Bioinformatical design of a vaccine against influenza virus N1 subtype
Bonaccorsi, Irene; Clausen, Martin Bau; Høj, Leif Howalt; Kjær, Jesper and Sayyad, Fhayaz Ahammad
Introduction
Morbidity and mortality of influenza
5-20% of all people in the US are infected with influenza every year, 200,000 are hospitalised and 36,000 die [1].
This makes flu a serious pathogen that causes massive losses in productivity in the industrialised world and even large number of deaths.
Influenza virus
Human influenza viruses are members of the orthomyxovirus family, which consists of: influenza A, B, and C vira, and Thogovirus (in ticks). In
humans, only influenza A and B viruses are of epidemiological interest. The main antigenic determinants of influenza A and B viruses are the
hemagglutinin (HA or N) and neuraminidase (NA or N) transmembrane glycoproteins. Based on the antigenicity of these glycoproteins, influenza
A viruses are further subdivided into sixteen H (H1-H16) and nine N (N1-N9) subtypes.
Neuraminidase
Like HA, neuraminidase is a glycoprotein, which is also found as projections on the surface of the virus where it forms a tetrameric
structure. The NA molecule presents its main part at the outer surface of the cell, spans the lipid layer, and has a small cytoplasmic tail.
NA acts as an enzyme, cleaving sialic acid from the HA molecule, from other NA molecules and from glycoproteins and glycolipids at the
cell surface. It also serves as an important antigenic site, and in addition, seems to be necessary for the penetration of the virus through
the mucin layer of the respiratory epithelium. Antigenic drift can also occur in the NA. The NA carries several important amino acid residues
which, if they mutate, can lead to resistance against neuraminidase inhibitors.
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Targeting a distinct protein as
vaccine candidate
Choosing the protein
Sequences of the human influenza A (covering the period 20002006) were taken from the NCBI Influenza Virus Resource
database [2]. A multiple alignment with ClustalW [3] of the NA
subtype N1 revealed it to be the most conserved gene (see NJ tree
in figure 1). A consensus sequence based on 197 protein
sequences was then chosen to be our vaccine candidate.
Neighbour-joining phylogenetic trees were then built using MEGA
3.1 [4], in order to ascertain the degree of conservation of NA in
different influenza subtypes.
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gi|i|905788423||ggb5| | ggbb||ABBDB98P5 85
g |89 42 17 99 7| gb |A B A 82
gi 331660 71 6342| | gbb|Ab|ABF
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N1 subtypes
am
/
0.02
Figure 2. Neighbour Joining tree of 222 NA
sequences (N1 subtype). The 25 H5N1
sequences forming the distinct lower clade
were removed from the dataset before we
generated the consensus sequence.
Analysing the protein
Class I MHC epitopes for all supertypes were found using NetCTL
[5]. Epitopes for HLA-DR4 (an MHC II allele) were found using
EasyGibbs [6]. A B-cell epitope was identified using BepiPred [7]
using an HMM model and provides the residue scores as log odds.
Table 2 Three highest scoring epitopes for
HLA-DR4 MHC class-II. Predictions are made
with EasyGibbs [6]. All identified epitopes are
in highly conserved regions of NA.
Epitope
location
155
239
100
Epitope
EasyGibbs
score
YRALMSCPL
19.381
FTIMTDGPS
17.788
YTKDNSIRI
16.758
Table 1 Shown below are the best scoring predictions of epitopes in NA subtype N1
obtained with NetCTL. Predictions are made for MHC-1 binding (Affinity column),
proteasome cleavage (C-terminal cleavage column) and TAP translocation. All 3 measures
are combined to a single score (combined score) in which the listed epitopes were the
highest scoring for the respective HLA supertypes. Epitope location column lists the position
of the first amino acid in epitopes placement in the NA protein sequence
Epitope
HLA
location supertype
304
22
254
132
150
100
151
276
182
274
A1
A2
A3
A24
B7
B8
B27
B44
B58
B62
Epitopes
VSFNQNLDY
LMLQIGNII
KIFKIEKGK
FFLTQGALL
KDRSPYRAL
YTKDNSIRI
DRSPYRALM
YEECSCYPD
SACHDGMGW
HQNEQGSGY
B-Cell epitopes
With BepiPred we predicted the
region 322 to 348 to be the best
region for a linear B-cell epitope.
