Simultaneous Engineering of the Thermostability and Activity of a Novel Aldehyde Dehydrogenase
Kangjie Xu, Qiming Chen, Haiyan Fu, Qihang Chen, Jiahao Gu, Xinglong Wang, Jingwen Zhou
Abstract
Acetaldehyde is a toxic pollutant that can be detoxified by acetaldehyde dehydrogenases (ADAs) through its conversion to acetyl-CoA. This study developed an integrated approach combining virtual screening, rational design, and a dual scoring mechanism to identify and engineer hyperactive ADA variants. A library of 5000 Dickeya parazeae ADA (DpADA) homologues was created through protein BLAST, and deep learning tools predicted their K cat values. The top 100 candidates were selected based on acetaldehyde binding affinity, evaluated through molecular docking and phylogenetic analysis. Among these, ADA6 from Buttiauxella sp. S04-F03 exhibited the highest activity, converting 57.6% of acetaldehyde to acetyl-CoA, which was 14.1 times higher than DpADA. To improve ADA6’s thermostability, folding engineering was applied, resulting in the P443C variant with an 80.7% increase in residual activity after heat treatment. Molecular dynamics simulation pinpointed I440 as a bottleneck in the substrate tunnel, guiding the design of a dual-scoring system that integrates structural adjustments and electronic optimization to evaluate mutations for improved substrate exposure and activity. The final optimized variant, P443C-I440T, achieved a conversion efficiency of 93.2%. This study demonstrates the effectiveness of combining computational tools and rational mutagenesis to enhance enzyme activity and stability in enzyme engineering.