Generic placeholder image

Recent Patents on Biotechnology

Editor-in-Chief

ISSN (Print): 1872-2083
ISSN (Online): 2212-4012

Research Article

Computational Elucidation of Phylogenetic, Functional and Structural Features of Methioninase from Pseudomonas, Escherichia, Clostridium and Citrobacter Strains

Author(s): Cambyz Irajie, Milad Mohkam , Bahareh Vakili and Navid Nezafat*

Volume 15, Issue 4, 2021

Published on: 09 September, 2021

Page: [286 - 301] Pages: 16

DOI: 10.2174/1872208315666210910091438

Price: $65

Abstract

Background: L-Methioninase (EC 4.4.1.11; MGL) is a pyridoxal phosphate (PLP)-dependent enzyme that is produced by a variety of bacteria, fungi, and plants. L-methioninase, especially from Pseudomonas and Citrobacter sp., is considered as the efficient therapeutic enzyme, particularly in cancers such as glioblastomas, medulloblastoma, and neuroblastoma that are more sensitive to methionine starvation. Objective: The low stability is one of the main drawbacks of the enzyme; in this regard, in the current study, different features of the enzyme, including phylogenetic, functional, and structural from Pseudomonas, Escherichia, Clostridium, and Citrobacter strains were evaluated to find the best bacterial L-Methioninase.

Methods: After the initial screening of L-Methioninase sequences from the above-mentioned bacterial strains, the three-dimensional structures of enzymes from Escherichia fergusonii, Pseudomonas fluorescens, and Clostridium homopropionicum were determined through homology modeling via GalaxyTBM server and refined by GalaxyRefine server.

Results and Conclusion: Afterwards, PROCHECK, verify 3D, and ERRAT servers were used for verification of the obtained models. Moreover, antigenicity, allergenicity, and physico-chemical analysis of enzymes were also carried out. In order to get insight into the interaction of the enzyme with other proteins, the STRING server was used. The secondary structure of the enzyme is mainly composed of random coils and alpha-helices. However, these outcomes should further be validated by wet-lab investigations.

Keywords: L-Methioninase, in silico analysis, Escherichia, Pseudomonas, Clostridium, phylogenetic.

