A Study On Artificial Enzymes Bringing Together Computational Design and Directed Evolution

References

[1] Arnold, F. H. (2018). Directed evolution: Bringing new chemistry to life. Angewandte Chemie International Edition, 57(16), 4143–4148. https://doi.org/10.1002/anie.201708408

[2] Baker, D., & Roth, S. (2019). Computational methods in enzyme design and optimization. Trends in Biotechnology, 37(4), 387–398. https://doi.org/10.1016/j.tibtech.2018.12.002

[3] Bornscheuer, U. T., & Kazlauskas, R. J. (2021). Catalytic promiscuity in biocatalysis: Using old enzymes to form new bonds and follow new pathways. Angewandte Chemie International Edition, 60(1), 88–119. https://doi.org/10.1002/anie.202005589

[4] Chen, K., & Arnold, F. H. (2020). Engineering new catalytic activities in enzymes. Nature Catalysis, 3(3), 203–213. https://doi.org/10.1038/s41929-020-0413-4

[5] Chica, R. A., Doucet, N., & Pelletier, J. N. (2021). Computational design and directed evolution for protein engineering. Current Opinion in Structural Biology, 69, 1–8. https://doi.org/10.1016/j.sbi.2020.11.002

[6] Cobb, R. E., Chao, R., & Zhao, H. (2013). Directed evolution: Past, present, and future. AIChE Journal, 59(5), 1432–1440. https://doi.org/10.1002/aic.13995

[7] Damborsky, J., & Brezovsky, J. (2021). Computational tools for designing and engineering biocatalysts. Current Opinion in Chemical Biology, 61, 102–110. https://doi.org/10.1016/j.cbpa.2020.11.010

[8] Doyle, L., Hallinan, J., & Ranganathan, R. (2022). Machine learning approaches in protein engineering. Annual Review of Biomedical Data Science, 5, 51–75. https://doi.org/10.1146/annurev-biodatasci-020222-104428

[9] Frushicheva, M. P., Cao, J., Chu, Z. T., & Warshel, A. (2010). Exploring challenges in rational enzyme design. Journal of Physical Chemistry B, 114(19), 5971–5980. https://doi.org/10.1021/jp100629p

[10] Gao, C., Wang, Y., & Liu, H. (2022). Combining directed evolution and computational design for efficient biocatalyst development. Current Opinion in Green and Sustainable Chemistry, 35, 100595. https://doi.org/10.1016/j.cogsc.2022.100595

[11] Giger, L., Caner, S., Obexer, R., & Kast, P. (2016). Directed evolution of enzyme stereoselectivity. Current Opinion in Chemical Biology, 31, 50–56. https://doi.org/10.1016/j.cbpa.2015.12.004

[12] Goldsmith, M., & Tawfik, D. S. (2017). Enzyme engineering by targeted libraries. Methods in Enzymology, 580, 31–50. https://doi.org/10.1016/bs.mie.2016.09.011

[13] Gong, L., Li, J., & Liu, X. (2020). Artificial enzymes for synthetic biology and biomedical applications. Nature Reviews Chemistry, 4(6), 339–353. https://doi.org/10.1038/s41570-020-0109-0

[14] Huang, X., & Groves, J. T. (2018). Beyond ferryl-mediated hydroxylation: 40 years of the rebound mechanism and C–H activation. Journal of Biological Inorganic Chemistry, 23(1), 13–29. https://doi.org/10.1007/s00775-017-1509-2

[15] Jiang, Y., Yu, H., & Wang, Y. (2021). Directed evolution of artificial enzymes for catalytic efficiency and beyond. ACS Catalysis, 11(12), 6980–6996. https://doi.org/10.1021/acscatal.1c00385

[16] Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2

[17] Kiss, G., Çelebi-Olcum, N., Moretti, R., Baker, D., & Houk, K. N. (2013). Computational enzyme design. Angewandte Chemie International Edition, 52(22), 5700–5725. https://doi.org/10.1002/anie.201204077

[18] Kumar, R., & Yadav, P. (2020). Challenges in enzyme design and their applications. Biotechnology Advances, 42, 107589. https://doi.org/10.1016/j.biotechadv.2020.107589

[19] Lee, S. Y., Kim, H. U., & Chae, T. U. (2019). A comprehensive metabolic map for production of bio-based chemicals. Nature Catalysis, 2(1), 18–33. https://doi.org/10.1038/s41929-018-0210-4

[20] Lewis, J. C. (2018). Artificial metalloenzymes for synthetic organic chemistry. Accounts of Chemical Research, 51(2), 298–306. https://doi.org/10.1021/acs.accounts.7b00527

[21] Lin, Y. W. (2020). Artificial metalloenzymes: Design and engineering. Current Opinion in Chemical Biology, 55, 32–38. https://doi.org/10.1016/j.cbpa.2019.12.007

[22] Miao, Z., et al. (2020). Advancements in computational enzyme design. Current Opinion in Chemical Biology, 57, 51–59. https://doi.org/10.1016/j.cbpa.2020.03.001

