Using a novel data-driven combinatorial mutagenesis strategy to engineer an alcohol dehydrogenase for efficient geraniol synthesis

Abstract

The monoterpene geraniol is widely applied in perfume, food, pharmaceutical, and other industries. However, conventional methods of geraniol production, including chemical synthesis and microbial fermentation, are associated with low selectivity and poor yields. Here, we engineered the medium-chain alcohol dehydrogenase AdhP from Escherichia coli K12 as a biocatalyst for the efficient bioconversion of geranial into geraniol. First, a single point mutation library targeting 12 key residues identified based on computational approaches was constructed, and 24 positive mutants with improved reducing activity toward geranial were obtained. Subsequently, we established a data-driven combinatorial mutagenesis strategy for five rounds of engineering with reference to a mathematical “weighting” principle, yielding the six-point mutant M6, which exhibited 8.5-fold higher catalytic efficiency than the wild type. In silico analysis revealed its enhanced structural stability and shortened critical catalytic distances. Recombinant bacterias co-expressing M6 and glucose dehydrogenase were employed as biocatalysts for the reduction of geranial. As a result, 100.0 g/L of geranial was converted to geraniol in 8 h with a space-time yield of up to 291.3 g·L−1d−1. This study provides a multipoint mutant library construction strategy for the accumulation of beneficial mutations to tailor enzymes and provides a potential biocatalyst for efficient geraniol synthesis.