A lipophilicity-based energy function for membrane-protein modelling and design.

Area of Science:

• Biochemistry
• Computational Biology
• Structural Biology

Background:

• Membrane protein design is advancing, but complex proteins require accurate energy functions balancing intra-protein and protein-membrane interactions.
• Existing water-soluble protein energy functions do not fully capture the unique solvation constraints of the plasma membrane.

Purpose of the Study:

• To develop and validate a new lipophilicity-based energy function within Rosetta for improved membrane protein modeling and design.
• To address the specific physical constraints imposed by the plasma membrane on protein solvation.

Main Methods:

• Developed a high-throughput experimental screen (dsTβL) to determine amino acid insertion energies across the bacterial plasma membrane.
• Integrated these experimental profiles as lipophilicity energy terms into the Rosetta computational modeling software.
• Validated the new energy function using Rosetta ab initio simulations and sequence-design benchmarks.

Main Results:

• The new Rosetta energy function significantly outperforms previous versions in modeling and design benchmarks.
• Ab initio simulations accurately predicted structures of membrane-spanning homo-oligomers (<2.5Å RMSD for two-thirds of models).
• The energy function effectively discriminates stabilizing mutations and recapitulates natural membrane protein sequences.

Conclusions:

• The developed lipophilicity energy function enhances the accuracy of membrane protein modeling and design.
• This tool is recommended for future research in computational membrane protein engineering.
• The findings provide a more robust computational approach for understanding and designing proteins within the membrane environment.