± 8
0.80 ± 0.1
10%
20%
Prediction of peptide-MHC binding using RANKEP
http://bio.dfci.harvard.edu/Tools/rankpep.html
• Most used peptide-mhc binding tool on the internet
(google)
• Prediction peptide binding to MHCI and MHCII.
• Largest number of predictors (44 MHCI and 40
MHCII PSSMs, respectively)
• Flexibility
• Sorting by percentage or total number of
molecular weight
• Restriction of searches by MW
• Prediction of proteasome cleavage of peptide
binding to MHCI.
• Immunodominance Filter
• INPUT: single file of protein/s in fasta format or
multiple sequence alignment. Variability masking
Reche et al. Human Immunol. 2002, 63:701-9
Reche et al. Immunogenetics 2004, 56:405-19
In humans, MHC molecules are extremely
polymorphic
HLA
Chr6
DP DQ DR B C
HLA II
HLA MOLECULE
SEQUENCES
230
HLA-B
447
HLA-C
97
HLA-DPA
12
HLA-DPB
90
HLA-DQA
17
HLA-DQB
42
HLA-DRA
2
HLA-DRB1
271
HLA-DRB3
30
HLA-DRB4
7
HLA-DRB5
14
CLASS II
HLA I
HLA-A*0101
CLASS I
HLA-A
A
Black
Caucas.
Hispan.
Nat.Ame
Asian
GF
5.6%
15.1%
6.0%
7.5%
1.5%
PF
10.8%
27.9%
11.6%
14.4%
3.0%
Black
Caucas.
Hispan.
Nat.Ame
Asian
GF
2.0%
11.75%
6.7%
3.4%
0.7%
PF
3.9 %
22.0 %
12.39%
8.3 %
1.3 %
HLA-B*4402
HLA polymorphisms match peptide binding
residues
HLA-DR1(DRA*0101xDRB1*0101)
a1
a1
b11
b37
b13
b1
b70-71
b1
VAR
DRB1*0101
R F L E Y S T S E C H F F N G T E R V R F L D R Y F Y N Q E E Y V R F D S D V G E Y R A V T E L G R P D A E YWN S Q K D L L E D R R A A V D T Y C R H N Y G V G E S F T V Q
6
1 0
2 0
S1
3 0
S2
c c
4 0
S3
Reche and Reinherz, J Mol Biol. 2005;331(3):623-41
5 0
S4
6 0
H1a
7 0
8 0
H1b
9 0
HLA polymorphisms complicate epitope
based immunotherapy
 Vaccine coverage. Required HLA restriction and ethnic variation in HLA
distribution, suggest that epitope vaccines might not be effective across
populations.
 Epitope prediction. T-cell epitopes are anticipated on the basis of their binding to
MHC molecules.
 Which MHC allele to choose?
 It would appear that peptide predictions to many MHC molecules will have to be
provided. How to limit the number of peptides?
 HLA supertypes: Peptide binding specificities HLA molecules can be largely
overlapping (promiscuity) HLA molecules with similar binding specificities are termed as
HLA supertypes
Defining HLA supertypes by clustering the overlap between their peptidebinding repertoires
Defining HLA supertypes by clustering the
peptide-binding repertoires
PSSMj (HLA Ij)
PSSMi (HLA Ii)
protein
(1)
i
(2)
ni,j
j
i
(3)
j
k
di,j (4)
( N - ni,j)
g
n
(1) Prediction of the peptide biding repertoire (i,j sets in figure).
(2) Compute common peptides between the binding repertoire of any two HLA I molecules.
(3) Build a distance matrix.
(4) Use a phylogenic clustering algorithm to compute and visualize HLA I supertypes (clusters
of HLA I molecules with overlapping peptide binding repertoires).
