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Deciphering the functional role of hypothetical proteins from Chloroflexus aurantiacs J-10-f1 using bioinformatics approach | ||
| Molecular Biology Research Communications | ||
| دوره 9، شماره 3، 2020، صفحه 129-139 اصل مقاله (292.07 K) | ||
| نوع مقاله: Original article | ||
| شناسه دیجیتال (DOI): 10.22099/mbrc.2020.36894.1495 | ||
| نویسندگان | ||
| Chander Jyoti Thakur* 1؛ Sandeep Saini1، 2؛ Aayushi Notra1؛ Bhawanshu Chauhan1؛ Sarthak Arya1؛ Rishabh Gupta1؛ Jyotsna Thakur1؛ Varinder Kumar1 | ||
| 1Department of Bioinformatics, GGDSD College, Sector 32-C, 160030, Chandigarh, India | ||
| 2Department of Biophysics, Panjab University, Sector 25, 160014, Chandigarh, India | ||
| چکیده | ||
| Chloroflexus aurantiacus J-10-f1 is an anoxygenic, photosynthetic, facultative autotrophic gram negative bacterium found from hot spring at a temperature range of 50-60°C. It can sustain itself in dark only if oxygen is available thereby exhibiting a dark orange color, however display a dark green color when grown in sunlight. Genome of the organism contains total of 3853 proteins out of which 785 (~20%) proteins are uncharacterised or hypothetical proteins (HPs). Therefore in this work we have characterized the 785 hypothetical proteins of Chloroflexus aurantiacus J-10-f1 using bioinformatics tools and databases. HPs annotated by more than five domain prediction tools were filtered and named high confidence-hypothetical proteins (HC-HPs). These HC-HPs were further annotated by calculating their physiochemical properties, homologous, subcellular locations, signal peptides and transmembrane regions. We found most of the HC-HPs were involved in photosynthesis, carbohydrate metabolism, biofuel production and cellulose synthesis processes. Furthermore, few of these HC-HPs could provide resistance to bacteria at high temperature due to their thermophilic nature. Hence these HC-HPs have the potential to be used in industrial as well as in biomedical needs. To conclude, the bioinformatics approach used in this study provides an insight to better understand the nature and role of Chloroflexus aurantiacus J-10-f1hypothetical proteins. | ||
| کلیدواژهها | ||
| Domains؛ Physicochemical properties؛ Thermophilic proteins؛ Carbohydrate metabolism | ||
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| مراجع | ||
|
1. Stackebrandt E, Rainey FA, Ward-Rainey N. Anoxygenic phototropy across the phylogenetic spectrum: current understanding and future perspectives. Arch Microbial 1996;166:211-223.
2. Kondo T, Itoh S, Matsuoka M, Azai C, Oh-oka H. Menaquinone as the Secondary Electron Acceptor in the Type I Homodimeric Photosynthetic Reaction Center of Heliobacterium modesticaldum. J Phys Chem B 2015; 119:8480-8489.
3. Bachar A, Omoregie E, de Wit R, Jonkers HM. Diversity and function of Chloroflexus-like bacteria in a hypersaline microbial mat: phylogenetic characterization and impact on aerobic respiration. Appl Environ Microbiol. 2007;73:3975-3983.
4. Oyaizu H1, Debrunner-Vossbrinck B, Mandelco L, Studier JA, Woese CR. The green non-sulfur bacteria: a deep branching in the eubacterial line of descent. Syst Appl Microbiol 1987;9:47-53.
5. van Dorssen RJ, Vasmel H, Amesz J. Pigment organization and energy transfer in the green photosynthetic bacterium Chloroflexus aurantiacus III. Photosynth Res 1986:33-45.
6. Woese CR. Bacterial evolution. Microbiol Rev 1987;51:221-271.
7. Tang KH, Barry K, Chertkov O, Dalin E, Han CS, Hauser LJ, Honchak BM, Karbach LE, Land ML, Lapidus A, Larimer FW, Mikhailova N, Pitluck S, Pierson BK, Blankenship RE. Complete genome sequence of the filamentous anoxygenic phototrophic bacterium Chloroflexus aurantiacus. BMC Genomics 2011;12:334.
