Open Access

Sequence-based in silico analysis of well studied Hepatitis C Virus epitopes and their variants in other genotypes (particularly genotype 5a) against South African human leukocyte antigen backgrounds

BMC Immunology201213:67

DOI: 10.1186/1471-2172-13-67

Received: 25 June 2012

Accepted: 30 November 2012

Published: 10 December 2012

Abstract

Background

Host genetics influence the outcome of HCV disease. HCV is also highly mutable and escapes host immunity. HCV genotypes are geographically distributed and HCV subtypes have been shown to have distinct repertoires of HLA-restricted viral epitopes which explains the lack of cross protection across genotypes observed in some studies. Despite this, immune databases and putative epitope vaccines concentrate almost exclusively on HCV genotype 1 class I-epitopes restricted by the HLA-A*02 allele. While both genotype and allele predominate in developed countries, we hypothesise that HCV variation and population genetics will affect the efficacy of proposed epitope vaccines in South Africa. This in silico study investigates HCV viral variability within well-studied epitopes identified in genotype 1 and uses algorithms to predict the immunogenicity of their variants from other less studied genotypes and thus rate the most promising vaccine candidates for the South African population. Six class I- and seven class II- restricted epitope sequences within the core, NS3, NS4B and NS5B regions were compared across the six HCV genotypes using local genotype 5a sequence data together with global data. Common HLA alleles in the South African population are A30:01, A02:01, B58:02, B07:02; DRB1*13:01 and DRB1*03:01. Epitope binding to 13 class I- and 8 class –II alleles were described using web-based prediction servers, Immune Epitope Database, (IEDB) and Propred. Online population coverage tools were used to assess vaccine efficacy.

Results

Despite the homogeneity of genotype 1 and genotype 5 over the epitopes, there was limited promiscuity to local HLA-alleles.Host differences will make a putative vaccine less effective in South Africa. Of the 6 well-characterized class I- epitopes, only 2 class I- epitopes were promiscuous and 3 of the 7 class-II epitopes were better conserved and promiscuous. By fine tuning the putative vaccine using an optimal cocktail of genotype 1 and 5a epitopes and local HLA data, the coverage was raised from 65.85% to 91.87% in South African Blacks.

Conclusion

While in vivo and in vitro studies are needed to confirm immunogenic epitopes, in silico HCV epitope vaccine design which takes into account HCV variation and host allele frequency will maximize population coverage in different ethnic groups.

Keywords

Epitope vaccines Viral variation Population coverage HCV HLA Epitope prediction

Background

As a relatively “new” virus, only identified in 1989 [1] and first cultured successfully in 2005 [2], there is still much that is unknown about the hepatitis C virus (HCV) and this has hindered the development of an effective vaccine. The following are some of the challenges to successful HCV vaccine design.
  1. 1)

    The virus is highly mutable and exists as a quasispecies within the host and genotypes cluster geographically.

     
  2. 2)

    Host cell responses to HCV infection are poorly defined and inconsistent among infected individuals.CD4+ and CD8+ T-cell responses are also not cross-protective to heterologous genotypes [3] and, to date, there is no immunodominant epitope that is consistently found in HCV-positive individuals [4].

     
  3. 3)

    Humans are the only natural host of HCV, and suitable laboratory models have only been developed recently. The chimpanzee has been infected in the laboratory [5], but studies using this model are expensive and limited. The mouse model for viral pathogenesis studies promises a more practical and plausible alternative [6, 7].

     

Epitope-based vaccines promote an immune response by presenting immunogenic peptides (viral genotype-specific) bound to major histocompatibility (MHC) molecules (host specific) to the T cell receptor. Class II- proteins are presented to T helper cells by antigen presenting cells (APCs) with the aid of the CD4 co-receptor whereas class I- proteins are presented by the infected target cell to cytotoxic T cells with the aid of the CD8 co-receptor. The T helper response is important in directing and activating the immune response, including the effectiveness of CD8+ T cells [8].An effective vaccine must be capable of inducing and maintaining powerful CD4 and CD8 T-cell immunity in the greatest proportion of its target population.

Both HCV genotype and HLA allele frequency are distributed geographically. Viral genotype, host genetic background [9] and HLA class I- [10] and class II- alleles [11] are associated with both HCV disease progression and sustained response to therapy [12]. South Africa has diverse ethnic groups, hence a high diversity of HLA genetic background [13]. Black Africans, including the well-studied Zulu ethnic group, constitute the majority (79.4%) population in the country (Statistics South Africa, [14], http://www.statssa.gov.za/PublicationsHTML/P03022010/html/P03022010.html). Other major population groups include Caucasians (Europeans and Indian/Asian,11.8%) and those of mixed race (8.8%). The predominant HCV genotype in South Africa is genotype 5a. This little studied genotype accounts for 57% of the HCV infections in South Africa with the very well studied genotype 1 accounting for 23% [15]. In comparison, genotype 1 accounts for 70% of HCV infections in USA [16]. Hence, most peptide-based vaccines studies concentrate mainly on HCV genotype 1 epitopes restricted by HLA-A*02 which is the most common HLA allele in populations of European/Caucasian descent (New allele Frequency Database [17], http://www.allefrequencies.net).

The binding of the epitope to the HLA-molecule is a highly selective process as only 1 in 40–200 peptides would bind to the HLA class I- or II- allele with high affinity to produce an efficient immune response [18]. Computer prediction servers have made it possible to identify potentially strong peptide binders to HLA molecules that can then be tested in vitro and in vivo as putative epitopes for peptide-based vaccines. This is a cost- and time-saving exercise as it is expensive and laborious to synthesize and test several 9-mer or overlapping peptides over long target antigens. There are various computational prediction servers available and their sensitivity is constantly improving, including more than 20 prediction servers to identify HLA-II binding peptides [19].

We hypothesize that putative vaccines based on restriction by the HLA-A*02 allele and genotype 1 sequences will not perform optimally in South Africa. The aim of the study was, therefore, to investigate the heterogeneity of well studied HCV epitope sequences across HCV genotypes (with particular reference to genotype 5a) and assess their immunogenicity against prevalent local HLA-types in order to assess vaccine efficacy and population coverage in the ethnically diverse South African population. This descriptive study used web-accessible prediction servers to predict epitope binding of recently published putative epitopes for HCV vaccines against the South African HLA background. The main objectives of the study were:
  1. 1)

    To characterise the variation of selected published immunogenic epitopes within popular target antigens, focusing on South African genotype 5a data.

     
  2. 2)

    To predict the immunogenicity of these epitopes and their variants against the background of prevalent alleles in the South African target population.

