alexa An Immunoinformatics Approach to Design Synthetic Peptide Vaccine from Dendroaspis polylepis polylepis Dendrotoxin-K(DTX-K)
ISSN: 2161-0525
Journal of Environmental & Analytical Toxicology

Like us on:

Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business
  • Research Article   
  • J Environ Anal Toxicol 2012, Vol 2(7): 157
  • DOI: 10.4172/2161-0525.1000157

An Immunoinformatics Approach to Design Synthetic Peptide Vaccine from Dendroaspis polylepis polylepis Dendrotoxin-K(DTX-K)

Changbhale S.S1, Chitlange N.R2, Gomase V.S2* and Kale K.V3
1Department of Computer Science, Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, India
2Bioinformatics, Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, India
3Professor and Head, Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
*Corresponding Author: Gomase V.S, Bioinformatics, Jagdishprasad Jhabarmal Tibrewala University, Jhunjhunu, Rajasthan, 333001, India, Tel: 91-9987770696, Email: [email protected]

Received Date: Aug 03, 2012 / Accepted Date: Oct 15, 2012 / Published Date: Oct 19, 2012

Abstract

Dendroaspis polylepis polylepis is the most toxic snake commonly known as black mamba, the black mamba venom contains Dendrotoxin-K which is highly specific and virulently toxic protein. Antigenic peptides of Dendrotoxin toxic protein are most suitable for peptide vaccine development because with single epitope, the immune response can be generated in large population. Analysis shows MHC class II binding peptides of antigenic protein from Dendroaspis polylepis polylepis DTX-K are important determinant for protection against several venom toxins. In this assay we predicted the binding affinity of Dendroaspis polylepis polylepis DTX-K protein having 79 amino acids, which shows71nonamers. In this analysis, we found the High affinity TAP Transporter peptide regions as, 37-KRKIPSFYY(score-9.550), 45-YKWKAKQCL (Score-8.581) 36-CKRKIPSFY (Score-7.685), 24-AKYCKLPLR (Score-7.669), 42-SFYYKWKAK (Score-6.859), 31-LRIGPCKRK (Score-6.848) 65-NRFKTIEEC (Score-6.698), 25-KYCKLPLRI (Score-6.632), 49-AKQCLPFDY (Score-6.576), 66-RFKTIEECR (Score-6.464), 47-WKAKQCLPF (Score-6.197), 23-AAKYCKLPL (Score-6.166). We also found the SVM based MHCII-IAb peptide regions, 61-GGNANRFKT, 12-TLWAELTPV, 41-PSFYYKWKA, 25-KYCKLPLRI (optimal score is 0.946); MHCII-IAd peptide regions, 2-GHLLLLLGL, 57-SGCGGNAN, 3-HLLLLLGLL, 1-SGHLLLLLG (optimal score is 0.488); MHCII-IAg7 peptide regions 60-CGGNANRFK, 21-SGAAKYCKL, 61-GGNANRFKT, 20-VSGAAKYCK (optimal score is 1.468); and MHCII-RT1.B peptide regions 46-KWKAKQCLP, 24-AKYCKLPLR, 10-LLTLWAELT, 45-YKWKAKQCL (optimal score is 0.569) which represented predicted binders from dendrotoxin. The method integrates prediction of peptide MHC class I binding; proteasomal C terminal cleavage and TAP transport efficiency of the Dendroaspis polylepis polylepis DTX-K. Thus a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines.

Keywords: Dendroaspis polylepis polylepis; Dendrotoxin-K; Antigenic peptides; MHC-Binders; SVM; Nonamers

Introduction

Dendroaspis polylepis polylepiscommonly known as black mamba is the aggressive and highly venomous land snake; Dendroaspis polylepis polylepisvenom contains Dendrotoxin-K (DTX-K), which has ability to kill a mouse within 5 minutes after bite. The dendrotoxin is highly specific and virulently toxic protein of low molecular weight that can spread very rapidly within the bitten tissue, so black mamba venom is the most rapid-acting of all snake venoms. Dendrotoxin inhibits the exogenous process of muscle contraction by means of the sodium potassium pump. Dendrotoxin-K is a selective blocker of voltage-gated potassium channels [1,2].

