ANALYSIS OF A HYPOTHETICAL PROTEIN FROM VIBRIO HARVEYI IDENTIFIED POSSIBLE CONNECTION WITH BIOPOLYMER METABOLISM: AN IN-SILICO APPROACH
DOI:
https://doi.org/10.71336/jabs.1027Keywords:
Hypothetical protein, , molecular docking, , ExbD/TolR protein,, DNA, , V. HarveyiAbstract
Because of our limited understanding of the mechanisms governing Vibrio species persistence and spread in the face of global warming, finding ways to control the increasing spread of pathogenic vibrios is challenging. To determine whether the persistence of Vibrio harveyi is associated with physiological and gene expression patterns, it is important to do research on several proteins in its genome which are classified as hypothetical proteins (HPs). As a result, the current work sought to elucidate the roles of a HP found in the genome of V.harveyi. To determine the structure, function and homologous model of this protein, quality bioinformatics methods were used to predict and confirm the function as well as secondary and tertiary structure. Additionally, the active site and interacting proteins were examined using CASTp and the STRING server. An important biological activity of the HP is that it contains single functional domains that may be act as DNA binding site. Further, protein-protein interactions within selected HP revealed several functional partners that are essential for bacterial survival with different functional activity. In addition, molecular docking and simulation results showed stable bonding between HP and ExbD/TolR family protein which might be of significant relevance to future bacterial genetics research.
References
Campbell, S., Harada, R.M., DeFelice, S.V., Bienfang, P.K., Li, Q.X. (2009): Bacterial production of tetrodotoxin in the pufferfish Arothron hispidus. Natural Product Research 23(17):1630-1640. DOI: https://doi.org/10.1080/14786410903003780
Ruwandeepika, H.A.D., Defoirdt, T., Bhowmick P.P., Shekar, M., Bossier, P., Karunasagar, I. (2010): Presence of typical and atypical virulence genes in vibrio isolates belonging to the Harveyi clade. Journal of Applied Microbiology 109(3):888-899. DOI: https://doi.org/10.1111/j.1365-2672.2010.04715.x
Roux, F.L., Wegner, K.M., Baker-Austin, C., Vezzulli, L., Osorio, C.R., Amaro, C., Ritchie, J.M., Defoirdt, T., Destoumieux-Garzón, D., Blokesch, M. (2015): The emergence of Vibrio pathogens in Europe: ecology, evolution, and pathogenesis. Frontiers in Microbiology 6(830). DOI: https://doi.org/10.3389/fmicb.2015.00830
Baker-Austin, C., Oliver, J.D., Alam, M., Ali, A., Waldor, M.K., Qadri, F., Martinez-Urtaza, J. (2018): Vibrio spp. infections. Nature Reviews Disease Primers 4(1):1-19. DOI: https://doi.org/10.1038/s41572-018-0005-8
Martin, G., Rubin, N., Swanson, E. (2004): Vibrio parahaemolyticus and V. harveyi cause detachment of the epithelium from the midgut trunk of the penaeid shrimp Sicyonia ingentis. Diseases of aquatic organisms 60:21-29. DOI: https://doi.org/10.3354/dao060021
Lee, K.K., Liu, P-C., Chuang, W.H. (2002): Pathogenesis of Gastroenteritis Caused by Vibrio carchariae in Cultured Marine Fish. Marine biotechnology (New York, NY) 4:267-277. DOI: https://doi.org/10.1007/s10126-002-0018-9
Austin, B., Zhang, X.-H. (2006): Vibrio harveyi: a significant pathogen of marine vertebrates and invertebrates. Letters in Applied Microbiology 43(2):119-124. DOI: https://doi.org/10.1111/j.1472-765X.2006.01989.x
Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O'Donovan, C., Phan, I. (2003): The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research 31(1):365-370. DOI: https://doi.org/10.1093/nar/gkg095
Johnson, M., Zaretskaya, I., Raytselis, Y., Merezhuk, Y., McGinnis, S., Madden, T.L. (2008): NCBI BLAST: a better web interface. Nucleic Acids Research 36(suppl_2): W5-W9. DOI: https://doi.org/10.1093/nar/gkn201
Alzohairy, A. (2011): BioEdit: An important software for molecular biology. GERF Bulletin of Biosciences 2:60-61.
Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D., Bairoch, A. (2003): ExPASy: the proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Research 31(13):3784-3788. DOI: https://doi.org/10.1093/nar/gkg563
Islam, S., Mou, M., Sanjida, S., Mahfuj, MsE. (2022): An In-silico Approach for Identifying Phytochemical Inhibitors Against Nervous Necrosis Virus (NNV) in Asian Sea Bass by Targeting Capsid Protein. Genetics of Aquatic Organisms 6:487. DOI: https://doi.org/10.4194/GA487
Yu, C., Hwang, J. (2008): Prediction of Protein Subcellular Localizations. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications: 26-28 Nov 2008. 165-170. DOI: https://doi.org/10.1109/ISDA.2008.306
Yu, N.Y., Wagner, J.R., Laird, M.R., Melli, G., Rey, S., Lo, R., Dao, P., Sahinalp, S.C., Ester, M., Foster, L.J. (2010): PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26(13):1608-1615. DOI: https://doi.org/10.1093/bioinformatics/btq249
Bhasin, M., Garg, A., Raghava, G.P.S. (2005): PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21(10):2522-2524. DOI: https://doi.org/10.1093/bioinformatics/bti309
Möller, S., Croning, M.D.R.., Apweiler, R. (2001): Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics 17(7):646-653. DOI: https://doi.org/10.1093/bioinformatics/17.7.646
Tusnády, G.E., Simon, I. (2001): The HMMTOP transmembrane topology prediction server. Bioinformatics 17(9):849-850. DOI: https://doi.org/10.1093/bioinformatics/17.9.849
Dobson, L., Reményi, I., Tusnády, G.E. (2015): CCTOP: a Consensus Constrained TOPology prediction web server. Nucleic Acids Research 43(W1): W408-W412. DOI: https://doi.org/10.1093/nar/gkv451
Marchler-Bauer, A., Anderson, J.B., Cherukuri, P.F., DeWeese-Scott, C., Geer, L.Y., Gwadz, M., He S., Hurwitz, D.I., Jackson, J.D., Ke, Z. (2005): CDD: a Conserved Domain Database for protein classification. Nucleic Acids Research 33(suppl_1): D192-D196. DOI: https://doi.org/10.1093/nar/gki069
Kanehisa, M., Goto, S., Kawashima, S., Nakaya, A. (2002): The KEGG databases at GenomeNet. Nucleic Acids Research 30(1):42-46. DOI: https://doi.org/10.1093/nar/30.1.42
Finn, R.D. (2005): Pfam: the protein families database. In: Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics. DOI: https://doi.org/10.1002/047001153X.g306303
Wilson, D., Madera, M., Vogel, C., Chothia, C., Gough J. (2006): The SUPERFAMILY database in 2007: families and functions. Nucleic Acids Research 35(suppl_1):D308-D313. DOI: https://doi.org/10.1093/nar/gkl910
Hunter, S., Apweiler, R., Attwood, T.K., Bairoch, A., Bateman, A., Binns, D., Bork, P., Das, U., Daugherty, L., Duquenne, L. (2008): InterPro: the integrative protein signature database. Nucleic Acids Research 37(suppl_1):D211-D215. DOI: https://doi.org/10.1093/nar/gkn785
Shen, H.-B., Chou, K.-C. ( 2009): Predicting protein fold pattern with functional domain and sequential evolution information. Journal of Theoretical Biology 256(3):441-446. DOI: https://doi.org/10.1016/j.jtbi.2008.10.007
McGuffin, L.J., Bryson, K., Jones, D.T. (2000): The PSIPRED protein structure prediction server. Bioinformatics 16(4):404-405. DOI: https://doi.org/10.1093/bioinformatics/16.4.404
Islam S., Mou, M., Sanjida, S., Mahfuj, MsE. (2022): Functional Annotation and Characterization of a Hypothetical Protein from Pseudoalteromonas spp. Identify Potential Biomarker: An In-silico Approach. Aquatic Food Studies 2:57. DOI: https://doi.org/10.4194/AFS57
Xu, J., McPartlon, M., Li, J. (2021): Improved protein structure prediction by deep learning irrespective of co-evolution information. Nat Mach Intell 3:601-609. DOI: https://doi.org/10.1038/s42256-021-00348-5
