Combining optimal control theory and molecular dynamics for protein folding.

Area of Science:

• Computational Biology
• Biophysics
• Protein Folding Dynamics

Background:

• Protein folding is crucial for biological function.
• Predicting low-energy folding routes remains a significant challenge in computational biology.
• Existing methods often struggle to accurately capture the complex dynamics of protein folding.

Purpose of the Study:

• To develop a novel computational method for determining low-energy protein folding routes.
• To synergistically integrate optimal control theory and Molecular Dynamics (MD) for enhanced protein folding simulations.
• To provide a generalizable approach applicable to various protein systems.

Main Methods:

• Utilized optimal control theory to compute the force field and optimal folding trajectory for a Coarse-Grained (CG) protein model.
• Employed Targeted Molecular Dynamics (TMD) guided by CG optimization to generate all-atom conformations.
• Implemented an iterative cycle of CG optimization and MD simulations, updating contacts and potentials at each step.

Main Results:

• Successfully computed low-energy folding routes for proteins.
• Demonstrated the method's utility through simulations on the model protein Villin.
• The iterative approach effectively accommodates dynamic changes in residue contacts during folding.

Conclusions:

• The novel optimal control theory and MD integration provides an efficient method for predicting protein folding pathways.
• The approach is widely applicable to diverse biological systems due to its foundation in general computational tools.
• The method is readily implementable with existing Molecular Dynamics software packages.