Enhancing the activity and succinyl-CoA specificity of 3-ketoacyl-CoA thiolase Tfu_0875 through rational binding pocket engineering.

The 3-ketoacyl-CoA thiolase is the rate-limiting enzyme for linear dicarboxylic acids production. However, the promiscuous substrate specificity and suboptimal catalytic performance have restricted its application. Here we present both biochemical and structural analyses of a high-efficiency 3-ketoacyl-CoA thiolase Tfu_0875. Notably, Tfu_0875 displayed heightened activity and substrate specificity for succinyl-CoA, a key precursor in adipic acid production. To enhance its performance, a deep learning approach (DLKcat) was employed to identify effective mutants, and a computational strategy, known as the greedy accumulated strategy for protein engineering (GRAPE), was used to accumulate these effective mutants. Among the mutants, Tfu_0875 exhibited the highest specific activity (320% of wild-type Tfu_0875), the greatest catalytic efficiency (/ = 1.00 minmM), the highest succinyl-CoA specificity ( = 0.59 mM, 28.1% of Tfu_0875) and dramatically reduced substrate binding energy (-30.25 kcal mol -15.94 kcal mol). A structural comparison between Tfu_0875 and the wild type Tfu_0875 revealed that the increased interaction between the enzyme and succinyl-CoA was the primary reason for the enhanced enzyme activity. This interaction facilitated rapid substrate anchoring and stabilization. Furthermore, a reduced binding pocket volume improved substrate specificity by enhancing the complementarity between the binding pocket and the substrate in stereo conformation. Finally, our rationally designed mutant, Tfu_0875, increased the adipic acid titer by 1.35-fold compared to the wild type Tfu_0875 in shake flask. The demonstrated enzymatic methods provide a promising enzyme variant for the adipic acid production. The above effective substrate binding pocket engineering strategy can be beneficial for the production of other industrially competitive biobased chemicals when be applied to other thiolases.