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Short Peptide Vaccine Design and Development: Promises and Challenges

  • Pandjassarame KangueaneEmail author
  • Gopichandran Sowmya
  • Sadhasivam Anupriya
  • Sandeep Raja Dangeti
  • Venkatrajan S. Mathura
  • Meena K. Sakharkar
Chapter

Abstract

Vaccine development for viral diseases is a challenge where subunit vaccines are often ineffective. Therefore, the need for alternative solutions is crucial. Thus, short peptide vaccine candidates promise effective answers under such circumstances. Short peptide vaccine candidates are linear T-cell epitopes (antigenic determinants that are recognized by the immune system) that specifically function by binding human leukocyte antigen (HLA) alleles of different ethnicities (including Black, Caucasian, Oriental, Hispanic, Pacific Islander, American Indian, Australian aboriginal, and mixed ethnicities). The population-specific allele-level HLA sequence data in the public IMGT/HLA database contains approximately 12542 nomenclature defined class I (9437) and class II (3105) HLA alleles as of March 2015 present in several ethnic populations.

The bottleneck in short peptide vaccine design and development is HLA polymorphism on the one hand and viral diversity on the other hand. Hence, a crucial step in its design and development is HLA allele-specific binding of short antigen peptides. This is usually combinatorial and computationally labor intensive. Mathematical models utilizing structure-defined pockets are currently available for class I and class II HLA-peptide-binding peptides. Frameworks have been developed to design protocols to identify the most feasible short peptide cocktails as vaccine candidates with superantigen properties among known HLA supertypes. This approach is a promising solution to develop new viral vaccines given the current advancement in T-cell immuno-informatics, yet challenging in terms of prediction efficiency and protocol development.

Keywords

Short peptide vaccine T-cell epitope Ethnicity Epitope design HLA alleles Polymorphism HLA-peptide binding HLA supertypes Superantigen Prediction Immune response Virus Specificity Sensitivity 

Core Message

There is a need for novel vaccine technologies where existing viral vaccine types (viruses, killed or inactivated viruses, and conjugate or subunits) are unsuitable against many viruses. Hence, short peptide (10–20 residues) vaccine candidates are considered promising solutions in recent years. These function on the principle of short epitopes developed through the binding of CD8+/CD4+-specific HLA alleles (12542 known so far). Thus, the specific binding of short peptide antigens to HLA alleles is rate limiting with high sensitivity in producing T-cell-mediated immune responses. Identification of HLA allele-specific antigen peptide binding is mathematically combinatorial and thus complex. Therefore, prediction of HLA allele-specific peptide binding is critical. Recent advancement in immune-informatics technologies with the aid of known X-ray-determined HLA-peptide structure data provides solutions for the accurate identification of short peptides as vaccine candidates for further consideration. Thus, we document the possibilities and challenges in the prediction, large-scale screening, development, and validation of short peptide vaccine candidates in this chapter.

1 Introduction

The types of approved viral vaccines include live attenuated viruses, killed/inactivated viruses, and conjugate/subunits. However, these types of vaccine technologies may prove unsuitable against some viruses. In some cases, there is interest in the development of short peptide vaccines to fill the gaps. For example, the use of live attenuated HIV-1/AIDS vaccines is not as yet approved due to safety concerns [1]. There are several subunit vaccines under consideration and evaluation. However, one of these, the NIAID and Merck Co.-sponsored 2004 STEP (HVTN 502 or Merck V520-023) trial using three recombinant adenovirus-5 (rAD5) vectors containing HIV-1 genes Ad5-gag, Ad5-pol, and Ad5-Nef, did not show promising results [2]. This has led to the development of a multifaceted strategy for HIV-1/AIDS vaccine development. However, encouraging results were observed with four priming injections of a recombinant canary pox vector (ALVAC-HIV) and two booster injections of gp120 subunit (AIDSVAX-B/E) in a community-based, randomized, multicenter, double-blind, placebo-controlled efficacy trial (NCT00223080) in Thailand [3]. The main concern following this study was that this vaccine did not affect the degree of viremia or the CD4 T-cell count in patients who later seroconverted. Further studies indicated that the challenges with the development of an HIV-1/AIDS vaccine are viral diversity and host-virus molecular mimicry [4, 5, 6]. Nonetheless, there is considerable amount of interest to develop gp160 (gp120-gp41 complex) TRIMER envelope (ENV) protein as a potential vaccine candidate [4].