HLA I Promiscuity: Supertypes
A0209
A0214
Cw0102
Cw0304
B2704
B2705
B2703
B2706
B2701
B2709
B27
A0204
A6802
A0207
A0203
A0206
A0201
A0202
A0205 A6601
A2
A3301
A3101
A3
A6801
B2702
A1101
B3909
A0301
B1517
Cw0702
B1501 B62
B4402
B44
B1508
B15
B4403
B1502
B5801
B5702
A*0101
B5701
B1513
B1516
B0702
B5401
B4002
B5301
B3501
A2902
B5101
B5103 B5502
B39011
B5102
B1510
B8
B1509
A2402 B3801
B7
BX
A24
Supertype
A2
A3
B7
B15
A24
B57
Alleles
Blacks
Caucasians
Hispanics *N.A.Na tives
Asians
A*0201-7, A*6802
A*0301, A *1101, A *3101, A *3301, A *6801, A *6601
B*0702, B*3501, B*5101-02, B*5301, B*5401
A*0101, B*1501_B62, B1502
A*2402, B*3801
43.7%
35.4%
45.9%
13.06%
15.5%
49.9%
46.9%
42.2%
37.80%
17.28%
51.8%
41.5%
40.5%
16.75%
25.85%
44.7%
47.9%
31.3%
21.04%
35.0%
52.4%
40.7%
52.0%
27.26%
41.94%
Promiscuous epitopes
0.8%
0.6%
1.2%
0.6%
0.5%
PEPVAC Features
http://bio.dfci.harvard.edu/PEPVAC/
SUPERTYPES AND COVERAGE:
A2: A*0201,
A3: A*0301,
A24:A*2301,
B7: B*0702,
B15:A*0101,
ALL:
A*0202, A*0205, A*0206
A*1101, A*3101, A*3301, A*6801
A*2402, A*2403, A*2405, A*2407
B*3501, B*5101, B*5301, B*5401
B*1501_B62, B1502
27.8%
32.1%
15.5%
30.6%
13.1%
>95.0%
NUMBER OF PEPTIDES (2% THRESHOLD)
( Influenza Virus A (PR/8); 11 ORF; 4617 amino acids)
PROTEOSOMA OFF : 210 (4.56% of all 9mers)
PROTEOSOMA ON: 148 (3.21% of all 9mers)
 Reche, P. A., and Reinherz, E. L. (2004). Definition of MHC
supertypes through clustering of MHC peptide binding repertoires.
Proceedings of 3rd International Conference on Artificial Immune
Systems. ICARIS 2004, LNCS 3239, pp. 189-196. Eds. G. Nicosia, V.
Cutello, P. J. Bentley and T. Timmis. Springer-Verlag Berling
Heidelberg. Publisher Abstract.
 Reche, P. A., and Reinherz, E. L. (2004). PEPVAC: a web server for
multi-epitope vaccine development based on the prediction of supertypic
MHC ligands. Nucleic Acids Res. In press
Computer-aided vaccine design flow
Pathogenic target
IN SILICO
Epitope selection:
MHC- binding,
processing,
conservation,
coverage
Experimental determination
Binding analysis
population coverage corrections
Immunogenicity experiments
EX SILICO
Vaccine optimization
Binding analysis and HLA-restriction mapping must be determined unambiguously
Genome wide identification of T cell epitopes in an
Influenza disease model in H2b Mouse (Kb and Db MHCI)
Zhong et al. J Biol Chem. 2003 Nov 14;278(46):45135-44.
1.
2.
3.
172 peptides were predicted to bind to Kb or Db
80% of peptides were found to bind to Kb and Db
Only 10% were immunogenic (activated T cells) and only 5 were
immunodominants)
Immu nog .
Immunodominant
Pept
Sequence
b
Cleav
Bind*
CTL ¦
PA224-233
SSLENFRAYV/D
*
+++
+++
NP366-374
ASNENMETM/Db
*
+++
+++
PB1703-
SSYRRPVGI/Kb
*
+++
++ ~ +++
M1128-135
MGLIYNRM/Kb
*
+++
-~±
NA425-432
SSISFCGV/Kb
*
+++
±
NS1133-
FSVIFDRL/Kb
*
+++
±
RTFSFQLI/Kb
*
++
+
HP43-50
GGLPFSLL/Kb
*
++
+
NA181-189
SGPDNGAVAV/Db
*
++
+
NA335-343
YRYGNGVWI/Db
*
TBD
+
PB1214-
RSYLIRAL/Kb
*
+
+
PA238-245
NGYIEGKL/Kb
*
+
+
PB2689-
VLRGFLIL/Kb
*
+
±
711
Subdominant
140
NS2114121
221
696
*+++: strong. ++: intermediate. +: weak. -: non-binder.
¦ +++: ~ 14%. ++: 5-14%. +: 1.5-3.0%. ±: ~1% in intracellular IFNg staining assay.
