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J. Biomedical Science and Engineering, 2009, 2, 273-279
doi: 10.4236/jbise.2009.24041 Published Online August 2009 (http://www.SciRP.org/journal/jbise/
Published Online August 2009 in SciRes. http:// www.scirp.org/journal/jbise
Determination of inter- and intra-subtype/species varia-
tions in polymerase acidic protein from influenza A virus
using amino-acid pair predictability
Shaomin Yan1, Guang Wu2*
1National Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, Nanning,
Guangxi, 530007, China; 2Computational Mutation Project, DreamSciTech Consulting, 301, Building 12, Nanyou A-zone, Jiannan
Road, Shenzhen, Guangdong Province 518054, China.
Received 23 March 2009; revised 15 April 2009; accepted 28 April 2009.
The polymerase acidic protein is an important
family of proteins from influenza A virus, which
is classified as many different subtypes or spe-
cies. Thus, an important question is if these
classifications are numerically distinguishable
with respect to the polymerase acidic protein.
The amino-acid pair predictability was used to
transfer 2432 polymerase acidic proteins into
2432 scalar data. The one-way ANOVA found
these polymerase acidic proteins distinguish-
able in terms of subtypes and species. However,
the large residuals in ANOVA suggested a pos-
sible large intra-subtype/species variation.
Therefore, the inter- and intra-subtype/species
variations were studied using the model II
ANOVA. The results showed that the in-
tra-subtype/species variations accounted most
of variation, which was 100% in total for both
inter- and intra- subtype/species variations. Our
analysis threw lights on the issue of how to de-
termine a wide variety of patterns of antigenic
variation across space and time, and within and
between subtypes as well as hosts.
Keywords: Amino-Acid Pair; Influenza A Virus; ln-
ter- and Intra-; Model II ANOVA; Polymerase Acidic
Protein; Species; Subtype; Variation
The unpredictable mutations in proteins from influenza
A virus threaten the humans with possible pandemics or
epidemics, therefore it is considered important to accu-
rately, precisely and reliably predict the mutations. In
this way, the new vaccines, which would be more effec-
tive against the influenza A virus, could be manufactured
Currently, the manufactured vaccines are designed to
target the influenza A virus according to their subtypes,
for example, the focus in recent year would be the H5N1
subtype of influenza A virus [7,8,9,10,11,12,13,14], and
anti-flu drugs are designed to target neuraminidases and
M2 protein [15,16]. It would be understandable that
proteins should be different from one subtype to another.
Otherwise, there would be no classification of subtype.
Moreover, the proteins under the same subtype should be
different one another, otherwise a single subtype would
contain only a single protein. The same holds for pro-
teins classified according to species, where the sample
Here, an important question is if these classifications
are numerically distinguishable, say, if a protein is dif-
ferent from species to species and from subtype to sub-
type in number. This is the base for prediction of muta-
tion using mathematical modeling.
However this work has yet to be done, because the
difference between proteins is different in terms of let-
ters, which represent the amino acids in proteins. It is
difficult to use any statistical method to determine these
differences cross a protein family.
For this aim, it needs to transfer a protein into a datum
that should be different from protein to protein. Then it
would be possible to conduct an ANOVA statistics to
answer the question above.
Actually there are quite a few methods, which can
transfer a protein sequence into a series of numerical
codes or numerical sequence for predicting its various
attributes (see, e.g., [17,18,19,20,21,22,23,24,25]).
Since 1999, we have developed three approaches to
transfer each amino acid in a protein as well as a whole
protein (for reviews, see [26,27,28]) into either a single
datum or numerical sequence, which resulted in many
studies on proteins.
Afterward, another question would be the inter- and
intra-subtype/species variations. This issue is important
because the vaccines and anti-flu drugs manufactured
based on subtype would be more efficient and effective
if the difference within subtype/species would be smaller
274 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279
SciRes Copyright © 2009 JBiSE
than that between subtypes/species.
Influenza viruses replicate and transcribe their seg-
mented negative-sense single-stranded RNA genome in
the nucleus of the infected host cell. All RNA synthesiz-
ing activities associated with influenza virus are per-
formed by the virally encoded RNA-dependent RNA
polymerase that consists of three subunits, polymerase
acidic protein (PA), polymerase basic proteins 1 and 2
. The PA subunit is involved for the conversion of
RNA polymerase from transcriptase to replicase  and
contains the endonuclease active site. A recent study
strongly implicates the viral RNA polymerase complex
as a major determinant of the pathogenicity of the 1918
pandemic virus .
Many studies have indicated that sequence-based pre-
diction approaches, such as protein subcellular location
prediction [32,33,34], protein quaternary attribute pre-
diction [25,35], identification of membrane proteins and
their types [36,37], identification of enzymes and their
functional classes , identification of GPCR and their
types [24,39,40], identification of proteases and their
types [41,42], protein cleavage site prediction [43,44,45],
signal peptide prediction [46,47], and protein 3D struc-
ture prediction based on sequence alignment , can
timely provide very useful information and insights for
both basic research and drug design.
