Machine-Learning-Guided Engineering of an NADH-Dependent 7β-Hydroxysteroid Dehydrogenase for Economic Synthesis of Ursodeoxycholic Acid

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.