8. Schmidt K, Maarzahl M, Mayer F. Development and pigmentation of chlorosomes in Chloroflexus aurantiacus strain Ok-70-fl. Archives of Microbiology 1980;127:87-97.
9. Blankenship RE. Early evolution of photosynthesis. Plant Physiol 2010;154:434-438.
10. Herter S, Farfsing J, Gad'On N, Rieder C, Eisenreich W, Bacher A, Fuchs G. Autotrophic CO(2) fixation by Chloroflexus aurantiacus: study of glyoxylate formation and assimilation via the 3-hydroxypropionate cycle. J Bacteriol 2001;183:4305-4316.
11. Eisenreich W, Strauss G, Werz U, Fuchs G. Retrobiosynthetic analysis of carbon fixation in the phototrophic eubacterium Chloroflexus aurantiacus. Eur J Biochem 1993;215:619-632.
12. Strauss G1, Fuchs G. Enzymes of a novel autotrophic CO2 fixation pathway in the phototrophic bacterium Chloroflexus aurantiacus, the 3-hydroxypropionate cycle. Eur J Biochem 1993; 215:633-643.
13. Claassens NJ. A warm welcome for alternative CO2 fixation pathways in microbial biotechnology. Microb Biotechnol 2017;10:31-34.
14. Zadvornyy OA, Boyd ES, Posewitz MC, Zorin NA, Peters JW. Biochemical and Structural Characterization of Enolase from Chloroflexus aurantiacus: Evidence for a Thermophilic Origin. Front Bioeng Biotechnol 2015;3:74.
15. Watanabe A, Kamio Y, Kimura W, Izaki K. Purification and characterization of a thermostable neutral metalloprotease I from Chloroflexus aurantiacus J-10-fl. Biosci Biotechnol Biochem 1993;57:2160-2165.
16. Ratanakhanokchai K, Kaneko J, Kamio Y, Izaki K. Purification and properties of a maltotetraose- and maltotriose-producing amylase from Chloroflexus aurantiacus. Appl Environ Microbiol 1992;58:2490-2494.
17. Bharat Siva Varma P, Adimulam YB, Kodukula S. In silico functional annotation of a hypothetical protein from Staphylococcus aureus. J Infect Public Health 2015;8:526-532.
18. Kolker E, Makarova KS, Shabalina S, Picone AF, Purvine S, Holzman T, Cherny T, Armbruster D, Munson RS Jr, Kolesov G, Frishman D, Galperin MY. Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae. Nucleic Acids Res 2004;32:2353-2361.
19. Ijaq J, Chandrasekharan M, Poddar R, Bethi N, Sundararajan VS. Annotation and curation of uncharacterized proteins- challenges. Front Genet 2015;6:119.
20. Pawłowski K. Uncharacterized/hypothetical proteins in biomedical ‘omics’ experiments: is novelty being swept under the carpet?. Brief Funct Genomics 2008;7:283-290.
21. Islam MS, Shahik ShM, Sohel M, Patwary NI, Hasan MA. In silico structural and functional Annotation of hypothetical proteins of Vibrio cholerae O139. Genomics Inform 2015;13:53-59.
22. Singh A, Singal B, Nath O, Singh IK. Functional annotation and classification of the hypothetical proteins of Neisseria meningitidis H44/76. AJBIO 2015;3:57-64.
23. Malhotra H, Kaur H. A bioinformatics approach for functional and structural analysis of hypothetical proteins of Clostridium difficile. Imp J Interdiscip Res 2016;2:1601-1609.
24. School K, Marklevitz J, Schram WK, Harris LK. Predictive characterization of hypothetical proteins in Staphylococcus aureus NCTC 8325. Bioinformation 2016;12:209-220.
