Functional, and phylogenetic analysis of maleylacetate reductase of Pseudomonas sp strain PNPG3: An in-silico approach
DOI:
https://doi.org/10.18006/2022.10(6).1331.1343Keywords:
Maleylacetate reductase, Pseudomonas, PNP biodegradation, Homology modeling, STRING databaseAbstract
Shrinking freshwater ecosystems are under tremendous pollution threat due to anthropocentric activities. Para nitrophenol (PNP), a well-documented priority pollutant extensively used in dyes, petrochemical, pharmaceutical, explosives, pesticides, leather industries, and agrochemicals, is responsible for contaminating aquatic ecosystems globally. It is highly toxic and has carcinogenic and mutagenic effects on living organisms like humans and several animal models. Bioremediation approaches mainly involving bacteria are considered the best, most eco-friendly, cost-effective, green, and clean method for effective removal PNP from its contaminated sites. This manuscript highlights the structural and functional analysis of a lower pathway enzyme involved in PNP degradation, maleylacetate reductase (MR), from Pseudomonas sp strain PNPG3, which was recently isolated from a freshwater ecosystem. This enzyme plays a role in converting maleylacetate to 3-oxoadipate. Despite its crucial functional role, no model is available for this protein in the protein database (PDB). Therefore, attempts were made for the computational investigation of physicochemical, functional, and structural properties, including secondary, and tertiary structure prediction, model quality analysis, and phylogenetic assessment using several standard bioinformatics tools. This enzyme has a molecular weight of about ~37.6 kDa, is acidic and thermostable, belonging to a member of iron-containing alcohol dehydrogenase. Moreover, this study will benefit the scientific community in deciphering the prediction of the function of similar proteins of interest.
References
Benkert, P., Biasini, M., & Schwede, T. (2011). Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics, 27, 343-350. DOI: https://doi.org/10.1093/bioinformatics/btq662
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., et al. (2000). The Protein Data Bank. Nucleic Acids Research, 28, 235–242. DOI: https://doi.org/10.1093/nar/28.1.235
Bhushan, B., Chauhan, A., Samanta, S.K., & Jain, R.K. (2000). Kinetics of biodegradation of p-nitrophenol by different bacteria. Biochemical and Biophysical Research Communications, 274, 626-630. DOI: https://doi.org/10.1006/bbrc.2000.3193
Booth, S.C., Weljie, A.M., & Turner RJ (2013). Computational tools for the secondary analysis of metabolomics experiments. Computational and Structural Biotechnology Journal, 4, e201301003. DOI: https://doi.org/10.5936/csbj.201301003
Delgove, M.A., Elford, M.T., Bernaerts, K.V., & De Wildeman, S.M. (2018). Application of a thermostable Baeyer–Villiger monooxygenase for the synthesis of branched polyester precursors. Journal of Chemical Technology & Biotechnology, 93, 2131-2140. DOI: https://doi.org/10.1002/jctb.5623
Gasteiger, E., Hoogland, C., Gattiker, A., Wilkins, M.R., Appel, R.D., & Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. The Proteomics Protocols Handbook, pp. 571-607. DOI: https://doi.org/10.1385/1-59259-890-0:571
Grasso, S., van Rij, T., & van Dijl, J.M. (2021). GP4: an integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting. Briefings in Bioinformatics, 22, 302. DOI: https://doi.org/10.1093/bib/bbaa302
Klausen, M.S., Jespersen, M.C., Nielsen, H., Jensen, K.K., et al. (2019). NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning. Proteins: Structure, Function, and Bioinformatics, 87, 520-527. DOI: https://doi.org/10.1002/prot.25674
Koonin, E.V., Mushegian, A.R., & Rudd, K.E. (1996). Sequencing and analysis of bacterial genomes. Current Biology, 6, 404-416. DOI: https://doi.org/10.1016/S0960-9822(02)00508-0
Krogh, A., Larsson, B., Von Heijne, G., & Sonnhammer, E.L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of Molecular Biology, 305, 567-580. DOI: https://doi.org/10.1006/jmbi.2000.4315
Kuang, S., Le, Q., Hu, J., Wang, Y., et al. (2020). Effects of p-nitrophenol on enzyme activity, histology, and gene expression in Larimichthys crocea. Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology, 228, 108638. DOI: https://doi.org/10.1016/j.cbpc.2019.108638
Kumar, S., Tsai, C.J., & Nussinov, R. (2000). Factors enhancing protein thermostability. Protein engineering, 13, 179-191. DOI: https://doi.org/10.1093/protein/13.3.179
Kumar, T.A. (2013). CFSSP: Chou and Fasman secondary structure prediction server. Wide Spectrum, 1, 15-19.
