Computational and Structural Biotechnology Journal (Jan 2021)

Evolutionary coupling-inspired engineering of alcohol dehydrogenase reveals the influence of distant sites on its catalytic efficiency for stereospecific synthesis of chiral alcohols

  • Jie Gu,
  • Byu Ri Sim,
  • Jiarui Li,
  • Yangqing Yu,
  • Lei Qin,
  • Lunjie Wu,
  • Yu Shen,
  • Yao Nie,
  • Yi-Lei Zhao,
  • Yan Xu

Journal volume & issue
Vol. 19
pp. 5864 – 5873

Abstract

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Alcohol dehydrogenase (ADH) has attracted much attention due to its ability to catalyze the synthesis of important chiral alcohol pharmaceutical intermediates with high stereoselectivity. ADH protein engineering efforts have generally focused on reshaping the substrate-binding pocket. However, distant sites outside the pocket may also affect its activity, although the underlying molecular mechanism remains unclear. The current study aimed to apply evolutionary coupling-inspired engineering to the ADH CpRCR and to identify potential mutation sites. Through conservative analysis, phylogenic analysis and residues distribution analysis, the co-evolution hotspots Leu34 and Leu137 were confirmed to be highly evolved under the pressure of natural selection and to be possibly related to the catalytic function of the protein. Hence, Leu34 and Leu137, far away from the active center, were selected for mutation. The generated CpRCR-L34A and CpRCR-L137V variants showed high stereoselectivity and 1.24–7.81 fold increase in kcat/Km value compared with that of the wild type, when reacted with 8 aromatic ketones or β-ketoesters. Corresponding computational study implied that L34 and L137 may extend allosteric fluctuation in the protein structure from the distal mutational site to the active site. Moreover, the L34 and L137 mutations modified the pre-reaction state in multiple ways, in terms of position of the hydride with respect to the target carbonyl. These findings provide insights into the catalytic mechanism of the enzyme and facilitate its regulation from the perspective of the site interaction network.

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