[
1. Zhu, B., Ni, F., Sun, Y., Zhu, X., Yin, H., Yao, Z., & Du, Y. (2018). Insight into carrageenases: major review of sources, category, property, purification method, structure, and applications. Critical reviews in biotechnology, 38(8), 1261-1276. DOI: 10.1080/07388551.2018.1472550
]Open DOISearch in Google Scholar
[
2. Zhao, Y., Chi, Z., Xu, Y., Shi, N., Chi, Z., & Liu, G. (2018). High-level extracellular expression of κ- carrageenase in Brevibacillus choshinensis for the production of a series of κ-carrageenan oligosaccharides. Process biochemistry, 64, 83-92. DOI: 10.1016/j.procbio.2017.09.013
]Open DOISearch in Google Scholar
[
3. Chauhan, P. S., & Saxena, A. (2016). Bacterial carrageenases: an overview of production and biotechnological applications. 3 Biotech, 6(2), 1-18. DOI: 10.1007/s13205-016-0461-3
]Open DOISearch in Google Scholar
[
4. Zhao, D., Jiang, B., Zhang, Y., Sun, W., Pu, Z., & Bao, Y. (2021). Purification and characterization of a cold-adapted κ-carrageenase from Pseudoalteromonas sp. ZDY3. Protein Expression and Purification, 178, 105768. DOI: 10.1016/j.pep.2020.105768
]Open DOISearch in Google Scholar
[
5. Van de Velde, F., Knutsen, S., Usov, A., Rollema, H., & Cerezo, A. (2002). 1H and 13C high resolution NMR spectroscopy of carrageenans: application in research and industry. Trends in Food Science & Technology, 13(3), 73-92. DOI: 10.1016/S0924-2244(02)00066-3
]Open DOISearch in Google Scholar
[
6. Kobayashi, T., Uchimura, K., Koide, O., Deguchi, S., & Horikoshi, K. (2012). Genetic and biochemical characterization of the Pseudoalteromonas tetraodonis alkaline κ-carrageenase. Bioscience, biotechnology, and biochemistry, 76(3), 506-511. DOI: 10.1271/bbb.110809
]Open DOISearch in Google Scholar
[
7. Bakli, M., Pașcalău, R., & Șmuleac, L. (2020). Rare Codon Analysis in Affecting Recombinant Protein Expression in. Advanced Research in Life Sciences, 4(1), 30-35. DOI: 10.2478/arls-2020-0015
]Open DOISearch in Google Scholar
[
8. Bakli, M., Karim, L., Mokhtari-Soulimane, N., Merzouk, H., & Vincent, F. (2020). Biochemical characterization of a glycosyltransferase Gtf3 from Mycobacterium smegmatis: a case study of improved protein solubilization. 3 Biotech, 10(10), 1-13. DOI: 10.1007/s13205-020-02431-x
]Open DOISearch in Google Scholar
[
9. Rahimnahal, S., Shams, M., Tarrahimofrad, H., & Mohammadi, Y. (2020). Analysis to describe the catalytic critical residue of keratinase mojavensis using peptidase inhibitors: A docking-based bioinformatics study. J. Bas. Res. Med. Sci, 7, 13-28.
]Search in Google Scholar
[
10. NCBI Resource Coordinators. (2017). Database resources of the national center for biotechnology information. Nucleic acids research, 46(D1), D8-D13. DOI: 10.1093/nar/gkx1095
]Open DOISearch in Google Scholar
[
11. 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, 571-607. DOI: 10.1385/1-59259-890-0:571
]Search in Google Scholar
[
12. Geourjon, C., & Deleage, G. (1995). SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics, 11(6), 681-684. DOI: 10.1093/bioinformatics/11.6.681
]Open DOISearch in Google Scholar
[
13. Arnold, K., Bordoli, L., Kopp, J., & Schwede, T. (2006). The SWISS-MODEL workspace: a web-based environment for protein structure homology modeling. Bioinformatics, 22(2), 195-201. DOI: 10.1093/bioinformatics/bti770
]Open DOISearch in Google Scholar
[
14. Xu, D., & Zhang, Y. (2011). Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophysical journal, 101(10), 2525-2534. DOI: 10.1016/j.bpj.2011.10.024
]Open DOISearch in Google Scholar
[
15. 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(2), 283-291. DOI: 10.1107/S0021889892009944.
