A sweeter future: Using protein language models for exploring sweeter brazzein homologs

Area of Science:

• Biotechnology
• Protein Engineering
• Computational Biology

Background:

• Growing health concerns regarding sugar consumption necessitate alternative sweeteners.
• Brazzein is a promising natural sweetener known for its sweetness, thermostability, and safety profile.
• Conventional protein engineering methods have limitations in optimizing complex traits like thermostability and sweetness.

Purpose of the Study:

• To utilize protein language models for designing novel brazzein homologs with enhanced thermostability and sweetness.
• To generate diverse, optimized amino acid sequences for improved structural and functional features of brazzein.
• To develop an efficient system for expressing, purifying, and analyzing engineered brazzein variants.

Main Methods:

• Application of protein language models for de novo protein design.
• Generation and optimization of amino acid sequences for brazzein.
• Expression and purification of brazzein mutants using Lactococcus lactis (L. lactis).
• Evaluation of sweetness using taste receptor assays.

Main Results:

• Successful design of new brazzein homologs with improved thermostability and potential for higher sweetness.
• Identification of unexpected mutations leading to novel protein engineering avenues.
• Development of a simplified and efficient protocol for brazzein mutant characterization.
• Demonstration of a computationally designed, more heat-resistant, and palatable brazzein variant (V23).

Conclusions:

• Protein language models are powerful tools for engineering enhanced protein functionalities, such as improved thermostability and sweetness in brazzein.
• Computational design offers a pathway beyond conventional methods for creating superior protein variants.
• The developed methodology facilitates efficient characterization of engineered proteins, accelerating future research in protein engineering and sweetener development.