     

Results

Degree of conservation between epitopes

The Weblogo consensus was generated from individual alignments of all available sequence data of HCV genotypes (1a, 1b, 2, 3, 4, 5a and 6). Thus, seven web logos were generated for each of the 13 chosen class I- (N=6) and class II- (N=7) epitopes (Table 1). The epitopes chosen for this study are well characterized and referenced (Table 1). NS4B2422-2433 has only one reference (others have 22–78 references) but it is also the only one that has a different restriction allele i.e. B15. The HCV consensus was derived from the 7 generated weblogos and the percentage conservation within each genotype over the epitope region was calculated as described in the Methods (Table 2 and Additional file 1: Figure S1).
Table 1

Six well studied HLA class I- and seven class II- restricted HCV immunodominant epitope sequences were chosen from previous publications for this study

CLASS I EPITOPES

SEQUENCE (Subtype)

RESTRICTION

REFERENCE

NUMBER OF REFERENCES LISTED AT IEDB

NS3 1073-1081

CINGVCWTV (1a)

A02

[20, 21]

78

NS3 1406-1415

KLVALGINAV (1a)

A02

[22, 23]

70

NS4 1807-1816

LLFNILGGWV (1a)

A02

[24, 25]

39

NS4 1851-1859

ILAGYGAGV (1)

A02

[22]

29

NS5B 2422-2433

MSYSWTGALVTP (1)

B15

[22]

1

NS5B 2727-2735

GLQDCTMLV (1)

A02

[22]

22

CLASS II

    

Core 17-35^

RRPQDVKFPGGGQIVGGVY (1)

Undetermined Class II allele

[26]

1

Core 21-40^

DVKFPGGGQIVGGVYLLPRR (1)

HLA-DRB1*1501

[21, 26, 27]

13

NS3 1248-1261

GYKVLVLNPSVAAT (1)

HLA-DRB1*1201; 1101; 1301; 0401

[21, 25, 28]

5

NS4A 1781-1800

LPGNPAIASLMAFTAAVTSP (1a)

Undetermined Class II allele

[25]

3

NS4A 1801-1820

LTTSQTLLFNILGGWVAAQL (1a)

Undetermined Class II allele

[25, 27, 29]

4

NS5 2571-2590

KGGRKPARLIVFPDLGVRVC (1a)

Undetermined Class II allele

[4, 25, 27, 29]

4

NS5 2661-2680

QCCDLDPQARVAIKSLTERL (1a)

Undetermined Class II allele

[27, 29]

4

^Class II- restricted epitopes in the core region are overlapping sequences.

Table 2

The sequences of the chosen epitopes were compared to the consensus sequence and conservation scores (as percentages) were calculated

CLASS I EPITOPE

Consensus Epitope sequence

HCV GENOTYPES

Mean across genotypes

MAX.

MIN.

SD

p-value

  

1

2

3

4

5

6

     

NS31073-1081

CINGVMWTV

78

67

67

67

78

67

70.67

67

78

5.680

0.3062

NS31406-1415

LTSLGLNAV

67

56

67

78

67

56

65.17

56

78

8.280

0.1645

NS41807-1816

LLFNILGGW

100

78

78

100

100

78

89.00

78

100

12.049

0.6513

NS41851-1859

ILAGYGAGV

89

67

89

78

89

67

79.83

67

89

10.815

0.2231

NS5B2422-2433

MSYSWTGAL

89

89

89

100

89

67

87.17

67

100

10.815

0.406

NS5B2727-2735

GLRDCTMLV

78

56

44

78

78

33

61.17

33

78

19.823

0.4142

 

Mean within genotypes

83.50

68.83

72.33

83.50

83.50

61.33

     

CLASS II EPITOPE

Consensus Epitope sequence

1

2

3

4

5

6

Mean across genotypes

MAX.

MIN.

SD

p-value

CORE17-40

RRPQDVKFPGGGQIVGGVYLLPRR

100

96

66

96

96

96

91.67

67

78

5.680

0.3062

NS31248-1261

GYKVLVLNPSVAAT

100

93

93

100

100

93

96.50

93

100

3.834

0.32

NS31781-1800

LPGNPAVASLMATAAVTSP

85

80

95

85

90

65

83.33

65

95

10.327

0.4142

NS41801-1820

LTTSQTLLFNILGGWVASQL

85

65

80

90

85

70

79.17

65

90

9.703

0.962

NS5B2571-2590

KGGRKPALIVYPDLGVRVC

80

80

90

95

95

80

86.67

80

95

7.527

0.2231

NS5B2661-2680

QCCDLEPEARVAIKSLTERL

85

55

70

80

60

50

66.67

50

85

14.023

0.4159

 

Mean within genotypes

89.17

78.17

82.33

91.00

87.67

75.67

     

The comparative variability of the epitope sequences within and across the different genotypes is shown in Table 2. Genotypes 2 and 6 have the lowest mean intra-genotype scores for both class I- and II- epitope sequences, indicating a greater variation among subtypes within these genotypes. There is only one subtype within genotype 5 so not surprisingly the epitope sequences, including our sequences, from subtype 5a are relatively conserved. Because a large proportion of sequences on the database belong to genotype 1a or 1b, the consensus sequences that were generated is mostly representative of genotype 1 sequences. Mean conservation scores of genotype 5 sequences are the same as that of genotype 1 for class I- (both had an average score of 83.5%) and similar for class II- (87.67% versus 89.17%, for genotypes 5 and 1, respectively for the class II epitopes). The intra-genotype variation was not statistically significant for any of the epitopes selected. Two class I- epitopes (NS4B1807-1816 and NS5B2422-2433) and four of the six class II-epitopes had the highest average conservation scores of more than 80% (Table 2). Published class II-restricted epitopes were, in general, better conserved than the class I- epitopes, both within and across the genotypes (Table 2).Some epitopes were well conserved (NS4B1807-1816 and NS5B2422-2433) while others (NS5B2727-2735 and NS5B2661-2680) were highly variable (Table 2).

Most epitopes were identified using genotype 1a sequences, hence it follows that the epitope sequences had greater identity with genotype 1. Genotype 4 epitope sequences showed a consistently high degree of correspondence with the consensus but since this genotype was represented by the smallest data set, this may not be a true reflection of variation within the genotype. Genotype 6 showed the most variability, with a mean conservation score of 61.33% within this genotype, which is to be expected since this genotype is known to be highly variable (Table 2).

Major HLA alleles

The most common HLA-A, -B and –C alleles in the South African Black population are classified into supertypes as described by [30]. For example, and as seen in Table 3, the A02 supertype includes the A*02:01 and A*68:02 alleles. The A*30:01 allele belongs to the supertype A01A03. This study predicted binding to 13 HLA class I- alleles in 8 supertypes and 8 class II- HLA-DR alleles predominant in the South African population.
Table 3

Binding affinity scores of published epitopes and their variants were determined by the IEDB prediction program to relevant supertypes in South Africa

Gene

Epitope sequence

Genotype of epitope

 

Class A- Alleles

Class B- Alleles

   

Supertypes

A01

A02

A24

A01A03

A01A24

B07

B58

B27

   

Allele type

A*01:01

A*02:01

A*68:02

A*23:01

A*30:01

A*29:02

B*07:02

B*35:01

B*53:01

B*57:01

B*58:01

B*15:03

B*27:05

NS3 (A*02)

CINGVCWTV

1a

 

17802

67

61

14908

15501

12611

23637

20927

25523

19827

13679

19257

23485

1073-1081

CVNGVCWTV

1b

 

