Creating a more robust 5-hydroxymethylfurfural oxidase by combining computational predictions with a novel effective library design

Abstract

Abstract Background HMF oxidase (HMFO) from Methylovorus sp. is a recently characterized flavoprotein oxidase. HMFO is a remarkable enzyme as it is able to oxidize 5-hydroxymethylfurfural (HMF) into 2,5-furandicarboxylic acid (FDCA): a catalytic cascade of three oxidation steps. Because HMF can be formed from fructose or other sugars and FDCA is a polymer building block, this enzyme has gained interest as an industrially relevant biocatalyst. Results To increase the robustness of HMFO, a requirement for biotechnological applications, we decided to enhance its thermostability using the recently developed FRESCO method: a computational approach to identify thermostabilizing mutations in a protein structure. To make this approach even more effective, we now developed a new and facile gene shuffling approach to rapidly combine stabilizing mutations in a one-pot reaction. This allowed the identification of the optimal combination of seven beneficial mutations. The created thermostable HMFO mutant was further studied as a biocatalyst for the production of FDCA from HMF and was shown to perform significantly better than the original HMFO. Conclusions The described new gene shuffling approach quickly discriminates stable and active multi-site variants. This makes it a very useful addition to FRESCO. The resulting thermostable HMFO variant tolerates the presence of cosolvents and also remained thermotolerant after introduction of additional mutations aimed at improving the catalytic activity. Due to its stability and catalytic efficiency, the final HMFO variant appears to be a promising candidate for industrial scale production of FDCA from HMF.

Keywords

• Enzyme engineering
• Carbohydrate
• Poly(ethylene-furandicarboxylate) PEF
• Computational library
• Golden Gate gene shuffling