The production of an HIV-1 ENV spike protein trimer complex is nontrivial due to protein size, protein type, sequence composition, and residue charge polarity. Therefore, the need for the consideration of alternative approaches for vaccine development such as T-cell-based HLA-specific short peptide vaccines is promising [6, 7]. The LANL HIV molecular immunology database provides comprehensive information on all known T-cell epitopes in the literature [8]. Thus, these resources in combination with other predictive advancements described in this chapter are collectively useful for the design, development, evaluation, and validation of short peptide vaccine candidates.

2 Methodology

2.1 Structural Data

A structural dataset of complexes for class I HLA-peptide (Table 1.1) and class II HLA-peptide (Table 1.2) is created from the protein databank (PDB) [9]. The characteristic features of the datasets are presented in Tables 1.1 and 1.2.
Table 1.1

Dataset of class 1 HLA-peptide structures downloaded from PDB

S

Code

Allele

Peptide sequence

L

Source

Year

Group

Country

State

1

1 W72

A*0101

EADPTGHSY

9

Melanoma related

2.15

2004

Ziegler A

Germany

Berlin

2

3BO8

A*0101

EADPTGHSY

9

Melanoma related

1.8

2008

Ziegler UB

Germany

Berlin

3

3UTS

A*0201

ALWGPDPAAA

10

Insulin

2.71

2012

Andrew SK

UK

Cardiff

4

3UTT

A*0201

ALWGPDPAAA

10

Insulin

2.6

2012

Sewell AK

UK

Cardiff

5

1I4F

A*0201

GVYDGREHTV

10

Melanoma related

1.4

2001

Mabbutt BC

Australia

Sydney

6

1JHT

A*0201

ALGIGILTV

9

Mart-1

2.15

2001

Wiley DC

USA

Cambridge

7

1B0G

A*0201

ALWGFFPVL

9

Human-peptide

2.6

1998

Collins EJ

USA

North Carolina

8

1I7U

A*0201

ALWGFVPVL

9

Synthetic

1.8

2001

Collins EJ

USA

North Carolina

9

1I7T

A*0201

ALWGVFPVL

9

Synthetic

2.8

2001

Collins EJ

USA

North Carolina

10

1I7R

A*0201

FAPGFFPYL

9

Synthetic

2.2

2001

Collins EJ

USA

North Carolina

11

1I1F

A*0201

FLKEPVHGV

9

HIV RT

2.8

2000

Collins EJ

USA

North Carolina

12

1HHI

A*0201

GILGFVFTL

9

Synthetic

2.5

1993

Wiley DC

USA

Massachusetts

13

1AKJ

A*0201

ILKEPVHGV

9

HIV-1 RT

2.65

1997

Jakobsen BK

UK

Oxford

14

1HHJ

A*0201

ILKEPVHGV

9

Synthetic

2.5

1993

Wiley DC

USA

Massachusetts

15

1QRN

A*0201

LLFGYAVYV

9

Tax peptide P6A

2.8

1999

Wiley DC

USA