Strategy

The phenotype of the resistant transgenic plants includes fewer centers of initial virus infection, a delay in symptom development, and low bacterial accumulation. Protoplasts from disease resistant transgenic plants are also resistant, suggesting that the protection is largely operational at the cellular level. Transgenic plants expressing nucleocapsid protein are protected against infection by bacteria but are susceptible to bacterial DNA, indicating that the protection may primarily involve an inhibition of bacterial cell wall. This approach is based on the phenomenon of cross-protection [3], hereby a plant infected with a mild strain of bacteria is protected against a more severe strain of the same bacteria. Plant Proteins are necessary for its production in or on all food commodities. An exemption from the requirement of a tolerance is established for residues of the biological plant pesticide.

MHC class binding peptides

The new paradigm in vaccine design is emerging, following essential discoveries in immunology and development of new MHC Class-I binding peptides prediction tools [4-7]. MHC molecules are cell surface glycoproteins, which take active part in host immune reactions. The involvement of MHC class-I in response to almost all antigens and the variable length of interacting peptides make the study of MHC Class I molecules very interesting. MHC molecules have been well characterized in terms of their role in immune reactions. They bind to some of the peptide fragments generated after proteolytic cleavage of antigen [8]. This binding acts like red flags for antigen specific and to generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. Antigenic peptides are most suitable for subunit vaccine development because with single epitope, the immune response can be generated in large population. MHC peptide complexes will be translocated on the surface of antigen presenting cells (APCs). This theme is implemented in designing subunit and synthetic peptide vaccines [9]. One of the important problems in subunit vaccine design is to search antigenic regions in an antigen [10] that can stimulate T cells called T-cell epitopes. In literature, fortunately, a large amount of data about such peptides is available. Pastly and presently, a number of databases have been developed to provide comprehensive information related to T-cell epitopes [11-14].

Materials and Methods

Protein sequence analysis

The antigenic protein sequence of Dendroaspis polylepis polylepisDTX-K was analyzed to study the antigenicity [15], solvent accessible regions and MHC class peptide binding, which allows potential drug targets to identify active sites against plant diseases.

Prediction of antigenicity

Prediction of antigenicity program predicts those segments from within bacterial pathogenicity protein that are likely to be antigenic by eliciting an antibody response. Antigenic epitopes are determined using the Gomase [9], Hopp and Woods, Welling, Parker, B-EpiPred Server and Kolaskar and Tongaonkar antigenicity methods [14,16-20].

Prediction of protein secondary structure

The important concepts in secondary structure prediction are identified as: residue conformational propensities, sequence edge effects, moments of hydrophobicity, position of insertions and Deletions in aligned homologous sequence, moments of conservation, auto-correlation, residue ratios, secondary structure feedback effects, and filtering [21,22].

Finding the location in solvent accessible regions

Finding the location in solvent accessible regions in protein, type of plot determines the hydrophobic and hydrophilic scales and it is utilized for prediction. This may be useful in predicting membranespanning domains, potential antigenic sites and regions that are likely exposed on the protein surface [1,2,23-42].

Prediction of MHC binding peptide

The MHC peptide binding is predicted using neural network strained on C terminals of known epitopes. In analysis predicted MHC/ peptide binding is a log-transformed value related to the IC50 values in nM units. MHC2Pred predicts peptide binders to MHCI and MHCII molecules from protein sequences or sequence alignments using Position Specific Scoring Matrices (PSSMs). Support Vector Machine (SVM) based method for prediction of promiscuous MHC class II binding peptides. The average accuracy of SVM based method for 42 alleles is ~80%. For development of MHC binder, an elegant machine learning technique SVM has been used. SVM has been trained on the binary input of single amino acid sequence. In addition, we predicts those MHCI ligands whose C-terminal end is likely to be the result of proteosomal cleavage [43-45].

Results and Interpretation

A antigenic sequence is 79 residues long as-GEDGYIADGDNCT YICTFNNYCHALCTDKKGDSGACDWWVPYGVVCWCEDLPTP VPIRGSGKCR

Prediction of antigenic peptides

In these methods we found the antigenic determinants by finding the area of greatest local hydrophilicity. The Hopp-Woods scale was designed to predict the locations of antigenic determinants in a protein, assuming that the antigenic determinants would be exposed on the surface of the protein and thus would be located in hydrophilic regions (Figure 1). Its values are derived from the transferfree energies for amino acid side chains between ethanol and water. Welling antigenicity plot gives value as the log of the quotient between percentage in a sample of known antigenic regions and percentage in average proteins (Figure 2). We also study B-EpiPred Server, Parker, Kolaskar and Tongaonkar antigenicity methods and the predicted antigenic fragments can bind to MHC molecule is the first bottlenecks in vaccine design (Figure 3- 6).

