REGULAR ARTICLE

In Silico Functional and Structural Characterizationof H1N1 Influenza A Viruses Hemagglutinin,2010–2013, Shiraz, Iran

Afagh Moattari1 • Behzad Dehghani1 •

Nastaran Khodadad1 • Forogh Tavakoli1

Received: 8 September 2014 /Accepted: 6 May 2015 / Published online: 12 May 2015

� Springer Science+Business Media Dordrecht 2015

Abstract Hemagglutinin (HA) is a major virulence factor of influenza viruses and

plays an important role in viral pathogenesis. Analysis of amino acid changes,

epitopes’ regions, glycosylation and phosphorylation sites have greatly contributed

to the development of new generations of vaccine. The hemagglutinins of 10 se-

lected isolates, 8 of 2010 and 2 of 2013 samples were sequenced and analyzed by

several bioinformatic softwares and the results were compared with those of 3

vaccine isolates. The study detected several amino acid changes related to altered

epitopes’ sites, modification sites and physico-chemical properties. The results

showed some conserved modification sites in HA structure. This study is the first

analytical research on isolates obtained from Shiraz, Iran, and our results can be

used to better understand the genetic diversity and antigenic variations in Iranian

and Asian H1N1 pathogenic strains.

Keywords Influenza � Hemagglutinin � In silico � Glycosylation � Phosphorylation

1 Introduction

In recent years, the influenza A virus has generally caused pandemic influenza

where only type A influenza (H1N1) virus has infected millions of people and the

infection has caused 18,000 deaths prior to May 30, 2010 globally (Girard et al.

Electronic supplementary material The online version of this article (doi:10.1007/s10441-015-9260-

1) contains supplementary material, which is available to authorized users.

& Afagh Moattari

Moattaria@sums.ac.ir

1 Influenza Research Center, Department of Bacteriology and Virology, Shiraz University of

Medical Sciences, 71348-45794 Shiraz, Iran

123

Acta Biotheor (2015) 63:183–202

DOI 10.1007/s10441-015-9260-1

2010; Taubenberger and Morens 2010). Among many subtypes of influenza A virus,

H1N1, H2N2, and H3N2 subtypes have efficiently been adapted to transmit to and

infect humans (Bouvier and Lowen 2010; Schrauwen and Fouchier 2014). For many

years, H1N1 has accounted for most influenza epidemics. Unlike seasonal influenza,

it has caused severe respiratory illness with high mortality rates (worldwide, 20–50

million deaths in 1918) (Ma et al. 2011; Taubenberger and Morens 2006). The

emergence and transition of type A (H1N1) pdm09 in 2009 resulted in a new

pandemic as declared by the World Health Organization (WHO, 6 August 2010).

The virus RNA encodes eleven proteins including HA, NA, NP, M1, M2, NS1,

NEP, PA, PB1, PB1-F2, PB2, of which hemagglutinin (HA) and neuraminidase

(NA) are two surface glycoproteins that interact with cellular receptors and play an

important role in cellular attachment (Kapoor and Dhama 2014; Mishin et al. 2005).

Mishin et al. (2005) reported the role of HA in binding to cellular receptors and

the functional balance between HA and NA in influenza virus infection. HA is

synthesized in the endoplasmic reticulum as an HA precursor (HA0) that is post-

translationally cleaved into two subunits of HA1 and HA2 (Boulay et al. 1988).

Influenza A virus cellular receptors contain terminal neuraminic acid (NeuAc)

moieties (Mishin et al. 2005). Pathogenicity, virus infection and spread of the virus

depend on the HA0 cleavage. The HA1 subunit carries the NeuAc-binding site, and

the HA2 subunit is responsible for fusion of viral and cellular membrane (Mishin

et al. 2005).

Structurally, HA is a trimer glycoprotein and comprises a globular head and stem

regions. Globular region includes receptor binding domain and major antigenic sites

and the stem consists of fusion peptide that supports globular domain (Das et al.

2010; Wang et al. 2009).

HA modification includes glycosylation and phosphorylation. HA co-transla-

tional or posttranslational glycosylation modification is essential for folding and

transport (Anwar et al. 2006; Das et al. 2010; Wang et al. 2009).

Frequent mutations in HA are related to variation in antigenic epitopes that affect

the antibody recognition, escape from the immune responses, and impacts on

vaccination (Han and Marasco 2011).

Eighteen HA subtypes were recognized and for some subtypes high resolution

crystal structures were determined (H1, H2, H3, H5, H7, H9, H14) (Sun et al. 2010;

Tong et al. 2012, 2013).

Several studies have focused on the relationship between functional and

structural properties of HA subtypes and determined important structures related

to special function (Isin et al. 2002; Sriwilaijaroen and Suzuki 2012; Sun et al.

2010).

These studies provided beneficial data to identify the corresponding structural

and functional modules in HA. Comparing the similarities and differences between

HA modules could usefully define other HA molecule’s properties.