Islam, S., Sanjida, S., Mou, M., Mahfuj, MsE., Nasir, S. (2022): In-silico functional annotation of a hypothetical protein from Edwardsiella tarda revealed Proline metabolism and apoptosis in fish. International Journal of Life Sciences and Biotechnology 5:78-96. DOI: https://doi.org/10.38001/ijlsb.1032171
Wiederstein, M., Sippl, M.J. (2007): ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research 35(Web Server issue): W407-W410. DOI: https://doi.org/10.1093/nar/gkm290
Anderson, R., Deng, Z., Campbell, R., Jiang, X. (2005): Main-chain conformational tendencies of amino acids. Proteins 60:679-689. DOI: https://doi.org/10.1002/prot.20530
Colovos, C., Yeates, T.O. (1993): Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511-1519. DOI: https://doi.org/10.1002/pro.5560020916
Laskowski, R.A., MacArthur, M.W., Thornton, J.M. (2006): PROCHECK: validation of protein-structure coordinates. In: International Tables for Crystallography 684-687. DOI: https://doi.org/10.1107/97809553602060000882
Lüthy, R., Bowie, J.U., Eisenberg, D. (1992): Assessment of protein models with three-dimensional profiles. Nature 356(6364):83-85. DOI: https://doi.org/10.1038/356083a0
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K.P. (2015): STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue): D447-452. DOI: https://doi.org/10.1093/nar/gku1003
Islam, S., Mou, M., Sanjida, S., Mahfuj, MsE., Alam, M., Juthy, Y. (2022): An In-silico analysis of the molecular interactions between PmCBP-VP24 and PmCBP-VP28 protein complex to understand the initial initiating events of shrimp WSSV infection. International Journal of Life Sciences and Biotechnology DOI: https://doi.org/10.38001/ijlsb.1055840
Grützner, A., Garcia-Manyes, S., Kötter, S., Badilla, C.L., Fernandez, J.M., Linke, W.A. (2009): Modulation of titin-based stiffness by disulfide bonding in the cardiac titin N2-B unique sequence. Biophysical journal 97(3):825-834. DOI: https://doi.org/10.1016/j.bpj.2009.05.037
Ferrè, F., Clote, P. (2005): DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res 33(Web Server issue):W230-232. DOI: https://doi.org/10.1093/nar/gki412
Heo, L., Shin, W.H., Lee, M.S., Seok, C. (2014): GalaxySite: ligand-binding-site prediction by using molecular docking. Nucleic Acids Res 42(Web Server issue): W210-214. DOI: https://doi.org/10.1093/nar/gku321
Dundas, J., Ouyang, Z., Tseng, J., Binkowski, A., Turpaz, Y., Liang, J. (2006): CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Research 34(suppl_2): W116-W118. DOI: https://doi.org/10.1093/nar/gkl282
Kozakov, D., Hall, D.R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D., Vajda, S. (2017): The ClusPro web server for protein-protein docking. Nature protocols 12(2):255-278. DOI: https://doi.org/10.1038/nprot.2016.169
Laskowski, R.A., Jabłońska, J., Pravda, L., Vařeková, R.S., Thornton, J.M. (2018): PDBsum: Structural summaries of PDB entries. Protein Sci 27(1):129-134. DOI: https://doi.org/10.1002/pro.3289
Weng, G., Wang, E., Wang, Z., Liu, H., Zhu, F., Li, D., Hou, T. (2019): HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res 47(W1): W322-w330. DOI: https://doi.org/10.1093/nar/gkz397
Källberg, M., Margaryan, G., Wang, S., Ma, J., Xu, J. (2014): RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol 1137:17-27. DOI: https://doi.org/10.1007/978-1-4939-0366-5_2
Tian, W., Chen, C., Lei, X., Zhao, J., Liang, J. (2018): CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research 46(W1): W363-W367. DOI: https://doi.org/10.1093/nar/gky473
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Journal of Applied Biological Sciences

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