environmental-analytical-toxicology-NMR-Solution

Figure 1: The NMR Solution Structure of Dendrotoxin K from the Venom of Dendroaspis polylepis polylepis showing hydrophobicity.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 2: Hydrophobicity plot of Hopp and Woods (1981) of Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 3: Hydrophobicity plot of Welling et al. (1985) of Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-cell-epitopes

Figure 4: B-cell epitopes are the sites of molecules that are recognized by antibodies of the immune system of the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Kolaskar-Tongaonkar

Figure 5: Kolaskar and Tongaonkar antigenicity are the sites of molecules that are recognized by antibodies of the immune system for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 6: Hydrophobicity plot of HPLC / Parker et al. (1986) of Dendroaspis polylepis polylepis DTX-K.

Secondary alignment

The Robson and Garnier method predicted the secondary structure of the Dendroaspis polylepis polylepisDTX-K. Each residue is assigned values for alpha helix, beta sheet, turns and coils using a window of 7 residues (Figure 7). Using these information parameters, the likelihood of a given residue assuming each of the four possible conformations alpha, beta, reverse turn, or coils calculated, and the conformation with the largest likelihood is assigned to the residue.

environmental-analytical-toxicology-structure-GOR

Figure 7: Secondary structure GOR plot of the Dendroaspis polylepis polylepis DTX-K.

Solvent accessible regions

Solvent accessible scales for delineating hydrophobic and hydrophilic characteristics of amino acids and scales are developed for predicting potential antigenic sites of globular proteins, which are likely to be rich in charged and polar residues. It was shown that a Dendroaspis polylepis polylepisDTX-K is hydrophobic in nature and contains segments.

Prediction of MHC binding peptides

These MHC binding peptides are sufficient for eliciting the desired immune response. The prediction is based on cascade support vector machine, using sequence and properties of the amino acids. The correlation coefficient of 0.88 was obtained by using jack-knife validation test. In this test, we found the MHCI and MHCII binding regions (Tables 1 and 2). MHC molecules are cell surface glycoproteins, which take active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens. In this assay we predicted the binding affinity of Dendroaspis polylepis polylepisDTX-K having 79 amino acids, which shows different nonamers (Tables 1 and 2). For development of MHC binder prediction method, an elegant machine learning technique support vector machine (SVM) has been used. SVM has been trained on the binary input of single amino acid sequence. In this assay we predicted the binding affinity of Dendroaspis polylepis polylepisDTX-K sequence (IsTX) having 79 amino acids, which shows 71nonamers. Small peptide regions found as High affinity TAP Transporter peptide regions as, 37- KRKIPSFYY (score-9.550), 45-YKWKAKQCL (Score-8.581), 36-CKRKIPSFY (Score-7.685), 24-AKYCKLPLR (Score-7.669), 42-SFYYKWKAK (Score-6.859), 31-LRIGPCKRK (Score-6.848), 65-NRFKTIEEC (Score-6.698), 25-KYCKLPLRI (Score-6.632), 49-AKQCLPFDY (Score-6.576), 66-RFKTIEECR (Score-6.464), 47-WKAKQCLPF (Score-6.197), 23-AAKYCKLPL (Score-6.166). We also found the SVM based MHCII-IAb peptide regions, 61-GGNANRFKT, 12-TLWAELTPV, 41-PSFYYKWKA, 25-KYCKLPLRI (optimal score is 0.946); MHCII-IAd peptide regions, 2-GHLLLLLGL, 57-SGCGGNAN, 3-HLLLLLGLL, 1-SGHLLLLLG (optimal score is 0.488); MHCII-IAg7 peptide regions 60-CGGNANRFK, 21-SGAAKYCKL, 61-GGNANRFKT, 20-VSGAAKYCK (optimal score is 1.468); and MHCII-RT1.B peptide regions 46-KWKAKQCLP, 24-AKYCKLPLR, 10-LLTLWAELT, 45-YKWKAKQCL (optimal score is 0.569) which represented predicted binders from Dendroaspis polylepis polylepisDTX-K. (Table 2). The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on C terminals of known epitopes. In analysis predicted MHC/peptide binding is a log-transformed value related to the IC50 values in nM units. These MHC binding peptides are sufficient for eliciting the desired immune response. Predicted MHC binding regions in an antigen sequence and there are directly associated with immune reactions, in analysis we found the MHCI and MHCII binding region.