T cell epitope predictions
1. Algorithms for the prediction of T cell epitopes can narrow the
number of potential epitopes by 95%
1. Low false negative rate
2. Aproximadetly 10% of predicted epitopes are immunogenic (activate
T cell epitopes)
1. High False positive rate
3. Lack of immunogenicity of predicted epitopes is due to lack of
appropriated processing.
4.
For pathogens such as HIV-1 there are hundreds of experimentally
determined epitopes (immunogenic in vivo) identified from patients
5.
Optimizing T-cell epitope selection for vaccine design?
CTL EPITOPES IN HIV INFECTED HUMANS
• AIDS is a sexually transmitted infectious disease caused by the HIV virus. The virus infects
primeraly cells of the immune system (CD4-T-cells) and it is subject of extreme sequence variability
which is basis for the HIV immune evasion.
• Los Alamos HIV database is depositary of T-cell epitopes from HIV and SIV
http://hiv-web.lanl.gov/content/index
• CTL epitope example record
Displaying record number 1
HXB2 Location p17(18-26)
Author Location p17(18-26 IIIB)
Sequence KIRLRPGGK
Species(HLA) human (A3)
Immunogen HIV-1 infection
Keywords
Notes
References
• 1567 CTL epitope records => 592 unique seq =>195 9mers
Immunogen HIV-1 infection
Species(HLA) human
• CTL epitopes from HIV infected patients infected could be used as the basis of a vaccine
against HIV1. How?
Strategy for Development of a HIV1 Vaccine Using CTL
Epitopes
1.
Collect CTL epitope HIV-database
 Immunogen:HIV1 INFECTION;
 SPECIE: Human
2.
Select only conserved CTL epitopes (9 mers)
3.
Determine the extended HLAI binding profile of each CTL epitope (experimental from
HIV + predicted)
4.
Determine population coverage for each peptide
5.
Vaccine against HIV1 should be made from a combination of peptides providing 95 %
population coverage including all ethnics
Selection of Conserved CTL
HIV1
GAG
POL
ENV
Clustalw
VIF
TAT
REV
VPU/VPX
VPR
NEF
GAG.aln
POL.aln
ENV.aln
VIF.aln
TAT.aln
REV.aln
VPU.aln
VPR.aln
NEF.aln
GAG.seq
POL.seq
ENV.seq
VIF.sea
TAT.seq
REV.seq
VPU.seq
VPR.seq
NEF.seq
Mask variability
Qu i ck Ti me ™a nd a TIFF (Unc om pres se d) de co mp re ss or are n ee de d to s ee th is pi ctu re .
2
1 .8
1 .6
H=1
1 .4
1 .2
1
0 .8
0 .6
0 .4
0 .2
0
G
G
I
I
W
G
C
W G C
S
S
G
G
K
K
L
L
I
I
C
C
T
T
T
T
N
N
V
V
P
W
N
P W N
S
S
S
S
W
S
W S
N
N
K
K
S
S
Q
S
Q S
E
E
I
I
W
D
W D
N
N
M
M
Shannon filter
G I W G C S G K L I C T T . V P W N S S W S N K S . . E I W . N M
Peptide binding scores are given only for segments without "dots"
Conserved HIV1 CTL Epitopes From HIV1 Infection
1.
Collect CTL epitope HIV-database (195 9mers)
2.
