β-lactoglobulin a new whey: Computational redesign improves stability and nutritional composition
Greis, M.; Castet, U.; Berlin, E.; Klangby, S.; Bancerz-Aleksiejczuk, O.; Vilaplana, F.; Keppler, J. K.; Hudson, E. P.
Show 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 ({beta}LG) 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 {beta}LG residues for increased stability. Second, the physics-based modeling platform PyRosetta was used to find positions in {beta}LG 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 {beta}LG. ProteinMPNN variants gave increased titers and increased thermal stability up to 15 {degrees}C, 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.
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