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. 2015 May 12:1–14. doi: 10.1007/978-1-4939-2410-3_1

Short Peptide Vaccine Design and Development: Promises and Challenges

Pandjassarame Kangueane 5,6,7,✉, Gopichandran Sowmya 5,6,7,8, Sadhasivam Anupriya 5,6, Sandeep Raja Dangeti 5,6,7,9, Venkatrajan S Mathura 10, Meena K Sakharkar 11
Editors: Paul Shapshak1, John T Sinnott2, Charurut Somboonwit3, Jens H Kuhn4
PMCID: PMC7121995

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.

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 [4–6]. 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.

Methodology

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

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.

Fig. 1.1

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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

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].

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.

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.

Fig. 1.2

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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

Results and Discussion

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.

Fig. 1.3

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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 [37–47].

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.

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.

Contributor Information

Paul Shapshak, Email: pshapsha@health.usf.edu.

John T. Sinnott, Email: johntsinnott@gmail.com

Charurut Somboonwit, Email: charurut@gmail.com.

Jens H. Kuhn, Email: kuhnjens@niaid.nih.gov

Pandjassarame Kangueane, Email: kangueane@bioinformation.net, Email: kangueane@gmail.com.

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