# Quaternary stabilization of a GH2 β‐galactosidase from the psychrophile A. ikkensis, a flexible and unstable dimeric enzyme

## TL;DR

This paper studies a cold-active enzyme from a psychrophilic bacterium, revealing how it adapts to low temperatures and how its structure and stability can be engineered.

## Contribution

The paper introduces a deep learning approach to engineer enzyme variants with stabilized oligomeric states.

## Key findings

- AiLac has high specific activity and biphasic Michaelis–Menten behavior at room temperature.
- The enzyme is highly sensitive to heat and urea but can be stabilized by trehalose.
- A deep learning model successfully engineered functional enzyme variants with stabilized dimers and tetramers.

## Abstract

Studies of cold‐active enzymes may elucidate the basis for low‐temperature activity and contribute to their wider application in energy‐efficient processes. Here we investigate the cold‐active GH2 β‐galactosidase from the psychrophilic bacterium Alkalilactibacillus ikkensis (AiLac). AiLac has a specific activity twice as high as its closest structural homolog (the mesophilic Escherichia coli GH2 β‐galactosidase) toward the lactose analog ONPG at room temperature and neutral pH, and shows biphasic behavior in Michaelis–Menten plots. AiLac is activated by Mg2+ and Na+ and is most effective at pH 7.0 and 30°C. However, early unfolding events are observed already at room temperature. Stability studies using intrinsic fluorescence, circular dichroism, and small‐angle x‐ray scattering (SAXS), combined with activity assays, showed AiLac to be highly sensitive to heat and urea and to be stabilized, but also inhibited, by loss of structural flexibility induced by the osmolyte trehalose. AlphaFold structure prediction combined with SAXS and flow‐induced dispersion analysis support a reversible monomer‐dimer model, suggesting structural adaptation to cold temperatures on a quaternary level. The low amount of dimeric buried surface area, high flexibility, and remarkably low chemical and thermal stability present an extreme example of cold adaptation promoted by high levels of solvent interactions. To investigate the relationship between evolution and oligomerization, we trained a generative deep learning model to successfully engineer functional variants that form stabilized dimers and tetramers by introducing high evolutionary fitness mutations at the interface, demonstrating an efficient way to explore the local sequence fitness landscape to modulate the equilibrium of oligomerization.