Massachusetts

16

1QSE

A*0201

LLFGYPRYV

9

Tax peptide V7R

2.8

1999

Wiley DC

USA

Massachusetts

17

1QSF

A*0201

LLFGYPVAV

9

Tax peptide Y8A

2.8

1999

Wiley DC

USA

Massachusetts

18

1AO7

A*0201

LLFGYPVYV

9

HTLV-1 Tax

2.6

1997

Wiley DC

USA

Massachusetts

19

1BD2

A*0201

LLFGYPVYV

9

HTLV-1 Tax

2.5

1998

Wiley DC

USA

Massachusetts

20

1DUZ

A*0201

LLFGYPVYV

9

HTLV-1 Tax

1.8

2000

Wiley DC

USA

Massachusetts

21

1HHK

A*0201

LLFGYPVYV

9

Synthetic

2.5

1993

Wiley DC

USA

Massachusetts

22

1IM3

A*0201

LLFGYPVYV

9

HTLV-1 Tax

2.2

2001

Wiley DC

USA

Boston

23

1HHG

A*0201

TLTSCNTSV

9

HIV-1 gp120

2.6

1993

Wiley DC

USA

Massachusetts

24

1I1Y

A*0201

YLKEPVHGV

9

HIV-1 RT

2.2

2000

Collins EJ

USA

North Carolina

25

3FQN

A*0201

YLDSGIHSGA

10

Beta-catenin

1.65

2009

Purcell AW

Australia

Victoria

26

3FQR

A*0201

YLDGIHSGA

10

Beta-catenin

1.7

2009

Purcell AW

Australia

Victoria

27

3FQT

A*0201

GLLGSPVRA

9

Tyrosine-phosphatase

1.8

2009

Purcell AW

Australia

Victoria

28

3FQU

A*0201

GLLGSPVRA

9

Tyrosine-phosphatase

1.8

2009

Purcell AW

Australia

Victoria

29

3FQW

A*0201

RVASPTSGV

9

Insulin receptor

1.93

2009

Purcell AW

Australia

Victoria

30

3FQX

A*0201

RVASPTSGV

9

Insulin receptor

1.7

2009

Purcell AW

Australia

Victoria

31

1QQD

A*0201

QYDDAVYKL

9

HLA-CW4

2.7

1999

Wiley DC

USA

Massachusetts

32

1P7Q

A*0201

ILKEPVHGV

9

POL polyprotein

3.4

2003

Bjorkman PJ

USA

California

33

2HN7

A*1101

AIMPARFYPK

9

DNA polymerase

1.6

2006

Gajhede M

Denmark.

Copenhagen

34

1X7Q

A*1101

KTFPPTEPK

9

SARS nucleocapsid

1.45

2005

Gajhede M

Denmark.

Copenhagen

35

3BVN

B*1402

RRRWRRLTV

9

Latent membrane

2.55

2009

Ziegler A

Germany

Berlin

36

3BP4

B*2705

IRAAPPPLF

9

Lysosomal

1.85

2008

Ziegler A

Germany

Berlin

37

1HSA

B*2705

ARAAAAAAA

9

N/A

2.1

1992

Wiley DC

USA

Massachusetts

38

1JGE

B*2705

GRFAAAIAK

9

Synthetic (M9)

2.1

2002

Ziegler UB

Germany

Berlin

39

1OF2

B*2709

RRKWRRWHL

9

Intestinal

2.2

2004

Ziegler UB

Germany

Berlin

40

1JGD

B*2709

RRLLRGHNQY

10

s10R

1.9

2003

Ziegler A.

Germany

Berlin

41

1K5N

B*2709

GRFAAAIAK

9

Synthetic (M9)

1.09

2002

Ziegler UB

Germany

Berlin

42

3BP7

B*2709

IRAAPPPLF

9

Lysosomal

1.8

2008

Ziegler A.

Germany

Berlin

43

1ZSD

B*3501

EPLPQGQLTAY

11

BZLF1

1.7

2005

McCluskey J

Australia

Brisbane

44

1A9B

B*3501

LPPLDITPY

9

EBNA-3C

3.2

1998

Saenger W

Germany

Berlin

45

1A9E

B*3501

LPPLDITPY

9

EBV-Ebna3c

2.5

1998

Saenger W

Germany

Berlin

46

3LN4

B*4103

AEMYGSVTEHPSPSPL

16

Ribonucleo protein

1.3

2010

Blasczyk R

Germany

Hannover

47

3LN5

B*4104

HEEAVSVDRVL

11

Thioadenosine

1.9

2010

Blasczyk R

Germany

Hannover

48

3DX6

B*4402

EENLLDFVRF

10

EBV decapeptide

1.7

2009

Rossjohn J

Australia

Victoria

49

3DX7

B*4403

EENLLDFVRF

10

EBV decapeptide

1.6

2009

Rossjohn J

Australia

Victoria

50

1SYS

B*4403

EEPTVIKKY

9

Sorting nexin 5

2.4

2004

McCluskey J

Australia

Victoria

51

3DXA

B*4405

EENLLDFVRF

10

EBV decapeptide

3.5

2009

Rossjohn J

Australia

Victoria

52

3DX8

B*4405

EENLLDFVRF

10

EBV decapeptide

2.1

2009

Rossjohn J

Australia

Victoria

53

1E27

B*5101

LPPVVAKEI

9

HIV-1 Kml

2.2

2000

Jones EY

UK

Oxford

54

1A1M

B*5301

TPYDINQML

9

HIV-2 gag

2.3

1998

Jones EY

UK

Oxford

55

1A1O

B*5301

KPIVQYDNF

9

HIV-1 Nef

2.3

1998

Jones EY

UK

Oxford

56

3VRJ

B*57:01

LTTKLTNTN

10

Cytochrome c Oxidase

1.9

2012

McCluskey J

Australia

Victoria

57

3UPR

B*57:01

HSITYLLPV

9

Synthetic construct

2

2012

Peters B

USA

Gainesville

58

3VRI

B*57:01

RVAQLEQVYI

10

SNRPD3

1.6

2012

McCluskey J

Australia

Victoria

59

2RFX

B*5701

LSSPVTKSF

9

Synthetic construct

2.5

2008

McCluskey J

Australia

Victoria

60

3VH8

B*5701

LSSPVTKSF

9

Ig kappa chain C region

1.8

2011

Rossjohn J

Australia

Victoria

61

2DYP

B27

RIIPRHLQL

9

Histone H2A.x

2.5

2006

Maenaka K

Japan

Fukuoka

62

2D31

B27

RIIPRHLQL

9

Histone H2A.x

3.2

2006

Maenaka K

Japan

Fukuoka

63

1EFX

Cw*0304

GAVDPLLAL

9

Importin-2

3

2000

Sun PD

USA

Maryland

64

1IM9

Cw*0401

QYDDAVYKL

9

Synthetic

2.8

2001

Wiley DC

USA

Cambridge

65

3CDG

G

VMAPRTLFL

9

Synthetic construct

3.1

2008

Rossjohn J

Australia

Victoria

66

3KYN

G

KGPPAALTL

9

Synthetic construct

2.4

2010

Clements CS

Australia

Victoria

67

3KYO

G

KLPAQFYIL

9

Synthetic construct

1.7

2010

Clements CS

Australia

Victoria

S = Serial number; Code = PDB code; L = Length of peptide; R = Resolution

Table 1.2

Dataset of class 2 HLA-peptide structures downloaded from PDB

S

Code

Allele

Peptide sequence

L

Source

Year

Group

Country

State

1

1UVQ

DC1

EGRDSMNLPSTKVSWAAVGGGGSLVPRGSGGGG

33

Human Orexin

1.8

2004

Fugger L

UK

Oxford

2

1S9V

DQ1

LQPFPQPELPY

11

Synthetic

2.2

2004

Sollid LM

USA

Stanford

3

2NNA

DQ8

QQYPSGEGSFQPSQENPQ

18

Gluten

2.1

2006

Anderson RP

Australia

Victoria

4

1JK8

DQ8

LVEALYLVCGERGG

14

Human insulin

2.4

2001

Wiley DC

USA

Boston

5

4GG6

DQ1

QQYPSGEGSFQPSQENPQ

18

MM1

3.2

2012

Rossjohn J

Australia

Victoria

6

1KLG

DR1

GELIGILNAAKVPAD

15

Synthetic

2.4

2001

Mariuzza RA

USA

Maryland

7

1KLU

DR1

GELIGTLNAAKVPAD

15

Synthetic

1.9

2001

Mariuzza RA

USA

Maryland

8

1T5W

DR1

AAYSDQATPLLLSPR

15

Synthetic

2.4

2004

Stern LJ

USA

Massachusetts

9

2IAN

DR1

GELIGTLNAAKVPAD

15

Human

2.8

2006

Mariuzza RA

USA

Maryland

10

2FSE

DR1

AGFKGEQGPKGEPG

14

Collagen

3.1

2006

Park HW

USA

Memphis

11

1SJH

DR1

PEVIPMFSALSEG

13

HIV1

2.2

2004

Stern LJ

USA

Cambridge

12

2Q6W

DR1

AWRSDEALPLGS

12

Integrin

2.2

2007

Stern LJ

USA

Cambridge

13

1ZGL

DR2

VHFFKNIVTPRTPGG

15

Myelin

2.8

2005

Mariuzza RA

USA

Maryland

14

1H15

DR2

GGVYHFVKKHVHES

14

EPV related

3.1

2002

Fugger L

UK

Oxford

15

1A6A

DR3

PVSKMRMATPLLMQA

15

Human CLIP

2.7

1998

Wiley DC

USA

Massachusetts

16

2SEB

DR4

AYMRADAAAGGA

12

Collagen

2.5

1997

Wiley DC

USA

Massachusetts

S = Serial number; Code = PDB code; L = Length of peptide; R = Resolution

2.2 Structural Superposition of HLA Molecules

The peptide-binding grooves of both class I HLA (Fig. 1.1a) and class II HLA (Fig. 1.1c) molecules were superimposed using the molecular overlay option in the Discovery Studio software from Accelrys® [10].
Fig. 1.1

The structural basis for short peptide vaccine design is illustrated. The allele-specific nomenclature defined, ethnicity profiled using known HLA sequences at the IMGT/HLA database [11], and the striking backbone structural similarity of antigen peptides at the HLA binding groove is the bottleneck. This is generated with using a dataset (Tables 1.1 and 1.2) of HLA-peptide complexes (67 class I and 16 class II) retrieved from protein databank (PDB) [9] using with Discovery Studio® (Accelrys Inc.) [10]. (a) The peptide-binding groove (superimposed) in class I HLA is structurally similar among known alleles and complexes. (b) The peptide-binding groove (superimposed) in class II HLA is structurally similar among known alleles and complexes; (c) class I HLA-bound peptides overlay showing structural constraints (bend peptides) at the groove; (d) class II bound peptides overlay showing extended conformation at the groove. This clearly suggests that class I (panel c) and class II (panel d) bound peptides do not have identical binding patterns at the groove

2.3 Molecular Overlay of HLA-Bound Peptides

HLA-bound peptides in the groove of both class I HLA (Fig. 1.1b) and class II HLA (Fig. 1.1d) molecules were overlaid using the molecular overlay option in the Discovery Studio software from Accelrys® [10].

2.4 Accessible Surface Area Calculations

Accessible surface area (ASA) was calculated using the WINDOWS software Surface Racer [12] with Lee and Richard implementation [13]. A probe radius of 1.4 Å was used for ASA calculation.

2.5 Relative Binding Measure

Relative binding measure (RBM) is defined as the percentage ASA Å2 of residues in the peptide at the corresponding positions buried as a result of binding with the HLA groove. This is the percentage change in ASA (ΔASA) of the position-specific peptide residues upon complex formation with the HLA groove (Fig. 1.2).
Fig. 1.2

The peptide binding pattern at the groove is illustrated as function of residue position for class I and class II alleles using a dataset (Tables 1.1 and 1.2) of HLA-peptide complexes (67 class I and 16 class II) retrieved from protein databank (PDB). This dataset is represented by several class I and class II alleles (see Tables 1.1 and 1.2). The peptide lengthwise distribution of the binding pattern is shown as relative binding measure using change in solvent-accessible surface area upon complex formation with the HLA groove

3 Results and Discussion

3.1 HLA-Peptide Binding Prediction for T-Cell Epitope Design

The rate-limiting step in T-cell epitope design is allele-specific HLA-peptide binding prediction. The number of known HLA alleles is over 12542 in number as of March 2015 at the IMGT/HLA database [11]. Hence, a number of methods have been formulated so far and optimized for HLA-peptide binding prediction during the last two decades. Structural information on HLA-peptide complexes has increased our understanding of their binding patterns (Tables 1.1 and 1.2). The HLA-binding groove is structurally similar among class I (Fig. 1.1a) and class II (Fig. 1.1b) alleles. The class I (Fig. 1.1c) and class II (Fig. 1.1d) bound peptides do not show an identical binding pattern at the groove. A detailed illustration of peptide binding patterns (Fig. 1.2) at the groove of class I and class II alleles provides valuable insights using mean and deviation profiles (Fig. 1.3).
Fig. 1.3

The mean peptide binding pattern with standard deviation (SD) at the groove is illustrated as function of residue position for class I and class II alleles using a dataset (Tables 1.1 and 1.2) of HLA-peptide complexes (67 class I and 16 class II) retrieved from protein databank (PDB). This provides insight into the understanding of the nature of peptide binding at the groove towards the design of an effective T-cell epitope candidate

A comprehensive description of HLA-peptide binding prediction is documented [14, 15]. Lee and McConnell [16] proposed a general model of invariant chain association with class II HLA using the side-chain packing technique on a known structural template complex with self-consistent ensemble optimization (SCEO) [17, 18] using the program CARA in the molecular visualization/modeling software LOOK (Molecular Application Group (1995), Palo Alto, CA) [16, 19]. This was an important development in the field and the approach was extended to a large dataset of known HLA-binding peptides. Kangueane et al. [20] collected over 126 class I peptides with known IC50 values from literature with defined HLA allele specificity. These peptides were modeled using available templates for a large-scale assessment of peptide binding to defined HLA alleles. Thus, a structural framework was established for discriminating allele-specific binders from non-binders using rules derived from a dataset of HLA-peptide complexes. This procedure was promising.

An extended dataset of class 1 and class 2 complexes were manually created, curated, and analyzed for insights into HLA-peptide binding patterns at the groove [21]. These studies lead to a detailed analysis of the HLA-peptide interface at the groove and the importance of peptide side chain and backbone atomic interactions were realized [22]. Meanwhile, the amount of structural data on HLA-peptide complexes was increasing in size leading to the development of an online database [23]. Thus, information gleaned from HLA-peptide structural complexes helped to identify common pockets among alleles in the binding groove and provided insights into functional overlap among them [24]. The need for a simple, robust, generic HLA-peptide binding prediction was evident. Therefore, a model was formulated by defining virtual pockets at the peptide-binding groove using information gleaned from a structural dataset of HLA-peptide complexes [25]. The model (average accuracy of 60 %) was superior because of its application to any given class I allele whose sequence is clearly defined. The model (53 % accuracy) was then extended for class II prediction using a class II-specific HLA-peptide structural dataset [26].

The techniques thus far established are highly promising towards short peptide vaccine design and development [27, 28]. Nonetheless, it was observed that alleles are covered within few HLA supertypes, where different members of a supertype bind similar peptides, yet exhibiting distinct repertoires [29]. These principles led to the development of frameworks to group alleles into HLA supertypes [30, 31], understand their structural basis [32], and cluster alleles based on electrostatic potential at the groove [33]. These observations should aid in the design of peptide vaccine candidates for viruses including HIV/AIDS [5, 6]. Further, for example, the importance of protein modifications to enhance HIV-1 ENV trimer spike protein vaccine across multiple clades, blood, and brain is discussed [4]. Currently available types of vaccine technology [34, 35], such as live virus, killed virus, and conjugate vaccines, have failed to produce a promising vaccine against several clinically important viruses, including HIV/AIDS [36]. Therefore, short peptide vaccines are promising solutions for viral vaccine development. It should be noted that there are many other viruses for which vaccines are needed. Examples of additional viruses for which there are no vaccines available, vaccines are still under development, vaccine failures occurred, or more effective vaccines are needed include RSV, measles, HBV, WNV, Coronaviruses, H5N1 influenza virus, HCV, Adenovirus, Hantavirus, and Filoviruses [37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47].

4 Conclusion

The design and development of short peptide cocktail vaccines is a possibility in the near future. This function on the principle of short epitopes developed through the binding of CD8+/CD4+-specific HLA alleles. HLA molecules are specific within ethnic populations and are polymorphic with more than 12542 known alleles as of March 2015. Thus, the binding of short peptide antigens to HLA alleles is rate limiting yet specific, with high sensitivity, while producing T-cell-mediated immune responses. Our understanding of this specific peptide binding to HLA alleles has improved using known HLA-peptide complexes. There is a search for superantigen peptides covering major HLA supertypes. Thus, peptide-binding predictions with large coverage, accuracy, sensitivity, and specificity are essential for vaccine candidate design and development. It should be noted that available HLA-peptide binding prediction methods are highly promising in these directions.

Notes

Acknowledgements

We wish to express our sincere appreciation to all members of Biomedical Informatics (India) for many discussions on the subject of this chapter. We also thank all scientists, research associates, “then” students, and collaborators of the project over a period of two decades since 1993. Pandjassarame Kangueane thanks all associated members and institutions, namely Bioinformatics Centre and Department of Microbiology @ NUS (Singapore), Supercomputer Centre @ NTU (Singapore), Biomedical Informatics (India), VIT University (India), AIMST University (Malaysia), Roskamp Institute (USA), RCSB, X-ray crystallographers for immune biological molecules, reviewers, editors, readers with critical feedback, open-access movement, and publishers for all their support on the subject of this chapter towards its specific advancement.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Pandjassarame Kangueane
    • 1
    • 2
    • 3
    Email author
  • Gopichandran Sowmya
    • 1
    • 2
    • 3
    • 4
  • Sadhasivam Anupriya
    • 1
    • 2
  • Sandeep Raja Dangeti
    • 1
    • 2
    • 3
    • 5
  • Venkatrajan S. Mathura
    • 6
  • Meena K. Sakharkar
    • 7
  1. 1.Biomedical InformaticsChennaiIndia
  2. 2.Biomedical InformaticsPondicherryIndia
  3. 3.Biomedical InformaticsPondicherryIndia
  4. 4.Department of Chemistry and Biomolecular SciencesMacquarie UniversitySydneyAustralia
  5. 5.School of Environment and SustainabilityUniversity of SaskatchewanSaskatoonCanada
  6. 6.Bioinformatics DivisionRoskamp InstituteSarasotaUSA
  7. 7.College of Pharmacy and NutritionUniversity of SaskatchewanSaskatoonCanada

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