The present study was attempted to use the model II
ANOVA to investigate the inter- and intra- sub-
type/species variations in polymerase acidic protein from
influenza A virus in hope to shed lights on helping find
effective drugs against influenza A virus.
2. MATERIALS AND METHODS
A total of 5165 full-length PA sequences of influenza A
virus sampled from 1918 to 2008 was obtained from the
influenza virus resources . After excluded identical
sequences, 2432 PA proteins were used in this study.
2.2. Transferring Symbolized PA Proteins
into Scalar Data
Among these methods developed by us, the amino-acid
pair predictability is the simplest, which was thus used in
this study. According to the permutation, the adjacent
amino-acid pairs in a protein can be classified as predict-
able and unpredictable, which provided a measure to dis-
tinguish protein one another and was used in many our
previous studies (for example 2008, [28,50,51,52,53,54]).
For example, there was an avian influenza virus
(strain A/quail/Hong Kong/1721-20/99(H6N1)) and its
PA was composed of 716 amino acids (accession number
CAC84865). The first and second amino acids could be
counted as an amino-acid pair, the second and third as
another amino-acid pairs, the third and fourth, until the
715th and 716th, thus there were 715 amino-acid pairs.
Then, how many amino-acid pairs can be explained by
the permutation or random mechanism in this PA? This
can be determined using the percentage of predictable
and unpredictable amino-acid pairs.
There were 37 aspartic acids “D” and 76 glutamic ac-
ids “E” in CAC84865 PA. If the permutation could ex-
plain the appearance of amino-acid pair DE, it could
appear four times in this PA (37/716×76/715×715=3.927).
Actually there were 4 DEs in this PA. Thus, the appearance
of DE could be explained by permutation or predicted by
random mechanism. By clear contrast, there were 50 iso-
leucines “I” in the PA. If the permutation could explain the
appearance of IE, it could appear five times
(50/716×76/715×715=5.307). However, it appeared 12
times in realty, which could not be explained by permuta-
tion or randomly unpredictable. In this way, all amino-acid
pairs in this PA could be classified as predictable and un-
predictable. For this particular PA, its predictable and un-
predictable portions were 25.45% and 74.55%.
Taking another PA (accession number CAC84866) as
example, this PA had only one amino acid different from
CAC84865 PA at position 437. However, its predictable
and unpredictable portions were 25.59% and 74.41%.
Thus, the amino-acid pair predictability distinguished
the difference between different PA proteins as a very
2.3. Difference among Subtypes/Species
Influenza A viruses are classified by the serological sub-
types of the primary viral surface proteins. Currently, there
are 16 haemagglutinin subtypes from H1 to H16 and 9
neuraminidase subtypes from N1 to N9 . Also, influ-
enza A viruses can be classified according to their host.
After computation of 2432 PA proteins, the predict-
able portions of PA proteins were grouped according to
their classifications of subtypes and species.
As there were more than two subtypes and two species,
and the number of PA proteins was highly different from
subtype to subtype, and from species to species, the
one-way ANOVA followed by the Holm-Sidak’s compari-
son test was used to compare the difference among and
between subtypes/species using the SigmaStat software
. P < 0.05 was considered statistically significant.
2.4. Inter- and Intra-Subtype Variation
The single classification model II ANOVA with unequal
sample sizes  was used to determine the inter- and
3. RESULTS AND DISCUSSION
The one-way ANOVA showed that there were statisti-
cally significant difference among HA subtypes (Figure
1), NA subtypes (Figure 2) and species (Figure 3). Even
the statistical difference was found between subtypes
and between species. The detailed results were listed in
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279 275
SciRes Copyright © 2009
Predictable portion (%)
Figure 1. HA subtype comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a sta-
tistically significant difference (P < 0.001) among sixteen subtypes, and the Holm-Sidak’s comparison test indi-
cated the statistical difference between two subtypes as follows: H5 versus H9, H1 versus H9, H3 versus H9, H2
versus H9, H7 versus H9, H5 versus H6, H6 versus H9, H1 versus H6, H5 versus H7, H2 versus H6, H5 versus
H4, H3 versus H6, H1 versus H4, H1 versus H7, H2 versus H4, H4 versus H9, H5 versus H11, H2 versus H11,
H10 versus H9, H1 versus H11, H2 versus H7, H3 versus H4, H3 versus H11, H13 versus H9, H3 versus H7, H11
versus H9, H2 versus H8, H5 versus H8, H1 versus H8, H2 versus H12, H12 versus H9, H5 versus H12, H5 ver-
sus H3, H3 versus H8, and H1 versus H12.
