Rosetta FunFolDes - A general framework for the computational design of functional proteins.

Area of Science:

• Protein Engineering
• Computational Biology
• Structural Biology

Background:

• Computational protein design aims to create novel proteins with specific functions, but faces challenges with low success rates and extensive optimization.
• Transplanting functional motifs from natural proteins is a strategy, but success depends on the motif's complexity and the host protein's 'designability'.
• Backbone flexibility is a promising avenue to improve the 'designability' of protein structures for functional embedding.

Purpose of the Study:

• To present a novel computational approach, Rosetta Functional Folding and Design (FunFolDes), that couples conformational folding with sequence design.
• To embed functional motifs into heterologous proteins, addressing the challenge of designing proteins with complex functional sites.
• To test the efficacy of FunFolDes by transplanting viral epitopes into diverse structural templates.

Main Methods:

• Developed and implemented the FunFolDes computational approach, integrating conformational folding and sequence design.
• Conducted computational benchmarks to assess design outcomes, particularly the relationship between functional requirements and energetic minima.
• Experimentally characterized designed proteins by transplanting viral epitopes into different protein scaffolds, including a de novo fold.

Main Results:

• FunFolDes designs were computationally found to be distant from the global energetic minimum, consistent with observed function-stability tradeoffs.
• Designed proteins exhibited high binding affinities to specific monoclonal antibodies, demonstrating successful functional motif transplantation.
• The approach successfully repurposed existing protein folds for new functions, including a de novo fold.

Conclusions:

• FunFolDes offers an accessible strategy for computational protein design, enabling the embedding of complex functional sites into proteins.
• The method facilitates the repurposing of protein folds for new biochemical functions, such as binding and catalysis.
• Designed proteins show promise for applications in translational research, including vaccine development.