Open Access

In silico Structural and Functional Characterization of an Endoglucanase from Actinoalloteichus hoggarensis


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Sharma, A., Thakur, M., Bhattacharya, M., Mandal, T., & Goswami, S. (2019). Commercial application of cellulose nano-composites–A review. Biotechnology Reports, 21, e00316. DOI: 10.1016/j.btre.2019.e00316 Search in Google Scholar

Li, Y., Song, W., Han, X., Wang, Y., Rao, S., Zhang, Q., Zhou, J., Li, J., Liu, S., Du, G. (2022). Recent progress in key lignocellulosic enzymes: Enzyme discovery, molecular modifications, production, and enzymatic biomass saccharification. Bioresource Technology, 127986. DOI: 10.1016/j.biortech.2022.127986 Search in Google Scholar

Gupta, G. K., Dixit, M., Kapoor, R. K., & Shukla, P. (2022). Xylanolytic enzymes in pulp and paper industry: new technologies and perspectives. Molecular biotechnology, 1-14. DOI: 10.1007/s12033-021-00396-7 Search in Google Scholar

Ejaz, U., Sohail, M., & Ghanemi, A. (2021). Cellulases: from bioactivity to a variety of industrial applications. Biomimetics, 6(3), 44. DOI: 10.3390/biomimetics6030044 Search in Google Scholar

Boudjelal, F., Zitouni, A., Bouras, N., Schumann, P., Spröer, C., Sabaou, N., & Klenk, H.-P. (2015). Actinoalloteichus hoggarensis sp. nov., an actinomycete isolated from Saharan soil. International journal of systematic and evolutionary microbiology, 65(6), 2006-2010. DOI: 10.1099/ijs.0.000216 Search in Google Scholar

Xie, F., Rangseekaew, P., & Pathom-aree, W. (2022). Actinobacteria from Arid Environments and Their Biotechnological Applications. Natural Products from Actinomycetes: Diversity, Ecology and Drug Discovery (pp. 91-118): Springer. DOI: 10.1007/978-981-16-6132-7_4 Search in Google Scholar

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 Search in Google Scholar

Mandeep, Liu, H., & Shukla, P. (2021). Synthetic biology and biocomputational approaches for improving microbial endoglucanases toward their innovative applications. ACS omega, 6(9), 6055-6063. DOI: 10.1021/acsomega.0c05744 Search in Google Scholar

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

Kyte, J., & Doolittle, R. F. (1982). A simple method for displaying the hydropathic character of a protein. Journal of molecular biology, 157(1), 105-132. DOI: 10.1016/0022-2836(82)90515-0 Search in Google Scholar

Hirokawa, T., Boon-Chieng, S., & Mitaku, S. (1998). SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics (Oxford, England), 14(4), 378-379. DOI: 10.1093/bioinformatics/14.4.378 Search in Google Scholar

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 Search in Google Scholar

Buchan, D. W., Minneci, F., Nugent, T. C., Bryson, K., & Jones, D. T. (2013). Scalable web services for the PSIPRED Protein Analysis Workbench. Nucleic acids research, 41(W1), W349-W357. DOI: 10.1093/nar/gkt381 Search in Google Scholar

Arnold, K., Bordoli, L., Kopp, J., & Schwede, T. (2006). The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics, 22(2), 195-201. DOI: 10.1093/bioinformatics/bti770 Search in Google Scholar

Källberg, M., Wang, H., Wang, S., Peng, J., Wang, Z., Lu, H., & Xu, J. (2012). Template-based protein structure modeling using the RaptorX web server. Nature protocols, 7(8), 1511-1522. DOI: 10.1038/nprot.2012.085 Search in Google Scholar

Chen, C.-C., Hwang, J.-K., & Yang, J.-M. (2009). (PS)2-v2: template-based protein structure prediction server. Bmc Bioinformatics, 10(1), 1-13. DOI: 10.1186/1471-2105-10-366 Search in Google Scholar

Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. (2015). The Phyre2 web portal for protein modeling, prediction and analysis. Nature protocols, 10(6), 845-858. DOI: 10.1038/nprot.2015.053 Search in Google Scholar

Wu, S., & Zhang, Y. (2007). LOMETS: a local meta-threading-server for protein structure prediction. Nucleic acids research, 35(10), 3375-3382. DOI: 10.1093/nar/gkm251 Search in Google Scholar

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(1), 7-8. DOI: 10.1038/nmeth.3213 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

DeLano, W. (2019). The PyMOL Molecular Graphics System, version 2.3. 1. Schrodinger LLC: New York, NY, USA. Search in Google Scholar

Yang, J., Roy, A., & Zhang, Y. (2013). Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics, 29(20), 2588-2595. DOI: 10.1093/bioinformatics/btt447 Search in Google Scholar

Yang, J., Roy, A., & Zhang, Y. (2012). BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions. Nucleic acids research, 41(D1), D1096-D1103. DOI: 10.1093/nar/gks966 Search in Google Scholar

Yu, N. Y., Wagner, J. R., Laird, M. R., Melli, G., Rey, S., Lo, R., 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: 10.1093/bioinformatics/btq249 Search in Google Scholar

Teufel, F., Almagro Armenteros, J. J., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., Heijne, G. O., & Nielsen, H. (2022). SignalP 6.0 predicts all five types of signal peptides using protein language models. Nature biotechnology, 40(7), 1023-1025. DOI: 10.1038/s41587-021-01156-3 Search in Google Scholar

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(3), 567-580. DOI: 10.1006/jmbi.2000.4315 Search in Google Scholar

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 Search in Google Scholar

Cai, L.-N., Xu, S.-N., Lu, T., Lin, D.-Q., & Yao, S.-J. (2022). Salt-tolerant and thermostable mechanisms of an endoglucanase from marine Aspergillus niger. Bioresources and Bioprocessing, 9(1), 1-15. DOI: 10.1186/s40643-022-00533-3 Search in Google Scholar

Akram, F., ul Haq, I., Imran, W., & Mukhtar, H. (2018). Insight perspectives of thermostable endoglucanases for bioethanol production: a review. Renewable Energy, 122, 225-238. DOI: 10.1016/j.renene.2018.01.095 Search in Google Scholar

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