25. Shahbaaz M, Hassan MI, Ahmad F. Functional annotation of conserved hypothetical proteins from Haemophilus influenzae Rd KW20. PLoS One 2013;8:e84263.
26. Ravooru N, Ganji S, Sathyanarayanan N, Nagendra GH. In silico analysis of hypothetical proteins unveils putative metabolic pathways and essential genes in Leishmania donovani. Front Genet 2014;5:291.
27. Gazi MA, Mahmud S, Fahim SM, Kibria MG, Palit P, Islam MR, Rashid H, Das S, Mahfuz M, Ahmeed T. Functional prediction of hypothetical proteins from shigella flexneri and validation of the predicted models by using ROC curve analysis. Genomics Inform 2018;16: e26.
28. Park SJ, Son WS, Lee BJ. Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins. Int J Mol Sci 2012;13:7109-7137.
29. da Costa WLO, Araújo CLA, Dias LM, Pereira LCS, Alves JTC, Araújo FA, Folador EL, Henriques I, Silva A, Folador ARC. Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance. PLoS One 2018;13:e0198965.
30. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 2004;32:D115-D119.
31. Bagowski CP, Bruins W, Te Velthuis AJ. The nature of protein domain evolution: shaping the interaction network. Curr Genomics 2010;11:368-376.
32. Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu Sh, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, Lanczycki CJ, Lu F, Marchler GH, Song JS, Thanki N, Wang Z, Yamashita RA, Zhang D, Zheng C, Bryant SH. CDD: NCBI's conserved domain database. Nucleic Acids Res 2015;43:D222-D226.
33. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer EL, Tate J, Punta M. Pfam: the protein families database. Nucleic Acids Res 2014;42:D222-230.
34. Mitchell AL, Attwood TK, Babbitt PC, Blum M, Bork P, Bridge A, Brown ShD, Chang HY, El-Gebali S, Fraser MI, Gough J, Haft DR, Huang H, Letunic I, Lopez R, Luciani A, Madeira F, Marchler-Bauer A, Mi H, Natale DA, Necci M, Nuka G, Orengo C, Pandurangan AP, Paysan-Lafosse T, Pesseat S, Potter SC, Qureshi MA, Rawlings ND, Redaschi N, Richardson LJ, Rivoire C, Salazar GA, Sangrador-Vegas A, Sigrist CJA, Sillitoe I, Sutton GG, Thanki N, Thomas PD, Tosatto SCE, Yong SY, Finn RD. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res 2019; 47:D351-D360.
35. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 2003;31:3692-3697.
36. Schultz J, Copley RR, Doerks T, Ponting CP, Bork P. SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res 2000;28:231-234.
37. Sillitoe I, Dawson N, Thornton J, Orengo C. The history of the CATH structural classification of protein domains. Biochimie 2015;119:209-217.
38. Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982;157:105-132.
39. McGinnis S, Madden TL. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 2004;32:W20-W25.
40. Gardy JL, Spencer C, Wang K, Ester M, Tusnády GE, Simon I, Hua S, deFays K, Lambert C, Nakai K, Brinkman FS. PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria. Nucleic Acids Res 2003;31:3613-3617.
41. Yu CSh, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004;13:1402-1406.
42. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21:2522-2524.
43. Yu CS, Chen YC, Lu CH, Hwang JK: Prediction of protein subcellular localization. Proteins 2006,64:643-651.
44. Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, von Heijne G, Nielsen H. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019;37:420-423.
45. Bendtsen JD, Kiemer L, Fausbøll A, Brunak S. Non-classical protein secretion in bacteria. BMC Microbiol 2005;5:58.
46. Bendtsen JD, Jensen LJ, Blom N, Von Heijne G, Brunak S. Feature-based prediction of non-classical and leaderless protein secretion. Protein Eng Des Sel 2004;17:349-356.
47. Nielsen H, Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng 1997;10:1-6.
48. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001;305:567-580.
