The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.
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May 2021
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In-cell and in vitro study of protein folding has been significantly advanced by using biophysical approaches including FRET, NMR, CEST-MRI and optical tweezers. Read more about this in the review by Zhang et al. (pp. 29–38) of the special biophysics issue, ‘Emerging trends in biophysics and their applications in modern biology’, guest edited by Kakoli Bose (ACTREC, India).
Review Article|
April 09 2021
Revolutionizing enzyme engineering through artificial intelligence and machine learning Available to Purchase
Nitu Singh;
Nitu Singh
*
1Laboratory of Biocatalysis and Enzyme Engineering, Regional Centre for Biotechnology, Faridabad, Haryana 121001, India
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Sunny Malik;
Sunny Malik
*
1Laboratory of Biocatalysis and Enzyme Engineering, Regional Centre for Biotechnology, Faridabad, Haryana 121001, India
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Anvita Gupta;
2AINovo Biotech Inc, 725 W Elliot Rd, Suite 112, Gilbert, AZ 85233, U.S.A.
Correspondence: Anvita Gupta ([email protected]) or Kinshuk Raj Srivastava ([email protected])
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Kinshuk Raj Srivastava
1Laboratory of Biocatalysis and Enzyme Engineering, Regional Centre for Biotechnology, Faridabad, Haryana 121001, India
Correspondence: Anvita Gupta ([email protected]) or Kinshuk Raj Srivastava ([email protected])
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Publisher: Portland Press Ltd
Received:
December 17 2020
Revision Received:
March 17 2021
Accepted:
March 22 2021
Online ISSN: 2397-8562
Print ISSN: 2397-8554
© 2021 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology
2021
Emerg Top Life Sci (2021) 5 (1): 113–125.
Article history
Received:
December 17 2020
Revision Received:
March 17 2021
Accepted:
March 22 2021
Citation
Nitu Singh, Sunny Malik, Anvita Gupta, Kinshuk Raj Srivastava; Revolutionizing enzyme engineering through artificial intelligence and machine learning. Emerg Top Life Sci 14 May 2021; 5 (1): 113–125. doi: https://doi.org/10.1042/ETLS20200257
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