An Immunoinformatics Approach to Design Synthetic Peptide Vaccine from Dendroaspis polylepis polylepis Dendrotoxin-K(DTX-K)
Received Date: Aug 03, 2012 / Accepted Date: Oct 15, 2012 / Published Date: Oct 19, 2012
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
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].
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 , 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 . 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 . One of the important problems in subunit vaccine design is to search antigenic regions in an antigen  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 , 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 , 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).
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.
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|
*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|
*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 , 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.
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.
- Aboderin AA (1971) A new fluorescent probe for protein and nucleoprotein conformation. Binding of 7-(p-methoxybenzylamino)-4-nitrobenzoxadiazole to bovine trypsinogen and bacterial ribosomes. Biochemistry 10: 4433-4440.
- Abraham DJ, Leo AJ (1987) Extension of the fragment method to calculate amino acid zwitterions and side chain partition coefficients. Proteins 2: 130-152.
- Valkonen JP, Rajamäki ML, Kekarainen T (2002) Mapping of viral genomic regions important in cross-protection between strains of a potyvirus. Mol Plant Microbe Interact 15: 683-692.
- Bhasin M, Singh H, Raghava GP (2003) MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665-666.
- Singh H, Raghava GP (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17: 1236-1237.
- Cui J, Han LY, Lin HH, Tang ZQ, Jiang L, et al. (2006) MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics 58: 607-613.
- Beaver JE, Bourne PE, Ponomarenko JV (2007) Epitope Viewer: a Java application for the visualization and analysis of immune epitopes in the Immune Epitope Database and Analysis Resource (IEDB). Immunome Res 3: 3.
- Kumar M, Gromiha MM, Raghava GP (2007) Identification of DNA-binding proteins using support vector machines and evolutionary profiles. BMC Bioinformatics 8: 463.
- Gomase VS, Kale KV, Chikhale NJ, Changbhale SS (2007) Prediction of MHC Binding Peptides and Epitopes from Alfalfa mosaic virus. Curr Drug Discov Technol 4: 117-215.
- Schirle M, Weinschenk T, Stevanovic S (2001) Combining computer algorithms with experimental approaches permits the rapid and accurate identification of T cell epitopes from defined antigens. J Immunol Methods 257: 1-16.
- Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50: 213-219.
- Blythe MJ, Doytchinova IA, Flower DR (2002) JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics 18: 434-439.
- Schonbach C, Koh JL, Flower DR, Wong L, Brusic V (2002) FIMM, a database of functional molecular immunology: update 2002. Nucleic Acids Res 30: 226-229.
- Kolaskar AS, Tongaonkar PC (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276: 172-174.
- Saritha RK, Jain RK (2007) Nucleotide sequence of the S and M RNA segments of a Groundnut bud necrosis virus isolate from Vigna radiata in India. Arch Virol 152: 1195-1200.
- Gomase VS (2006) Prediction of Antigenic Epitopes of Neurotoxin Bmbktx1 from Mesobuthus martensii. Curr Drug Discov Technol 3: 225-229
- Hopp TP, Woods KR (1981) Prediction of Protein Antigenic Determinants from Amino Acid Sequences. Proc Natl Acad Sci USA 78: 3824-3828.
- Welling GW, Weijer WJ, van der Zee R, Welling-Wester S (1985) Prediction of sequential antigenic regions in proteins. FEBS Lett 188: 215-218.
- Larsen JE, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2.
- Parker JM, Guo D, Hodges RS (1986) New Hydrophilicity Scale Derived from High-Performance Liquid Chromatography Peptide Retention Data: Correlation of Predicted Surface Residues with Antigenicity and X ray-Derived Accessible Sites. Biochemistry 25: 5425-5432.
- Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120: 97-120.
- Robson B, Garnier J (1993) Protein structure prediction. Nature 361: 506.
- Sweet RM, Eisenberg D (1983) Correlation of sequence hydrophobicities measures similarity in three-dimensional protein structure. J Mol Biol 171: 479-488.
- Kyte J, Doolittle RF (1982) A Simple Method for Displaying the Hydropathic Character of a Protein. J Mol Biol 157: 105-132.
- Bull HB, Breese K (1974) Surface tension of amino acid solutions: A hydrophobicity scale of the amino acid residues. Arch Biochem Biophys 161: 665-670.
- Guy HR (1985) Amino acid side chain partition energies and distributions of residues in soluble proteins. Biophys J 47: 61-70.
- Miyazawa S, Jernigen RL (1985) Estimation of Effective Interresidue Contact Energies from Protein Crystal Structures: Quasi-Chemical Approximation. Macromolecules18: 534-552.
- Roseman MA (1988) Hydrophilicity of polar amino acid side-chains is markedly reduced by flanking peptide bonds. J Mol Biol 200: 513-522.
- Wolfenden R, Andersson L, Cullis PM, Southgate CC (1981) Affinities of amino-acid side-chains for solvent water. Biochemistry 20: 849-855.
- Wilson KJ, Honegger A, Stotzel RP, Hughes GJ (1981) The behaviour of peptides on reverse-phase supports during high-pressure liquid chromatography. Biochem J199: 31-41.
- Chothia C (1976) The nature of accessible and buried surfaces in proteins. J Mol Biol 105: 1-12.
- Eisenberg D, Schwarz E, Komaromy M, Wall R (1984) Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol 179: 125-142.
- Manavalan P, Ponnuswamy PK (1978) Hydrophobic character of amino acid residues in globular proteins. Nature 275: 673-674.
- Black SD, Mould DR (1991) Development of Hydrophobicity Parameters to Analyze Proteins Which Bear Post- or Cotranslational Modifications. Anal Biochem193: 72-82.
- Fauchere JL, Pliska V (1983) Hydrophobic parameters of amino-acid side-chains from the partitioning of N-acetyl-amino-acid amide. Eur J Med Chem 18: 369-375
- Janin J (1979) Surface and inside volumes in globular proteins. Nature 277: 491-492.
- Rao MJK, Argos P (1986) A conformational preference parameter to predict helices in integral membrane proteins. Biochim Biophys Acta 869: 197-214.
- Tanford C (1962) Hydrophobicity scale (Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 84: 4240-4274.
- Cowan R, Whittaker RG (1990) Hydrophobicity indices at ph 3.4 determined by HPLC. Pept Res 3: 75-80.
- Rose GD, Geselowitz AR, Lesser GJ, Lee RH, Zehfus MH (1985) Hydrophobicity of amino acid residues in globular proteins. Science 229: 834-838.
- Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, et al. (1999) Protein identification and analysis tools in the ExPASy server. Methods Mol Biol 112: 531-552.
- Eisenberg D, Weiss RM, Terwilliger TC (1984) The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci USA 81: 140-144
- Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L (1998) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14: 121-130.
- Bhasin M, Raghava GP (2005) P cleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res 33: W202-W207.
- Gomase VS, Changbhale SS, Kale KV (2008) Insilico analysis of Mesobuthustamulus neurotoxin from groundnut ringspot virus. Advancements in Information Technology and Internet Security 370-378
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.
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