Graphical Abstract

[1]
Mohkam M, Taleban Y, Golkar N, et al. Isolation and identification of novel l-Methioninase producing bacteria and optimization of its production by experimental design method. Biocatal Agric Biotechnol 2020; 26: 101566.
[http://dx.doi.org/10.1016/j.bcab.2020.101566]
[2]
Tanaka H, Esaki N, Soda K. A versatile bacterial enzyme: L-methionine γ-lyase. Enzyme Microb Technol 1985; 7: 530-7.
[http://dx.doi.org/10.1016/0141-0229(85)90094-8]
[3]
Soda K, Tanaka H, Esaki N. Multifunctional biocatalysis: methionine γ-lyase. Trends Biochem Sci 1983; 8: 214-7.
[http://dx.doi.org/10.1016/0968-0004(83)90216-5]
[4]
Sharma B, Singh S, Kanwar SS. L-methionase: a therapeutic enzyme to treat malignancies. BioMed Res Int 2014; 2014: 506287.
[http://dx.doi.org/10.1155/2014/506287] [PMID: 25250324]
[5]
Breillout F, Antoine E, Poupon MF. Methionine dependency of malignant tumors: a possible approach for therapy. J Natl Cancer Inst 1990; 82(20): 1628-32.
[http://dx.doi.org/10.1093/jnci/82.20.1628] [PMID: 2213904]
[6]
Tan Y, Xu M, Tan X, et al. Overexpression and large-scale production of recombinant L-methionine-α-deamino-γ-mercaptomethane-lyase for novel anticancer therapy. Protein Expr Purif 1997; 9(2): 233-45.
[http://dx.doi.org/10.1006/prep.1996.0700] [PMID: 9056489]
[7]
Anderson ME. Glutathione: an overview of biosynthesis and modulation. Chem Biol Interact 1998; 111-112: 1-14.
[http://dx.doi.org/10.1016/S0009-2797(97)00146-4] [PMID: 9679538]
[8]
Hoffman RM. Altered methionine metabolism, DNA methylation and oncogene expression in carcinogenesis. A review and synthesis. Biochim Biophys Acta 1984; 738(1-2): 49-87.
[PMID: 6204687]
[9]
Mecham JO, Rowitch D, Wallace CD, Stern PH, Hoffman RM. The metabolic defect of methionine dependence occurs frequently in human tumor cell lines. Biochem Biophys Res Commun 1983; 117(2): 429-34.
[http://dx.doi.org/10.1016/0006-291X(83)91218-4] [PMID: 6661235]
[10]
Hoffman RM, Yang Z, Tan Y, Han Q, Li S, Yagi S. Methionine dependence of cancer and aging Heidelberg Springer. 2019; 8: pp. 211-29.
[http://dx.doi.org/10.1007/978-1-4939-8796-2_16]
[11]
Yoshioka T, Wada T, Uchida N, et al. Anticancer efficacy in vivo and in vitro, synergy with 5-fluorouracil, and safety of recombinant methioninase. Cancer Res 1998; 58(12): 2583-7.
[PMID: 9635582]
[12]
Sato D, Nozaki T. Methionine gamma-lyase: the unique reaction mechanism, physiological roles, and therapeutic applications against infectious diseases and cancers. IUBMB Life 2009; 61(11): 1019-28.
[http://dx.doi.org/10.1002/iub.255] [PMID: 19859976]
[13]
Takakura T, Mitsushima K, Yagi S, et al. Assay method for antitumor L-methionine γ-lyase: comprehensive kinetic analysis of the complex reaction with L-methionine. Anal Biochem 2004; 327(2): 233-40.
[http://dx.doi.org/10.1016/j.ab.2004.01.024] [PMID: 15051540]
[14]
Skehan P, Storeng R, Scudiero D, et al. New colorimetric cytotoxicity assay for anticancer-drug screening. J Natl Cancer Inst 1990; 82(13): 1107-12.
[http://dx.doi.org/10.1093/jnci/82.13.1107] [PMID: 2359136]
[15]
Ali V, Nozaki T. Current therapeutics, their problems, and sulfur-containing-amino-acid metabolism as a novel target against infections by “amitochondriate” protozoan parasites. Clin Microbiol Rev 2007; 20(1): 164-87.
[http://dx.doi.org/10.1128/CMR.00019-06] [PMID: 17223627]
[16]
Mateo C, Palomo JM, Fernandez-Lorente G, Guisan JM, Fernandez-Lafuente R. Improvement of enzyme activity, stability and selectivity via immobilization techniques. Enzyme Microb Technol 2007; 40: 1451-63.
[http://dx.doi.org/10.1016/j.enzmictec.2007.01.018]
[17]
Damborsky J, Brezovsky J. Computational tools for designing and engineering enzymes. Curr Opin Chem Biol 2014; 19: 8-16.
[http://dx.doi.org/10.1016/j.cbpa.2013.12.003] [PMID: 24780274]
[18]
Gholami A, Shahin S, Mohkam M, Nezafat N, Ghasemi Y. Cloning, characterization and bioinformatics analysis of novel cytosine deaminase from Escherichia coli AGH09. Int J Pept Res Ther 2015; 21: 365-74.
[http://dx.doi.org/10.1007/s10989-015-9465-9]
[19]
Bjellqvist B, Hughes GJ, Pasquali C, et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 1993; 14(10): 1023-31.
[http://dx.doi.org/10.1002/elps.11501401163] [PMID: 8125050]
[20]
Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 2007; 8: 4-14.
[http://dx.doi.org/10.1186/1471-2105-8-4] [PMID: 17207271]
[21]
Magnan CN, Zeller M, Kayala MA, et al. High-throughput prediction of protein antigenicity using protein microarray data. Bioinformatics 2010; 26(23): 2936-43.
[http://dx.doi.org/10.1093/bioinformatics/btq551] [PMID: 20934990]
[22]
Dimitrov I, Naneva L, Doytchinova I, Bangov I, Allergen FP. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics 2014; 30(6): 846-51.
[http://dx.doi.org/10.1093/bioinformatics/btt619] [PMID: 24167156]