Bioinformatic analysis of HA is a favorable and useful method to determine

several changes in amino acids, modification sites, B cell and T cell epitopes (Das

et al. 2010; Sun et al. 2010). The study of amino acid variations related to epitopes

could lead to a new generation of vaccine against influenza. This study attempted to

determine the major changes in the HA protein of influenza viruses isolated in the

184 A. Moattari et al.

123

Virology Department of Shiraz University Medical School in 2010 and 2013,

compared with those of vaccine strains introduced by the World Health Organi-

zation (WHO).

2 Materials and Methods

2.1 Sampling

The present study comprised 772 patients selected from pandemic Influenza A

(H1N1) infections in Shiraz, southern Iran, between May 2010 to February 2013.

The specimens collected from the patients were placed in viral transport media and

transported, under refrigeration to the virology laboratory of Shiraz University of

Medical School (SUMS) and stored at -70 �C until tested. The study was approved

by the ethics committee of SUMS.

2.2 RNA Extraction and Real Time Reverse Transcription (rRT)-PCR

RNA extraction was carried out using Roche High Pure Viral RNA Extraction Kit

(Roch, Mannheim, Germany) according to the manufacturer’s instructions.

Extracted RNAs were kept at -80 �C until further processing, where rRT-PCR

was carried out using SuperScript III Platinum One-Step Quantitative RT-PCR kit

manufactured by Invitrogen. Real time runs were performed on the Corbett 6000

Rotor Gene system. The reaction comprised 4 ll of the extracted RNA combined

with 16 ll of the master mix, including 29 reaction mix, SuperScript III RT/

Platinum Taq Mix, 5.4 ll RNase-DNase Free water and 0.4 ll of each primer and

probe. Each isolate of RNA was tested by separate primer/probe sets for detection of

influenza universal swine (swFLUA), swine H1 and RNase P. According to the

CDC Real time RT-PCR protocol, the cycling conditions included a 30 min RT step

at 50 �C, followed by enzyme inactivation at 95 �C for 2 min. PCR step included 45

cycles at 95 �C for 15 s, 55 �C for 30 s, and 72 �C for 30 s. Data collection and

analysis of the real-time PCR assay were accomplished using the Rotor-Gene data

analysis Software, Version 6.0A.

The isolates were positive for H1N1pdm09 grown in MDCK cells.

2.3 Virus Isolation

The swabs were vortexed in 5 ml DMEM medium for a few minutes to dislodge and

suspend adherent viruses. The Madin–Darby canine kidney cell confluent mono-

layers were inoculated with 200 microliters of the viral suspension proven positive

by Real Time PCR. The monolayers were maintained in the serum free Dulbeco’s

Modified Eagle’s Medium (Sigma) and supplemented with 2 mg/ml trypsin (Gibco

BRL, Life Technologies), 100 lg/ml streptomycin and 100 units/ml penicillin G.

The cultures were incubated at 34 �C and examined daily for cytopathic effect

which was confirmed by the ability of infected cultures to agglutinate guinea pig

erythrocytes no later than 7 days post-infection.

In Silico Functional and Structural Characterization of… 185

123

2.4 Sequencing

The PCR products of 8 HA gene isolated in 2010 and 2 HA gene isolated in 2013

were purified by a commercial gel extraction kit (QiagenGmbH, Hilden, Germany)

and subsequently sequenced. The nucleotide sequences obtained in this study were

submitted to Gen Bank under the following accession numbers.

2.5 Selection of HA for Analysis

For bioinformatic analysis, 10 sequences were submitted (full length: 1701 bp, 567

amino acids):

GenBank:HQ419004.1(A/Shiraz/1/2010(H1N1), GenBank:HQ419005.1(A/Shi-

raz/2/2010(H1N1), GenBank:HQ419006.1(A/Shiraz/3/2010(H1N1), GenBank:HQ4

19007.1(A/Shiraz/4/2010(H1N1), GenBank:HQ419008.1(A/Shiraz/5/2010(H1N1),

GenBank:HQ419009.1(A/Shiraz/6/2010(H1N1), GenBank:HQ419010.1(A/Shiraz/

7/2010(H1N1), GenBank:HQ419011.1(A/Shiraz/8/2010(H1N1), GenBank:KJ7812

17.1(A/Shiraz/38/2013(H1N1), GenBank:KJ781218.1(A/Shiraz/43/2013(H1N1) and

three vaccine isolates GenBank:FJ981613(A/California/07/2009(H1N1), GenBank:

CY058519 (California/07/2009 x NYMC X-157), GenBank:CY030232(A/Brisbane/

59/2007(H1N1)) were obtained from http://www.ncbi.nlm.nih.gov.

For easier reading, abbreviations were used instead of the names of isolates:

Shiraz1–Shiraz 8, Shiraz 38, Shiraz 43, Calif, Calif X-157, and Brisbane (Table 1).

