Enzymatic synthesis of ursodeoxycholic acid (UDCA) catalyzed by an NADH-dependent 7β-hydroxysteroid dehydrogenase (7β-HSDH) is more economic compared with an NADPH-dependent 7β-HSDH when considering the much higher cost of NADP+/NADPH than that of NAD+/NADH. However, the poor catalytic performance of NADH-dependent 7β-HSDH significantly limits its practical applications. Herein, machine-learning-guided protein engineering was performed on an NADH-dependent Rt7β-HSDHM0 from Ruminococcus torques. We combined random forest, Gaussian Naïve Bayes classifier, and Gaussian process regression with limited experimental data, resulting in the best variant Rt7β-HSDHM3 (R40I/R41K/F94Y/S196A/Y253F) with improvements in specific activity and half-life (40 °C) by 4.1-fold and 8.3-fold, respectively. The preparative biotransformation using a "two stage in one pot" sequential process coupled with Rt7β-HSDHM3 exhibited a space-time yield (STY) of 192 g L-1 d-1, which is so far the highest productivity for the biosynthesis of UDCA from chenodeoxycholic acid (CDCA) with NAD+ as a cofactor. More importantly, the cost of raw materials for the enzymatic production of UDCA employing Rt7β-HSDHM3 decreased by 22% in contrast to that of Rt7β-HSDHM0, indicating the tremendous potential of the variant Rt7β-HSDHM3 for more efficient and economic production of UDCA.
Keywords: 7β-hydroxysteroid dehydrogenase; NADH dependency; biotransformation; machine learning; protein engineering; ursodeoxycholic acid.