Bioinformatics Analysis of Domain 1 of HCV-Core Protein: Iran.

Hepatitis C virus (HCV) infection is a serious global health problem and a cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (HCC). Bioinformatics software has been an effective tool to study the HCV genome as well as core domains. Our research was based on employing several bioinformatics software applications to find important mutations in domain 1 of core protein in Iranian HCV infected samples from 2006 to 2017, and an investigation of general properties, B-cell and T-cell epitopes, modification sites, and structure of domain 1. Domain 1 sequences of 188 HCV samples isolated from 2006 to 2017, Iran, were retrieved from NCBI gene bank. Using several tools, all sequences were analyzed for determination of mutations, physicochemical analysis, B-cell epitopes prediction, T-cell and CTL epitopes prediction, post modification, secondary and tertiary structure prediction. Our analysis determined several mutations in some special positions (70, 90, 91, and 110) that are associated with HCC and hepatocarcinogenesis, efficacy of triple therapy and sustained virological response, and interaction between core and CCR6. Several B-cell, T-cell, and CTL epitopes were recognized. Secondary and tertiary structures were mapped fordomain1 and core proteins. Our study, as a first report, offered inclusive data about frequent mutation in HCV-core gene domain 1 in Iranian sequences that can provide helpful analysis on structure and function of domain 1 of the core gene. © Springer Nature B.V. 2019.

Introduction

class="Disease">HCV infection is a class="Chemical">n class="Chemical">serious global health problem and causes chronic hepatitis, liver cirrhosis, and HCC (Akuta et al. 2007; Ajorloo et al. 2015; Alborzi et al. 2017, 2015; Moayedi et al. 2018; Hashempoor et al. 2018). It is estimated that 160 million people are infected worldwide (Lavanchy 2011). The prevalence rate of HCV infection is from 0.2 up to 40% in different countries, and this prevalence in Iran is 0.16% (Sefidi et al. 2013).

Lack of an effective vaccine and therapeutic choices has leaded to the rapid growth of n class="Disease">HCV infection (Lauer aclass="Chemical">nd Walker 2001).

class="Species">HCV has six lclass="Chemical">n class="Chemical">arge different genotypes (1–6) and 70 distinct subtypes (a, b, c, etc.) globally (Martro et al. 2011). Genotyping analysis showed 30–33% difference in each genotype, and in subtypes around 20–25% (Sefidi et al. 2013). According to the current investigations in Iran, the predominant HCV subtype is 1a, followed by 3a and 1b (Sefidi et al. 2013).

class="Species">HCV is a positive-straclass="Chemical">nd Rclass="Chemical">n class="Chemical">NA virus encoding three structural components, the core protein, and two E1 and E2 envelope glycoproteins (Ajorloo et al. 2015).

The core protein has many confirmed roles: core binds Rclass="Chemical">NA aclass="Chemical">nd Dclass="Chemical">n class="Chemical">NA and has an important function in RNA packing. It has been determined that HCV-core is a nucleic acid chaperone similar to retroviral nucleocapsid (NC) proteins in act acting to rearrange HCV 3′UTR, resulting in RNA dimerization in vitro (Caval et al. 2011; Cristofari Gl; Ivanyi-Nagy et al. 2004; Steinmann et al. 2008).

class="Species">HCV-core is a highly basic proteiclass="Chemical">n that forms the viral class="Chemical">n class="Chemical">NC and has interactions with cellular proteins and signal transduction pathways. As a result of HCV-core and host cell interactions, core may have a function in persistent infection and the pathogenesis of HCV liver disease (Polyak et al. 2006; Hashempour et al. 2015).

Core protein consisted of n class="Chemical">three predicted domaiclass="Chemical">ns: aa1-aa117 domaiclass="Chemical">n 1 (domaiclass="Chemical">n D1), aa117-aa177 domaiclass="Chemical">n 2 (domaiclass="Chemical">n D2), class="Chemical">n class="Gene">and 177–191 domain 3 (domain D3) (Strosberg et al. 2010).

Domain 1 contains freclass="Chemical">queclass="Chemical">nt positively chclass="Chemical">n class="Chemical">arged amino acids, and is involved in RNA binding, promotes dimerization of the viral RNA, and has a significant role in NC formation and core envelopment by endosomal membranes (Ivanyi-Nagy et al. 2006).

Several identified mutations in domain 1 are involved in the development of class="Disease">HCC and hepatocarcinogenesis, the efficacy of triple therapy, aclass="Chemical">nd iclass="Chemical">nteractioclass="Chemical">n betweeclass="Chemical">n core aclass="Chemical">nd class="Chemical">n class="Gene">CXCL6 (Akuta et al. 2007, 2010, 2011; Fishman et al. 2009; Ogata et al. 2002; Takahashi et al. 2001; Idrees and Ashfaq 2013).

Humoral and cellular immune responses against class="Disease">HCV infections are iclass="Chemical">nefficieclass="Chemical">nt aclass="Chemical">nd there is class="Chemical">no coclass="Chemical">nviclass="Chemical">nciclass="Chemical">ng explaclass="Chemical">natioclass="Chemical">n to uclass="Chemical">nderstaclass="Chemical">nd class="Chemical">n class="Species">HCV immune pathogenesis (Gremion and Cerny 2005). However, HCV-specific IgM and IgG together were detected in acute infection. There are some outstanding proofs supporting a role for Abs in control of HCV infection and especially in reinfection (Cashman et al. 2014).

Some researchers have described a rise in anti-class="Species">HCV humeral immuclass="Chemical">ne after immuclass="Chemical">nizatioclass="Chemical">n with core proteiclass="Chemical">n (Aghasadeghi et al. 2006). Other researchers have claimed that iclass="Chemical">n class="Chemical">n class="Disease">acute HCV infection the titer of antibodies is very low, and delay in neutralizing antibody production is responsible for ineffective ability to prevent HCV infection (Netski et al. 2005). Other sources have introduced a large diversity of epitopes in HCV proteins as a possible way to escape the humoral response (Pavio and Lai 2003).

Cellular immune responses have an important role in the clearance of class="Disease">HCV infection. class="Chemical">n class="Species">Patients with cellular immune dysfunction like human immunodeficiency virus (HIV) have rapid HCV progression. Some researchers have claimed that this system promotes liver injury by cytolysis activity of infected cells. In spite of cellular immune responses, HCV often evades recognition and has the ability to persist (Ward et al. 2002).

Several studies have described a number of pathways for class="Species">HCV to escape from cell respoclass="Chemical">nses, iclass="Chemical">ncludiclass="Chemical">ng impaired oligo-/moclass="Chemical">no-specific or class="Chemical">no virus-specific class="Chemical">n class="Gene">CD4+ and CD8+, mutation of epitopes, weakness of proliferative capacity, and cytotoxicity and ability to secrete TNF-α and IFN-γ by CD8+ T cells (Neumann-Haefelin et al. 2005). In addition, regulatory T cells (Tregs) induced by HCV infection have a significant role in the impaired activity of cellular immune response (Hashempour et al. 2015; Hashempoor et al. 2010).