16997

110

20

12228

13122

11766

21885

15696

13382

18288

12132

20367

23007

 

SISGVLWTV

2a variant

 

18961

11

16

21483

11417

11417

22455

22186

29702

18590

15055

15691

20667

 

TVGGVMWTV

3a

 

19940

64

8

12677

14750

9776

20729

21877

24623

16182

18054

26500

24303

 

AVNGVMWTV

4a variant

 

17734

23

14

24001

4015#

12036

10753

20258

20595

17093

12996

13641

18882

 

CINGVLWTV

5a

 

15172

26

39

17548

13613

13865

23524

21854

15854

18628

11203

17516

21090

 

CINGVMWTL

5a variant

 

17922

140

101

10449

14413

11435

18947

13165

11237

2239

13165

13572

19956

NS3 (A*02)

KLVALGINA

1a

 

22719

273

15048

32261

1830

18800

24242

25216

37253

23529

20557

4839

19019

1406-1415

KLSGLGLNA

1b

 

19133

475

21824

33559

2557

13152

20740

27147

37083

23891

19220

8973

18099

 

QLTSLGLNA

4a

 

20013

7051

15292

33674

12859

12517

26454

24440

37244

22168

26218

7165

19904

 

KLVALGINAV

1a

 

37929

52

8564

39134

NO VALUE

31977

19547

42247

34339

NO VALUE

NO VALUE

NO VALUE

26021

 

LTGLGINAV

5a

 

12100

5692

304

32426

10980

20519

21309

20981

33652

25012

21599

12577

26332

 

QLTGLGINA

5a variant

 

22408

6972

7419

34672

13389

17488

26117

23541

36968

25569

22283

15466

20054

NS4B (A*02)

LLFNILGGW

1a, 1b, 4, 5a

 

22942

14359

17095

18086

17906

9175

24903

19854

17154

956#

962#

5918

23118

1807-1816

MFFNILGGWV

3a

 

24613

23482

19706

343

15640

1707#

21757

11817

8151

10769

1251

13832

26621

 

LLFNILGGWV

1a, 1b, 4, 5a

 

32231

44

1159#

38969

NO VALUE

19453

32445

40287

25767

NO VALUE

NO VALUE

NO VALUE

25868

NS4B (A*02)

ILAGYGAGV

1a, 1b, 5a

 

20500

15

530#

30882

15492

10120

11883

21134

37213

22934

20702

3735

20143

1851-1859

ILAGYGTGV

5a variant

 

20351

18

193

32028

17493

12563

11272

21994

36657

23555

20603

2196

19849

NS5B (B*15)

MSYSWTGAL

1a, 1b, 4

 

12612

1522

24

2924

2372

5457

1530#

50

8456

10166

523#

80

16876

2422-2433

MSYTWTGAL

5a

 

12133

2640

22

8602

2141#

7606

2515#

58

9150

10680

787#

144

17267

 

YTWTGALIT

5a variant

 

15779

3000

13286

33166

13737

1561

18979

3920

27619

22480

17360

6553

18765

NS5B (A*02)

GLQDCTMLV

1a

 

18371

8

5733

11972

13187

6275

20996

27015

35681

25282

22002

10687

17601

2727-2735

KLQDCTMLV

1b

 

17735

7

3878

6160

2071#

9527

17308

26776

35038

23310

18296

3587

16634

 

KLRDCTLLV

5a

 

19744

13

14912

15150

10

5150

2800

27145

36627

21481

20362

1720#

18071

 

ALRDCTMLV

4a

 

19976

19

4673

19836

29

9982

5384

26302

36740

24190

22343

1206#

20027

<50 IC50nm, bold, high affinity.

>50 IC50nm, <500 IC50nm, italic, intermediate affinity.

>500 IC50nm, #, poor affinity.

No value indicates server produced no binding score.

Epitope binding prediction

The predicted binding values of the published and “newly predicted” epitopes to prevalent local class I-alleles were generated using the IEDB, ANN prediction server (Tables 3 and 4, respectively). Predicted binding values of the published epitopes to local HLA class II- alleles were generated using the prediction server Propred, Quantitative matrix (Table 5).
Table 4

Binding affinity scores of “newly predicted” epitopes and their variants were determined by the IEDB prediction program to relevant supertypes in South Africa

GENE

EPITOPE SEQUENCE

GENOTYPE OF EPITOPE

 

Class A- Alleles

Class B- Alleles

   

Supertypes

A01

A02

A24

A01A03

A01A24

B07

B58

B27

   

Allele type

A*01:01

A*02:01

A*68:02

A*23:01

A*30:01

A*29:02

B*07:02

B*35:01

B*53:01

B*57:01

B*58:01

B*15:03

B*27:05

NS3

LTGPTPLLY

5a, 1b

 

15

23679

24474

24873

4551

5

24599

6188

7688

448

28

1558#

22842

 

LHGPTPLLY

1a

 

10396

24884

27469

21381

17350

10

26731

12561

6443

21175

9987

442

23420

 

FLSTATQTF

5a

 

165

15329

17845

3634

1663

1886

15839

40

17977

16320

4231

8

18662

 

IVSTAAQTF

1a

 

20409

23323

22013

4758

11756

5496

11246

75

13372

814#

425

55

22273

 

VLSTVTQSF

1b, 2a

 

18550

13712

17004

4838

16940

5785

15666

988#

29052

11492

1654

26

21745

 

IVSTDTQSF

4a

 

19885

22289

20020

12440

14943

5300

6080

151

8229

4394

973#

47

24757

 

TLAGPKGPV

5a, 6a

 

23444

2081#

13

33957

16907

18949

6657

21854

39095

25027

22108

20499

22034

 

TLASPRGPV

1b

 

22044

1451#

8

32375

13790

18855

2453

21660

39379

25346

22237

6235

22501

 

TLASSRGPV

2a

 

22034

857#

11

29481

9965

20464

2095

19022

38571

24511

22225

3353

22267

 

TLASAKHPA

3a

 

21914

413

49

29038

10681

19694

16284

12935

39038

24637

21967

13015

23364

 

TIASPKGPV

1a

 

22885

7397

7

34010

15054

20663

7437

19533

38620

25493

22070

16303

24145

 

SVIDCNSAV

5a

 

21948

30

9

24435

8789

12923

1702

4571

35486

21627

21514

3381

25586

 

SVTDCNTCV

1b

 

21476

131

24

30991

15202

21169

19345

15846

19349

26045

22021

11521

22609

 

SVIDCNVAV

1b, 2a, 6a

 

21855

15

6

22019

7833

13308

3218

4376

31399

24317

21463

3412

24232

 

SVIDCNTCV

1a

 

22281

25

13

23478

14812

17452

17390

13879

20769

25334

21666

7032

23918

 

SVIDCNTSV

4a

 

22543

18

9

25166

10636

15522

3402

11164

17097

24124

20942

4646

24512

 

ITYSTYGKF

1b, 5a, 2a, 2b, 1a, 4a

 

