Prediction of an Epitope"‘based Computational Vaccine Strategy for Gaining Concurrent Immunization Against the Venom Proteins of Australian Box Jellyfish



C. fleckeri, docking simulation, epitope prediction, vaccine design, venom proteins


Background: Australian Box Jellyfish (C. fleckeri) has the most rapid acting venom known to in the arena of toxicological research and is capable enough of killing a person in less than 5 minutes inflicting painful, debilitating and potentially life-threatening stings in humans. It has been understood that C. fleckeri venom proteins CfTX-1, 2 and HSP70-1 contain cardiotoxic, neurotoxic and highly dermatonecrotic components that can cause itchy bumpy rash and cardiac arrest. Subjects and Methods: As there is no effective drug available, novel approaches regarding epitope prediction for vaccine development were performed in this study. Peptide fragments as nonamers of these antigenic venom proteins were analyzed by using computational tools that would elicit humoral and cell mediated immunity, were focused for attempting vaccine design. By ranking the peptides according to their proteasomal cleavage sites, TAP scores and IC50<250 nM, the predictions were scrutinized. Furthermore, the epitope sequences were examined by in silico docking simulation with different specific HLA receptors. Results: Interestingly, to our knowledge, this is the maiden hypothetical immunization that predicts the promiscuous epitopes with potential contributions to the tailored design of improved safe and effective vaccines against antigenic venom proteins of C. fleckeri which would be effective especially for the Australian population. Conclusion: Although the computational approaches executed here are based on concrete confidence which demands more validation and in vivo experiments to validate such in silico approach.


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Marine"‘ Chironex fleckeri"‘The north Australian box"‘jellyfish (2000). Available from: http://www.marine"‘ [Last accessed on 2013 Jul 22].

Brinkman DL. The molecular and biochemical characterisation of venom proteins from the box jellyfish, Chironex fleckeri. [PhD thesis], Queensland, Australia: James Cook Univ; 2008. [Available Online at:].

Beadnell CE, Rider TA, Williamson JA, Fenner PJ. Management of a major box jellyfish (Chironex fleckeri) sting. Lessons from the first minutes and hours. Med J Aust 1992;156:655"‘8.

Northern Territory Government of Australia. Centre for Disease Control"‘Chironex fleckeri (Box jellyfish) (2012). Available from: and siteID=1 and str_title=Box%20 Jellyfish.pdf [Last accessed on 2013 Jul 22].

Brinkman DL, Burnell JN. Biochemical and molecular characterisation of cubozoan protein toxins. Toxicon 2009;54:1162"‘73.

Brinkman DL, Aziz A, Loukas A, Potriquet J, Seymour J, Mulvenna J.Venom proteome of the box jellyfish Chironex fleckeri. PLoS One 2012;7:e47866.

Tsan MF, Gao B. Heat shock proteins and immune system.J Leukoc Biol 2009;85:905"‘10.

Pagetta A, Tramentozzi E, Frigo G, Finotti P. Heat shock protein"‘derived peptides as a potential immuno"‘regulatory vaccine in allergy. "In silico” analysis. Immunome Res 2012;8:27.

Wagstaff SC, Laing GD, Theakston RD, Papaspyridis C, Harrison RA.Bioinformatics and multiepitope DNA immunization to design rational snake antivenom. PLoS Med 2006;3:e184.

Kolaskar AS, Tongaonkar PC. A semi"‘empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 1990;276:172"‘4.

Hopp TP, Woods KR. Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A 1981; 78:3824"‘8.

Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy Server. In: Walker JM, editor. The Proteomics Protocols Handbook. New York: Humana Press; 2005. p. 571"‘607.

Garnier J, Osguthorpe DJ, Robson B. Analysis of the accuracy andimplications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 1978;120:97"‘120.

Robson B, Garnier J. Protein structure prediction. Nature 1993;361:506.

King RD, Sternberg MJ. Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Sci 1996;5:2298"‘310.

Wolfenden R, Andersson L, Cullis PM, Southgate CC. Affinities of aminoacid side"‘chains for solvent water. Biochemistry 1981;20:849"‘55.

Eisenberg D, Schwarz E, Komaromy M, Wall R. Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol 1984;179:125"‘42.

Eisenberg D, Weiss RM, Terwilliger TC. The hydrophobic moment detects periodicity in protein hydrophobicity. Proc Natl Acad Sci U S A 1984;81:140"‘4.

Levitt M. Conformational preferences of amino acids in globular proteins. Biochemistry 1978;17:4277"‘85.

Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455"‘61.

Nielsen M, Lundegaard C, Worning P, Lauemí¸ller SL, Lamberth K, Buus S, et al. Reliable prediction of T"‘cell epitopes using neural networks with novel sequence representations. Protein Sci 2003;12:1007"‘17.

Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC"‘3.0: Accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8"‘11. Nucleic Acids Res 2008;36:W509"‘12.

Nielsen M, Lund O. NN"‘align. An artificial neural network"‘based alignment algorithm for MHC class II peptide binding prediction.BMC Bioinformatics 2009;10:296.

Reche PA, Glutting JP, Zhang H, Reinherz EL. Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 2004;56:405"‘19.

Rini JM, Schulze"‘Gahmen U, Wilson IA. Structural evidence for induced fit as a mechanism for antibody"‘antigen recognition.Science 1992;255:959"‘65.

Hinds MG, Welsh JH, Brennand DM, Fisher J, Glennie MJ, Richards NG, et al. Synthesis, conformational properties, and antibody recognition of peptides containing beta"‘turn mimetics based on alpha"‘alkylproline derivatives. J Med Chem 1991;34:1777"‘89.

Muh HC, Tong JC, Tammi MT. AllerHunter: A SVM"‘Pairwise system for assessment of allergenicity and allergic cross"‘reactivity in proteins. PLoS One 2009;4:e5861.

Consogno G, Manici S, Facchinetti V, Bachi A, Hammer J, Conti"‘Fine BM, et al. Identification of immunodominant regions among promiscuous HLA"‘DR"‘restricted CD4+T"‘cell epitopes on the tumor antigen MAGE"‘3. Blood 2003;101:1038"‘44.

Alam MJ, Ashraf KU, Gupta SD, Emon MA. Computational approach for the prediction of potential MHC binding peptides and epitope mapping in order to develop sero"‘diagnostic immunogen against potato virus Y. Int J Comput Bioinfo Silico Model 2013;2:186"‘98.



How to Cite

Alam, M. J., & Ashraf, K. U. M. (2018). Prediction of an Epitope"‘based Computational Vaccine Strategy for Gaining Concurrent Immunization Against the Venom Proteins of Australian Box Jellyfish. Toxicology International, 20(3), 235–253. Retrieved from



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