Bioinformatics tools are efficient means to study viruses and different parts of the class="Species">HCV geclass="Chemical">nome like core domaiclass="Chemical">ns (Idrees aclass="Chemical">nd Ashfaclass="Chemical">n class="Chemical">q 2013; Moattari et al. 2015; Dehghani et al. 2017; Nezafat et al. 2018; Atapour et al. 2018; Sarvari et al. 2014; Behzad Dehghani and Zahra Hasanshahi 2019). Bioinformatics tools are efficient means to study viruses and different parts of the HCV genome like core domains. Many programs have been developed to analyze function, structures, and modification of core protein, providing a large amount of information about core domains and important mutation sites (Akuta et al. 2007, 2010, 2011).

Current data are useful for prediction of n class="Disease">HCV disease developmeclass="Chemical">nt aclass="Chemical">nd treatmeclass="Chemical">nt respoclass="Chemical">nse. Iclass="Chemical">n this study, we employed several bioiclass="Chemical">nformatics tools to ficlass="Chemical">nd importaclass="Chemical">nt mutatioclass="Chemical">ns iclass="Chemical">n domaiclass="Chemical">n 1 of the core proteiclass="Chemical">n, geclass="Chemical">neral properties of B-cell aclass="Chemical">nd T-cell epitopes, modificatioclass="Chemical">n sites, aclass="Chemical">nd structure of domaiclass="Chemical">n 1 iclass="Chemical">n class="Chemical">n class="Disease">Iranian HCV infected samples from 2006 to 2017.

Materials and Methods

Sequence Alignment and Phylogenetic Tree

Domain 1 seclass="Chemical">queclass="Chemical">nces of 188 Iraclass="Chemical">niaclass="Chemical">n class="Chemical">n class="Species">HCV samples and reference sequences of HCV genotypes that were registered in NCBI gene bank (http://www.ncbi.nlm.nih.gov/) from 2006 to 2017 were downloaded. Homology among sequences was determined using multiple sequence alignment available in CLC- sequence viewer software under the following parameters: gap open cost, 10; gap extension cost, 1.0; and very accurate progressive alignment algorithm. Also, phylogenetic trees were analyzed through CLUSTAL X software, version 1.81, by neighbor-joining times to confirm the reliability of phylogenetic trees. The accession numbers of all sequences are displayed in Table 1.

Table 1

The accession numbers of all 188 sequences that were used in this study

2006(16)	DQ065821-DQ065836 and DQ202323
2012(1)	JN129986
2013(84)	KC118250-KC118333, KC118288
2014(28)	KC285337-KC285362, JQ341409,KF218585,KC118320
2016(59)	KT329291-KT329341, KU736828-KU736837

The accession numbers of all 188 sen class="Chemical">queclass="Chemical">nces that were used iclass="Chemical">n this study

Determination of Mutations

By considering previous studies, several significant mutations that are involved in: class="Disease">1-hepatocellular carcinoma, 2-viral respoclass="Chemical">nse to triple therapy, aclass="Chemical">nd 3-the iclass="Chemical">nteractioclass="Chemical">n betweeclass="Chemical">n core aclass="Chemical">nd class="Chemical">n class="Gene">CXCL6, were determined. All sequences were compared to find mentioned mutations (Akuta et al. 2007; Fishman et al. 2009; Ogata et al. 2002).

Physico-chemical Analysis

General properties of domain 1 (genotype 1a) were determined by employing “Expasy’sProtParam” (http://expasy.org/tools/protparam.html) and ProtScale at (http://web.expasy.org/protscale/) (Gasteiger et al. 2005).

B-Cell Epitopes Prediction

Chou and Fasman, Karplus and Schulz, Kolaskar & Tongaonkar, Emini, Parker, and BepiPred methods at http://www.immuneepitope.org (http://tools.immuneepitope.org/tools/bcell/iedb_input) were run for prediction of B-Cell epitopes positions (Chou and Fasman 2009; Karplus and Schulz 1985; Emini et al. 1985; Parker et al. 1986; Larsen et al. 2006).

On hydrophilicity, flexibility/mobility, accessibility, polarity, exposed surface and turns features by BcePred (http://www.imtech.res.in/raghava/bcepred) B-cell epitopes prediction were performed (Saha and Raghava 2004).

ABCpred software (http://www.imtech.res.in/raghava/abcpred/) predicted 16 meric B-cell epitopes (Saha and Raghava 2006a, b).

Prediction of T-Cell, CTL Epitopes and Allergic Properties

ProPred-I (http://www.imtech.res.in/raghava/propred1/) (Singh and Raghava 2003) was employed for class="Gene">MHC Class-I biclass="Chemical">ndiclass="Chemical">ng peptide predictioclass="Chemical">n aclass="Chemical">nd proposed (http://www.imtech.res.iclass="Chemical">n/raghava/propred/) was used for class="Chemical">n class="Gene">MHC Class-II binding peptide prediction. Programs were worked at a 4% default threshold by the proteasome and immunoproteasome filters on at 5% threshold (Singh and Raghava 2001).

class="Gene">MHC class I and II predictioclass="Chemical">ns were determiclass="Chemical">ned usiclass="Chemical">ng the Immuclass="Chemical">ne Epitope Database (IEDB) (http://tools.immuclass="Chemical">neepitope.org/maiclass="Chemical">n/). For predictioclass="Chemical">n of CTL epitopes, “ctlpred” aclass="Chemical">nd Aclass="Chemical">n class="Chemical">NN methods were used (48).

Probability of antigenicity was expected by VaxiJen software at http://www.ddg-pharmfac.net (Doytchinova and Flower 2007).

IgE epitopes and n class="Disease">allergic properties were estimated at http://www.imtech.res.iclass="Chemical">n/raghava/algpred/iclass="Chemical">ndex.html by usiclass="Chemical">ng AlgPred (Saha aclass="Chemical">nd Raghava 2006).

Post-modification

class="Chemical">Serine, class="Chemical">n class="Chemical">threonine, and tyrosine phosphorylation sites prediction was done using 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). Kinase specific phosphorylation sites were determined by NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) (Blom et al. 2004). NetNGlyc (http://www.cbs.dtu.dk/services/NetNGlyc/) (Gupta and Brunak 2002) and GlycoEP (http://www.imtech.res.in/raghava/glycoep/submit.html) were employed for N-glycosylation sites prediction (Chauhan et al. 2013).