2.6 Amino Acid Changes and Phylogenetic Trees

For analysis of the mutations in all 13 HA sequences, translated and editing were

carried out with the CLC sequence viewer version Beta (QIAGEN). The alignment

of the translated peptides of all sequences was generated using CLUSTAL X

software, version 1.81. Phylogenetic trees were constructed by neighbor–joining

Table 1 Abbreviations were

used instead of isolated namesGenBank Abbreviations

HQ419004.1(A/Shiraz/1/2010(H1N1) Shiraz 1

HQ419005.1(A/Shiraz/2/2010(H1N1) Shiraz 2

HQ419006.1(A/Shiraz/3/2010(H1N1) Shiraz 3

HQ419007.1(A/Shiraz/4/2010(H1N1) Shiraz 4

HQ419008.1(A/Shiraz/5/2010(H1N1) Shiraz 5

HQ419009.1(A/Shiraz/6/2010(H1N1) Shiraz 6

HQ419010.1(A/Shiraz/7/2010(H1N1) Shiraz 7

HQ419011.1(A/Shiraz/8/2010(H1N1) Shiraz 8

KJ781217.1(A/Shiraz/38/2013(H1N1) Shiraz 38

KJ781218.1(A/Shiraz/43/2013(H1N1) Shiraz 43

FJ981613(A/California/07/2009(H1N1) Calif

CY058519 (California/07/2009 9 NYMC X-157) Calif x-157

CY030232(A/Brisbane/59/2007(H1N1) Brisbane

186 A. Moattari et al.

123

(NJ) and maximum-likelihood (ML) methods, 100 times, to confirm the reliability

of phylogenetic trees.

2.7 Primary Sequence Analysis

Theoretical isoelectric point (PI), molecular weight, total number of positive and

negative residues, extinction coefficient, instability index, aliphatic index and grand

average hydropathy (GRAVY) were evaluated using the ‘‘Expasy’sProtParam’’

(http://expasy.org/tools/protparam.html), (Gasteiger et al. 2005).

‘‘PROTSCALE’’ (http://us.expasy.org/tools/protscale.html) was used to calculate

the number of codons, bulkiness, polarity, refractivity, recognition factors, hy-

drophobicity, transmembrane tendency, percent buried residues, percent accessible

residues, average area buried, average flexibility, relative mutability, and the

number of amino acids (Gasteiger et al. 2005).

2.8 Immuno-Informatic Analysis

B cell epitopes’ positions were determined at www.immuneepitope.org (http://tools.

immuneepitope.org/tools/bcell/iedb_input). The server uses the following methods:

Chou and Fasman method of Chou and Fasman (2006) used for Beta-Turns (Karplus

and Schulz 1985) for predicting the flexibility; Emini method (Emini et al. 1985) for

predicting surface accessibility and Parker method (Parker et al. 1986) for hy-

drophilicity evaluation.

Linear B cell epitopes were also predicted by Bepipred (Larsen et al. 2006)

(http://www.cbs.dtu.dk/services/BepiPred/) software. BcePred software at http://

www.imtech.res.in/raghava/bcepred was run on sequences to detect polarity-based

B cell epitopes in addition to properties used by the previous server (Saha and

Raghava 2004). ABCpred software at http://www.imtech.res.in/raghava/abcpred/

predicted B cell epitopes (Saha and Raghava 2006b).

Probability of antigenicity was estimated at http://www.ddg-pharmfac.net/

vaxijen/VaxiJen/VaxiJen.html website using VaxiJen software (Doytchinova and

Flower 2007). Default threshold of the software was 0.4. Also AlgPred (Saha and

Raghava 2006a) at http://www.imtech.res.in/raghava/algpred/submission.html was

used regarding IgE epitopes.

2.9 Functional Characterization

DISPHOS (http://www.dabi.temple.edu/disphos/pred.html) (Iakoucheva et al. 2004)

and NetPhos (http://www.cbs.dtu.dk/services/NetPhos/) (Blom et al. 1999) were

used to predict serine, threonine and tyrosine phosphorylation sites in eukaryotic

proteins. NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) (Blom et al. 2004)

was used to determine kinase specific phosphorylation sites in eukaryotic proteins.

N-glycosylation sites were predicted using NetNGlyc (http://www.cbs.dtu.dk/

services/NetNGlyc/), (Gupta and Brunak 2002) and GlycoEP (http://www.imtech.

res.in/raghava/glycoep/submit.html) (Chauhan et al. 2013).

In Silico Functional and Structural Characterization of… 187

123

2.10 Secondary Structure Prediction

SOPMA software at http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_

sopma.html (Geourjon and Deleage 1995) was used to predict the secondary

structure of all sequences. The results were confirmed by Phyre server at http://

www.sbg.bio.ic.ac.uk/phyre (Kelley and Sternberg 2009); ALPHAPRED, Be-

taTpred2 (Kaur and Raghava 2003b), and GAMMAPred (Kaur and Raghava 2003a)

software at http://www.imtech.res.in and RONN at http://www.strubi.ox.ac.uk/

RONN (Yang et al. 2005).

