Kinetic Analysis of R-Selective ω-Transaminases for Determination of Intrinsic Kinetic Parameters and Computational Modeling of Kinetic Resolution of Chiral Amine

Sang Woo Han, Jong Shik Shin

Research output: Contribution to journalArticle

Abstract

Reliable kinetic parameters of enzymes are of paramount importance for a precise understanding of catalytic performance, which is essential for enzyme engineering and process optimization. Here, we developed a simple and convenient method to determine intrinsic kinetic parameters of R-selective ω-transaminases (ω-TAs) with a minimal set of kinetic data. Using (R)-α-methylbenzylamine ((R)-α-MBA) and pyruvate as a substrate pair, two R-selective ω-TAs from Arthrobacter sp. and Aspergillus fumigatus were subjected to kinetic measurements. In contrast to S-selective ω-TAs, both R-selective ω-TAs were observed to be devoid of substrate inhibition by pyruvate. Double reciprocal plot analysis was carried out with two sets of kinetic data obtained at varying concentrations of (R)-α-MBA under a fixed concentration of pyruvate and vice versa, leading to the determination of three intrinsic kinetic parameters, i.e., one kcat and two KM values, using three regression constants. The validity of the kinetic parameters was verified by a self-consistency test using a regression constant left out in the kinetic parameter determination, showing that deviations of calculated regression constants from the experimental ones were less than 15%. Because the kinetic parameters for (R)-α-MBA and pyruvate are not apparent but intrinsic, a cosubstrate substitution method enabled rapid determination of intrinsic parameters for a new substrate pair using just one set of kinetic data. Eventually, computational modeling of kinetic resolution of rac-α-MBA was carried out and showed a good agreement with experimental reaction progresses.

Original languageEnglish
Pages (from-to)92-103
Number of pages12
JournalApplied Biochemistry and Biotechnology
Volume191
Issue number1
DOIs
Publication statusPublished - 2020 May 1

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Biochemistry
  • Applied Microbiology and Biotechnology
  • Molecular Biology

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