Secondary and Tertiary Structure Prediction

To predict secondary and tertiary structures of core and domain 1 of genotype 1a, SOPMA at (http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html) (Geourjon and Deleage 1995), I-TASclass="Chemical">SER at (http://zhaclass="Chemical">nglab.ccmb.med.umich.edu/I-TASclass="Chemical">n class="Chemical">SER) (Roy et al. 2010), PHYRE2server at (http://www.sbg.bio.ic.ac.uk/~phyre2/html) (Kelley and Sternberg 2009), (PS)2-v2 Server at (http://ps2v2.life.nctu.edu.tw) (Chen et al. 2006) were employed. To evaluate the stereochemistry and quality of 3D structures Qmean at (http://swissmodel.expasy.org/qmean/cgi/index.cgi) (Benkert et al. 2008) was used, and the Ramachandaran plot was mapped by Rampage (http://mordred.bioc.cam.ac.uk/~rapper/rampage.php).

The Signal Peptide Prediction

The Signal peptide was predicted by “Signal-BLAST”, and “SignalP 4.1 n class="Chemical">Server”.

Prediction of Epitopes Digestion: Peptide Cutter was Used to Determine Potential Cleavage Sites Cleaved by Proteases

Research Ethics

All data were collected anonymously in accordance with legal ren class="Chemical">quiremeclass="Chemical">nts regardiclass="Chemical">ng data protectioclass="Chemical">n aclass="Chemical">nd medical coclass="Chemical">nfideclass="Chemical">ntiality. Approval from the Faculty class="Chemical">n class="Species">Human Research Ethics Committee (Shiraz University of Medical Sciences) was obtained before the commencement of the study.

Results

Mutation and Phylogenic Tree

By considering all submitted seclass="Chemical">queclass="Chemical">nces iclass="Chemical">n class="Chemical">n class="Chemical">NCBI GenBank we could not find any sequences related to 2017.

Phylogenetic tree for all seclass="Chemical">queclass="Chemical">nces was showclass="Chemical">n iclass="Chemical">n Fig. 1. All 2006 seclass="Chemical">n class="Chemical">quences were placed in a cluster at the bottom of the tree, and a sequence of 2012 has a high similarity to KF218585.1 (2014). The majority of sequences were closer to 1a and 3a than other reference sequences.

Fig. 1

Phylogenetic tree based on domain1 sequences and by using neighbor joining method. The phylogenetic tree was constructed by the NJ method. The numbers at the forks show the numbers of occurrences of the repetitive groups to the right out of 100 bootstrap samples. All used reference sequences were showed after accession numbers (1a, 1b, and etc.). Sequences were categorized in five major clusters

Phylogenetic tree based on domain1 seclass="Chemical">queclass="Chemical">nces aclass="Chemical">nd by usiclass="Chemical">ng class="Chemical">neighbor joiclass="Chemical">niclass="Chemical">ng method. The phylogeclass="Chemical">netic tree was coclass="Chemical">nstructed by the class="Chemical">n class="Chemical">NJ method. The numbers at the forks show the numbers of occurrences of the repetitive groups to the right out of 100 bootstrap samples. All used reference sequences were showed after accession numbers (1a, 1b, and etc.). Sequences were categorized in five major clusters

All important mutation positions were listed in Table 2; the majority of mutations happened in 2013 and 2016 samples. n class="Chemical">No mutatioclass="Chemical">n was detected iclass="Chemical">n12, 23, 25, 39, 45 positioclass="Chemical">ns.

Table 2

Frequency of all identified mutations in HCV-core domain1 sequences

	2006	2012	2013	2014	2016	Ref
11(T)	–	–	1(P)1.2%	–		1a(N)
12(K)	–	–	–	–		–
23(K)	–	–	–	–		–
25(P)	–	–	–	–		––
39(R)	–	–	–	–		–
45(G)	––	–	–	–		–
49(T)	2(V)11.7%	–	2(P) 2.4%, 5(A) 6.1%, 1(V) 1.2%	1(V) 3.5%	1(p)1.6%	–
69(R)	–	–	–	–		–
70(R)	2(Q)11.7%	–	3(Q) 3.7%, 2(H) 2.4%, 1(P) 1.2%	4(Q)14.3%	25(Q)42.3%,1(H)1.6%	3a,6a (Q)
74(R)	–	–	–	–		–
78(Q)	–	–	–	1 (R) 3.5%		2a,2b (K)
90(G)	–	–	1(L) 1.2%, 1(S)1.2%	–		–
91(C)	2(M)11.7%	–	13(M)16.4%, 3(L) 3.7%, 1(F) 1.2%	–	43(M)72.8%, 7(L)11.8	1b (M); 2a and 5a (L)
102(G)	–	–	–	–		5a (S)
110	8(T)47%, 9(N)53%	–	23(N)28.4%, 5(S)6.1%, 53(T)65.4%	18(T)64.3%, 8(N)28.57%	N (10)17%	1b, 1a, 2b(T); 1c(S); 4a, 3a, 6a, 2a, 5a(N)

The majority of mutations happened in amino acid residues 49, 70, 91, 110

Freclass="Chemical">queclass="Chemical">ncy of all ideclass="Chemical">ntified mutatioclass="Chemical">ns iclass="Chemical">n class="Chemical">n class="Species">HCV-core domain1 sequences

Protparam analysis

Protparam results for domain 1 are listed in Table 3. Because of the high percentage of n class="Chemical">basic amino acids, domaiclass="Chemical">n 1 is a highly basic peptide (Theoretical pI: 12). The iclass="Chemical">nstability iclass="Chemical">ndex, aclass="Chemical">n estimate of the stability of a proteiclass="Chemical">n iclass="Chemical">n a test tube, showed that domaiclass="Chemical">n 1 is aclass="Chemical">n uclass="Chemical">nstable peptide. Aliphatic iclass="Chemical">ndex, a positive factor for the iclass="Chemical">ncrease of thermostability of proteiclass="Chemical">ns, iclass="Chemical">ndicated that this peptide is a thermostable peptide. GRAVY is a hydropath city iclass="Chemical">ndex aclass="Chemical">nd iclass="Chemical">ncreasiclass="Chemical">ng positive score iclass="Chemical">ndicates a greater hydrophobicity, so this peptide is a hydrophilic peptide.

Table 3

Domain1 physicochemical properties computed by “Protparam”

Amino acid composition	Number	Percentage (%)
Number of amino acids: 117
Molecular weight: 13320.2
Theoretical pI: 12.05
Ala (A)	4	3.40
Arg (R)	21	17.90
Asn (N)	5	4.30
Asp (D)	2	1.70
Cys (C)	1	0.90
Gln (Q)	6	5.10
Glu (E)	3	2.60
Gly (G)	16	13.70
Ile (I)	2	1.70
Leu (L)	6	5.10
Lys (K)	7	6.00
Met (M)	1	0.90
Phe (F)	1	0.90
Pro (P)	17	14.50
Ser (S)	7	6.00
Thr (T)	6	5.10
Trp (W)	5	4.30
Tyr (Y)	3	2.60
Val(V)4		3.40
Total number of negatively charged residues (Asp + Glu)	5
Total number of positively charged residues (Arg + Lys)	28
The estimated half-life is
Mammalian reticulocytes, in vitro	30 h
Yeast, in vivo	> 20 h
Escherichia coli, in vivo	> 10 h
The instability index (II)	77.69 unstable
Aliphatic index	40
Grand average of hydropathicity (GRAVY)	− 1.424

ProtScale Analysis

Hydropathicity analysis by Kyte J. and Doolittle R.F. method showed that the major part of the peptide had a negative score; the maximum hydropath city score was on n class="Chemical">aa34 (valiclass="Chemical">n) aclass="Chemical">nd the miclass="Chemical">nimum hydropath city score was oclass="Chemical">n aa 14 (class="Chemical">n class="Chemical">asparagine).

Amino acids flexibility predicted by Bhaskaran R Ponnuswamy P.K method indicated that the maximum flexibility was around amino acid 58 (n class="Chemical">proline) aclass="Chemical">nd the miclass="Chemical">nimum was arouclass="Chemical">nd aa 95 (class="Chemical">n class="Chemical">glycine).

Transmembrane (TM) tendency calculated by Zhao, G., London E. method, showed that the major part of peptide had a negative score, and the maximum transmembrane tendency was on class="Chemical">aa34 (class="Chemical">n class="Chemical">valine) and the minimum was on aa10 (lysine).

Peptide polarity predicted by Grantham R method showed that the maximum polarity was on amino acid 12 class="Gene">and 13 (class="Chemical">n class="Chemical">lysine and arginine) and the minimum polarity was on aa 34 (valine).

http://www.immuneepitope.org online software:

Chou and Fasman Beta-Turn Prediction, which is based on the rationale for predicting turns to predict antibody epitopes, showed one high score region, 99–112.

Emini Surface Accessibility Prediction, which is based on surface accessibility scale, showed two high score positions (4–20 and 49–61).

Karplus and Schulz flexibility scale was used for B-cell prediction; this method is based on mobility of protein segments on the basis of the known temperature B factors of the a-n class="Chemical">carbons of 31 proteiclass="Chemical">ns of kclass="Chemical">nowclass="Chemical">n structure. Results democlass="Chemical">nstrated two positioclass="Chemical">ns with the highest score (50–60 aclass="Chemical">nd 5–12).

Kolaskar & Tongaonkar Antigenicity method is based on physicochemical properties of amino acid residues and their fren class="Chemical">queclass="Chemical">ncies of occurreclass="Chemical">nce iclass="Chemical">n experimeclass="Chemical">ntally kclass="Chemical">nowclass="Chemical">n segmeclass="Chemical">ntal epitopes to predict aclass="Chemical">ntigeclass="Chemical">nic determiclass="Chemical">naclass="Chemical">nts oclass="Chemical">n proteiclass="Chemical">n. Results showed five positioclass="Chemical">ns of 20–39, 43–49, 63–69, 78–85, aclass="Chemical">nd 93–101.

Parker Hydrophilicity Prediction method is based on peptide retention times during high-performance lin class="Chemical">quid chromatography (HPLC) oclass="Chemical">n a reversed-phase columclass="Chemical">n. By this method, two regioclass="Chemical">ns were fouclass="Chemical">nd: 9–16, 51–57.

Linear B-cell epitopes were determined using BepiPred. This method is based on a combination of a hidden Markov model and a propensity scale method. class="Chemical">Three regioclass="Chemical">ns (1–25, 51–84, class="Chemical">n class="Gene">and 102–116) were founded by BepiPred analysis.

Bcepred Results

For a combination of all physicochemical properties (hydrophilicity, flexibility/mobility, accessibility, polarity, exposed surface, and turns) for linear B-cell epitope prediction based on physicochemical properties on a non-redundant dataset:

Using bcepred online software, n class="Chemical">three regioclass="Chemical">ns (4–22, 47–59, class="Chemical">n class="Gene">and 109–115) with the highest combined score were found.

Five 16 meric conclass="Chemical">served regioclass="Chemical">ns (78, 49, 1, 64 class="Chemical">n class="Gene">and 102) were found by ABCpred prediction Server (Table 4).

Table 4

16 meric conserved B-cell epitopes regions in HCV-core domain1, predicted by ABCpred online software

Rank	Sequence	Position	Score
1	QPGYPWPLYGNEGCGW	78–93	0.92
2	TRKTSERSQPRGRRQP	49–64	0.9
3	MSTNPKPQKKNKRNTN	1–16	0.89
4	PIPKARRPEGRTWAQP	64–79	0.86
4	GSRPSWGPTDPRRRSR	102–117	0.86

16 meric conn class="Chemical">served B-cell epitopes regioclass="Chemical">ns iclass="Chemical">n class="Chemical">n class="Species">HCV-core domain1, predicted by ABCpred online software

VaxiJen Prediction

According to a predefined cutoff of VaxiJen program, domain 1 was confirmed as a probable antigen (model: virus and n class="Chemical">threshold: 0.4).

IgE Epitopes

The prediction of allergenic proteins by mapping of IgE epitope, SVM, and hybrid methods showed that domain 1 was not an allergen protein.

T Cell-Epitopes

Regarding T-cell responses against class="Species">HCV, previous researches fouclass="Chemical">nd some hosts’ class="Chemical">n class="Species">human leukocyte antigen (HLA) alleles associations with HCV infection in Iranian patients. We found several epitopes of HLA’s shown in Table 5.

Table 5

HLA predicted epitopes in HCV-core domain1 sequence (genotype 1a) for HLA’s that were determined by previous researches in Iranian patients

HLA types	Epitopes positions
HLA-A1	1_10
HLA-A2	29–44, 77–85
HLA-A3	2–10,29–59, 96–104
HLA-B*3501	6–14, 27–35, 41–49, 57–65,78–91, 99–116
HLA-B*3801	29–37, 77–85, 89–97
HLA-Cw*0401	23–37, 58–65, 78–93
HLA-Cw*0702	27–35, 73–86
HLA-DQA1*05:01	2–16,13–27
HLA-DQA1*0201	NO
HLA-DQB1*0602	2–16,17–31, 102–116
HLA-DQB1*0301	96–104
HLA-G: HLA-G*01:01, HLA-G*01:02	4–16,8–21, 22–33, 66–79, 100–112
HLA-DRB1*0301	96–104
HLA-DRB1*0305	96–104
HLA-DRB1*0309	96–104
HLA-DRB1*0701	No
HLA-DRB1*11	34–42

class="Gene">HLA predicted epitopes iclass="Chemical">n class="Chemical">n class="Species">HCV-core domain1 sequence (genotype 1a) for HLA’s that were determined by previous researches in Iranian patients

Some studies found both class="Gene">CD4 helper aclass="Chemical">nd class="Chemical">n class="Gene">CD8 CTL responses against HCV infection. ctlpred found several epitopes for CTL (Table 6).

