EnZight: A Structure-Guided Algorithm to Identify and Prioritize Substitution Hotspots for Enzyme Engineering

Abstract

Homologous protein structures contain valuable information about tolerated sequence variation. However, translating this information into practical enzyme design strategies remains challenging. Here we present EnZight, a user-friendly web server that integrates homologous structural alignment with intuitive visualization to identify substitution hotspots in protein cores. EnZight exploits structurally aligned homologs to identify positions where the surrounding structural environment is conserved while the residue at the position varies across homologs. This enables prediction of substitutions that preserve fold integrity while modulating function and thermostability. The approach further provides interactive structural outputs that allow users to inspect and prioritize substitutions manually. To validate the use of EnZight, we used a polyurethane-degrading amidase as a proof of concept. We constructed 34 variants, and 97% were successfully expressed, indicating high foldability of the predicted substitutions. Several substitutions improved both catalytic turnover and thermostability, and, importantly, beneficial substitutions combined additively, enabling stepwise accumulation of improvements. The best triple mutant variant exhibited a six-fold increase in catalytic turnover and 2{degrees}C increase in apparent melting temperature. Enhanced activity toward the pharmaceutical micropollutant flutamide further demonstrates EnZight's broad applicability in identifying substitutions that enable enzyme optimization across diverse substrates.