[23]
Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2- a server for in silico prediction of allergens. J Mol Model 2014; 20(6): 2278.
[http://dx.doi.org/10.1007/s00894-014-2278-5] [PMID: 24878803]
[24]
Sievers F, Wilm A, Dineen D, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011; 7: 539.
[http://dx.doi.org/10.1038/msb.2011.75] [PMID: 21988835]
[25]
Schwartz R. Matrices for detecting distant relationships. Atlas Protein Seq Struc 1978; 5: 353-9.
[26]
Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 1999; 292(2): 195-202.
[http://dx.doi.org/10.1006/jmbi.1999.3091] [PMID: 10493868]
[27]
Combet C, Jambon M, Deléage G, Geourjon C. Geno3D: automatic comparative molecular modelling of protein. Bioinformatics 2002; 18(1): 213-4.
[http://dx.doi.org/10.1093/bioinformatics/18.1.213] [PMID: 11836238]
[28]
Heo L, Park H, Seok C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res 2013; 41: W384-8.
[http://dx.doi.org/10.1093/nar/gkt458] [PMID: 23737448]
[29]
Lovell SC, Davis IW, Arendall WB III, et al. Structure validation by Calpha geometry: ϕ,ψ and Cbeta deviation. Proteins 2003; 50(3): 437-50.
[http://dx.doi.org/10.1002/prot.10286] [PMID: 12557186]
[30]
Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007; 35: W407-10.
[http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]
[31]
Colovos C, Yeates TO. Verification of protein structures: patterns of nonbonded atomic interactions. Protein science : a publication of the Protein Society 1993; 2: 1511-9.
[http://dx.doi.org/10.1002/pro.5560020916]
[32]
Lüthy R, Bowie JU, Eisenberg D. Assessment of protein models with three-dimensional profiles. Nature 1992; 356(6364): 83-5.
[http://dx.doi.org/10.1038/356083a0] [PMID: 1538787]
[33]
von Mering C, Jensen LJ, Kuhn M, et al. STRING 7--recent developments in the integration and prediction of protein interactions. Nucleic Acids Res 2007; 35(Database issue): D358-62.
[http://dx.doi.org/10.1093/nar/gkl825] [PMID: 17098935]
[34]
El-Sayed AS. Purification and characterization of a new L-methioninase from solid cultures of Aspergillus flavipes. J Microbiol 2011; 49(1): 130-40.
[http://dx.doi.org/10.1007/s12275-011-0259-2] [PMID: 21369990]
[35]
Verma A, Singh VK, Gaur S. Computational based functional analysis of Bacillus phytases. Comput Biol Chem 2016; 60: 53-8.
[http://dx.doi.org/10.1016/j.compbiolchem.2015.11.001] [PMID: 26672917]
[36]
Pramanik K, Soren T, Mitra S, Maiti TK. In silico structural and functional analysis of Mesorhizobium ACC deaminase. Comput Biol Chem 2017; 68: 12-21.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.02.005] [PMID: 28214450]
[37]
Pramanik K, Rajbhar P, Soren T, Maiti TK. In Silico structural, functional and phylogenetic analyses of Corynebacterium aspartokinase: an Enzyme of aspartate family of amino acids. Int J Rec Inov Trends Comput Commun 2017; 5: 981-7.
[38]
Pramanik K, Ghosh PK, Ray S, Sarkar A, Mitra S, Maiti TK. An in silico structural, functional and phylogenetic analysis with three dimensional protein modeling of alkaline phosphatase enzyme of Pseudomonas aeruginosa. J Genet Eng Biotechnol 2017; 15(2): 527-37.
[http://dx.doi.org/10.1016/j.jgeb.2017.05.003] [PMID: 30647696]
[39]
Rani S, Pooja K. Elucidation of structural and functional characteristics of collagenase from Pseudomonas aeruginosa. Process Biochem 2018; 64: 116-23.
[http://dx.doi.org/10.1016/j.procbio.2017.09.029]
[40]
Salim N, Santhiagu A, Joji K. Process modeling and optimization of high yielding L-methioninase from a newly isolated Trichoderma harzianum using response surface methodology and artificial neural network coupled genetic algorithm. Biocatal Agric Biotechnol 2019; 17: 299-308.
[http://dx.doi.org/10.1016/j.bcab.2018.11.032]
[41]
Takakura T, Ito T, Yagi S, et al. High-level expression and bulk crystallization of recombinant L-methionine γ-lyase, an anticancer agent. Appl Microbiol Biotechnol 2006; 70(2): 183-92.
[http://dx.doi.org/10.1007/s00253-005-0038-2] [PMID: 16012835]
[42]
Panja AS, Majee S, Bandyopadhyay B, Maity S. Antifungal signature: physicochemical and structural in silico analysis of some antifungal peptides. Int J Pept Res Ther 2016; 22: 163-9.
[http://dx.doi.org/10.1007/s10989-015-9493-5]
[43]
Morya VK, Yadav S, Kim E-K, Yadav D. In silico characterization of alkaline proteases from different species of Aspergillus. Appl Biochem Biotechnol 2012; 166(1): 243-57.
[http://dx.doi.org/10.1007/s12010-011-9420-y] [PMID: 22072140]
[44]
Pramanik K, Saren S, Mitra S, Ghosh PK, Maiti TK. Computational elucidation of phylogenetic, structural and functional characteristics of Pseudomonas Lipases. Comput Biol Chem 2018; 74: 190-200.
[http://dx.doi.org/10.1016/j.compbiolchem.2018.03.018] [PMID: 29627694]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy