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<p>Biocatalysis provides a sustainable approach for highly selective chemical transformations, however the complex, non-additive nature of enzyme fitness landscapes remains a significant barrier hindering the predictive design of optimized enzymes. Here, we document a curated dataset of kinetic colorimetric activity measurements of galactose oxidase variants for biocatalytic oxidation of a secondary alcohol, 1-phenylbutan-1-ol. Using the engineered galactose oxidase variant, GOh1052, as the backbone, site saturation mutagenesis libraries covering 352 residue positions were constructed, yielding 6,686 unique single mutants. These mutants were subsequently screened via a colorimetric assay to capture their kinetic activity profiles over a 16-hour reaction period. This dataset provides a data foundation for the development of machine learning models for applications such as enzyme-substrate activity prediction or biocatalytic process optimization.</p>

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Kinetic Activity Profiling of Galactose Oxidase Mutants for Biocatalytic Secondary Alcohol Oxidation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Dillon W. P. Tay,
  • Wan Lin Yeo,
  • Ying Sin Koo,
  • Jhoann M. T. Miyajima,
  • Charmaine Chia,
  • Shreyas Supekar,
  • Nazreen Abdul Muthaliff,
  • Tong Mei Teh,
  • Hao Fan,
  • Sebastian Maurer-Stroh,
  • Ee Lui Ang,
  • Yee Hwee Lim

摘要

Biocatalysis provides a sustainable approach for highly selective chemical transformations, however the complex, non-additive nature of enzyme fitness landscapes remains a significant barrier hindering the predictive design of optimized enzymes. Here, we document a curated dataset of kinetic colorimetric activity measurements of galactose oxidase variants for biocatalytic oxidation of a secondary alcohol, 1-phenylbutan-1-ol. Using the engineered galactose oxidase variant, GOh1052, as the backbone, site saturation mutagenesis libraries covering 352 residue positions were constructed, yielding 6,686 unique single mutants. These mutants were subsequently screened via a colorimetric assay to capture their kinetic activity profiles over a 16-hour reaction period. This dataset provides a data foundation for the development of machine learning models for applications such as enzyme-substrate activity prediction or biocatalytic process optimization.