Data-Driven Construction of an Imine Reductase Library Capable of Broad-Scope Reductive Amination at Equimolar Substrate Concentrations

Reductive amination catalysed by imine reductase (IRED) and reductive aminase (RedAm) enzymes has recently been established as a powerful method for the asymmetric synthesis of chiral amines. While this biocatalytic technology has rapidly progressed from proof of concept to initial industrial applications, its scope and limitations remain to be fully explored. In this work, we report a broad and systematic profiling of reductive amination performance in the sequence space of IREDs and RedAms. This investigation employed an iterative strategy for activity and stereoselectivity screening, guided by chemo- and bioinformatic modelling as well as machine learning. By evaluating the catalytic performance of 175 IREDs against structurally diverse panels of 36 carbonyl compounds and 24 amines, we show that the majority of these enzymes is capable of asymmetric reductive amination at equimolar concentrations of the two substrates (50 mM each). The most effective enzymes identified in this study display sequence characteristics of RedAms, are active on 29–42% of the analysed substrate combinations, and combine high specific activities for the most favourable substrate pair (1.7−27.7 U/mgIRED) with excellent stereoselectivity. Beyond assembling this high-performance enzyme panel, we demonstrate extrapolation from our collected screening data to new substrate combinations by deep learning and the scale-up of selected reactions to a preparative batch size (10 mmol substrate, 200 mL reaction volume), delivering gram amounts of reductive amination products in high yield (63–89%) and optical purity (98% to >99% ee).