Select only conserved CTL epitopes (Shannon Filter 0-4.3)
HIV CTL
H<1
SOURCE POS
SPRTLNAWV:p24
AVFIHNFKR:Integrase
MAVFIHNFK:Integrase
TLFCASDAK:gp160
FPVRPQVPL:Nef
RAMASDFNL:Integrase
KLTPLCVTL:gp160
TLNAWVKVI:p24
VIYQYMDDL:RT
LVGPTPVNI:Protease
TVLDVGDAY:RT
PLVKLWYQL:RT
TLNFPISPI:POL
NTPVFAIKK:RT
SEGATPQDL:p24
EKEGKISKI:RT
LLWKGEGAV:Integrase
KLVGKLNWA:RT
LTFGWCFKL:Nef
YQYMDDLYV:RT
GPKVKQWPL:RT
WASRELERF:p17
RAIEAQQHL:gp160
GLNKIVRMY:p24
KEKGGLEGL:Nef
YFPDWQNYT:Nef
WYIKIFIMI:gp160
YVDRFFKTL:p24
FVNTPPLVK:RT
DRFFKTLRA:p24
KIQNFRVYY:Integrase
KLNWASQIY:RT
QGWKGSPAI:RT
IRLRPGGKK:p17
DLSHFLKEK:Nef
KIRLRPGGK:p17
GIPHPAGLK:RT
MTKILEPFR:RT
AETFYVDGA:RT
EEKAFSPEV:p24
CRAPRKKGC:p2p7p1p6
ITLWQRPLV:Protease
16-24
179-187
178-186
51-59
68-76
20-28
121-129
19-27
179-187
76-84
107-115
421-429
POL
57-65
44-52
42-50
241-249
259-267
137-145
181-189
18-26
36-44
557-565
137-145
92-100
120-128
680-688
164-172
416-424
166-174
219-227
263-271
151-159
19-27
86-94
18-26
93-101
164-172
437-445
28-36
42-50
03-11
Total: 42 peptides
ALLELES (HIVDB)
B0702
A0301
A0301
A0301
B3501
A0201
A0201
A0201
A0201
A0201
B3501
A0201
A0201
A0301
B5101
A0201
A0201
A0201
A0201
B0801
B3501
B5101 B1501 C0304
B1501
B4002
A1 B3701 B5701
A2402
A2601
A1101
B1402
A3002
A3002
B5101
B2705
A0301
A0301 B0301
A0301
A0301
B4501
B4415
B1402
Conser.
CTL
SOURCE
SPRTLNAWV
AVFIHNFKR
MAVFIHNFK
LVGPTPVNI
TVLDVGDAY
GLNKIVRMY
SEGATPQDL
PLVKLWYQL
LLWKGEGAV
FVNTPPLVK
KLVGKLNWA
YQYMDDLYV
QGWKGSPAI
GIPHPAGLK
LTFGWCFKL
TLNFPISPI
KLNWASQIY
p24
Integrase
Integrase
Protease
RT
p24
p24
RT
Integrase
RT
RT
RT
RT
RT
Nef
POL
RT
H < 0.5
POS
16-24
179-187
178-186
76-84
107-115
137-145
44-52
421-429
241-249
416-424
259-267
181-189
151-159
93-101
137-145
263-271
ALLELES (HIVDB)
B0702
A0301
A0301
A0201
B3501
B1501
B60
A0201
A0201
A1101
A0201
A0201
B5101
A0301
A0201
A2
A3002
B7
A2
B35
B62
A30
Total: 17 peptides
Experimental HLAI binding from
HIV database
Extended HLAI binding Profile of Conserved HIV1 CTL
Epitopes From HIV1 Infection
CTL
SOURCE
SPRTLNAWV
AVFIHNFKR
MAVFIHNFK
LVGPTPVNI
TVLDVGDAY
GLNKIVRMY
SEGATPQDL
PLVKLWYQL
LLWKGEGAV
FVNTPPLVK
KLVGKLNWA
YQYMDDLYV
QGWKGSPAI
GIPHPAGLK
LTFGWCFKL
TLNFPISPI
KLNWASQIY
p24
Integrase
Integrase
Protease
RT
p24
p24
RT
Integrase
RT
RT
RT
RT
RT
Nef
POL
RT
H < 0.5
POS
16-24
179-187
178-186
76-84
107-115
137-145
44-52
421-429
241-249
416-424
259-267
181-189
151-159
93-101
137-145
263-271
ALLELES (HIVDB)
B0702
A0301
A0301
A0201
B3501
B1501
B60
A0201
A0201
A1101
A0201
A0201
B5101
A0301
A0201
A2
A3002
B7
A2
B35
B62
A30
ALLELES (HIVDB + PREDICTED)
B0702 B3501 B5101 B5102 B5103 B5301 B5401 B5502
A0301 A1101 A3101 A3301 A6601 A6801
A0301 A1101 A3101 A3301 A6601 A6801
A0201 A0202 A0205 A0209 B1501 B1516
B1501 B3501 B5701 C0304
A0203 A1 B1501
A2902 B39011 B4002 B4402 B4403
A0201 A0202 A0203
A0201 A0204 A0205 A0209
A1101
A0201
A0201
B5101
A0301
A0201
A0201 A0207
A1 A3002
HLA NÞ % COV
8
6
6
6
4
3
5
3
4
1
1
1
1
1
1
2
2
0.35
0.35
0.35
0.27
0.26
0.13
0.2
0.26
0.18
0.05
0.18
0.18
0.02
0.02
0.18
0.23
0.03
Apply an algorithm algorithm to identify combinations of epitopes providing a
population coverage of 95%
A minimum of 5 peptides are required to cover the whole population
Selected HIV1 Epitopes
# 37 epitopes resulting from parsing out all epitopes with H ≤ 1 sites with regard to a HIV1
all clades consensus sequence
# Epitope selection: There are 52 different combinations of 5 peptides each with a predicted
95% coverage.