Table 6

CTL epitopes in HCV-core domain 1; the high score epitopes are displayed

Peptide Rank	Position	Sequence	Score	Prediction
1	48–56	ATRKTSERS	1	Epitope
2	80–88	GYPWPLYGN	1	Epitope
3	2–10	STNPKPQKK	0.99	Epitope

CTL epitopes in n class="Species">HCV-core domaiclass="Chemical">n 1; the high score epitopes are displayed

Postmodification

Prediction of class="Chemical">serine, class="Chemical">n class="Chemical">threonine, and tyrosine phosphorylation sites by DISPHOS showed one position (116) in domain1.

By “class="Chemical">NetPhos” software we fouclass="Chemical">nd 10 phosphorylatioclass="Chemical">n sites (Fig. 2), 6 sites for class="Chemical">n class="Chemical">serine (53, 56, 99, 103, 106, and 116) 3 sites for threonine (15, 49, and 52) and one site for tyrosine (86).

Fig. 2

Phosphorylation sites prediction for domain 1 using “NetPhos” online software. Green lines indicate 6 sites for serine, blues lines show 3 sites for threonine, and one purple line shows tyrosine. All sites with scores above the threshold of 0.5 were considered as phosphorylation sites

Phosphorylation sites prediction for domain 1 using “class="Chemical">NetPhos” oclass="Chemical">nliclass="Chemical">ne software. Greeclass="Chemical">n liclass="Chemical">nes iclass="Chemical">ndicate 6 sites for class="Chemical">n class="Chemical">serine, blues lines show 3 sites for threonine, and one purple line shows tyrosine. All sites with scores above the threshold of 0.5 were considered as phosphorylation sites

class="Chemical">NetPhosK results determiclass="Chemical">ned four phosphorylatioclass="Chemical">n sites, class="Chemical">n class="Chemical">three threonine amino acids (3, 15, and 49) for protein kinase C and one serine (116) for protein kinase A. No glycosylation site was found by NetNGlyc and GlycoEP.

Secondary structure prediction for core and domain1 by using SOMPA software was summarized in Table 7 and Fig. 3.

Table 7

Percentage of secondary structures in core and domain1

	Alpha helix (Hh)	Extended strand (Ee)	Beta turn (Tt)	Random coil (Cc)
Core	39, 20.42%	25, 13.09%	14, 7.33%	113, 59.16%
Domain 1	NO	19, 16.24%	10, 8.55%	88, 75.21%

Fig. 3

Secondary structure prediction using SOMPA. Red region is extended strand, blue is the alpha helix, green is beta turn, and purple is the random coil. The majority of core structure belongs to random coil

SOMPA showed there was no alpha helix structure in domain1 and the major part of it was the random coil. But the combination of (PS)2-v2 and PHYRE2 showed there was an alpha helix structure in 8–15 region (Figs. 4, 5). All programs displayed extended strand in the 29–36 region.

Fig. 4

Secondary structure prediction using PHYRE2. The result of this tool shows that the majority of the core structure (40%) is alpha helix which is indicated with green helix, also the confidence keys of the predicted structure for these regions are high

Fig. 5

Secondary structure prediction using (PS)2-v2. C coil, H helix, and E extended strand. The majority of core structure contains coil structure

3D structures were determined by all class="Chemical">three oclass="Chemical">nliclass="Chemical">ne software but oclass="Chemical">nly structures predicted by I-TASclass="Chemical">n class="Chemical">SER were reliable. Final structures (Figs. 6, 7) were validated by Qmean. QMEANscore and Z-score for calculated for core were 0.242 and − 5.43. The scores were not satisfactory but at least provided an overview of the core protein structure. QMEANscore and Z-score for domain 1 were 0.61 and − 1.24 confirming the quality and reliability of the predicted structure.

Fig. 6

3D structure of the core protein using “I-TASSER” program. The selected model had the highest C score and it was qualified by “Qmean”

Fig. 7

3D structure of domain1 using I-TASSER program. The selected model had the highest C score and it was qualified by “Qmean”

3D structure of the core protein using “I-TASclass="Chemical">SER” program. The selected model had the highest C score aclass="Chemical">nd it was class="Chemical">n class="Chemical">qualified by “Qmean”

3D structure of domain1 using I-TASclass="Chemical">SER program. The selected model had the highest C score aclass="Chemical">nd it was class="Chemical">n class="Chemical">qualified by “Qmean”

Ramachandran plot was assessed by RAMPAGE, and percentages of the favoured region, allowed region, and outlier region for core were 63.0%, 27.5%, and 9.5% respectively (Fig. 8). RAMPAGE results for domain1 showed 61.7% of residues in favored region and 24.3 in allowed region (Fig. 9). Figure 10 showed the showed T cell and B-cell epitopes on the surface of the core protein.

Fig. 8

Ramachandran plot was used to visualize energetically allowed regions for backbone dihedral angles ψ against ϕ of amino acid residues in modeled protein structure (LCC model) for tertiary structure of core protein by RAMPAGE; the majority of amino acids residues were in favored region (119 amino acids) and allowed region (52 amino acids)

Fig. 9

Ramachandran plot was used to visualize energetically allowed regions for backbone dihedral angles ψ against ϕ of amino acid residues in modeled protein structure (LCC model) for tertiary structure of domain1 by RAMPAGE; majority of amino acids residues were in favored region (71 amino acids) and allowed region (28 amino acids)

Fig. 10

A: the position of the B-Cell epitopes (yellow region) on core tertiary structure and B: the T-cell epitopes (yellow region) on core 3D structure

Core Signal Peptide

Both online tools “Signal-BLAST” and “SignalP 4.1 n class="Chemical">Server” were class="Chemical">not able to predict aclass="Chemical">ny sigclass="Chemical">nal peptide for core proteiclass="Chemical">n.

Cleavage Sites Prediction

The results of “PeptideCutter” prediction were summarized in Table 8. The prediction was done for all the predicted epitopes. According to the results, the antigenic epitopes that had the lower number of cleavage positions for enzymes were more potential for B cell or T cell epitopes.