49. Tusnady GE, Simon I. The HMMTOP transmembrane topology prediction server. Bioinformatics 2001;17:849-850.
50. Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 1998;14:378-379.
51. Naqvi AA, Shahbaaz M, Ahmad F, Hassan MI. Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum. PLoS One 2015;10:e0124177.
52. Eng J. ROC analysis: web-based calculator for ROC curves. Baltimore: Johns Hopkins University.
53. Sivashankari S, Shanmughavel P. Functional annotation of hypothetical proteins-A review. Bioinformation 2006;1:335-338.
54. Close DW, D'Angelo S, Bradbury AR. A new family of β-helix proteins with similarities to the polysaccharide lyases. Acta Crystallogr D Biol Crystallogr 2014;70:2583-2592.
55. Akinosho H, Yee K, Close D, Ragauskas A. The emergence of Clostridium thermocellum as a high utility candidate for consolidated bioprocessing applications. Front Chem 2014;2:66.
56. Wong HC, Fear AL, Calhoon RD, Eichinger GH, Mayer R, Amikam D, Benziman M, Gelfand DH, Meade JH, Emerick AW. Genetic organization of the cellulose synthase operon in Acetobacter xylinum. Proc Natl Acad Sci U S A. 1990;87:8130-8134.
57. Omadjela O, Narahari A, Strumillo J, Melida H, Mazur O, Bulone V, Zimmer J. BcsA and BcsB form the catalytically active core of bacterial cellulose synthase sufficient for in vitro cellulose synthesis. Proc Natl Acad Sci U S A 2013;110:17856-17861.
58. Du J, Vepachedu V, Cho SH, Kumar M, Nixon BT. Structure of the Cellulose Synthase Complex of Gluconacetobacter hansenii at 23.4 Å Resolution. PLoS One 2016;11:e0155886.
59. Murakami T, Kanai T, Takata H, Kuriki T, Imanaka T. A novel branching enzyme of the GH-57 family in the hyperthermophilic archaeon Thermococcus kodakaraensis KOD1. J Bacteriol 2006;188:5915-5924.
60. Palomo M, Pijning T, Booiman T, Dobruchowska JM, van der Vlist J, Kralj S, Planas A, Loos K, Kamerling JP, Dijkstra BW, van der Maarel MJ, Dijkhuizen L, Leemhuis H. Thermus thermophilus glycoside hydrolase family 57 branching enzyme: crystal structure, mechanism of action, and products formed. J Biol Chem 2011;286:3520-3530.
61. Henrissat B, Callebaut I, Fabrega S, Lehn P, Mornon JP, Davies G. Conserved catalytic machinery and the prediction of a common fold for several families of glycosyl hydrolases. Proc Natl Acad Sci U S A 1995;92:7090-7094.
62. Davies G, Henrissat B. Structures and mechanisms of glycosyl hydrolases. Structure 1995;3:853-859.
63. Ogura T, Wilkinson AJ. AAA+ superfamily ATPases: common structure--diverse function. Genes Cells 2001;6:575-597.
64. Robinson PK. Enzymes: Principle and Biotechnolgy Application. Essays Biochem 2015; 59:1-41.
65. Hori H, Kawamura T, Awai T, Ochi A, Yamagami R, Tomikawa C, Hirata A. Transfer RNA modification enzymes from thermophiles and their modified nucleosides in tRNA. Microorganisms 2018;6:110.
66. De Oliveira TB, Gostincar C, Gunde-Cimerman N, Rodrigues A. Genome mining for peptidase in heat- tolerant and mesophillic fungi and putative adaptation for thermostability. BMC Genomics 2018;19:152.
67. Yabe Sh, Aiba Y, Sakai Y, Hazaka M, Yokota A. Thermosporothrix hazakensis gen. nov., sp. nov., isolated from compost, description of Thermosporotrichaceae fam. nov. within the class Ktedonobacteria Cavaletti et al. 2007 and emended description of the class Ktedonobacteria. Int J Syst Evol Microbiol 2010;60:1794-1801. | ||
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