EXPERIMENTAL
PREDICTIONS
EPITOPE
SPRTLNAWV
AVFIHNFKR
TLFCASDAK
FPVRPQVPL
RAMASDFNL
TLNAWVKVI
VIYQYMDDL
LVGPTPVNI
TVLDVGDAY
PLVKLWYQL
TLNFPISPI
NTPVFAIKK
EKEGKISKI
LLWKGEGAV
LTFGWCFKL
YQYMDDLYV
GPKVKQWPL
RAIEAQQHL
GLNKIVRMY
YFPDWQNYT
WYIKIFIMI
YVDRFFKTL
FVNTPPLVK
KIQNFRVYY
DRFFKTLRA
COV
K
K
A
R
P
E
Y
I
F
E
E
K
G
V
V
G
T
L
S
P
V
R
L
R
E
0.35
0.35
0.32
0.32
0.31
0.29
0.28
0.27
0.26
0.26
0.23
0.22
0.19
0.18
0.18
0.18
0.17
0.13
0.13
0.07
0.05
0.05
0.05
0.03
0.03
Restriction
B0702 B3501 B5101 B5102 B5103 B5301 B5401 B5502
A0301 A1101 A3101 A3301 A6601 A6801
A0301 A1101 A3101 A3301 A6801
A2902 B0702 B3501 B5101 B5102 B5103 B5301 B5401
A0201 B2709 C0304
A0201 A0202 A0203 A0204 A0206
A0201 A0205 A0207 A0214
A0201 A0202 A0205 A0209 B1501 B1516
B1501 B3501 B5701 C0304
A0201 A0202 A0203
A0201 A0207
A0301 A6601 C0102
B2701 B3801 B39011 B3909 B4402 B5101 B8
A0201 A0204 A0205 A0209
A0201
A0201
B0702 B0801 B3501 B8
B1501 B1517 B5101 C0304
A0203 A1 B1501
A1 B3701 B5701
A0203 A0206 A2402
A0203 A0204 A0207 A2601 B3801
A1101
A1 A3002
B1402 B2701 B2702 B2703 B2704 B2705 B2709
Source
p24
Integrase
gp160
Nef
Integrase
p24
RT
Protease
RT
RT
POL
RT
RT
Integrase
Nef
RT
RT
gp160
p24
Nef
gp160
p24
RT
Integrase
p24
Restriction
Position
16-24
179-187
51-59
68-76
20-28
19-27
179-187
76-84
107-115
421-429
B0702
A0301
A0301
B3501
A0201
A0201
A0201
A0201
B3501
A0201
57-65
42-50
241-249
137-145
181-189
18-26
557-565
137-145
120-128
680-688
164-172
416-424
219-227
166-174
A0301
B5101
A0201
A0201
A0201
B0801
B5101 B1501 C0304
B1501
A1 B3701 B5701
A2402
A2601
A1101
A3002
B1402
Possible caveats in designing epitopes based vaccines
 Processing: Epitopes have been isolated from a population, and to be presented by an
individual with the right MHC specificity processing must be conserved. Will two
individual with the same HLAI profile present the same peptides?
 TCR repertoire, immunodominance/competition issues. Is the TCR repertoire large
enough to recognize any peptide that is processed and presented? Is immunodominance
conserved in individual with the same HLAI profile? Can immunodominance be modulated
by priming with a peptides?
 Are these the right epitopes? These epitopes were isolated from infected people that
usually became ill and die: How do we know that these epitopes are "the good ones".
Peptide predictions suggest the presence of new unidentified T-cell epitopes
MIF BIOINFORMATICS
Jonn-Paul Glutting
Hong Zhang
Ellis Reinherz