16829

22979

16133

124

9722

352

21954

6132

16141

354

43

27

20982

 

LTYSTYGKF

3a

 

14296

22834

13829

263

10036

379

22076

3345

11660

860#

41

31

20046

 

KVLVLNPSV

1a, 1b, 2a, 2b, 4a, 5a, 6a

 

23587

50

6303

27046

21

18669

14670

21450

31145

20648

8842

3129#

18558

 

RAKAPPPSW

5a, 1b, 2a, 6a

 

25817

25080

27568

8387

308

25791

7172

18126

8580

31

11

596#

22382

 

RAQAPPPSW

1b, 3a, 1a

 

24980

24747

27454

22992

6443

24136

6017

14212

3253

38

8

1675#

17482

 

KVWLAPPPSW

4a

 

24000

4927

22172

26746

170

16220

18770

9620

39029

21580

20215

12296

20633

 

LTSLGVNAV

5a

 

5815

3795

42

33629

6533

20008

16663

13886

27357

24243

18277

3860#

24767

 

LTSLGLNAV

5a variant

 

5305

3082

64

32917

6065

18186

16615

16431

29952

24579

19519

7004

23118

<50 IC50nm, bold, high affinity.

>50 IC50nm, <500 IC50nm, italic, intermediate affinity.

>500 IC50nm, #, poor affinity.

Table 5

Binding affinity scores (as percentages) of Class II published epitopes and their variants were determined by the ProPred prediction program to common DRB1* alleles prevalent in the South African population

Epitope:

Sequence

HCV Genotype specificity

DRB1*0101

*0102

*0301

*0401

*0701

*1101

*1301

*1501

Core 17–42

RRPQDVKFPGGGQIVGGVYLLPRRGP

1, 2, 5 & 3 var & 6 var

        
 

VYLLPRRGP

1, 2, 4, 5, 6

0.0%

0.0%

18.0%

0.0%

0.0%

16.0%

48.0%

18.0%

 

VGGVYLLPR

1, 2, 4, 5, 6

0.0%

0.0%

17.0%

0.0%

9.0%

9.0%

10.0%

20.0%

NS3 1248–1261

GYKVLVLNPSVAAT

1, 2, 4, 5, 6

        
 

LVLNPSVAA

1, 2, 3, 4, 5, 6

37.0%

54.0%

36.0%

47.0%

28.0%

17.0%

34.0%

39.0%

 

YKVLVLNPS

1, 2, 4, 5, 6

5.0%

0.0%

0.0%

30.0%

9.0%

31.0%

27.0%

17.0%

NS4B 1781–1800

LPGNPAIASLMAFTAAVTSP

1a, 4 var

        
 

LPGNPAVAS

2,3, 5, 6

0.0%

2.0%

0.0%

4.0%

0.0%

0.0%

9.0%

0.0%

 

LPGNPAIAS

1, 4

0.0%

0.7%

15.0%

4.0%

0.0%

2.4%

0.0%

7.0%

 

IASLMAFTA

1

7.0%

23.0%

0.0%

0.0%

4.0%

0.0%

14.0%

21.0%

NS4B 1801–1820

LTTSQTLLFNILGGWVAAQL

1a, 1b var ,

        
 

LFNILGGWV

1, 4, 5

0.0%

0.0%

16.0%

0.0%

24.0%

0.0%

16.0%

28.0%

 

FNILGGWVA

1, 4, 5

47.0%

47.0%

0.0%

2.0%

16.0%

28.0%

16.0%

31.0%

 

ILGGWVASQ

4, 5

0.0%

0.0%

28.0%

0.0%

0.0%

2.4%

8.0%

0.0%

 

LGGWVASQI

4, 5

0.0%

0.0%

0.0%

0.0%

21.0%

0.0%

13.0%

21.0%

NS5B 2571–2590

KGGRKPARLIVFPDLGVRVC

1, 2 var & 6 var

        
 

VFPDLGVRV

1

0.0%

0.0%

34.0%

0.0%

0.0%

0.0%

0.0%

0.0%

 

VYPDLGVRV

3, 5

0.0%

0.0%

35.0%

0.0%

14.0%

0.0%

0.0%

19.0%

 

IVYPDLGVR

3, 5

0.0%

0.0%

28.0%

0.0%

0.0%

0.0%

7.0%

0.0%

 

LIVYPDLGV

3, 5

0.0%

0.0%

0.0%

0.0%

12.0%

0.0%

3.0%

60.0%

NS5B 2661–2680

QCCDLDPQARVAIKSLTERL

5 var

        
 

LAPEARQAI

1b

0.0%

0.0%

8.0%

0.0%

11.0%

0.0%

4.5%

11.0%

 

LDPQARVAI

5

0.0%

0.0%

8.0%

0.0%

0.0%

0.0%

0.0%

0.0%

 

LQPEARAAI

5var

0.0%

0.0%

22.0%

0.0%

12.0%

1.0%

22.0%

26.0%

HLA-A and –B class I- restricted binding

Binding predictions of epitopes and their variants for all available HLA alleles prevalent in the South African population are shown in Table 3.Five of the six HLA class I-published epitopes (NS31073-1081, NS31406-1415, NS4B1807-1816, NS4B1851-1859 and NS5B2727-2735) have been reported to be HLA-A*02 restricted (Table 1). Three of the five published HLA-A*02 restricted epitopes bound the A*02:01 allele as expected (Table 3).

Predictions for the different alleles were in agreement regardless of the programme or algorithm used (IEDB ANN, Propred I, SYFPEITHI) with two exceptions, binding of the 9 amino acid epitopes of NS4B1807-1816 LLFNILGGWV and the HLA-B*27:05 binding predictions. The original 10 amino acid NS4B1807-1816genotype 1 epitope LLFNILGGWV (which is conserved in genotype 1b, 4 and 5a) predicted to bind with high affinity (44.1 IC50nM) to HLA-A*02:01. Neither IEDB ANN nor ProPred I predicted binding between this allele and the two possible 9 mer epitopes, LLFNILGGW and LFNILGGWV while SYFPEITHI predicted binding of 18% and 14%, respectively. One of the shortcomings of IEDB ANN is that it can only predict binding peptides that are of the same length as those in the training set. For this reason, all peptides were re-analysed with all the alleles of interest using the “any length” parameter for epitope length. No other changes were observed to binding predictions listed in Table 3 using these parameters.

The second exception observed was the failure of IEDB ANN to predict binding between any of the epitopes (or their variants) and HLA-B*27:05 which SYFPEITHI and/or ProPred I scored. There was no data supporting restriction of these particular peptides by B*27:05 in the IEDB epitopes database. Both SYFPEITHI and ProPred I use peptide motifs and amino acid matrix based prediction. The following scores using x-[R (K)]-x (6–9) could explain the scoring of these two packages for NS31406-1415epitopes K LVALGINA, K LSGLGINA (21%ProPredI 7%SYFPEITHI, respectively) and variants K LQDCTMLV and K LRDCTLLV (32%ProPredI 12%SYFPEITHI, respectively). SYFPEITHI uses x-[R]-x (5–8)-[LFYRHK (MI)]. However, one would expect lower predictions for NS5B2422-2433 epitopes MSYSWTGAL and MSYTWTGAL (38%ProPredI 12%SYFPEITHI) since only the carboxyl anchor is present but this was not the case.

NS31073-1081, NS4B1851-1859 and NS5B2727-2735 bound with high affinity to A*02:01 allele, regardless of genotypic variation (Table 3). All variants tested for both NS5B2727-2735and NS4B1851-1859 were predicted to bind the A*02:01 allele with equal strength (<20 IC50nM, Table 3). High and intermediate binding affinities over all variants was also observed for NS31073-1081 and NS4B1851-1859 with allele A*68:02 (Table 3), of the A02 supertype.

Two of the variants, SIS GVLWTV (genotype 2a) and TVG GVMWTV (genotype 3a) had changes from the wild type N (Asparagine) in position 3 but none of the variants had changes in positions 4, 5 and 7. Interestingly, when all possible alanine exchange peptides were placed into IEDB ANN, the output scores reflected the experimental binding changes for all of the alanine exchange peptides with the exception of the total abrogation of signal for substitutions in positions 3, 4 and 5 (data not shown).Of note, while consistent binding was observed across the supertype A02 for all of the variants of the A*02 restricted epitope NS31073-1081, epitopes of genotypes 1, 3a and 5a (variant) were found to be intermediate binders (Table 3).

The genotype 4a and 5a variants of the HLA-A*02 restricted epitope NS5B2727-2735displayed some level of promiscuity as these were predicted to bind with high affinity to the A01A03 supertype allele, A*30:01 (29 and 10 IC50nM, respectively), while the genotype 1b variant had low affinity with this allele (2071 IC50nM) and the original genotype 1a peptide was not predicted to bind at all. The original peptide and one of the two of three variants of the published B*15-restricted NS5B2422-2433 epitope displayed intermediate binding IC50 nM values of 80 and 144 (Table 3). This epitope showed the highest cross-reactivity across the supertypes with both the original epitope and one of the genotype 5a variants binding very strongly to A*68:02 (supertype A02) and B*35:01 (B7 supertype; Table 3).

Of the 6 class I- epitopes used in this study, only two epitope variants were found to be promiscuous: MSYTWTGAL (supertypes A02, B07, B27) and KLRDCTLLV (A02, A01A03).In a preliminary attempt to identify conserved epitopes showing greater promiscuity across supertypes, strings of epitopes (other than the ones selected from publications for this study) of the NS3 protein were placed into the IEDB server. Table 4 indicates that five of the eight epitopes were predicted to be promiscuous, binding with high (<50 IC50nm) and intermediate (<500 IC50nm) affinities to two or more supertypes: LTGPTPLLY (A01, A01A24, B58), FLSTATQTF (A01, B07, B58, B27), ITYSTYGKF (A24, A01A24, B58, B27), KVLVLNPSV (A02, A01A03), RAKAPPPSW (A01A03, B58). Of the five epitopes above, three were conserved among genotypes 1, 2, 4 and 5 (Table 4), ITYSTYGKF, KVLVLNPSV and RAKAPPPSW.

Class II- alleles

ProPred II was used to predict binding of the longer class II- epitopes. Before calculating the predicted binding, the programme identifies all overlapping nine amino acid peptides within the input polypeptide. A predicted binding score is given as a percentage of the maximum possible binding (i.e. the highest log value achievable by an optimal peptide) with the chosen allele (Table 5). For example, CORE17-42, RRPQDVKFPGGGQIVGGVYLLPRRGP, returned two 9-mer peptides, VYLLPRRGP and VGGVYLLPR, which scored similarly for alleles HLA-DRB1*03:01 and HLA-DRB1*15:01 (Table 5). However, in the context of DRB1*13:01, VYLLPRRGP had a much higher percentage binding score (48%) than its flanking sequence VGGVYLLPR (10%). Note that no class II- epitopes were predicted in the first 14 amino acids of CORE17-42. The CORE17-42 epitope was well conserved across the genotypes (second only to NS31248-1261, Table 2), but was not predicted to bind with HLA-DRB1*01:01, HLA-DRB1*01:02 or HLA-DRB1*04:01 and only VGGVYLLPR was predicted to bind with HLA-DRB1*07:01 (9%, Table 5).

The most promiscuous class II-epitope was also the best conserved epitope, NS31248-1261(Table 2), specifically the region 1252–1260 LVLNPSVAA, bound all eight of the alleles tested and was the only epitope to bind HLA-DRB1*04:01.The allele HLA-DRB1*15:01 was predicted to bind with all but five of the 18 peptides output by the program (Table 5). The highest percentage of optimal binding (60%) was predicted between peptide LIVYPDLGV within NS5B2571-2590 and the HLA-DRB1*15:01 allele.This immunogenic epitope is one of three variants common to genotypes 3 and 5.

The NS31248-1261 epitope YKVLVLNPS was well conserved among genotypes and bound to three DRB1* alleles (Table 5). Interestingly, the epitope KVLVLNPSV, also conserved, bound to two class I- supertypes (Table 4). Another epitope that is a class I- and II- binder is FNILGGWVA (Table 3 and Table 5, respectively).

Coverage calculations

The predicted binding scores of published epitopes (Tables 3 and 5) were used to estimate population coverage. Selected programme output (which includes a list of the input epitopes) has been supplied as supplementary figures where indicated.

IEDB population coverage

The published class I- and II- epitopes had coverage of 65.85% (Additional file 2: Figure S2) in South African Blacks and 81.36% (Additional file 3: Figure S3) in South African Whites. Corresponding figures when calculations included only the class I- epitopes were 41.76% and 52.70%, respectively (results not shown). By choosing predominantly genotypes 1 and 5a epitopes (“best mix”) predicted to be immunogenic in South African Blacks, the combined class I- and II-coverage in Blacks improved to 91.87% (Additional file 4: Figure S4) while coverage improved to 94.77% (Additional file 5: Figure S5) in the South African Whites.

Optitope Population Coverage

The Optitope candidate epitopes were proposed whether the chosen population was “North American Europeans” or Europe (geographical) and results showed coverage of 94.28% (Additional file 6: Figure S6). Alternatively, candidate epitopes were sought using the same HCV alignment data and choosing the Zulu ethnic group (the only South African ethnic group available in OptiTope) and coverage of 75.16% was shown (Additional file 7: Figure S7).

Optitope Epitopes and IEDB population coverage

Candidate epitopes chosen for “optimal” vaccines for Caucasians and Zulus, respectively, from the OptiTope analyses described above, were then tested using the South African white and black populations. Local population data was placed into the IEDB population coverage web application as before.

Results indicated that South African Blacks had a 72.64% chance of responding to a putative European “optimal” vaccine while the same vaccine provided 90.55% coverage in the population for which it was designed. The putative “optimal” vaccine for Zulus provided coverage of 73.72% in South African Blacks with 90.79% coverage in Europeans (summarized in Additional file 8: Figure S8).

Discussion

HCV genotypes and host genetics vary geographically and yet proposed epitope vaccines are most often formulated based on genotype 1 peptide sequence data alone and their restriction confined to the alleles found predominantly in the Caucasian population. This study assesses the efficacy of a putative epitope vaccine designed with this typical sequence bias when used in South African populations. The heterogeneity of epitope regions proposed for HCV vaccines was explored together with their predicted binding, and that of their variants, to HLA alleles common in the South Africa population.

There is a need to examine viral variation within known epitopes, and assess the prevalence and immunogenicity of the variants for relevant host alleles within the target population, before choosing epitopes for inclusion in an epitope vaccine. This study, therefore, focused on subtype 1a, 1b and 5a sequences as these were found to predominate in South Africa [15]. This is the first time that South African genotype 5a data is being compared to well- studied epitope data of other genotypes. Genotypes 3 and 4 have also been found in the South African population but genotype 2 is rare and, to date, genotype 6 has not been identified. In order to improve the representation of genotype 5a, all available sequence data was included in the alignments, including sequences from our own studies and those of [31] (Belgium and South Africa) and [32] (France).

There are numerous epitopes meeting the inclusion criteria that could have been chosen for the study but a final subset was chosen so that it included well studied epitopes considered for multi-epitopic [22], therapeutic [21], minigene [25] and DNA polytope [23] vaccines.Genotype 1 is a well-studied genotype and considerably more sequences were available for the genotype 1 alignments. Class I- and II- epitope sequences of genotype 5a were found to be relatively conserved compared to some of the other genotypes, notably genotypes 2, 3 and 6.Genotype 5 is considered to be a relatively conserved genotype as to date, there is only one subtype of genotype 5 (5a), compared to the highly intra-genotypically variable genotype 6 that partitions into 22 different subtypes, 6a-6v, considerably more than any of the other genotypes [33].

There have been several studies which show a lack of cross-protection across the genotypes [3436]. With regard to the NS31073-1081epitope, an extensively studied epitope, our study has predicted high and intermediate binding of variant sequences to A02 supertype, indicating a level of cross-reactivity for this epitope. The consensus at the position 2 of NS31073-1081 was an isoleucine (I). The only other common amino acid in this anchor position was Valine (V). Valine was conserved at position 9 in all but the genotype 5a sequences where approximately one third of the sequences had a leucine (L) in this position. Despite the fact that substitutions at P2 were conservative (an I or V for the more favourable L), affinity of this epitope was lowered. When alanine exchange peptides were used in in vitro assays [37], substitutions at positions 3, 4, 5 and 7 of the published NS31073-1081 epitope abolished IFN-gamma production. Changes at positions 2, 8 and 9 only partially reduced production and only positions 1 and 6 had no effect. Even single amino acid exchanges at non-anchor sites can significantly limit the potential efficacy of a vaccine containing only the wild type peptide [37].

[36] identified distinct polymorphism profiles of genotypes 1a and 3a non-structural gene sequences. Only 2 of the 51 polymorphisms, observed to have significant HLA association, were common to both genotypes [36]. The extent of genetic diversity can result in a distinct repertoire of HLA-restricted viral epitopes for different genotypes. When we looked at consensus alignments of the chosen epitopes, we also observed this phenomenon. The consensus at each site of an epitope represents the amino acid best adapted to T cell responses across the host population [36]. A consequence of this is that escape of a mutant (driven by the selection pressure of dominant HLA alleles within the host population) can become the most dominant amino acid. When this happens, the polymorphism in the epitope, or negatope, as it is now called, is over-represented even in hosts not having the allele which drove the escape [36].

One of the shortcomings of IEDB ANN is that it can only predict binding peptides that are of the same length as those in the training set. Hence, the server will not pick up binding in longer epitopes if this is not specified [38]. However, by using older programs, such as SYFPEITHI and BIMAS that use peptide motifs and amino acid matrix based prediction ([39]; Singh and Raghava 200) both of which are popular, updated and have relevance [40] we were able to flag the longer epitopes and repeat the prediction in IEDB ANN for the 10 amino acid epitope.

Epitopes which are well conserved and show good binding affinities to many HLA alleles (promiscuous) are the best candidates for in vitro and/or in vivo testing. Epitopes like NS4B1801-1820are particularly appealing since they contain substrings which act as class I- and class II- alleles. While in silico planning has been found to greatly facilitate peptide design, not all peptides predicted in silico are optimally immunogenic in vivo[41] and it remains essential to test predicted peptides in vivo so as to ascertain that the needed T-cell response is elicited. Numerous in silico studies have shown the value of using prediction programs to assess the efficiency of binding of putative epitopes to human alleles [4245]. Also, [46] showed an increase in the use of in silico prediction studies with an improvement of epitope prediction programs available. Of the published epitopes used in this study, only 2 class I- (based on binding to ≥supertypes) and 3 class II- (binding to >2 DRB1* alleles) epitopes were found to be promiscuous using the prediction programs.

The NS3 protein is a large protein and has been shown to generate effective immune responses, which can resolve acute infection. This study looked across the NS3 protein to identify possible additional epitopes (other than the ones chosen from the published papers) that may be good binders to predominant HLA-alleles in the South African population. The results of this search (Table 4) which we have called, “newly predicted” NS3 epitopes were found to be well-conserved and bind to more than one HLA class I- allele. Three class I- epitope sequences were found to be highly conserved, particularly among genotypes 1 and 5, and were predicted to be strong binders to two or more supertypes. None of these “newly predicted” NS3 epitopes were found on the Los Alamos HCV immunology database (http://hcv.lanl.gov/content/immuno/tables/ctl_summary.html, accessed 05-09-2012). This exercise illustrates the usefulness of in silico studies to identify potential binders which will suit the target populations. In vivo studies will always be needed to confirm immunogenicity of these predicted peptides but this study has shown that in silico prediction can consider both host and viral variation, particularly in countries like South Africa and Egypt where genotypes other than genotype 1 predominate. In silico coverage calculations can not only identify promiscuous epitopes but also optimise the best cocktail for an effective multi-epitope vaccine. A recent in silico study identified 69 promiscuous HCV class I- and 150 class II- epitopes that were predicted to bind to genotype 3a [44]. A string of 18 conserved and promiscuous immunodominant epitopes spanning 8 HIV-1 proteins produced an effective immunogen [47], 23 epitopes were found promiscuous to MHC class I- and II- within E-coli 536 genome [45] and 15 promiscuous epitopes were predicted within M. tuberculosis peptide [43].

This study focused mainly on A02 –restricted epitopes and promiscuity was poor. However, immunogenic epitopes restricted to other alleles have been identified [4850]. Two B alleles, B57 and B27, have been found to provide spontaneous control of HCV. Neither of these alleles are prevalent in South African Blacks (Paximadis et al., 2011) but preliminary investigations on NS5B (B*57-restricted) epitope, KSKKTPMGF (genotype 1a, [48]), and genotype 5a variants RSKKTPMAF and KSKKIPMAF showed promiscuity to B*58:01, B*15:03 and A*30:01(data not shown). Indeed, this reiterates the need to look at viral variation and promiscuity as this is particularly important to vaccine design.

The following class I- and II-restricted epitopes were selected from the original epitope set as likely to provide the best vaccine in the South African setting. This was based on binding affinities predicted for epitopes expected in the local population and binding to several supertypes recently recommended for inclusion in a vaccine which is optimal for both White and Black South Africans (supertypes A1, A2, B07, B27 and B58; [13]).
  1. 1.

    NS31073-1081 both wild type genotype 1a CINGVCWTV and genotype 1b CVNGVCWTV because they are so well studied and show cross-reactivity within variants and across the supertype A02.

     
  2. 2.

    NS4B1807-1816 (LLFNILGGWV; [22, 24, 25]) because the 10-mer peptide is well conserved (genotypes 1a, 1b, 4, 5a) and is immunogenic for both class I- and class II- alleles.

     
  3. 3.

    NS5B2422-2433, both the original MSYSWTGAL (genotypes 1a, 1b and 4; Table 3; [22]) and the genotype 5a variant MSYTWTGAL as they cover the supertypes B27 as well as B07 and are also the best available B58 candidate in the recommended supertype set [13].

     
  4. 4.

    NS5B2727-2735genotype 5a variant KLRDCTLLV of the published epitope sequence GLQDCTMLV [22] as it brings the most prevalent HLA-A allele in the Black population (A*30:01) and the most prevalent HCV genotype 5a in South Africa into the mix.

     
  5. 5.

    The class II-restricted epitopes NS31252-1260 LVLNPSVAA [27] which is conserved in all genotypes and also very promiscuous.

     
  6. 6.

    NS4B1809-1817 which overlaps class I-restricted 1807 (FNILGGWVA; [25]) and is restricted by the 2 HLA-DR alleles in the Black population (HLA DRB1*13:01 and *11:01) and is also promiscuous.

     
  7. 7.

    Core class II- epitope VYLLPRRGP (genotypes 1,2,4,5,6) included as it is the most reactive of the class II- epitopes to HLA DRB1*13:01.

     

The frequencies of the most common HLA alleles in the South African Caucasian and Indian populations closely correlate with values from their respective populations globally. However, the frequencies of the most common HLA-A and –B alleles in the South African Black population are both heterogeneous and unique and quite distinct even from other Black populations in Western and Northern Africa [51]. Many of the well studied published and “newly predicted” epitopes assessed in this study bound to A*68:02 (supertype A02). HLA-A*68:02 was found 2.6x more often in the Black population than HLA-A*68:01 (A03 supertype, [13]).

There is a good correlation between immunogenicity and MHC class I- binding affinity [52]. Based on this principle, several web-based resources are available which can assess the population coverage of putative epitope vaccines based on the predicted binding of the epitopes and their variants to chosen HLA alleles relevant to the population being assessed. The predicted coverage of the original well studied class I- and II-epitopes selected for this study to illustrate the drawbacks of a vaccine using South African host population frequencies was found to be 65.85% and 81.36% for Blacks and Whites, respectively (Additional file 8: Figure S8).The OptiTope example highlighted the fact that the greater the knowledge of local viral variation and the immunogenicity of these variants together with accurate high resolution population allele frequencies allows the design of superior epitope vaccines with much better coverage for more groups within the target population. Fine tuning the vaccine by using an optimal cocktail of genotype 1 and 5a epitopes raised the coverage of the vaccine to 91.87% and 94.77%, close to the 100% coverage predicted by [13] in their study population.

Conclusion

In light of data generated in this study, epitope-based HCV vaccines should contain a mixture of epitope variants from all of the genotypes as wild-type genotype 1 response is not guaranteed to cross-protect against variants, even if the variant is restricted by the same allele. In addition the efficacy of a proposed epitope vaccine will differ between the major population groups. While coverage estimates can be made based on South African supertypes, cross-reaction of peptides with all supertype members is not universal. Clearly for a set of epitopes to elicit a broad and potent immune response in the target population, viral variation and population genetics data should be factored into the algorithm particularly in the light of less-studied variants such a genotype 5a.

Even where proposed epitopes are conserved, host differences will make the vaccine less effective in the South African setting. Of the 13 published and well-characterised epitopes selected for this analysis (including variants from two of these) four class I- and three class II-restricted epitopes would be beneficial in a multi-topic therapeutic vaccine for genotype 5a infection in our population. Hepatitis C genotypes and high resolution population data is necessary when planning epitope vaccine design. While in vivo and in vitro studies are needed to confirm predicted immunogenic epitopes, in silico “reverse immunology” studies provide a sound basis with which to screen the many possible candidates. This study has shown that with the ease and usefulness of web-based sequence- and structure-based prediction servers, non-bioinformaticians can predict potential binders, without expensive computer hardware and programming knowledge.

Methods

Epitope sequences

The literature was searched for known immunogenic class I- and II-restricted epitope vaccine candidates. All of the open reading frames (ORF), from the core to the NS5B protein, yielded putative epitopes and these ranged in length from 9 base pairs (bp; [22]) to 683 bp [53]. Six class I- and seven class II- epitopes were chosen for the analyses (Table 1) based on the following criteria:
  1. 1.

    All were extensively studied immunogenic epitopes (as indicated by the number of references in Table 1).

     
  2. 2.

    All had been published in the peer reviewed literature.

     
  3. 3.

    All class I- epitopes had known HLA restriction.

     
  4. 4.

    All had been recommended for putative vaccines.

     
  5. 5.

    All were from conserved regions of the genome (core to NS5 region).

     

Alignments of representative reference sequences were obtained over the chosen putative epitope regions using sequence data from each of the genotypes with the aid of pre-aligned and updated amino acid sequence data from the International Nucleotide Sequence Database Collaboration (INSDC; [54]).

The total number of sequences, available per epitope region, varied in numbers by genotype and region on the genome. Genotype 1 (subtypes 1a and 1b) sequences form by far the major number of sequences on the database ranging from 54% (of the total number of sequences) to 84% in some regions. In contrast, the little studied genotypes, genotype 4 and 5, accounted for only 4 to 24% of available sequences, respectively. Genotype 5a is one of the major genotypes found in South Africa together with genotype 1. Thus, to have this local type adequately represented in the data set, we included our own sequence data (25 patients) from the core [GenBank:JX571010-JX571031], NS4B [GenBank: JX571032-JX571039] and NS5B [GenBank: DQ482799-DQ482824] regions of genotype 5a.Care was taken to ensure that all our own data, as well as data used from public databases, corresponded to one sequence per subject. The study was retrospective and approved by the ethics committee of the University of the Witwatersrand, Johannesburg, South Africa (WITS HREC M051114), and was therefore performed in accordance with the ethical standards of the 1964 Declaration of Helsinki. PCR and sequencing was performed as previously described [15, 31].