Table 8

The results of predicted cleavage positions for 12 common proteases: B-cell, T-cell, and CTL predicted epitopes

Positions	Caspase	Chymotrypsin	Clostripain	Elastase	Pepsin	Proteinase K	Asp-N	Staphylococcal peptidase I	Thrombin	Proline	Trypsin
B-cell epitopes
78–93		3(8,9,16)			5(4, 7, 9,15, 16)	6(4, 6, 8, 9, 12, 16)	1(11)	1(12)
49–64			5 (2, 7, 11, 13, 14)			3(1, 4, 6)	1(5)	1(6)			6(2, 3, 7, 11, 13, 14)
1_16		1(1)	1(13)			2(3,15)				1(7)	4(9, 10, 12, 13)
64–79		1(13)	3(6, 7, 11)	2(5,14)	1(12)	6(2, 5, 9, 12, 13, 14)	1(8)	1(9)		1(8)	3(4, 6, 11)
102–117		1(6)	5(3, 12, 13, 14, 16)			2(6,9)	1(9)			1(4)	3(12, 14, 16)
CTL epitopes
48–56			2(3,8)	1(1)		4(1, 2, 5, 7)	1(6)	1(7)			3(3, 4 ,8)
80–88		2(6,7)			3(2, 5, 7)	4(2, 4, 6, 7)
2_10						1(2)				1(6)	2(8,9)
T-cell epitopes
HLA-A1
1_10		1(1)				1(3)				1(7)	2(9,10)
HLA-A2
29–44		3(6, 7, 15)	3(10, 11, 14)	2(2, 5)	4(5, 6, 8, 15)	7(1, 2, 5, 6, 7, 8, 15)			1(10)		3(10, 11, 14)
HLA-A2
77–85		1(9)		1(1)	2(5,8)	4(1, 5, 7, 9)
HLA-A3
2_10						1(2)				1(6)	2(8,9)
HLA-A3
29–59	1(25)	3(7,8,16)	7(11, 12, 15, 19, 22, 27, 31)	4 (3, 6, 18, 20)	4(6 7 9 16)	12(2, 3, 6, 7, 8, 9, 16, 18, 20, 21, 24, 26,)		1(26)	1(11)		8(11 12 15 19 22 23 27 31)
HLA-A3
96–104
HLA-B*3501
6_14			1(8)							1(2)	4(4, 5, 7, 8)
HLA-B*3501
27_35		1(9)		2(5,8)	2(8,9)	4(4, 5, 8, 9)
HLA-B*3501
41–49		1(4)	2(3,7)	2(6,8)	1(4)	4(4 6 8 9)					2(3,7)
HLA-B*3501
57–65			3(3 5 6)			1(9)					4(3, 5, 6, 11)
HLA-B*3501
78–91		2(8,9)			3(4, 7, 9)	5(4, 6, 8, 9, 12)	1(11)	1(12)
HLA-B*3501
99–116		1(9)	5(3, 6, 15, 16, 17)			2(9, 12)	1(12)			1(7)	3(3, 15, 17)
HLA-B*3801
29–37		3(7, 8, 9)	2(3, 6)	4(6, 7, 8, 9)		6(2 3 6 7 8 9)
HLA-B*3801
77–85		1(9)		1(1)	2(5,8)	4(1, 5, 7, 9)
HLA-B*3801
89–97		3(5, 8, 9)		1(6)	5(4, 5, 7, 8, 9)	5(1, 5, 6, 8, 9)		1(1)
HLA-Cw*0401
23–37		3(7, 8, 9)		2(3, 6)	4(6, 7, 8, 9)	6(2, 3, 6, 7, 8, 9)
HLA-Cw*0401
58–65			3(2,4,5)								3(2,4,5)
HLA-Cw*0401
78–93		3(8, 9 ,16)			5(4, 7, 9, 15, 16)	6(4, 6, 8, 9, 12, 16)	1(11)	1(12)
HLA-Cw*0702
27–35		1(9)		2(5, 8)	2(8,9)	4(4, 5, 8, 9)
HLA-Cw*0702
73–86		3(4, 13, 14)	1(2)	1(5)	4(3 9 12 14)	7(3 4 5 9 11 13 14)					1(2)
HLA-DQA1*05:01
2_16			1(12)			2(2,14)				1(6)	4(8 ,9, 11, 12)
HLA-DQA1*05:01
13–27			3(1,5,6)	1(10)	1(12)	3(3,10,12)	1(8)			1(7)	3(1,5,11)
HLA-DQB1*0602
2_16			1(12)			2(2,14)				1(6)	4(8 ,9, 11, 12)
HLA-DQB1*0602
17–31							1(4)
HLA-DQB1*0602
102–116			2(1,2)	2(6,15)	1(8)	4(6, 8, 14, 15)				1(3)	2(1,7)
HLA-DQB1*0301		3(1, 2, 3)	2(6, 9)	2(1, 2)		3(1, 2, 3)					2(6, 9)
HLA-G: HLA-G*01:01, HLA-G*01:02
4_16			1(10)			1(12)				1(4)	4(6 7 9 10)
8_21			3(6,10,11)			1(8)	1(13)			1(12)	5(2, 3, 5 ,6, 10)
22–33				2(1,10)	1(3)	4(1,3,9,10)					1(2)
66–79		1(11)	3(4,5,9)	2(3,12)	1(10)	5(3, 7, 10, 11, 12)	1(6)	1(7)		1(6)	3(2,4,9)
100–112		1(8)	2(2,5)			2(8,11)	1(11)			1(6)	1(2)
HLA-DRB1*0301
96–104		3(1,2,3)	2(6,9)		2(1,2)	3(1,2,3)					2(6,9)
HLA-DRB1*0305
96–104		3(1,2,3)	2(6,9)		2(1,2)	3(1,2,3)					2(6,9)
HLA-DRB1*0309
96–104		3(1,2,3)	2(6,9)		2(1,2)	3(1,2,3)					2(6,9)
HLA-DRB1*11
34–42		2(3,4)	2(7,8)	1(2)	3(2,3,5)	4(2,3,4,5)			1(7)		2(7,8)

The position of each epitope, the number of cleave sites in each epitope as well as the position of the cleave sites in each epitope are mentioned in the table

Discussion

Although emerging bacterial class="Disease">viral diseases have caused great catastrophes iclass="Chemical">n class="Chemical">n class="Species">human history which can affect from a small and localized group to millions of people across continents, several vaccine and therapies have been introduced to control them (Dehghani et al. 2014, 2013).

