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Combining Flexible Region Design and Automatic Design to Enhance the Thermal Stability and Catalytic Efficiency of Leucine Dehydrogenase.

Xiang ZhangXichuan ZhangHongling ShiHuimin ZhangJianhui ZhangChao YueDandan LiLunguang YaoCun-Duo Tang
Published in: Journal of agricultural and food chemistry (2024)
Leucine dehydrogenase (LeuDH, EC 1.4.1.9) can reversibly catalyze the oxidative deamination of l-leucine and some other specific α-amino acids to form the corresponding α-ketoacids. This reaction has great significance in the field of food additives and the pharmaceutical industry. The LeuDH from Exiguobacterium sibiricum ( Es LeuDH) has high catalytic efficiency but limited thermal stability, hindering its widespread industrial application. In this study, a mutant N5F/I12L/A352Y of Es LeuDH (referred to as M2) was developed with enhanced thermal stability and catalytic activity through rational modification. The M2 mutant exhibits a half-life at 60 °C ( t 1/2 (60 °C)) of 975.7 min and a specific activity of 69.6 U mg -1 , which is 5.4 and 2.1 times higher than those of Es LeuDH, respectively. This research may facilitate the utilization of Es LeuDH at elevated temperatures, enhancing its potential for industrial applications. The findings offer a practical and efficient approach for optimizing LeuDH and other industrial enzymes.
Keyphrases
  • wastewater treatment
  • heavy metals
  • amino acid
  • machine learning
  • wild type
  • deep learning
  • risk assessment
  • crystal structure