Predictable portion (%)
Figure 2. NA subtype comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a sta-
tistically significant difference (P < 0.001) among nine subtypes, and the Holm-Sidak’s comparison test indicated
the statistical difference between two subtypes as follows: N1 versus N8, N2 versus N8, N1 versus N5, N1 versus
N2, N2 versus N5, N1 versus N6, N1 versus N9, N1 versus N3, N3 versus N8, N7 versus N8, N7 versus N5, N3
versus N5, N6 versus N8, N2 versus N6, N2 versus N9 and N6 versus N5.
276 S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279
SciRes Copyright © 2009
Stone marten n=1
Unknown species n=36
Predictable portion (%)
Figure 3. Species comparison of PA proteins from influenza A viruses. The one-way ANOVA indicated a
statistically significant difference (P < 0.001) among ten species, and the Holm-Sidak’s comparison test
indicated the statistical difference between two species as follows: human versus avian, human versus
equine, swine versus equine, swine versus avian, avian versus equine, human versus unknown, swine
versus unknown, environment versus equine, human versus environment, swine versus environment,
unknown versus equine, tiger versus equine, civet versus equine, cat versus equine, civet versus mink,
swine versus mink, human versus mink, and swine versus canine.
During the one-way ANOVA test, a particular phe-
nomenon got our attention, i.e. the residual was very
large in standard ANOVA table. For example, the sum of
squares (SS) was 668.97 and 6293.13 for between
groups and residual under HA subtype (Table 1).
Table 1 suggested that there were very large varia-
tions in PA proteins within each subtype or species,
which further suggested that the model II ANOVA was
in need to determine the inter- and intra-subtype/species
Table 2 listed the inter- and intra-subtype/species
variations. The model II ANOVA defined the total varia-
tion as 100%, which was further divided into inter- and
intra-subtype/species variations. As seen in Table 2, the
intra-subtype/species variation is far much larger than
the inter-subtype/species variation. For example, the
inter-subtype HA variation was 10.71% while the in-
tra-subtype HA variation was 89.28%.
Table 1. Standard ANOVA table regarding HA subtype, NA subtype and species of PA proteins from influenza A viruses.
Source of variation Degree of freedom Sum of Squares Mean Square F
HA subtype Between groups 16 668.97 41.81 16.05
Residual 2415 6293.13 2.61
Total 2431 6962.10
NA subtype Between groups 9 377.13 41.90 15.41
Residual 2422 6584.97 2.72
Total 2431 6962.10
Species Between groups 11 547.64 49.79 18.78
Residual 2420 6414.46 2.65
Total 2431 6962.10
S. M. Yan et al. / J. Biomedical Science and Engineering 2 (2009) 273-279 277
SciRes Copyright © 2009 JBiSE
Table 2. Inter- and intra-subtype/species variations of PA pro-
teins from influenza A viruses.
HA subtype 10.71% 89.28%
NA subtype 7.54% 92.46%
Species 11.88% 88.12%
At this point, one might wonder why the statistical
difference was found among subtypes and species while
there were so large variations within subtype and species.
These results in fact were very reasonable. In plain
words, there would be statistical difference between
males and females in performing a sport, for example,
however the difference between male sportsman and
ordinary male would be also huge, thus it could be pos-
sible this variation would be larger than that between
males and females.
In fact, the single classification model II ANOVA has
many important applications although this method is less
familiar with most researchers [58,59]. For example, it is
better to know the inter- and intra-patient variations be-
fore planning clinical experiments, say, if an experimen-
tal design should include two parallel groups (inter- pa-
tients) or a two-part crossover design (intra-patients).
In the context of this study, generally, a small intra-
subtype/species variation suggested a cost-effective way
in collecting of samples, it said, not many samples for a
particular subtype/species were in need, by clear contrast,
many samples were in need regarding a particular sub-
type/species if there was a large intra- subtype/species
Actually, the inter- and intra-subtype/species varia-
tions were the mutations occurred in the same subtype or
species, and occurred cross subtypes or species. There
was a wide variety of patterns of antigenic variation
across space and time, and within and between subtypes
as well as hosts and we did not yet understand the de-
terminants of these different patterns . Our analysis
could shed lights on this issue.
The far much large intra-subtype/species variation
found in this study suggested: 1) much more PA proteins
belonging to the same subtype/species were needed in
order to better understand the mutation pattern in the
same subtype/species, and 2) the current classification
was based on the surface proteins from influenza A virus,
while under the same subtype, the PA mutations were
On the other hand, the statistically significant differ-
ence between subtypes suggested the current classifica-
tion valid even for the PA, which was an internal protein.
The most important requirement for producing vaccines
against viruses displaying antigenic diversity is a method
of measuring antigenic distances between strains and
developing an understanding of how these distances re-
late to cross-protection . The current results sup-
ported the idea to develop vaccines and anti-flu drugs
that generate effective heterosubtypic immunity based
on immune recognition of influenza A virus antigens
conserved across all viral strains [62,63].
This study was partly supported by International Science
& Technology Cooperation Projects (2008DFA30710),
Guangxi Science Foundation (No. 0630003A2 and
0991080), and Guangxi Academy of Sciences (project
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