Peptide Rank Start Position Sequence Score Predicted Affinity
1 37 KRKIPSFYY 9.550 High
2 45 YKWKAKQCL 8.581 High
3 36 CKRKIPSFY 7.685 High
4 24 AKYCKLPLR 7.669 High
5 42 SFYYKWKAK 6.859 High
6 31 LRIGPCKRK 6.848 High
7 65 NRFKTIEEC 6.698 High
8 25 KYCKLPLRI 6.632 High
9 49 AKQCLPFDY 6.576 High
10 66 RFKTIEECR 6.464 High
11 47 WKAKQCLPF 6.197 High
12 23 AAKYCKLPL 6.166 High

*Optimal Score for given MHC binder in Mouse

Table 1: TAP Peptide binders of Dendroaspis polylepis polylepis DTX-K.

Prediction method Rank Sequence ResidueNo. Peptide Score
ALLELE I-Ab 1 GGNANRFKT 61 0.946
ALLELE I-Ab 2 TLWAELTPV 12 0.918
ALLELE I-Ab 3 PSFYYKWKA 41 0.687
ALLELE I-Ab 4 KYCKLPLRI 25 0.639
ALLELE I-Ad 1 GHLLLLLGL 2 0.488
ALLELE I-Ad 2 YSGCGGNAN 57 0.484
ALLELE I-Ad 3 HLLLLLGLL 3 0.467
ALLELE I-Ad 4 SGHLLLLLG 1 0.396
ALLELE I-Ag7 1 CGGNANRFK 60 1.468
ALLELE I-Ag7 2 SGAAKYCKL 21 1.467
ALLELE I-Ag7 3 GGNANRFKT 61 1.369
ALLELE I-Ag7 4 VSGAAKYCK 20 1.208
ALLELE RT1.B 1 KWKAKQCLP 46 0.569
ALLELE RT1.B 2 AKYCKLPLR 24 0.344
ALLELE RT1.B 3 LLTLWAELT 10 0.257
ALLELE RT1.B 4 YKWKAKQCL 45 0.248

*Optimal Score for given MHC II peptide binder in Mouse

Table 2: Peptide binders to MHCII molecules of Dendroaspis polylepis polylepis DTX-K.

Discussion and Conclusion

Gomase method [9], B-EpiPred Server, Hopp and Woods, Welling, Parker, Kolaskar and Tongaonkar antigenicity scales were designed to predict the locations of antigenic determinants in Dendroaspis polylepis polylepisDTX-K. Nucleocapsid shows beta sheets regions, which are high antigenic response than helical region of this peptide and shows highly antigenicity (Figure 1-5). We also found the Sweet hydrophobicity, Kyte & Doolittle hydrophobicity, Abraham & Leo, Bull & Breese hydrophobicity, Guy, Miyazawa hydrophobicity, Roseman hydrophobicity, Cowan HPLC pH7.5 hydrophobicity, Rose hydrophobicity, Eisenberg hydrophobicity, Manavalan hydrophobicity, Black hydrophobicity, Fauchere hydrophobicity, Janin hydrophobicity, Rao & Argos hydrophobicity, Wolfenden hydrophobicity, Wilson HPLC hydrophobicity, Cowan HPLC pH- 3.4, Tanford hydrophobicity, Rf mobility hydrophobicity and Chothia hydrophobicity scales, Theses scales are essentially a hydrophilic index, with a polar residues assigned negative values (Figures 7-28). In this assay we predicted the binding affinity of Dendroaspis polylepis polylepisDTX-K having 79 amino acids, which shows 71nonamers.Small peptide regions found as, 37-KRKIPSFYY (score-9.550), 45-YKWKAKQCL (Score-8.581) 36-CKRKIPSFY (Score-7.685), 24-AKYCKLPLR (Score-7.669), 42-SFYYKWKAK (Score-6.859), 31-LRIGPCKRK (Score-6.848) 65-NRFKTIEEC (Score-6.698), 25-KYCKLPLRI (Score-6.632), 49-AKQCLPFDY (Score-6.576), 66-RFKTIEECR (Score-6.464), 47-WKAKQCLPF (Score-6.197), 23-AAKYCKLPL (Score-6.166). Adducts of MHC and peptide complexes are the ligands for T cell receptors (TCR) (Table 1). MHC molecules are cell surface glycoproteins, which take active part in host immune reactions and involvement of MHC class-I and MHC II in response to almost all antigens (Table 2). Kolaskar and Tongaonkar antigenicity are the sites of molecules that are recognized by antibodies of the immune system for the Dendroaspis polylepis polylepisDTX-K, analysis shows epitopes present in the Dendroaspis polylepis polylepisDTX-K the desired immune response. The region of maximal hydrophilicity is likely to be an antigenic site, having hydrophobic characteristics, because C-terminal regions of Dendroaspis polylepis polylepisDTX-K is solvent accessible and unstructured, antibodies against those regions are also likely to recognize the native protein. For the prediction of antigenic determinant site of Dendroaspis polylepis polylepisDTX-K, we got eighteen antigenic determinant sites in the sequence. The SVM based MHCII-IAb peptide regions, 61-GGNANRFKT, 12-TLWAELTPV, 41-PSFYYKWKA, 25-KYCKLPLRI (optimal score is 0.946); MHCIIIAd peptide regions, 2-GHLLLLLGL, 57-SGCGGNAN, 3-HLLLLLGLL, 1-SGHLLLLLG (optimal score is 0.488); MHCII-IAg7 peptide regions 60-CGGNANRFK, 21-SGAAKYCKL, 61-GGNANRFKT, 20-VSGAAKYCK (optimal score is 1.468); and MHCII-RT1.B peptide regions 46-KWKAKQCLP, 24-AKYCKLPLR, 10-LLTLWAELT, 45-YKWKAKQCL (optimal score is 0.569) which represented predicted binders from Dendroaspis polylepis polylepisDTX-K (Table 2). Which is a larger percentage of their atoms are directly involved in binding as compared to larger molecules.