2.11 Tertiary Structure Prediction and Validation

All 3D structures were built in I-TASSER (Roy et al. 2010) at http://zhanglab.ccmb.

med.umich.edu/I-TASSER, Phyre2server (Kelley and Sternberg 2009) at http://

www.sbg.bio.ic.ac.uk/*phyre2/html and (PS)2 Server (Chen et al. 2006) at http://

ps2v2.life.nctu.edu.tw. Qmean (Benkert et al. 2008) at http://swissmodel.expasy.

org/qmean/cgi/index.cgi was employed to evaluate the stereochemistry and quality

of the models. The Ramachandran plots were mapped by Rammpage at http://

mordred.bioc.cam.ac.uk/*rapper/rampage.php.

3 Results

3.1 Phylogenetic Results

Phylogenetic tree for 13 isolates is shown in Fig. 1 by NJ method. The two main

clades are shown in tree. The upper clade was divided into two clusters. In the first

cluster Calif and CalifX-157 were closer than Brisbane and in the second cluster

Shiraz 38 and Shiraz 43 were very close with 94 bootstrap score. Down clades were

divided into two clusters, where the first cluster included Shiraz 4, Shiraz 5 and the

second cluster contained other isolates. In addition, by ML method, two main clades

are shown in tree, and the upper clade was divided into two clusters; in the first

cluster Shiraz 1–8 and Calif X-157 and calif were close, and in the second cluster

Shiraz 38 and 43 were very closely related. Down clade include Brisbane isolate.

3.2 Amino Acid Changes

Comparison of the patient and vaccines’ isolates showed changes in several amino

acids. Computable changes were observed in 100, 220, and 104 positions in Calif

and Calif X-157., and similar changes were found in all isolates’ sequences

(Table 2).

3.3 ProtParam and Protscale Properties

A variety of protein sequences were evaluated using ProtParam, ProtParam physico-

chemical properties, molecular weight, aliphatic index. The grand averages of

188 A. Moattari et al.

123

hydropathicity were similar in patients’ isolates and vaccine sequences. PI analysis

results were divided into 3 groups. The first group was vaccine and Shiraz 6 isolates

(pI 7.19–6.74), the second was Shiraz 7 (pI 7.51) isolate, and the third group

included Shiraz 1–Shiraz 5, Shiraz 8, Shiraz 38 and Shiraz 43 (pI 7.81–8.22)

isolates. There was no significant difference between the instability indexes of the

isolates, which was predicted as stable proteins.

‘‘PROTSCALE’’ results for several properties of patient and vaccine isolates

showed no significant difference; the results revealed a high degree of similarity in

many of the isolates’ features.

Fig. 1 Phylogenetic tree for 13 sequences, bootstrap 100. By NJ and Ml methods

In Silico Functional and Structural Characterization of… 189

123

Table

2Comparisonofam

inoacid

residues

changes

amongisolatesandvaccineisolatesCalifornia

andCalifornia

X-157

Shiraz

1Shiraz

2Shiraz

3Shiraz

4Shiraz

5Shiraz

6Shiraz

7Shiraz

8Shiraz

38

Shiraz

43

Popular

amino

acid

changes

P100S

??

??

??

??

?

S220T

??

??

??

??

?

N104K

?N104E

??

??

??

S102T

??

??

??

G105W

??

??

??

?

D291N

??

?

I338V

??

??

?

V251I

?

E391K

??

?

H155R

?

S202T

?

E516K

?

I477M

? G157A

?

C320Y

?

S468N

?

V226L

?

N461D

?

T305P

?

Unique

amino

acid

changes

G242R,

E252Q,

E260Q

A261P

D103H,

T262P,

T316K,

P321S

Y175F,P176S,

S181T,T183L,

A278V,G378K

I341V,

G478K

S138N,

R222K

D239G

Y10F,I22L,

D114N,K300Q,

P314L,D489N

Q205H,S285P,

K300E,V428L,

N472K,A549E

190 A. Moattari et al.

123

3.4 B Cell Epitopes Analysis

Proteins’ sequences position containing the B cell epitopes at 80 % identity level by

immuneepitope results are shown in Table 3,where they confirmed three common

regions except for Brisbane (141–145, 173–175, 501); 104–108 regions were found

in Shiraz 38, Calif, and Calif X-157.

Linear B cell epitopes by Bepipred analysis demonstrated 3 distinct conserved

regions (28–32, 138–142, 499–507) and some common regions among the isolates.

These include 100–107 regions in Shiraz 38, Shiraz 43, Brisbane, Calif, Calif X-157

and 289 region in Shiraz 4, Shiraz 7, Shiraz 38, Shiraz 43, Calif, and Calif X-157.

BcePred results identified several similar B cell epitopes’ regions in all isolates

including 114–120, 129–133, 462–468, 370–372 and 506–518. The regions shared

by all isolates, except Brisbane, were 4. Shiraz 43 showed two new regions as

156–160 and 506–518.

ABCpred result revealed 16 meric peptide sequences as B cell epitopes for 13

protein sequences (Table 4). Three 16 meric conserved regions (279, 357, 351) were

determined; region 300 was common to all sequences except Brisbane.

Epitopes having vaxijen cutoff value was considered as 0.4 for identification of T

cell epitopes, the results showed no significant difference and all them were

probably antigenic. Based on prediction by AlgPred, none of the proteins’

sequences was allergen.

