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Molecular Biotechnology

, Volume 61, Issue 1, pp 53–59 | Cite as

In-Silico Bioprospecting: Finding Better Enzymes

  • Asmita Kamble
  • Sumana Srinivasan
  • Harinder SinghEmail author
Review
  • 71 Downloads

Abstract

Enzymes are essential biological macromolecules, which catalyse chemical reactions and have impacted the human civilization tremendously. The importance of enzymes as biocatalyst was realized more than a century ago by eminent scientists like Kuhne, Buchner, Payen, Sumner, and the last three decades has seen exponential growth in enzyme industry, mainly due to the revolution in tools and techniques in molecular biology, biochemistry and production. This has resulted in high demand of enzymes in various applications like food, agriculture, chemicals, pharmaceuticals, cosmetics, environment and research sector. The cut-throat competition also pushes the enzyme industry to constantly discover newer and better enzymes regularly. The conventional methods to discover enzymes are generally costly, time consuming and have low success rate. Exploring the exponentially growing biological databases with the help of various computational tools can increase the discovering process, with less resource consumption and higher success rate. Present review discusses this approach, known as in-silico bioprospecting, which broadly involves computational searching of gene/protein databases to find novel enzymes.

Keywords

In-silico Bioprospecting Enzyme 

Notes

Funding

The manuscript is a review article and was not supported by any funding agency.

Compliance with Ethical Standards

Conflict of interest

The authors declare no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

For this type of study, formal consent is not required.

