β-lactoglobulin a new whey: Computational redesign improves stability and nutritional composition

Authors

Greis, M., Castet, U., Berlin, E., Klangby, S., Bancerz-Aleksiejczuk, O., Vilaplana, F., Keppler, J. K., Hudson, E. P.

Abstract

Protein engineering and precision fermentation provide an opportunity to increase the value of food proteins by improving their solubility, stability, functionality, or nutritional composition. Here, we use Beta-lactoglobulin (BLG) as a model protein to investigate how state-of-the-art computational protein design approaches affect these properties. First, the deep learning-based design tool ProteinMPNN was used to alter up to 20% of BLG residues for increased stability. Second, the physics-based modeling platform PyRosetta was used to find positions in BLG accommodating increased branched-chain amino acid (BCAA) content and up to 10 residues were simultaneously exchanged. Experimental characterisation of ProteinMPNN and stabilised BCAA-enriched variants showed similar secondary structure and oligomeric state as native BLG. ProteinMPNN variants gave increased titers and increased thermal stability up to 15C, and this correlated with changes in the rate of surface pressure in droplet tensiometry. Stabilized BCAA-enriched mutants had altered acid solubility. Correlations between computationally derived biophysical metrics and experimental properties are presented and suggest some predictive power for surface hydrophobicity on protein yield.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 19 Aug 2026.

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