3α-Hydroxysteroid dehydrogenase (3α-HSD) from Comamonas testosteroni is widely used in clinical settings to measure serum total bile acid levels. However, its low enzymatic activity leads to high operational costs. In this study, we employed a combinatorial mutagenesis approach to systematically identify potential key mutation sites within the enzyme. The enzyme molecule was segmented into distinct regions, and a comprehensive strategy integrating substrate pocket engineering, binding energy calculations, and deep learning techniques was used. Through experimental verification, single-point mutants from the mutation library with enhanced enzymatic activity by at least 1.5-fold were identified. Through iterative combinatorial mutations of them, the optimal mutant H119A/R201G/R216L was obtained. This mutant exhibited a specific activity of 34.18 U/mg towards deoxycholic acid, representing a 6.85-fold increase over the wild-type (WT) enzyme. Additionally, the optimal temperature of the mutant increased from 35 °C to 40 °C, and its turnover number and catalytic efficiency increased by 6.4-fold and 9.4-fold, respectively. Quantum mechanics/molecular mechanics (QM/MM) calculations indicated that the energy barrier of the dehydrogenase reaction was reduced in the H119A/R201G/R216L mutant compared to that of the WT enzyme. Specifically, the R201G mutation significantly reduced the electric field strength along the 3α-hydroxyl group, facilitating its deprotonation. This study provides insights into enhancing enzymatic efficiency through strategic mutagenesis and elucidates mechanistic changes that optimize enzyme performance for clinical and biotechnological applications.
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