Fishman et al. (2009) using multivariable logistic regression models ,found 10 class="Species">HCV-core geclass="Chemical">ne polymorphisms exteclass="Chemical">nsively associated with iclass="Chemical">ncreased HCC risk (36G/C, 209A, 271C/U, 309A/C, 384U, 408U, 435A/C, 465U, 481A, 546A/C) aclass="Chemical">nd oclass="Chemical">ne sigclass="Chemical">nificaclass="Chemical">ntly liclass="Chemical">nked with decreased HCC risk (78U). Meclass="Chemical">ntioclass="Chemical">ned mutatioclass="Chemical">ns related to chaclass="Chemical">nge iclass="Chemical">n domaiclass="Chemical">n1 amiclass="Chemical">no acid seclass="Chemical">n class="Chemical">quence: N11S/T, K12Silent/N, A25V, G69S, Q70R, M91L, and L102P. All current amino acid changes decreased HCC risk except A25V (78U) (Fishman et al. 2009).

In selected seclass="Chemical">queclass="Chemical">nces, we fouclass="Chemical">nd that amiclass="Chemical">no acid 11 was T iclass="Chemical">n all seclass="Chemical">n class="Chemical">quences except one sequence in 2013 (P) and in 1a (N).

Positions 12(K) did not show any change; amino acid 25 was P and amino acid 69 was R in all seclass="Chemical">queclass="Chemical">nces. Amiclass="Chemical">no acid 70 iclass="Chemical">n class="Chemical">nearly all seclass="Chemical">n class="Chemical">quences was R except in 2 sequences in 2006 (Q), 5 sequences in 2013 (3(Q), 2(H), and 1(P)), 4 sequences in 2014 (Q), in 2016 [25(Q), 1(H) and 2 reference sequences (Q)].

Amino acid 91 in most seclass="Chemical">queclass="Chemical">nces was C except 2 seclass="Chemical">n class="Chemical">quences in 2006(M), 17 sequences in 2013 (13(M), 3(L), 1(F)),50 sequences in 2016(43(M), 7(L)) and three ref sequences, (1b(M), 2a and 5a (L)). In amino acid 102 we did not find any change but in one of the reference sequences, we found one change (5a (G to S)).

Akuta et al. (2007) employed PCR for detecting substitutions of aa 70 and aa 91 in class="Species">HCV-core geclass="Chemical">ne of geclass="Chemical">notype 1b by usiclass="Chemical">ng the mutatioclass="Chemical">n-specific primer as aclass="Chemical">n importaclass="Chemical">nt predictor of class="Chemical">n class="Disease">hepatocarcinogenesis. For wild samples, aa 70 was arginine (R) and aa 91 was leucine (L) but for mutant aa 70 was glutamine(Q)/histidine (H) and aa 91 was methionine (M) (Akuta et al. 2007).

Also, Furui et al. (2011) identified aa 70 and aa 91 substitutions among Japanese volunteer blood donors (Furui et al. 2011).

In terms of aa 70 substitutions, we recognized 2 class="Chemical">glutamine substitutioclass="Chemical">ns iclass="Chemical">n 2006 seclass="Chemical">n class="Chemical">quences and 3 glutamine, 2 histidines, one proline in 2013 sequences; also 4 glutamine substitutions in 2014 sequences, and 25 glutamine, and 1 histidine in 2016. We found several methionine residues in aa 91 in 2006, 2014, and 2016.

Ogata et al. (2002) compared seclass="Chemical">queclass="Chemical">nces of the core proteiclass="Chemical">n of Subtype 1b class="Chemical">n class="Species">HCV strains obtained from patients with and without HCC and found some amino acid mutation sites (Ogata et al. 2002). K23Q, Q70R, and T110M substitutions were found by Ogata et al. (2002). In comparison with our results, in all sequences, aa 23 was K, in the majority of sequences aa 70 was R and in 34 sequences it was Q. In aa 110 we did not find any methionine (Ogata et al. 2002).

Akuta et al. (2010) confirmed the role of class="Chemical">Gln70 (or class="Chemical">n class="Chemical">His70) in the efficacy of triple therapy and sustained a virological response, the patient with both genotype non-TT and Gln70 (His70) had the worst sustained virological response. Also, Akuta et al. (2011) by following up twenty-six patients determined the role of Gln70 (His70) substitution in the development of HCC They suggested detection of aa substitutions in the core region before antiviral therapy (Akuta et al. 2010, 2011).

Alestig et al. (2011) showed substitution in aa 70 of the core was related to treatment response, but that was less important than class="Gene">IL28B polymorphism. Iclass="Chemical">n our study, 37 seclass="Chemical">n class="Chemical">quences had Gln70 (or His70) substitution (Alestig et al. 2011).

Tokita et al. (2000) approved the role of class="Species">HCV-core regioclass="Chemical">n (class="Chemical">n class="Chemical">Thr49Pro) to reduce the fluorescence enzyme immunoassay (FEIA) sensitivity. We found 2 sequences in 2013 and one in 2016 with T to P substitutions (Tokita et al. 2000).

Findings of Horie et al. (1999) indicated that alteration from class="Chemical">glycine to class="Chemical">n class="Chemical">serine at core codon 45 was dominant in noncancerous liver portions rather than in cancerous liver portions and sera from HCC patients (Horie et al. 1999). Our results did not show any glycine to serine mutation in aa 45 and in all sequences, it was glycine.

Idrees and Ashfaclass="Chemical">q (2013) by usiclass="Chemical">ng molecular dockiclass="Chemical">ng software, reported iclass="Chemical">nteractioclass="Chemical">ns of amiclass="Chemical">no acid residues class="Chemical">n class="Chemical">arginine 149, arginine 39, arginine 74, and arginine 78 in HCV-core protein and Leu44, Ala71, Ser76 and Pro97 in CXCL6. This finding clues to understanding HCV pathogenesis. Any change in these positions can relate to HCV Infection and HCC. Our results showed no alteration in aa 39 and 74, and in aa 78 just one sequence was glutamine to arginine (Idrees and Ashfaq 2013).

Using a combination of predicted B-cell antibody epitopes by all methods on the immune epitope website and also considering bcepred and ABCpred prediction ,we could define n class="Chemical">three major epitopes (4–20, 50–60, class="Chemical">n class="Gene">and 100–112) for domain 1.

Ferroni et al. (1993) by using the algorithm of Jameson and Wolf identified four epitopes in n class="Species">HCV-core proteiclass="Chemical">n (7–21, 31–45, 49–63, aclass="Chemical">nd 99–113).

Harase et al. (1995) analyzed the response to class="Species">HCV-core proteiclass="Chemical">n iclass="Chemical">n class="Chemical">n class="Species">mice and found a major B cell epitope (21–40).

Pirisi et al. (1995) analyzed class="Chemical">Sera from 97 class="Chemical">n class="Species">HCV infected patients and found three 15-mer peptides as antigens in an enzyme immunoassay. They concluded that anti-R15P (50–64, RKTSERSQPRGRRQP) as a potent antigen might help to identify a subgroup at higher risk to develop HCC (Pirisi et al. 1995).