3.5 Functional Analysis

Prediction of serine, threonine and tyrosine phosphorylation sites by DISPHOS,

NetPhos and kinase specific phosphorylation sites by NetPhosK are shown in

Table 5. DISPHOS program output showed that Shiraz 1–Shiraz 8 had some similar

regions (123, 127, 209, 215, 287, 501, 507); these were also shown in Calif and

Table 3 Results of B cell epitopes by ‘‘immuneepitope’’

Isolates B cell epitopes positions

Shiraz 1 141–145 173–175 501

Shiraz 2 ? ? ?

Shiraz 3 ? ? ?

Shiraz 4 ? ?

Shiraz 5 ? ? ?

Shiraz 6 ? ? ?

Shiraz 7 ? ? ?

Shiraz 8 ? ? ?

Shiraz 38 ? ? ? 104–108

Shiraz 43 ? ? ? 102–103 108–110

Brisbane ? 107, 156, 500

Calif ? ? ? ? 100–102

Calif X-157 ? ? ? ? ?

In Silico Functional and Structural Characterization of… 191

123

Calif X-157. Phosphorylation sites in Shiraz 38 (123, 126, 127, 209, 215, 287, 501,

507) were found in Calif and Calif X-157 but a new site (101) was found in Shiraz

43. Brisbane phosphorylation sites showed a very different Phosphorylation pattern.

The study of the numbers of phosphorylation sites in the isolates revealed various

changes. These include 14 serine sites in Shiraz 1, Shiraz 2, Shiraz 5, Shiraz 8, Calif

and Calif X-157. On the other hand, 17 and 16 serine sites were found in Shiraz 3,

Shiraz 4 as well as 13 in Shiraz 38 and Shiraz 43, respectively.

In addition 8 threonine phosphorylation sites were detected in Shiraz 1, Shiraz 3,

Shiraz 38, Calif and Calif X-157. Also 9 threonine phosphorylation sites were found

in Shiraz 4 and Shiraz 8 with 7 in Shiraz 43 and 4 in Brisbane. Similar kinase

phosphorylation sites (124, 220, 224, 326, 393, 500, and 524) were identified in all

isolates except in Brisbane.

Comparison of the results of the patient and vaccine isolates indicated lack of site

221 in Shiraz 1,Shiraz 5, Shiraz 7,Shiraz 8 and Shiraz 38; 294 in Shiraz 1–Shiraz 7,

Shiraz 8. Also our analysis indicated addition of site 321 in Shiraz 3, site176 in

Shiraz 4, and site 201 in Shiraz 43. Brisbane had only 4 sites (124, 220, 392, and

499).

The outcomes of glycosylation site prediction for all protein sequences by using

NetNGlyc and GlycoEP are displayed in Table 6. NetNGlyc results showed 4

conserved glycosylation sites (28, 40, 304, and 557) for all isolates except for

Brisbane, that glycosylation sites located on 28, 40, 71, 142, 176, 303, and 556.

Similar glycosylation sites’ prediction (27, 28, 293, and 498) was shown by

GlycoEP in all isolates but not in Brisbane.

Comparison all sequences with Calif and Calif X-157 showed loss of sites 71 and

176 in Shiraz 1–Shiraz 8, Shiraz 38, and Shiraz 43 and deletion of site 304 in Shiraz

38 and Shiraz 43 and addition of 40 and 557 in Shiraz 8. Brisbane had some similar

sites with Calif and Calif X-157 (27, 28, 71, 176, and 498) and one different site

(497).

Table 4 Results of 16 meric peptide sequences as B cell epitopes by ‘‘ABCpred’’

Isolates Start codon of 16 meric peptide sequences as B cell epitopes

Shiraz 1 279 338 300 250 357 94 449 351 383

Shiraz 2 ? ? ? ? ? ? ? ? ?

Shiraz 3 ? ? ? ? ? ? ? ?

Shiraz 4 ? ? ? ? ? ? ? ?

Shiraz 5 ? ? ? ? ? ? ?

Shiraz 6 ? ? ? ? ? ? = ? ?

Shiraz 7 ? ? ? ? ? ? ? ? ?

Shiraz 8 ? ? ? ? ? ?

Shiraz 38 ? ? ? ? ?

Shiraz 43 ? ? ? ? ?

Brisbane ? 356 350 497, 337

Calif ? ? ? ? 356 93 ? ? ?

Calif X-157 ? ? ? ? ? 93 ? ? ?

192 A. Moattari et al.

123

Tab

le5

Resultsofpositionofphosphorylationsites,number

ofphosphorylationsites,andKinasephosphorilationsites

Isolates

Positionofphosphorylationsites

Numbersphosphorylationsites

Kinasephosphorilationsites

Shiraz

1123,127,209,215,287,501,507

Ser:14Thr:8Tyr:11

124,220,224,326,393,500,524

Shiraz

2?,?,?,?,?,?,?

Ser:14Thr:8Tyr:11

124,220,224,326,393,500,524

Shiraz

3?,?,?,?,?,?,?

