Rosetta:MSF: a modular framework for multi-state computational protein design.

Area of Science:

• Protein engineering
• Computational biology
• Biochemistry

Background:

• Computational protein design (CPD) uses algorithms for protein modeling.
• Rosetta software suite offers protocols for protein engineering tasks.
• Single-state design optimizes sequences based on one conformation.

Purpose of the Study:

• To develop a multi-state framework (MSF) for Rosetta.
• To enable simultaneous assessment of multiple protein states for complex design objectives.
• To improve the performance of CPD by incorporating multi-state design.

Main Methods:

• Developed the multi-state framework (MSF) for Rosetta.
• Implemented and utilized two existing Rosetta protocols within MSF.
• Applied MSF to a ligand-binding benchmark and de novo enzyme design.

Main Results:

• Multi-state design showed a 15% higher performance than single-state design on a ligand-binding benchmark.
• Successfully designed nine de novo retro-aldolases using MSF.
• All designed enzyme variants exhibited measurable catalytic activity.

Conclusions:

• The multi-state framework (MSF) enhances Rosetta's capabilities for complex protein design.
• Multi-state design is a successful strategy for engineering novel enzymes with desired functions.
• This approach significantly improves the success rate of computational protein engineering.