[23] Nobeli, I., Favia, A. D., & Thornton, J. M. (2009). Protein promiscuity and its implications for biotechnology. Nature Biotechnology, 27(2), 157–167. https://doi.org/10.1038/nbt1519

[24] Obexer, R., Godina, A., Garrabou, X., Mittl, P. R. E., Baker, D., Griffiths, A. D., & Hilvert, D. (2017). Emergence of a catalytic tetrad during evolution of a highly active artificial aldolase. Nature Chemistry, 9(1), 50–56. https://doi.org/10.1038/nchem.2605

[25] Packer, M. S., & Liu, D. R. (2015). Methods for the directed evolution of proteins. Nature Reviews Genetics, 16(7), 379–394. https://doi.org/10.1038/nrg3927

[26] Rothlisberger, D., et al. (2008). Kemp elimination catalysts by computational enzyme design. Nature, 453(7192), 190–195. https://doi.org/10.1038/nature06879

[27] Savile, C. K., & Lalonde, J. J. (2011). Biotechnology for the acceleration of green chemistry. Annual Review of Chemical and Biomolecular Engineering, 2, 409–434. https://doi.org/10.1146/annurev-chembioeng-061010-114148

[28] Siegel, J. B., et al. (2010). Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction. Science, 329(5989), 309–313. https://doi.org/10.1126/science.1190239

[29] Tee, K. L., & Wong, T. S. (2013). Polishing the craft of genetic diversity creation in directed evolution. Biotechnology Advances, 31(8), 1707–1721. https://doi.org/10.1016/j.biotechadv.2013.08.002

[30] Tóth-Petróczy, Á., & Tawfik, D. S. (2014). The robustness and innovability of protein folds. Current Opinion in Structural Biology, 26, 131–138. https://doi.org/10.1016/j.sbi.2014.06.004

[31] Turner, N. J. (2009). Directed evolution drives the next generation of biocatalysts. Nature Chemical Biology, 5(8), 567–573. https://doi.org/10.1038/nchembio.203

[32] Wang, X., Chen, J., & Liu, W. (2021). Enzyme engineering in the era of artificial intelligence: Challenges and opportunities. Biotechnology Journal, 16(5), 2000022. https://doi.org/10.1002/biot.202000022

[33] Wu, Y., & Zhang, L. (2020). Synergy between computational design and directed evolution in enzyme engineering. Trends in Biotechnology, 38(8), 1017–1029. https://doi.org/10.1016/j.tibtech.2020.02.010

[34] Yang, K. K., Wu, Z., & Arnold, F. H. (2019). Machine-learning-guided directed evolution for protein engineering. Nature Methods, 16(8), 687–694. https://doi.org/10.1038/s

@article{1703816,
      author = {Dr. N. Y. Badannavar},
      title = {A Study On Artificial Enzymes Bringing Together Computational Design and Directed Evolution},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {3},
      pages = {248-259},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703816.pdf},
      abstract = {Artificial enzymes, which are engineered catalysts that mimic the functions of natural enzymes, represent a transformative advancement in biotechnology, as they offer the potential to revolutionize various fields, including synthetic biology, industrial catalysis, and medicine, by enabling highly specific and efficient reactions, and the integration of computational design with directed evolution, two cutting-edge approaches, forms the foundation of the next generation of enzyme engineering, with computational design providing a powerful tool to predict and optimize enzyme structures and functions before experimental validation, allowing for the rational development of novel catalytic properties, while directed evolution, a process inspired by natural selection, enables the fine-tuning of these designed enzymes by iteratively mutating and screening variants to select for desired traits, thereby overcoming the limitations of traditional enzyme discovery and development methods, and together, computational design and directed evolution complement each other by combining the precision of computational predictions with the robustness of empirical selection, ultimately enhancing the creation of artificial enzymes with improved stability, specificity, and catalytic efficiency for a wide range of applications in areas such as pharmaceutical production, biofuel synthesis, and environmental remediation, where enzymes can catalyze processes that are otherwise difficult or unsustainable using conventional chemical methods, and future developments of this integrated approach hold great promise, including the potential for creating enzymes capable of catalyzing highly complex reactions or even entirely new chemical transformations, pushing the boundaries of current chemical and biological processes, while also addressing critical challenges in sustainability by replacing toxic or energy-intensive catalysts with bioinspired systems, thus contributing to greener and more sustainable industrial practices; moreover, advancements in computational methods, machine learning, and high-throughput screening are expected to accelerate the optimization of artificial enzymes, further expanding their applications and allowing for the creation of tailored enzymes for specific processes, which could greatly impact innovation in various sectors such as agriculture, healthcare, and renewable energy, making the integration of computational design and directed evolution a key driver of future innovation in enzyme technology and industrial sustainability.},
      keywords = {Artificial enzymes, Computational design, Directed evolution, Synthetic biology, Industrial catalysis, Sustainability},
      month = {September},
  }