Also, a study on class="Species">HCV positive blood doclass="Chemical">nor by Lechmaclass="Chemical">nclass="Chemical">n et al. (1996) determiclass="Chemical">ned a regioclass="Chemical">n (aa 1–24) of domaiclass="Chemical">n1 that bouclass="Chemical">nd the aclass="Chemical">ntibodies from the class="Chemical">n class="Chemical">sera of all patients and showed a great potential for detection of HCV infection by using serological B-Cell responses tests.

Comparison of our results with previous studies revealed that all predicted epitopes in our research have a good potential for future studies of the immune response against n class="Disease">HCV infection, aclass="Chemical">nd are useful for recogclass="Chemical">nitioclass="Chemical">n of all kiclass="Chemical">nds of class="Chemical">n class="Disease">HCV infections.

Gededzha et al. (2014) used several bioinformatics tools to predict class="Gene">HLA class I aclass="Chemical">nd class="Chemical">n class="Gene">HLA class II in HCV genotype 5a. They found three T-cell epitopes of NS3, NS4B, and NS5B.

Some class="Gene">HLA class II alleles were fouclass="Chemical">nd iclass="Chemical">n Iraclass="Chemical">niaclass="Chemical">n class="Chemical">n class="Species">patients by Samimi-Rad et al. (2015); DRB1*0301, DQA1*0501, DQB1*0201, DRB1*1101, and DQB1*0301 were demonstrated in patients with HCV clearance, and DRB1*0701, DQA1*0201, DQB1*0602, DRB1*0301, DRB1*11, and DQB1*0201 occurred more frequently in chronic patients (Samimi-Rad et al. 2015).

Khorrami et al. (2015) found a relationship between class="Gene">HLA-G, class="Chemical">n class="Gene">IL-10, and response to combined therapy in HCV positive patients. They concluded that HLA-G, and IL-10 have a significant role in response to therapy with IFN-α2α and ribavirin.

Also, class="CellLine">HLA-A01 aclass="Chemical">nd class="Chemical">n class="Gene">HLA-B38 were determined as important alleles associated with Peg-IFN plus ribavirin therapy in Egyptian patients by Farag et al. (2013).

Pourhassan et al. (2014) found several class="Gene">HLA alleles associated with class="Chemical">n class="Species">HCV in Iranian patients (2011–2013). A2, A3, B35, B38, BW4, CW4and CW7 were the most frequent alleles found by this group.

In accordance with the above-mentioned studies, we collected class="Gene">HLA alleles associated with class="Chemical">n class="Disease">HCV infection, and by employing in-silico analysis we established numerous T-cell epitopes for domain1 that can be helpful for future studies to design effective vaccine against HCV genotype 1a, and can provide benefit data for better understanding the role of domain1 in immune response.

Many researchers have proved broader CTL responses to n class="Disease">HCV infection aclass="Chemical">nd the usefulclass="Chemical">ness of CTL epitopes mappiclass="Chemical">ng to develop therapeutic iclass="Chemical">nterveclass="Chemical">ntioclass="Chemical">ns or vacciclass="Chemical">nes (Sabet et al. 2014; Saeedi et al. 2014; Jazayeri aclass="Chemical">nd Carmaclass="Chemical">n 2005; Arashkia et al. 2011). Iclass="Chemical">n our research, we utilized reliable software to predict CTL epitopes aclass="Chemical">nd extracted data that caclass="Chemical">n be useful for vacciclass="Chemical">ne developmeclass="Chemical">nt studies.

By considering results of phosphorylation sites prediction by 3 programs, we concluded that 3 sites (15, 49, class="Gene">and 116) were the maiclass="Chemical">n phosphorylatioclass="Chemical">n sites iclass="Chemical">n domaiclass="Chemical">n 1. Amiclass="Chemical">no acid 116 is a class="Chemical">n class="Chemical">serine that located in Arg-Arg-Arg-Ser-Arg region; this region was similar to the usual target sequence for protein kinase A [Arg-Arg-X-Ser/Thr-X]. Two threonine amino acid residues (15, 49) were calculated as protein kinase c target sites where this kinase acts through the phosphorylation of hydroxyl groups of amino acid residue. Previous studies indicated that core protein is phosphorylated by PKA and PKC.

Core phosphorylation regulates the suppressive activity of class="Species">HCV-core proteiclass="Chemical">n oclass="Chemical">n HBV geclass="Chemical">ne replicatioclass="Chemical">n aclass="Chemical">nd expressioclass="Chemical">n. Also, it has beeclass="Chemical">n showclass="Chemical">n that phosphorylatioclass="Chemical">n iclass="Chemical">n core relates to the class="Chemical">nuclear localizatioclass="Chemical">n of the core proteiclass="Chemical">n. They democlass="Chemical">nstrated class="Chemical">n class="Chemical">three serine residues (Ser-53, Ser-116, and Ser-99) as the potential phosphorylated sites in core protein, that were similar to NetPhos software results in our research (Yassin 2001; Shih et al. 1995; Lu and Ou 2002).

Secondary structure prediction indicated that the majority of domain1 was the random coil, and all B-cell epitopes and important mutations placed on random coil structure.

Tertiary structures were designed by class="Chemical">three sigclass="Chemical">nificaclass="Chemical">nt aclass="Chemical">nd reliable oclass="Chemical">nliclass="Chemical">ne programs, but just oclass="Chemical">ne of them provided a reliable aclass="Chemical">nd high-class="Chemical">n class="Chemical">quality protein structure model for domain1. The quality and reliability of models were confirmed by QMEAN and RAMPAGE software.

By examining all the predictions core epitopes with and without signal peptide, we found out that there is no difference between these two different strategies of analysis (Pene et al. 2009; Tn class="Chemical">argett-Adams et al. 2008; Ma et al. 2007; Okamoto et al. 2008; Oehler et al. 2012).

Based on previous studies core protein has a C terminal signal peptide (170–191) and because the focus of our study was on domain 1, it was expected that the deletion of this region could not affect the epitope perdition results.

Digestion analysis to predict possible proteases was shown that each epitope can be digested by at least 5 selected proteases which can have a significant effect on the reduction of the half-life of epitopes.

Conclusion

Finally our investigations in this research provided comprehensive data about freclass="Chemical">queclass="Chemical">nt mutatioclass="Chemical">ns iclass="Chemical">n domaiclass="Chemical">n1, aclass="Chemical">nd as a first report caclass="Chemical">n be useful for future study about sigclass="Chemical">nificaclass="Chemical">nt mutatioclass="Chemical">ns aclass="Chemical">nd their role iclass="Chemical">n therapeutic pathway aclass="Chemical">nd respoclass="Chemical">nse to aclass="Chemical">ntiviral therapy for Iraclass="Chemical">niaclass="Chemical">n class="Chemical">n class="Species">patients. Also, identification of domain1 properties provides practical information for domain1 cloning and more researches.