Ser:13Thr:8Tyr:11

124,220,224,321,326,393,500,524

Shiraz

4?,?,?,?,?,?,?

Ser:13Thr:9Tyr:11

124,176,220,224,294,326,393,500,524

Shiraz

5?,?,?,?,?,?,?

Ser:14Thr:9Tyr:11

124,220,224,294,326,393,500,524

Shiraz

6?,?,?,?,?,?,?

Ser:14Thr:9Tyr:11

124,220,221,224,294,326,393,500,524

Shiraz

7?,?,?,?,?,?,?

Ser:14Thr:9Tyr:11

124,220,224,294,326,393,500,524

Shiraz

8?,?,?,?,?,?,?

Ser:14Thr:9Tyr:11

124,220,224,326,393,500,524

Shiraz

38

?,?,?,?,?,?,?,126

Ser:17Thr:8Tyr:10

124,220,224,294,326,393,500,524

Shiraz

43

?,?,?,?,?,?,?,101,106,126

Ser:16Thr:7Tyr:11

124,201,220,224,294,326,393,500,524

Brisbane

115,123,126,127,208,214,227,500,506

Ser:17Thr:4Tyr:11

124,220,392,499

Calif

95,99,106,123,126,127,209,215,220,287,501,507

Ser:14Thr:8Tyr:11

124,220,221,224,294,326,393,500,524

CalifX-157

95,99,106,123,126,127,209,215,287,501,507

Ser:14Thr:8Tyr:11

124,220,221,224,294,326,393,500,524

In Silico Functional and Structural Characterization of… 193

123

Table 6 Glycosylation sites of all 13 isolates by two softwares ‘‘NetNGlyc’’ and ‘‘GlycoEP’’

Isolates NetNGlyc GlycoEP

Shiraz 1 28, 40, 304, 557 27, 28, 293, 304, 498

Shiraz 2 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 3 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 4 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 5 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 6 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 7 ?, ?, ?, ? ?, ?, ?, ?, ?

Shiraz 8 ?, ?, ?, ? 27, 28, 40, 293, 304, 498, 557

Shiraz 38 ?, ?, ?, ? 27, 28, 293, 498

Shiraz 43 ?, ?, ?, ? 27, 28, 293, 498

Brisbane 28, 40, 71, 142, 176, 303, 556 27, 28, 71, 176, 303, 497

Calif ?, ?, ?, ? 27, 28, 71, 176, 293, 304, 498

Calif X-157 ?, ?, ?, ? 27, 28, 71, 176, 293, 304, 498

Fig. 2 Secondary structures of all sequences predicted by ‘‘SOMPA’’ and validated. Blue helix, redstrand, purple coil and green beta turn. (Color figure online)

194 A. Moattari et al.

123

3.6 Secondary Structure Prediction

Percentages of secondary structure constituents generated by SOPMA and other

softwares, and schematic display of proteins’ secondary structure are depicted in

Fig. 2.

3.7 Tertiary Structures Prediction

All 3D structures were determined by I-TASSER, Phyre2server and (PS)2, and the

predicted structures were validated using Qmean and Rammpage. Rammpage

identified 3D structure by 3 regions including favored region, allowed region and

outlying region. The analysis of the results showed that the predicted 3D structures

by Phyre2server were more reliable. Means of favored and allowed regions for

Phyre2server were 94.24 and 4.26 %; which was 89.07 and 7.11 % for I-TASSER

and 92.13 and 5.1 % for (PS)2, indicating the Phyre2server as a more credible

bioinformatic software to predict the tertiary structure of hemagglutinin.

Qmean results included two main scores, QMEAN score and Z-score, showing

the quality and reliability of tertiary structures. Means of QMEAN score and

Z-score for Phyre2server were 0.624 and 1.7; for I-TASSER as 0.49, -3.05 and for

(PS) 2 as 0.48, -3.1. The current results confirm better prediction of quality and

reliability structure by Phyre2server. The results of Qmean and Rammpage analyses

are shown in Table 7 and finally predicted 3D structure for each sequence is

displayed in Fig. 3.

The positions of phosphorylation and glycosylation sites of 2010, 2013 and

vaccine isolates are shown in Figs. 4 and 5, respectively.

4 Discussion

Bioinformatic tools are beneficial and useful methods used for analysis and

prediction of biological phenomena. Several bioinformatic tools have been

developed in recent years but validation tests are necessary to perform for all of

them. This research confirmed the validation of each tool, before they are used in

analytical studies.

The current study is a comparative analysis of some viral sequences derived from

patients between 2010 and 2013 in virology department of Shiraz University of

Medical Sciences and those of 3 vaccine isolates as control.

The results showed some amino acid changes in 13 sequences of HA related to

alignment tree. Also, the study of amino acids revealed similar changes in Shiraz 1,

Shiraz 8 in 105 and 102 positions. The study of Shiraz 38, and Shiraz 43 detected

changes in 9 amino acid residues including 391, 155, 202, 516, 320, 468, 226, 461,

and 305. The changes in amino acids could be related to diversity in modification

sites, epitopes, function and structure of HA. The comparison between amino acid

changes and properties of HA indicated widespread useful data supporting HA

functional and structural prediction of isolates derived from the patients (Das et al.

