An In Silico Comparative Study of Eleven Wasp Venom Allergens: An Arena for Therapeutic Approach
DOI:
https://doi.org/10.53555/jaz.v45i6.5035Keywords:
Wasp, venom allergen toxin, in silico, Docking, Anti-bacterialAbstract
Peptide toxins in animals as part of the chemical arsenal for predation and/or protection, and they can also safeguard the host from pathogenic infections. Insects including the hymenopterans generate a battery of toxic bactericidal or bacteriostatic molecules which are small multifunctional, linear peptides that cause pain, have antimicrobial effects, and inflammatory processes . The toxins under the experiment are active against gram-positive, gram-negative bacteria and fungi. The present in silico study aims to predict the physicochemical attributes like molecular weight, theoretical pI, amino acid composition, extinction co-efficient, estimated half-life, instability index, aliphatic index and grand average of hydropathy (gravy) of wasps’ (class: Insecta; order: Hymenoptera) 11 wasp venom allergens through Expasy Protparam & Pepstat software tools. The secondary structures of the toxins were predicted using psi-Blast-based secondary structure prediction GOR4 tools revealing the % α helix, extended β strand, random coil and ambiguous state reflecting a comparative picture of physicochemical parameters of these defensins. 3D Homology modelling of these toxins was accomplished through Swiss-model webserver tool and validated through various in silico tools like ANOLEA, ProSA-web, QMean4 determining Z score, PROCHEK establishing the 3D models of these toxins. Use of Inter Pro, CDD, PROSITE, P fam, Tox DL, PrDOS software predicted the protein family, protein toxicity, protein disorder respectively. Scratch Protein Predictor software tools predicted cysteine – cysteine bonds. Docking of the eleven (11) wasp venom allergens individually with bacterial cell wall component N-acetylglucosamine was done by CB DOCK webserver resulting negative affinity scores reflecting towards the strong binding between the mentioned 11 toxins and N-acetylglucosamine indicating that the mentioned wasps’ toxin molecules might be used as potential antibacterial therapeutic molecules binding to N-acetylglucosamine leading to an avenue to the probable bacterial drug discovery.
Downloads
References
1. King G. F, 2011.Venoms as a platform for human drugs: Translating toxins into therapeutics. Expert Opin Biol Ther.,11(11):1469–1484.
2. Bazon M.L.,A.Perez Riverol, J.R.A Dos Santos Pinto, L.G.R. Fernandes, A.M. Lasa, D.L. Justo Jacomini, et al., 2017. Heterologous expression, purification and immunoreactivity of the antigen-5 from Polybia paulista wasp venom. Toxins (Basel),9pii: E259.
3.Cociancich S, O.A. Ghazi, C. Hetru, A. Jules, S.N. Hoffmann & L. Letelliers, 1993. Insect Defensin, an Inducible Antibacterial Peptide, Voltage-dependent Channels in Micrococcus luteus. The Journal of Biochemistry, 19239-19245.
4.Bulet, P., C. Hetru, D. Jean-Luc & D. Hoffmann, 1999. Antimicrobial peptides in insects, structure and function. Developmental & Comparative Immunology,23(4–5):329-344.
5.Palma, M.S., 2011. Peptides as toxins/defensins. Amino Acids, 40:1-4.
6.Bazon, M.L., L.H.Silverid, P.U.Simioni, M.R.Brochetto-Braga,2018. Current Advances in Immunological Studies on the Vespidae Venom Antigen 5: Therapeutic and Prophylaxis to Hypersensitivity Responses. Toxins, 10(8), 305.
7.Casteels-Josson, K., W. Zhang, T. Capaci, P. Casteels & P. Tempst, 1994. Acute transcriptional response of the honey bees peptide-antibiotics gene repertoire, required posttranslational conversion of the precursor structures. J. Biol. Chem, 269: 28569-28575.
8.Bulet, P. & R. Stocklin,2005. Insect antimicrobial peptides: structure, properties and gene regulation. Protein & Peptide Letters,12: 3-11.
9.Chapot-Chartier, M. & S.Kulakauskas,2013. Cell wall structure and function in lactic acid bacteria.Microbial Cell Factories volume 13, Article number: S9 (2014).
9.Henriksen, A., T.P.King, O.Mirza et al,2001.Major venom allergen of yellow jackets, Ves v 5: Structural characterization of a pathogenesis-related protein superfamily. Proteins,45:438–448.
10.Mohammad, R.F.,2015. ‘In silico structural analysis, physicochemical characterization and homology modelling of arabidopsis thaliana Na+/H+ exchanger 1 (ATNHX1) protein. B.S. Thesis. BRAC University, p.96.
11.Prajapati,K.K. & R.K. Upadhyay, 2021.Bees and wasps venom toxins, its immune-allergic responses, diagnosis and therapeutics. International Journal of Pharmacy and Pharmaceutical Sciences, vol. 13, no. 1, pp. 1-9.
12.Obando, M.A., T.dorr ,2023.Novel role for peptidoglycan carboxypeptidases in maintaining the balance between bacterial cell wall synthesis and degradation. bioRxiv[Preprint],2023.07.12.548665. [Version 1].
13.Lu, G., M. Villalba,M. R. Coscia,D.R. Hoffman,T.P. King,1993. Sequence analysis and antigenic cross-reactivity of a venom allergen, antigen 5, from hornets, wasps, and yellow jackets. J Immunol, 150 (7): 2823–2830.
14.Yang Liu, M., Grimm, +3 authors C., Yang,2019. CB-Dock: a web server for cavity detection-guided protein–ligand blind docking.Acta Pharmacologica Sinica. 41(Suppl 14), DOI:10.1038/s41401-019-0228-6
15.de Magalhães, C.S., M. Almeida, H.J.C. Barbosa & L.E. Dardenne,2014 . A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Information Sciences ,289: 206–224.
