OSPREY: protein design with ensembles, flexibility, and provable algorithms.

Area of Science:

• Computational biology
• Protein engineering
• Biophysics

Background:

• Protein redesign algorithms are crucial for understanding protein function and developing new therapeutics.
• Accurate in silico modeling requires capturing conformational changes and binding dynamics.
• Existing methods often lack the flexibility and global optimization needed for realistic predictions.

Purpose of the Study:

• To introduce a novel suite of protein redesign algorithms and the OSPREY software.
• To demonstrate the importance of enhanced flexibility, ensemble modeling, and globally optimal search in protein design.
• To showcase OSPREY's application in predicting resistance mutations and understanding protein-ligand interactions.

Main Methods:

• Development of protein redesign algorithms incorporating backbone and side-chain flexibility.
• Modeling proteins and ligands as ensembles of low-energy structures to approximate binding affinity.
• Implementation of a globally optimal search strategy for protein design predictions.

Main Results:

• OSPREY successfully models conformational changes induced by mutations.
• Ensemble modeling improves the approximation of binding affinity.
• Globally optimal search guarantees optimal predictions based on the input model.
• Prospective experimental validation confirms OSPREY's accuracy in predicting resistance mutations.

Conclusions:

• The OSPREY suite provides a powerful tool for realistic in silico protein modeling and redesign.
• Improved flexibility, ensemble modeling, and provable optimality are essential for accurate prediction of protein behavior.
• OSPREY has demonstrated utility in biomedically relevant applications, including mutation prediction.