2010; Strengell et al. 2011; Sun et al. 2010, 2013).

In Silico Functional and Structural Characterization of… 195

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Table

7Validationofproteins3D

structures,Ram

mpageanalysis(%

ofresidues

infavouredregion,%

ofresidues

inallowed

region),QMEAN

score

(global

score

of

thewhole

model

reflectingthepredictedmodel

reliabilityrangingfrom

0to

1)andZ-score

isamasseurfortheabsolute

qualityofamodel

Isolates

Ram

mpageanalysis

QMEAN

score

Z-score

I-TASSER(%

)Phyre2(%

)(PS)2

(%)

I-TASSER

Phyre2

(PS)2

I-TASSER

Phyre2

(PS)2

Shiraz

189.5,6.6

94.4,4.2

93.3,4.6

0.479

0.63

0.475

-3.1

-1.63

-3.19

Shiraz

288.7,7.8

94.2,4.2

92.2,5.1

0.484

0.631

0.476

-3.09

-1.62

-3.17

Shiraz

389.5,7.4

94.0,4.4

93.1,3.9

0.501

0.622

0.482

-2.89

-1.73

-3.1

Shiraz

489.5,6.6

94.2,4.4

91.8,5.5

0.482

0.617

0.509

-3.16

-1.79

-2.8

Shiraz

588.7,7.8

94.2,4.4

91.8,5.5

0.484

0.616

0.459

-3.23

-1.8

-3.36

Shiraz

689.2,7.5

94.4,4.2

93.1,4.1

0.5

0.634

0.478

-2.83

-1.58

-3.15

Shiraz

789.3,6.6

94.4,4.2

93.1,4.1

0.47

0.625

0.462

-3.39

-1.69

-3.34

Shiraz

888.1,7.8

94.2,4.4

91.1,6.0

0.464

0.625

0.503

-2.89

-1.68

-2.87

Shiraz

38

89.9,7.4

94.2,4.2

91.1,6.0

0.511

0.623

0.481

-3.16

-1.72

-3.12

Shiraz

43

89.3,6.3

94.4,4.0

91.1,6.0

0.489

0.631

0.498

-3.13

-1.62

-2.92

Brisbane

88.2,7.3

93.8,4.4

93.1,4.6

0.49

0.602

0.562

-2.84

-1.97

-2.47

Calif

89,7.2

94.4,4.2

91.5,5.5

0.512

0.634

0.447

-3.19

-1.58

-3.5

CalifX-157

89.1,6.2

94.4,4.2

91.5,5.5

0.499

0.63

0.454

-2.79

-1.62

-3.43

196 A. Moattari et al.

123

Fig. 3 3D structure of proteins, 1 H1, 2 H2, 3 H3, 4 H4, 5 H5, 6 H6, 7 H7, 8 H8, 9 H38, 10 H43, 11Brisbane, 12 California, and 13 California X-157

Fig. 4 Position of phosphorylation sites. a 2010 isolates, b 2013 isolates, c vaccine isolates

In Silico Functional and Structural Characterization of… 197

123

The primary analysis of the properties of HA sequences did not show any

relationship to amino acid changes and protein properties. Such data will be

beneficial to future analyses like cloning, expression, and purification of HA.

The comparative study between B cells epitope regions by immuneepitope and

amino acid changes showed that 141–145 and 501 were conserved regions; the lack

of 173–175 in Shiraz 4 was related to tyrosine to phenylalanine change in amino

acid 175. Also, amino acid change in amino acid 104 was related to lack of 104–108

epitope region in patient isolates except Shiraz 38, with no change in position 104;

the proline changing to serine in amino acid 100 was related to lack of 100–102

region in all patient isolates. 108–110 and 102–103 regions in Shiraz 43 isolate was

not amenable to logical interpretation.

Bepipred showed 6 conserved regions in all patient isolates including 28–32,

138–148, 200–204, 238–239, 371–377, and 499–507. Tyrosine to phenylalanine in

175 and proline to serine in 176 positions were related to lack of 174–176 epitope

region. Lack of 100–107 region in Shiraz 1 and Shiraz 8 isolates was related to

changing of asparagine to lysine in 104, serine to threonine in 102, and glycine to

tryptophan in 105 but changing of glycine to tryptophan was more important. Shiraz

1, Shiraz 3 and Shiraz 8 did not contain 287–292 region, because aspartic acid

changed to asparagine in 291.

BcePred detected many conserved regions in B cell epitopes but lack of 413–429

and 320–324 in Shiraz 1 and Shiraz 2 was not related to amino acid changes.

On the other hand, 279, 300, 357, 351 are the start codons of conserved 16 meric

regions in all patient isolates. Lack of 338–354 region in Shiraz 5 isolates was

related to isoleucine change to valine in 341. Changing of glutamic acid to lysine in

391 was responsible for lack of 383–399 region in Shiraz 4, Shiraz 5, Shiraz 38 and

Shiraz 43.

