Data-Efficient Distal Engineering of Fluorinase Using Zero-Shot Models

Abstract

Fluorinases have high potential for industrial biofluorination, but any applications have been precluded by low catalytic efficiency and resistance to active site engineering. In this work, we employed PRIZM, a computational workflow utilizing an existing low-N data set and zero-shot models for in silico prediction of activity-enhancing mutations at distal sites. The combination of these predictions with expert opinion led to the identification of 21 fluorinase mutants with enhanced relative activities, while 3 variants showed increased melting temperatures. A mutation in the hexameric interface, K237R, resulted in the largest stability gain, a more than 3.2-fold improvement in catalytic efficiency at 57 °C, and an 8-fold increase in relative activity at 62 °C. These results highlight the potential of distal fluorinase engineering for improving properties required to realize its industrial applications.