NetCTL predictions
Affinity
CTAP
Combined
terminal binding
score
cleavage
0.5107
0.9953
3.0830
3.7221
0.4439
0.9847
0.7320
1.1974
0.5628
0.6765
0.7600
1.7818
0.7806
0.9461
1.0550
1.2154
0.3923
0.9796
0.8500
1.2284
0.2214
0.9986
0.5000
1.1964
0.1291
0.9990
0.2920
0.7520
0.1431
0.0124
-2.1380
0.8838
0.6493
0.4893
1.1530
2.3525
0.3828
0.9083
3.1650
1.2279
Figure 4 Zoom of the
predicted epitope area (red)
highest scoring 9mer is
coloured yellow (both figure 3
and 4 were generated in
PyMOL [8] with 1V0Z.pdb)
FGDNPRPKDGEGSCNPVTVDGANGVKG
The region in bold is the best scoring
9mer epitope of the region
(BepiPred score for all 9 residues:
19.788). Figure 3 and 4 show 3D
visualisations and figure 5 a logo of
the epitope.
Creating a plasmid vaccine
Sequences for all influenza proteins were taken from NCBI Influenza Virus
Resource database [2] (only genes sequenced in the period 2000-2006 were
used). Multiple alignments were made using ClustalW [3], giving consensus
sequences for all proteins.
Using NetCTL [5] and EasyGibbs [6], respectively, epitopes for all MHC-I
supertypes and HLA-DR4 were found.
Polytope of all epitopes were constructed with triple A linker regions binding
the epitopes together. The polytope was optimised with a Monte Carlo
Metropolis simulation (MCM) implemented in polytope_cont3 (unpublished).
MCM settings: all standard but with 700 iterations and 14 temperature steps.
The final polytope was evaluated with NetCTL to check C-terminal cleavage,
TAP translocation and affinity
Results
For the final polytope we replaced two epitopes (HLA: A3 and B44) with
suboptimal epitopes (both ranked 2nd in the NetCTL prediction) because
these had better C-terminal cleavage than the best predicted.
Results
T-Cell epitopes
Listed in the table 1 are all NetCTL
prediction of good MHC-I epitope
candidates in NA. Table 2 shows
MHC-II epitopes. All of these are in
highly conserved regions of NA
sequences covering 2000 to 2006.
Figure 1. Structure of an influenza A virus. Polymerase B1, B2 and A
proteins (PB1 + PB2 + PA), Hemagglutinin (HA), Nucleocapsid protein (NP),
Neuraminidase (NA), Matrix proteins (M1 + M2), Non-structural protein (NS)
All epitopes were validated to be in highly conserved regions of the genes.
polytope_cont3 only predicted three C-terminal cleavages within epitopes
(HLA: B7, A3 and B58) and no additional epitopes would arise from the
polytope. Given the stochastic nature of the proteasome we do not consider
these few internal cleavage sites to be a problem.
Table 3 Shown below is the polytope (top row to bottom row of the epitope column).
Further shown for each epitope is the location in gene and relative position (Epitope
location), HLA supertype for the epitope and the validated rank of the epitope in NetCTL.
DR4 is an MHC-II allele and thus not validated against NetCTL.
Epitope
location
PB1 (540-548)
PB1 (482-490)
PA (495-503)
PB2 (523-531)
HA (224-232)
PA (46-54)
PA (126-134)
PB2 (529-537)
PA (140-148)
HA (377-385)
HA (369-377)
HLA
Epitopes (upper case) and
supertype linker regions (lower case)
maaeyGPATAQMAL
B7
SYINRTGTFaavdd
A24
RRKTNLYGFiryq
B8
GTEKLTITYkrw
A1
RRFTPEIAKa
B27
FMYSDFHFInlwcek
A2
EVHIYYLEKc
A3
ITYSSSMMWavdy
B58
SEKTHIHIF
B44
YAADQKSTQwhyrw
DR4
HQNEQGSGYaa
B62
Rank in NetCTL
prediction
1
1
1
1
1
1
1
2
1
NA
1
Are these epitopes novel?