BioEdit (version 7.0; [55]), was used to align all the amino acid sequences. The consensus sequence of immunogenic regions, for each of the genotypes, was generated using the Web based software package, WebLogo (version 2.8.2; http://weblogo.berkeley.edu/logo.cg; 2008-09-08). Sequence numbering is according to [56]. WebLogo produces a consensus of the input sequences output as a series of “letter stacks”, each representing a single column of the sequence alignment (Additional file 1: Figure S1).The height of each letter within the stack is proportional to the relative frequency of the representative amino acid at that position in the sequence [57]. The Weblogo software incorporates a “small sample number” correction, to correct for potential bias.

The relative conservation of each epitope was calculated as a percentage of the number of polymorphic sites over the epitope length when compared to the overall HCV consensus sequence. The HCV consensus was determined by taking the most common amino acid at each amino acid site of the 7 respective genotype consensus sequences (genotypes 1a, 1b, 2, 3, 4, 5a and 6), irrespective of representation in the database. A minimal class I-restricted epitope length of 9 nucleotides was used for all class I-restricted epitopes. Since class II-restricted epitopes are longer and are made up of numerous overlapping regions, the number of amino acids per epitope varied. The statistical analysis was performed using the analysis of variance (ANOVA) tests of significance in the Statistica software, version 9.1.

Common South African HLA alleles

Initially, a literature search was conducted in order to collate available South Africa population HLA-A –B and –DR allele frequency data which included relevant data stored online in the New allele Frequency Database (http://www.allelefrequencies.net 2010-11-30). However, much of this data was low resolution with 2 digits. Hence, high resolution data [13], which is required for the predictions, were used for the study.

Immunogenicity prediction and population coverage calculations

Two servers (Immune Epitope Database, IEDB (http://tools.immuneepitope.org, [58]) and Propred II, http://www.imtech.res.in/raghava/propred/index.html, [59]) were chosen for this study because these were user-friendly, easily available online and displayed many of the HLA alleles prevalent in SA. To predict binding to HLA class1- alleles, the IEDB server was used. The Propred II server was used to predict binding to HLA class II- alleles.

Resources of the immune epitope database (IEDB)

The IEDB is a manually curated database of experimentally characterized immune epitopes. Its companion site, the IEDB resource, is a collection of tools for prediction and analysis of immune epitopes (http://tools.immuneepitope.org/main/jsp/menu.jsp; version 2.0, accessed 2009-09-09 to 2011-03-14, [60]). The “Peptide Binding to MHC class I- molecules” resource, which predicts MHC binding to T cell epitopes, was utilised for class I- predictions. Valid input data include proteins or peptides. The programme splits these into all possible overlapping peptides and then predicts their binding to each selected MHC allele using the chosen prediction method. The sequence-based method, using the artificial neural network (ANN) algorithm of [61] on the IEDB server was selected for all HLA class I-predictions as it is reported to be more reliable than earlier matrix algorithms [61].

In addition, however, the matrix-based methods, ProPred 1 (http://www.imtech.res.in/raghava/propredI/index.html, 2010-11-30, [62]) and SYFPEITHI [39] were used in parallel and binding efficiencies of the three methods compared. For brevity, only scores for IEDB are shown in the result tables and incompatible results are discussed where appropriate. ANN uses training data from the IEDB to calculate the affinity of a given peptide for specific MHC molecules. It calculates binding based on the position of each amino acid in the putative epitope while taking into account the probability of adjacent amino acids competing for a space in the MHC pocket. Predicted binding efficiencies are calculated in units of IC50nM (the half-maximal inhibitory concentration). IC50 values <50 nM indicate high affinity while values >500 but <5000 nM indicate low affinity and values in between the two extremes (>50 nM but <500 nM) indicate intermediate affinity (http://tools.immuneepitope.org/main/jsp/menu.jsp).

Sequence data in the NS3 region that was available on the database was used for the genotype 5 conservation score and binding to predominant HLA-alleles in the South African context were predicted.The promiscuity of “newly predicted” (i.e. other than published epitopes) class I-epitopes of the NS3 gene were analysed using the IEDB server. An epitope sequence that bound with <500 IC50nM to more than one HLA class I- allele was considered promiscuous.

ProPred MHC class II- binding prediction

A structure-based method with a quantitative matrix (QM) algorithm on the Propred II server (http://www.imtech.res.in/raghava/propred/index.html, 2010-10-20, [63]) was used to predict binding of HLA class II- epitopes. This tool uses a linear prediction model which scores the binding potential of the query peptide based on values stored in allele specific coefficient tables, or quantitative matrices. Matrices are generated based on experimental results taking into account the properties of each individual amino acid and its position within the epitope.

The program is useful in locating promiscuous, versus allele specific, binding regions in a query peptide sequence. Note that, by comparison to IEDB ANN, a high score is indicative of good binding between the relevant peptide and the specific HLA allele and vice versa. The score represents the percentage binding of the query peptide when compared to the highest possible binding score for the optimal peptide with the given allele and thus reflects the binding characteristics of the query peptide. However, there is no clear cut off as with IEDB ANN scoring, and actual percentages should not be compared between alleles. The stringency threshold of the analysis can be set between 1% and 10% where the highest stringency guarantees no false positives and the lowest stringency guarantees no false negatives. The highest stringency was, therefore, used in all programme runs to minimize the number of false positives and ensure that all binding had significance.

Population coverage calculations

Population coverage was calculated by the Population coverage tool on the IEDB server (http://tools.immuneepitope.org/tools/population/iedb) for South African Whites and Blacks for both the published class I- and II- epitopes and an adapted “best mix” which took into account the most prevalent alleles and epitope variants in South Africa and their predicted binding. In order to assess the efficacy of a vaccine epitope, the IEDB resource Tool calculates the fraction of individuals predicted to respond to a given set of epitopes with known MHC restrictions (http://tools.immuneepitope.org/main/html/analysis_tools.html last accessed 2011-04-20). The calculation is based on input HLA genotypic frequencies.

Recently released web-based software, OptiTope [64], looks at viral and host variation in order to customise and optimise candidate epitopes to a specific population. Since this approach used the same parameters as this study, it was decided to compare the coverage of the chosen epitopes with the coverage of putative optimal epitope vaccines generated in OptiTope using similar biases. For this reason OptiTope was asked to generate an optimal epitope vaccine from an alignment of “common” HCV sequences in a Caucasian population. This HCV sample data (available in OptiTope), while biased, was very comprehensive and consisted of an alignment of >100 sequences from 10 different HCV proteins (Core, E1, E2, NS2, NS3, NS4A, NS4B, NS5A, NS5B and p7) but only included the “common” subtypes 1a, 1b, 2a and 3a.

Declarations

Acknowledgement

The study was funded by the Poliomyelitis research foundation, PRF grant 07/17.

Authors’ Affiliations

(1)
Specialized Molecular Diagnostics, Hepatitis Unit, National Institute for Communicable Diseases (NICD), National Health Laboratory Services (NHLS)
(2)
Division of Virology and Communicable Diseases Surveillance, School of Pathology, University of Witwatersrand
(3)
Department of Medical Virology, University of Pretoria
(4)
Tshwane Academic Division, NHLS

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