References

  1. 1.
    Coker, J. A. (2016). Extremophiles and biotechnology: Current uses and prospects. F1000Research, 5, 396.CrossRefGoogle Scholar
  2. 2.
    Anastas, P. T., & Warner, J. C. (1998). Green chemistry: Theory and practice. Oxford University Press: New York.Google Scholar
  3. 3.
    Savile, C. K., Janey, J. M., Mundorff, E. C., Moore, J. C., Tam, S., Jarvis, W. R., … Hughes, G. J. (2010). Biocatalytic asymmetric synthesis of chiral amines from ketones applied to sitagliptin manufacture. Science, 329(5989), 305–309.CrossRefGoogle Scholar
  4. 4.
    Chamoli, M., & Pant, K. (2014). In-silico bioprospecting of the enzymes involved in the biosynthetic pathway of the alkaloid berberine and its distance study Through R. International Journal of Advanced Technology in Engineering and Science, 2(9), 165–178.Google Scholar
  5. 5.
    Musumeci, M. A., Lozada, M., Rial, D. V., Cormack, W. P. M., Jansson, J. K., Sjöling, S., … Dionisi, H. M. (2017). Prospecting biotechnologically-relevant monooxygenases from cold sediment metagenomes: An in silico approach. Marine Drugs, 15(4).Google Scholar
  6. 6.
    Tan, H., Wu, X., Xie, L., Huang, Z., Peng, W., & Gan, B. (2016). Identification and characterization of a mesophilic phytase highly resilient to high-temperatures from a fungus-garden associated metagenome. Applied Microbiology and Biotechnology, 100(5), 2225–2241.CrossRefGoogle Scholar
  7. 7.
    Berón, C. M., Curatti, L., & Salerno, G. L. (2005). New strategy for identification of novel cry-type genes from bacillus thuringiensis strains. Applied and Environmental Microbiology, 71(2), 761–765.CrossRefGoogle Scholar
  8. 8.
    Tan, H., Wu, X., Xie, L., Huang, Z., Peng, W., & Gan, B. (2016). A novel phytase derived from an acidic peat-soil microbiome showing high stability under acidic plus pepsin conditions. Journal of Molecular Microbiology and Biotechnology, 26(4), 291–301.CrossRefGoogle Scholar
  9. 9.
    Shakeel, T., Gupta, M., Fatma, Z., Kumar, R., Kumar, R., Singh, R., … Yazdani, S. S. (2018). A consensus-guided approach yields a heat-stable alkane-producing enzyme and identifies residues promoting thermostability. The Journal of Biological Chemistry, 1–30.Google Scholar
  10. 10.
    Sharma, N., Thakur, N., Raj, T., Savitri, & Bhalla, T. C. (2017). Mining of microbial genomes for the novel sources of nitrilases. BioMed Research International, 2017.Google Scholar
  11. 11.
    Gupta, S., Singh, Y., Kumar, H., Raj, U., Rao, A. R., & Varadwaj, P. K. (2018). Identification of novel abiotic stress proteins in triticum aestivum through functional annotation of hypothetical proteins. Interdisciplinary Sciences: Computational Life Sciences, 10(1), 205–220.Google Scholar
  12. 12.
    Thornbury, M., Sicheri, J., Guinard, C., Mahoney, D., Routledge, F., Curry, M., … Getz, L. (2018). Discovery and Characterization of Novel Lignocellulose-Degrading Enzymes from the Porcupine Microbiome. bioRxiv, (February).Google Scholar
  13. 13.
    Toyama, D., de Morais, M. A. B., Ramos, F. C., Zanphorlin, L. M., Tonoli, C. C. C., Balula, A. F., et al. (2018). A novel β-glucosidase isolated from the microbial metagenome of Lake Poraquê (Amazon, Brazil). Biochimica et Biophysica Acta, 1866(4), 569–579.CrossRefGoogle Scholar
  14. 14.
    Qu, Y., Egelund, J., Gilson, P. R., Houghton, F., Gleeson, P. A., Schultz, C. J., & Bacic, A. (2008). Identification of a novel group of putative Arabidopsis thaliana β-(1,3)-galactosyltransferases. Plant Molecular Biology, 68(1–2), 43–59.CrossRefGoogle Scholar
  15. 15.
    Foong, C. P., Lakshmanan, M., Abe, H., Taylor, T. D., Foong, S. Y., & Sudesh, K. (2018). A novel and wide substrate specific polyhydroxyalkanoate (PHA) synthase from unculturable bacteria found in mangrove soil. Journal of Polymer Research, 25(1), 23.CrossRefGoogle Scholar
  16. 16.
    Vaquero, M. E., De Eugenio, L. I., Martínez, M. J., & Barriuso, J. (2015). A novel CalB-type lipase discovered by fungal genomes mining. PLoS ONE, 10(4), 1–11.CrossRefGoogle Scholar
  17. 17.
    Adam, N., & Perner, M. (2018). Novel hydrogenases from deep-sea hydrothermal vent metagenomes identified by a recently developed activity-based screen. ISME Journal, 12(5), 1225–1236.CrossRefGoogle Scholar
  18. 18.
    Ferrer, M., Martínez-Martínez, M., Bargiela, R., Streit, W. R., Golyshina, O. V., & Golyshin, P. N. (2016). Estimating the success of enzyme bioprospecting through metagenomics: Current status and future trends. Microbial Biotechnology, 9(1), 22–34.CrossRefGoogle Scholar
  19. 19.
    Uria, A. R., & Zilda, D. S. (2016). Metagenomics-guided mining of commercially useful biocatalysts from marine microorganisms. In Advances in Food and nutrition research.Google Scholar
  20. 20.
    Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M. R., Appel, R. D., & Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. The Proteomics Protocols Handbook, 571–607.Google Scholar
  21. 21.
    Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017). A review of bioinformatics tools for bio-prospecting from metagenomic sequence data. Frontiers in Genetics.Google Scholar
  22. 22.
    Machielsen, R., Leferink, N. G. H., Hendriks, A., Brouns, S. J. J., Hennemann, H. G., Daußmann, T., & Van Der Oost, J. (2008). Laboratory evolution of Pyrococcus furiosus alcohol dehydrogenase to improve the production of (2S,5S)-hexanediol at moderate temperatures. Extremophiles, 12(4), 587–594.CrossRefGoogle Scholar
  23. 23.
    Wang, N. Q., Sun, J., Huang, J., & Wang, P. (2014). Cloning, expression, and directed evolution of carbonyl reductase from Leifsonia xyli HS0904 with enhanced catalytic efficiency. Applied Microbiology and Biotechnology, 98(20), 8591–8601.CrossRefGoogle Scholar
  24. 24.
    Jakoblinnert, A., Wachtmeister, J., Schukur, L., Shivange, A. V., Bocola, M., Ansorge-Schumacher, M. B., & Schwaneberg, U. (2013). Reengineered carbonyl reductase for reducing methyl-substituted cyclohexanones. Protein Engineering, Design and Selection, 26(4), 291–298.CrossRefGoogle Scholar
  25. 25.
    Hoelsch, K., Sührer, I., Heusel, M., & Weuster-Botz, D. (2013). Engineering of formate dehydrogenase: Synergistic effect of mutations affecting cofactor specificity and chemical stability. Applied Microbiology and Biotechnology, 97(6), 2473–2481.CrossRefGoogle Scholar
  26. 26.
    Koudelakova, T., Chaloupkova, R., Brezovsky, J., Prokop, Z., Sebestova, E., Hesseler, M., … Damborsky, J. (2013). Engineering enzyme stability and resistance to an organic cosolvent by modification of residues in the access tunnel. Angewandte Chemie - International Edition, 52(7), 1959–1963.CrossRefGoogle Scholar
  27. 27.
    Buller, A. R., Brinkmann-Chen, S., Romney, D. K., Herger, M., Murciano-Calles, J., & Arnold, F. H. (2015). Directed evolution of the tryptophan synthase β-subunit for stand-alone function recapitulates allosteric activation. Proceedings of the National Academy of Sciences, 112(47), 14599–14604.Google Scholar
  28. 28.
    Brinkmann-Chen, S., Flock, T., Cahn, J. K. B., Snow, C. D., Brustad, E. M., McIntosh, J. A., … Arnold, F. H. (2013). General approach to reversing ketol-acid reductoisomerase cofactor dependence from NADPH to NADH. Proceedings of the National Academy of Sciences, 110(27), 10946–10951.Google Scholar
  29. 29.
    Fox, R. J., & Huisman, G. W. (2008). Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space. Trends in Biotechnology, 26(3), 132–138.CrossRefGoogle Scholar
  30. 30.
    Reetz, M. T., Rentzsch, M., Pletsch, A., Maywald, M., Maiwald, P., Peyralans, J. J. P., … Taglieber, A. (2007). Directed evolution of enantioselective hybrid catalysts: a novel concept in asymmetric catalysis. Tetrahedron, 63(28), 6404–6414.CrossRefGoogle Scholar
  31. 31.
    Rubin-Pitel, S. B., Cho, C. M. H., Chen, W., & Zhao, H. (2007). Directed evolution tools in bioproduct and bioprocess development. Bioprocessing for Value-Added Products from Renewable Resources, 49–72.Google Scholar
  32. 32.
    Li, Y., & Cirino, P. C. (2014). Recent advances in engineering proteins for biocatalysis. Biotechnology and Bioengineering, 111(7), 1273–1287.CrossRefGoogle Scholar
  33. 33.
    Wang, M., Si, T., & Zhao, H. (2012). Biocatalyst development by directed evolution, Bioresource Technology, 40(6), 1301–1315.Google Scholar
  34. 34.
    Woodley, J. M. (2013). Protein engineering of enzymes for process applications. Current Opinion in Chemical Biology, 17(2), 310–316.CrossRefGoogle Scholar
  35. 35.
    Zheng, G. W., & Xu, J. H. (2011). New opportunities for biocatalysis: Driving the synthesis of chiral chemicals. Current Opinion in Biotechnology, 22(6), 784–792.CrossRefGoogle Scholar
  36. 36.
    Lane, M. D., & Seelig, B. (2014). Advances in the directed evolution of proteins. Current Opinion in Chemical Biology, 22, 129–136.CrossRefGoogle Scholar
  37. 37.
    Kaur, J., Kumar, A., & Kaur, J. (2018). Strategies for optimization of heterologous protein expression in E. coli: Roadblocks and reinforcements. International Journal of Biological Macromolecules, 106, 803–822.CrossRefGoogle Scholar
  38. 38.
    Rosano, G. L., & Ceccarelli, E. A. (2014). Recombinant protein expression in Escherichia coli: Advances and challenges. Frontiers in Microbiology.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biological Sciences, Sunandan Divatia School of ScienceNMIMS Deemed to be UniversityMumbaiIndia
  2. 2.Biosystems Engineering Lab, Department of Chemical EngineeringIIT BombayMumbaiIndia

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