Figure 3 Tetramer structure of
neuraminidase viewed from above
(white lines show the 4 proteins). Nonyellow coloured residues are part of
highly conserved sequence regions
Figure 5 Logo [9] of the 9mer B-Cell epitope from
figure 4 (across all 197 sequences). All positions
are highly conserved although position 5 and 8
indicate presence of mutation and risk of immune
escape.
Discussion
Conclusion
We limited our work to influenza A virus and subtype N1 as we believe vaccines for influenza will be most
efficient when targeted against a single subtype rather then multiple subtypes. The purpose of this approach
is to keep the epitopes specific rather than general in order to achieve a focused immune response. The
approach we made here can easily be repeated for other subtypes.
We are aware that immune escape does happen. Any vaccine against influenza will have to be updated often
and may also further be different based on specific regions in the world [11]. Question remains whether the
approach here will be easier than the current vaccines based on heat-killed strains. The vaccines we have
designed are based on a dataset for the period 2000 to 2006. As can be seen in figure 1, the N1 subtype
sequences show a very high degree of similarity. This does indicate that epitope based vaccines may be
useful for longer periods than the current methods which are updated every year [11].
The bioinformatical designed vaccines are in no way final, in vitro and in vivo tests are necessary to
determine the real effect of the vaccines. The benefit of the bioinformatical approach is that the preliminary
design is very cost efficient compared to standard laboratory trial and error approach to vaccine designs.
References
Figures:
Figure 1. Image copyright by Dr. Markus Eickmann, Institute for Virology, Marburg, Germany.
http://www.biografix.de
Litterature, webresources and tools:
[1] CDC - Influenza (Flu) | What Everyone Should Know About Flu and the Flu Vaccine
(http://www.cdc.gov/flu/keyfacts.htm)
[2] NCBI Influenza Virus Resource (http://www.ncbi.nlm.nih.gov/genomes/FLU/)
[3] ClustalW: Thompson, J.D., Higgins, D.G. and Gibson, T.J. (1994). CLUSTAL W:
improving the sensitivity of progressive multiple sequence alignment through sequence
We searched for all identified epitopes in the SYFPEITHI database [10] and
found no matches, however in general SYFPEITHI seems to be lacking
epitopes for influenza. Most vaccines against influenza are based on heatkilled influenza strains and not epitopes.
We have used bioinformatical tools to identify MHC-I and
II supertype T- and B-cell epitope candidates in the highly
conserved NA protein. In addition we constructed a
polytope of highly conserved epitopes found in the full
genome of the human Influenza A virus subtype N1.
The methods used can easily be applied to other subtypes.
Virus subtypes should be dealt with individually in order to
make the vaccines specific and effective.
weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Research,
submitted, June 1994.
[4] Mega 3, Version 3.1. S Kumar, K Tamura, and M Nei (2004) MEGA3: Integrated software for
Molecular Evolutionary Genetics Analysis and sequence alignment. Briefings in Bioinformatics
5:150-163.
[5] An integrative approach to CTL epitope prediction. A combined algorithm integrating MHC-I
binding, TAP transport efficiency, and proteasomal cleavage predictions. Larsen M.V.,
Lundegaard C., Kasper Lamberth, Buus S,. Brunak S., Lund O., and Nielsen M. European Journal
of Immunology. 35(8): 2295-303. 2005 (http://www.cbs.dtu.dk/services/NetCTL/)
[6] Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling
approach. Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S,
Lund O. Bioinformatics. 2004 20:1388-97
(http://www.cbs.dtu.dk/biotools/EasyGibbs/)
[7] Improved method for predicting linear B-cell epitopes Jens Erik Pontoppidan Larsen, Ole
Lund and Morten Nielsen Immunome Research 2:2, 2006.
(http://www.cbs.dtu.dk/services/BepiPred/)
[8] PyMOL version 0.99 (http://pymol.sourceforge.net/)
[9] Crooks GE, Hon G, Chandonia JM, Brenner SE WebLogo: A sequence logo generator,
Genome Research, 14:1188-1190, (2004) (http://weblogo.berkeley.edu/)
[10] Rammensee, Friede, Stevanovic, MHC ligands and peptide motifs: 1st listing,
Immunogenetics 41, 178-228, 1995 (http://www.syfpeithi.de/)
[11] The Influenza Sequence Database (http://www.flu.lanl.gov/)

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