Engineering of Halide Methyltransferase BxHMT through Dynamic Cross-correlation Network Analysis.

Abstract

Halide methyltransferases (HMTs) provide an effective way to regenerate S-adenosyl methionine (SAM) from S-adenosyl homocysteine and reactive electrophiles such as methyl iodide (MeI) or methyl toluene sulfonate (MeOTs). Compared with MeI, the cost-effective unnatural substrate MeOTs can be accessible directly from cheap and abundant alcohols, but shows only limited reactivity in SAM production. Herein, we developed a dynamic cross-correlation network analysis (DCCNA) strategy for quickly identifying hot spots influencing the enzyme's catalytic efficiency, and applied it to the evolution of HMT from Paraburkholderia xenovorans. Finally, the optimal mutant, M4 (V55T/C125S/L127T/L129P), exhibited remarkable improvements, with a specific activity of 4.08 U/mg towards MeOTs, representing an 82-fold increase compared to the wild-type (WT). Notably, M4 also demonstrated a positive impact on the catalytic ability with other methyl donors. The structural mechanism behind the enhanced enzyme activity was uncovered by molecular dynamics simulations. Our work not only contributes promising biocatalyst for the regeneration of SAM, but also offers a strategy for efficient enzyme engineering.