]Open DOISearch in Google Scholar
[
16. DeLano, W. (2019). The PyMOL Molecular Graphics System, version 2.3. 1. Schrodinger LLC: New York, NY, USA.
]Search in Google Scholar
[
17. Yu, C. S., Chen, Y. C., Lu, C. H., & Hwang, J. K. (2006). Prediction of protein subcellular localization. Proteins: Structure, Function, and Bioinformatics, 64(3), 643-651. DOI: 10.1002/prot.21018
]Open DOISearch in Google Scholar
[
18. Roy, A., Yang, J., & Zhang, Y. (2012). COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic acids research, 40(W1), W471-W477. DOI: 10.1093/nar/gks372
]Open DOISearch in Google Scholar
[
19. Szklarczyk, D., Gable, A.L., Nastou, K.C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N.T., Legeay, M., Fang, T., and Bork, P. (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of useruploaded gene/measurement sets. Nucleic acids research, 49(D1), D605-D612. DOI: 10.1093/nar/gkaa1074
]Open DOISearch in Google Scholar
[
20. Østgaard, K., Wangen, B., Knutsen, S., & Aasen, I. (1993). Large-scale production and purification of κ-carrageenase from Pseudomonas carrageenovora for applications in seaweed biotechnology. Enzyme and microbial technology, 15(4), 326-333. DOI: 10.1016/0141-0229(93)90159-Y
]Open DOISearch in Google Scholar
[
21. Ziayoddin, M., Lalitha, J., & Shinde, M. (2014). Increased production of carrageenase by Pseudomonas aeruginosa ZSL-2 using Taguchi experimental design. International Letters of Natural Sciences, 12(2). DOI: 10.18052/www.scipress.com/ILNS.17.194
]Search in Google Scholar
[
22. Khambhaty, Y., Mody, K., & Jha, B. (2007). Purification and characterization of κ-carrageenase from a novel γ-proteobacterium, Pseudomonas elongata (MTCC 5261) syn. Microbulbifer elongatus comb. Nov. Biotechnology and Bioprocess Engineering, 12(6), 668-675. DOI: 10.1007/BF02931084
]Open DOISearch in Google Scholar
[
23. Ikai, A. (1980). Thermostability and aliphatic index of globular proteins. The Journal of Biochemistry, 88(6), 1895-1898. DOI: 10.1093/bioinformatics/11.6.681
]Open DOISearch in Google Scholar
[
24. Nimrod, G., Glaser, F., Steinberg, D., Ben-Tal, N., & Pupko, T. (2005). In silico identification of functional regions in proteins. Bioinformatics, 21(suppl_1), i328-i337. DOI: 10.1093/bioinformatics/bti1023
]Open DOISearch in Google Scholar
[
25. Viborg, A. H., Terrapon, N., Lombard, V., Michel, G., Czjzek, M., Henrissat, B., & Brumer, H. (2019). A subfamily roadmap of the evolutionarily diverse glycoside hydrolase family 16 (GH16). Journal of Biological Chemistry, 294(44), 15973-15986. DOI: 10.1074/jbc.RA119.010619
]Open DOISearch in Google Scholar
[
26. Matard-Mann, M., Bernard, T., Leroux, C., Barbeyron, T., Larocque, R., Préchoux, Jeudy, A., Jam, A., Nyvall Collén, P., Michel, G., & Czjzek, M. (2017). Structural insights into marine carbohydrate degradation by family GH16 κ-carrageenases. Journal of Biological Chemistry, 292(48), 19919-19934. DOI: 10.1074/jbc.M117.808279
]Open DOISearch in Google Scholar
[
27. Michel, G., Chantalat, L., Duee, E., Barbeyron, T., Henrissat, B., Kloareg, B., & Dideberg, O. (2001). The κ-carrageenase of P. carrageenovora features a tunnel-shaped active site: a novel insight in the evolution of Clan-B glycoside hydrolases. Structure, 9(6), 513-525. DOI: 10.1016/S0969-2126(01)00612-8.
]Open DOISearch in Google Scholar