environmental-analytical-toxicology-Hydrophobicity-Sweet

Figure 8: Hydrophobicity Sweet plot of OMH for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 9: Hydrophobicity plot of Kyte and Doolittle (1982) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 10: Hydrophobicity plot of Abraham and Leo (1987) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 11: Hydrophobicity plot of Bull and Breese (1974) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 12: Hydrophobicity plot of Guy (1985) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 13: Hydrophobicity plot of Miyazawa, et al (1985) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 14: Hydrophobicity plot of Roseman (1988) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 15: Hydrophobicity plot of Wolfenden et al. (1981) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 16: Hydrophobicity Wilson et al. (1981) plot of HPLC for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-Cowan

Figure 17: Hydrophobicity Cowan (1990) plot of HPLC pH3.4 for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 18: Hydrophobicity plot of Rf mobility for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 19: Hydrophobicity plot of Chothia (1976) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 20: Hydrophobicity plot of Eisenberg et al. (1984) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 21: Hydrophobicity plot of Manavalan, et al (1978) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 22: Hydrophobicity plot of Black (1991) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 23: Hydrophobicity plot of Fauchere, et al (1983) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 24: Hydrophobicity plot of Janin (1979) for the Dendrotoxin-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 25: Hydrophobicity plot of Rao and Argos (1986) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 26: Hydrophobicity plot of Tanford (1962) for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-Cowan

Figure 27: Hydrophobicity Cowan (1990) plot of HPLC pH7.5 for the Dendroaspis polylepis polylepis DTX-K.

environmental-analytical-toxicology-Hydrophobicity-plot

Figure 28: Hydrophobicity plot of Rose et al. (1985) for the Dendroaspis polylepis polylepis DTX-K.

Future Perspectives

This method will be useful in cellular immunology, Vaccine design, immunodiagnostics, immunotherapeutics and molecular understanding of autoimmune susceptibility. Dendroaspis polylepis polylepisDTX-K sequence involved multiple antigenic components to direct and empower the immune system to protect the host from the dendrotoxin. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class in response to almost all antigens and it give effects on specific sites. Predicted MHC binding regions acts like red flags for antigen specific and generate immune response against the parent antigen. So, a small fragment of antigen can induce immune response against whole antigen. The method integrates prediction of peptide MHC class binding; proteosomal C terminal cleavage and TAP transport efficiency. This theme is implemented in designing subunit and synthetic peptide vaccines.

References

Citation: Changbhale SS, Chitlange NR, Gomase VS, Kale KV (2012) An Immunoinformatics Approach to Design Synthetic Peptide Vaccine from Dendroaspis polylepis polylepis Dendrotoxin-K(DTX-K). J Environ Anal Toxicol 2: 157. Doi: 10.4172/2161-0525.1000157

Copyright: © 2012 Changbhale SS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Select your language of interest to view the total content in your interested language

Post Your Comment Citation
Share This Article
Relevant Topics
Article Usage
  • Total views: 12071
  • [From(publication date): 11-2012 - Dec 10, 2019]
  • Breakdown by view type
  • HTML page views: 8269
  • PDF downloads: 3802
Share This Article
Top