Computer-aided understanding and engineering of enzymatic selectivity.

Biotechnol Adv

State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, MOE-LSB & MOE-LSC, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Published: March 2022

AI Article Synopsis

  • Enzymes provide targeted synthesis of valuable chiral molecules, but their natural inefficiency and selectivity issues limit their use in biotechnology.
  • Biochemists are using computer-aided approaches to improve and engineer enzymes, expanding the range of artificial options available for specific tasks.
  • This review outlines a systematic method for enhancing enzymatic selectivity, including quantum calculations, enzyme-substrate interaction design, and channel engineering, contributing to better asymmetric biosynthesis through computational strategies.

Article Abstract

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

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Source
http://dx.doi.org/10.1016/j.biotechadv.2021.107793DOI Listing

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