Phosphorylation is a major and important phase of HA post-translational

modifications and viral protein phosphorylation plays important roles in the

Fig. 5 Position of glycosylation sites. a 2010 isolates, b 2013 isolates, c vaccine isolates

198 A. Moattari et al.

123

influenza virus life cycle (Hutchinson et al. 2012; Wang et al. 2013). DISPHOS and

NetPhos are prevalent and helpful tools based on serine, threonine and tyrosine

phosphorylation sites in proteins.

Study of properties on proteins Complexity, hydrophobicity, and charge seem to

exist in multiple regions showed protein regions in and around the phosphorylation

sites were an important prerequisite for phosphorylation. Two dimensional analyses

of conserved phosphorylation sites (123, 127, 209, 215, 287, 501, 507) showed that

123, 209, 215 and 507 were on the helix and 127, 287 and 501 were on coil

structure.

The number of phosphorylation sites did not show a significant difference

between 2010, 2013 and vaccine isolates, but there was a limited increase in serine

sites.

Predictions of kinase specific eukaryotic protein phosphoylation sites by

NetPhosK 1.0 Server’’ with 0.7 threshold revealed all phosphoylation sites with

the highest score corresponding to the Protein kinase C (PKC) phosphorylation

sites. Some related studies have shown the important roles played by PKC in

infection and release from human cells (Root et al. 2000; Sieczkarski et al. 2003).

The analysis of data did not show any major changes in PKC phosphorylation

sites except for a new phosphorylation site in Shiraz 43 compared to vaccine

isolates.

Threonine change to serine amino acid was related to lack of 321 and 176 PKC

phosphorylation sites in Shiraz 3 and Shiraz 4, respectively.

HA is considered as a surface glycoprotein of influenza virus and glycosylation

has been shown to have important roles in many functions of HA molecules (Das

et al. 2010; Mir-Shekari et al. 1997; Sun et al. 2013).

Oligosaccharides can attach to the asparagine (Asn) side chain in N-X-(S/T)

Sequon, where X represents any residue other than proline in glycosylation

modification cotranslationally or posttranslationally.

Many types of glycans have been found on HA molecules, including high

mannose, complex type, and hybrid type. Regardless of glycan type, structure and

composition of glycans depends on the accessibility of glycosylation sequons to

host cell saccharide modifying enzymes.

In many previous studies, the great function of glycosylation has been found

including: (a) protein folding that is necessary to transport to the cell surface, (b) to

avoid accumulation in the Golgi complex, (Roberts et al. 1993) (c) receptor binding,

(d) escape from immune system by interfering with antibody recognition, (e) the

HA cleavage of glycans near the proteolytic activation site of HA modulate, and

(f) changes in receptor binding properties (Klenk et al. 2001). Studies on the

progressive increase in glycosylation sites since 1918, has shown that glycosylation

takes place specifically on the HA globular head region (Sun et al. 2013).

In the current study, glycosylation analysis showed two similar sites in 28 and

304 regions, indicated by NetNGlyc, GlycoEP softwares. Studies conducted from

2007 to 2013 showed that regions 27, 28 and 40 are the conserved sites.

Interestingly, comparison between 2009, 2010, and 2013 isolates detected a

decrease in the number of glycosylation sites without any new site.

In Silico Functional and Structural Characterization of… 199

123

The regions 27, 28, 40, 293, 304, and 498 were major locations on the coil

secondary structure and all major sites except 498(stalk) were found on globular

part of viral protein domain.

N-linked glycosylation sites in 304 were absent in Shiraz 38 and Shiraz 43

because threonine changed to proline in 305 region, regarding Asn-X-Ser/

Thrsequons where X is any amino acid except proline. This change was determined

by GlycoEP software but the results of NetNGlyc did not show any changes. This

indicates a better performance of GlycoEP compared to NetNGlyc.

Secondary and tertiary structure analysis did not show any significant differences

among patient and vaccine isolates; also the analysis showed that the main mass of

HA consisted of coils, helix, strand and turn.

Overview of all results confirmed widespread changes in 2013 isolates compared

with vaccine and 2010 isolates. Often the change in properties of HA shows

diversity in HA protein that could lead to changes in virulence and infection

mechanism of influenza virus, a condition reducing the efficiency of vaccine.

Similar studies, at different time periods, are necessary to distinguish the the

diversity and changes of HA protein as an important and multifunctional protein in

influenza virus virulence. Few studies have focused on the relationship between

experimental results and in silico analysis for HA proteins. Therefore, the results of

this study are useful for better understanding of the HA modification sites, epitope

sites, and structural analysis that are important in delineating the mechanism of

hemagglutinin action. Screening of hemagglutinin diversity is very important to

achieve better understanding of H1N1 antigenic variations, antigenic drift and

examination of vaccine efficacy of influenza vaccine.

Acknowledgments The authors would like to acknowledge Shiraz University of Medical Sciences for

financial support.

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