Directed Evolution and Computational Modeling of Galactose Oxidase toward Bulky Benzylic and Alkyl Secondary Alcohols
Wan Lin Yeo, Dillon W. P. Tay, Jhoann M.T. Miyajima, Shreyas Supekar, Tong Mei Teh, Jin Xu, Yee Ling Tan, Jie Yang See, Hao Fan, Sebastian Maurer‐Stroh, Yee Hwee Lim, Ee Lui Ang
Abstract
In the field of alcohol oxidation, galactose oxidase (GOase) is one of the most established enzymes capable of this important chemical transformation under benign conditions. However, the applicability of GOase toward more complex molecules such as those frequently found in the pharmaceutical or agrochemical industries remains restricted. Here, by employing a combined approach of directed evolution and computational modeling, we have identified improved GOases with significantly expanded substrate specificity toward both bulky benzylic and alkyl secondary alcohols, showing activity enhancements of up to 2400-fold compared to the reported benchmark M 3-5 mutant. Beneficial mutations conveying relaxed substrate enantioselectivity biases (R/S ratios down to 1.05) and higher thermostabilities (up to 1.6-fold improvement in residual activity versus benchmark) have also been identified. We have applied computational tools YASARA, FoldX, SCWRL, and Glide to show reasonable correlation with features related to GOase structure, protein stability, and catalytic activity. The generated enzyme activity models based on MM/GBSA ( r = −0.85) and YASARA ( r = −0.89) have successfully predicted the activity trend of a family of related substrates based on the 1-phenyl-1-alkyl alcohol scaffold with varying alkyl chain lengths. Together with curated experimental data sets and further optimization of these in silico models, these approaches can serve as gateway to explore desirable enzyme characteristics, establish enzyme substrate scopes, and accelerate biocatalyst development.