Laskowski, R.A., MacArthur, M.W., Moss, D.S., & Thornton, J.M. (1993). PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26, 283-291. DOI: https://doi.org/10.1107/S0021889892009944
Munn, M., Knuth, R., Van Horne, K., Shouse, A.W., & Levias, S. (2017). How do you like your science, wet or dry? How two lab experiences influence student understanding of science concepts and perceptions of authentic scientific practice. CBE—Life Sciences Education, 16, 39. DOI: https://doi.org/10.1187/cbe.16-04-0158
Niño-Gómez, D.C., Rivera-Hoyos, C. M., Morales-Álvarez, E.D., Reyes-Montaño, E.A., et al. (2017). “In silico” characterization of 3-phytase A and 3-phytase B from Aspergillus niger. Enzyme Research, 2017. DOI: https://doi.org/10.1155/2017/9746191. DOI: https://doi.org/10.1155/2017/9746191
Pal, S., Biswas, P., Ghosh, R., & Dam, S. (2021). In silico analysis and molecular identification of an anaphase-promoting complex homologue from human pathogen Entamoeba histolytica. Journal of Genetic Engineering and Biotechnology, 19, 1-10. DOI: https://doi.org/10.1186/s43141-021-00234-y
Pal, S., & Sengupta, K. (2020). Computational-based insights into the phylogeny, structure, and function of Rhodococcus alkane-1-monooxygenase. 3 Biotech, 10, 1-11. DOI: https://doi.org/10.1007/s13205-020-02388-x
Pettersen, E.F., Goddard, T.D., Huang, C.C., Couch, G.S., et al. (2004). UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25, 1605-1612. DOI: https://doi.org/10.1002/jcc.20084
Prabhu, D., Rajamanikandan, S., Anusha, S., Chowdary, M.S., Veerapandiyan, M., Jeyakanthan, J. (2020). In silico functional annotation and characterization of hypothetical proteins from Serratia marcescens FGI94. Biology Bulletin, 47, 319-331. DOI: https://doi.org/10.1134/S1062359020300019
Pramanik, K., Ghosh, P.K., Ray, S., Sarkar, A., Mitra, S., Maiti, T.K. (2017). An in silico structural, functional and phylogenetic analysis with three-dimensional protein modeling of alkaline phosphatase enzyme of Pseudomonas aeruginosa. Journal of Genetic Engineering and Biotechnology, 15, 527-537. DOI: https://doi.org/10.1016/j.jgeb.2017.05.003
Pramanik, K., Kundu, S., Banerjee, S., Ghosh, P.K., & Maiti, T.K. (2018). Computational-based structural, functional and phylogenetic analysis of Enterobacter phytases. 3 Biotech, 8, 1-12. DOI: https://doi.org/10.1007/s13205-018-1287-y
Roy, A., Yang, J., & Zhang, Y. (2012). COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40, 471477. DOI: https://doi.org/10.1093/nar/gks372
Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., et al. (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47, 607-613. DOI: https://doi.org/10.1093/nar/gky1131
Tamura, K., Stecher, G., & Kumar, S. (2021). MEGA11: molecular evolutionary genetics analysis version 11. Molecular biology and evolution, 38, 3022-3027. DOI: https://doi.org/10.1093/molbev/msab120
Teufel, F., Almagro Armenteros, J.J., Johansen, A.R., Gíslason, M.H., et al. (2022). SignalP 6.0 predicts all five types of signal peptides using protein language models. Nature biotechnology, 40, 1023–1025. DOI: https://doi.org/10.1038/s41587-021-01156-3
Vikram, S., Pandey, J., Kumar, S., & Raghava, G.P.S. (2013). Genes involved in degradation of para-nitrophenol are differentially arranged in form of non-contiguous gene clusters in Burkholderia sp. strain SJ98. PLoS One, 8, e84766. DOI: https://doi.org/10.1371/journal.pone.0084766
Yadav, P.K., Singh, G., Gautam, B., Singh, S., et al. (2013). Molecular modeling, dynamics studies and virtual screening of Fructose 1, 6 biphosphate aldolase-II in community acquired- DOI: https://doi.org/10.6026/97320630009158
methicillin resistant Staphylococcus aureus (CA-MRSA). Bioinformation, 9, 158.
Yakimov, A.P., Afanaseva, A.S., Khodorkovskiy, M.A., & Petukhov, M.G. (2016). Design of stable α-helical peptides and thermostable proteins in biotechnology and biomedicine. Acta Naturae, 8, 70-81. DOI: https://doi.org/10.32607/20758251-2016-8-4-70-81
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015). The I-TASSER Suite: protein structure and function prediction. Nature Methods, 12, 7-8. DOI: https://doi.org/10.1038/nmeth.3213
Yu, C.S., Cheng, C.W., Su, W.C., Chang, K.C., et al. (2014). CELLO2GO: a web server for protein subCELlularLOcalization prediction with functional gene ontology annotation. PloS one, 9, e99368. DOI: https://doi.org/10.1371/journal.pone.0099368
Yu, C.S., Lin, C.J., & Hwang, J.K. (2004). Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions. Protein Science, 13, 1402-1406. DOI: https://doi.org/10.1110/ps.03479604
Yu, N.Y., Wagner, J.R., Laird, M.R., Melli, G., et al. (2010). PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics, 26, 1608-1615. DOI: https://doi.org/10.1093/bioinformatics/btq249
Downloads
Published
How to Cite
License
Copyright (c) 2023 Journal of Experimental Biology and Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.