16.Guedes, I.A., A.M.S. Barreto, D. Marinho, E. Krempser, M.A. Kuenemann & O. Sperandio, et al,2021. New Machine Learning and Physics-Based Scoring Functions for Drug Discovery. Sci Rep., 11(3198).
17.Santos Ricardo, N., L.G.L. Ferreira & D.A. Andricopulo,2018. Practices in Molecular Docking and Structure-Based Virtual Screening. Methods in molecular biology, 1762:31-50.
18.Gasteiger, E., A. Gattiker, C. Hoogland, I. Ivanyi, R.D. Appel & A. Bairoch,2003. ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. ,31:3784-3788.
19.Gasteiger, E., C. Hoogland, A. Gattiker, S. Duvaud, M.R. Wilkins, R.D. Appel & A. Bairoch,2005. Protein Identification and Analysis Tools on the ExPASy Server. In: Walker JM, editor: The Proteomics Protocols Handbook, Humana Press,p.
571-607.
20.Moffat, L. & D.T. Jones,2021. Increasing the Accuracy of Single Sequence Prediction Methods Using a Deep Semi-Supervised Learning Framework. Bioinformatics,37(21):3744–51.
21.Garnier, J., D.J. Osguthorpe & Robson, B. Robson,1978. Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120, 97-120.
22.Waterhouse, A., M. Bertoni, S. Bienert, G. Studer, G. Tauriello & R. Gumienny, et al.,2018. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 46: W296-W303.
23.Wiederstein, S.,2007. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research,35: W407-W410.
24.Benkert, P., M. Biasini & T. Schwede,2011. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics, 27: 343-350.
25.Jain, C.K., M. Gupta, Y. Prasad, G. Wadhwa & S. Sharma,2014. Homology modeling and protein engineering of alkane monooxygenase in Burkholderia thailandensis MSMB121: In silico insights. Journal of molecular modelling, 20: 2340.
26.Fariselli, P., P. Riccobelli & R. Casadio, 1999. The role of evolutionary information in predicting the disulfide bonding state of cysteines in proteins. Proteins,36: 340-346.
27.Yang, J., Bao-Ji. He, R. Jang, Y. Zhang & H. Shen, 2015. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins, Bioinformatics,31: 3773-3781.
28.Blum, M., H.Chang, S. Chuguransky, T. Grego, S. Kandasaamy & A. Mitchell, et al, 2020. The InterPro protein families and domains database: 20 years on. Nucleic Acids Research,49(D1): D344–D354.
29.Marchler-Bauer, A., J.B. Anderson, P.F. Cherukuri, C. DeWeese-Scott, L.Y. Geer & M. Gwadz, et al,2005. Nucleic Acids Res,33(Database issue).
30.Pan ,X., J. Zuallaert, X. Wang, H.B. Shen, E.P. Campos & D.O. Marushchak ,et al, 2021. ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity. Bioinformatics,36(21):5159-5168.
31.Ishida ,T. & K. Kinoshita, 2007. PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res,35(2): W460–W464.
32.National Center for Biotechnology Information. PubChem Compound Summary for CID 1738118,1,4-beta-D-Glucan. https://pubchem.ncbi.nlm.nih.gov/compound/N-acwtylglucosamine. Accessed Mar. 5, 2024.
33.Guedes, A.I., C.S. de Magalhães & L.E. Dardenne,2014. Receptor–ligand molecular docking, Biophysical Reviews,6(1): 75–87.
34.Kaur, A., P.K. Pati, A.M. Pati & A.K. Nagpal, 2020. Physico-chemical characterization and topological analysis of pathogenesis-related proteins from Arabidopsis thaliana and Oryza sativa using in-silico approaches. PLoS ONE,15(9): e0239836.
35.Panda,S. & G. Chandra, 2012. Physicochemical characterization and functional analysis of some snake venom toxin proteins and related non-toxin proteins of other chordates. Bioinformation,8(18):891–896.
36.Guruprasad, K., B.V.B. Reddy & M.W. Pandit, 1990. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Engineering, 4: 155–161.
37.Shen, C.H., 2019. Chapter 4 - Gene Expression: Translation of the Genetic Code,In: Shen CH, editor. Diagnostic Molecular Biology, Academic Press,87-116.
38.Gupte, T.M., M. Ritt & S. Sivaramakrishnan, 2021. Chapter Seven - ER/K-link—Leveraging a native protein linker to probe dynamic cellular interactions, In: Maarten M, editor. Methods in Enzymology, Academic Press, 64:173-208.
39.Robinson, S.W., A.M. Afzal & D.P. Leader, 2014. Chapter 13 - Bioinformatics: Concepts, Methods, and Data,In: Padmanabhan S, editor. Handbook of Pharmacogenomics and Stratified Medicine, Academic Press, 259-287.
40.Xue, B., A.K. Dunker & V.N. Uversky, 2010. Retro-MoRFs: identifying protein binding sites by normal and reverse alignment and intrinsic disorder prediction. Int J Mol Sci., 11(10):3725-3747.
41.Waterhouse, A., M. Bertoni, S. Bienert, G. Studer, G. Tauriello & R. Gumienny, 2018. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Research, 46: W296–W303.
42.Ramachandran, G.N., C. Ramakrishnan & V. Sasisekharan, 1963. Stereochemistry of polypeptide chain configurations. Journal of Molecular Biology,7: 95–9.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Satarupa Das, Dr. Neeta Shweta Kerketta, Jayanta Sinha

This work is licensed under a Creative Commons Attribution 4.0 International License.