Coarse-grained free energy functions for studying protein conformational changes: a double-well network model

Area of Science:

• Computational Biology
• Biophysics
• Molecular Dynamics

Background:

• Understanding protein conformational changes is crucial for molecular biology.
• Existing models often use simplified potentials to connect conformational states.
• Characterizing complex energy landscapes remains a challenge.

Purpose of the Study:

• To introduce a double-well network model (DWNM) for generating coarse-grained free energy functions.
• To investigate the impact of a rough free energy landscape on protein transition pathways.
• To apply the DWNM to specific protein conformational transitions.

Main Methods:

• Developed a double-well network model (DWNM) using interconnected double-well potentials.
• Implemented the DWNM to create a rough free energy landscape with multiple intermediate states.
• Reduced the free energy function to an elastic-network model near reference states.
• Applied the DWNM to study the coil-to-helix transition in G-actin and the open-to-closed transition in adenylate kinase.

Main Results:

• The DWNM successfully generated a rough free energy landscape.
• Application to G-actin and adenylate kinase revealed distinct minimum free energy paths.
• The model identified unique reaction pathways influenced by the landscape's roughness.
• DWNM's rough landscape effectively characterized transition mechanisms.

Conclusions:

• The double-well network model (DWNM) provides a robust method for modeling complex protein free energy landscapes.
• DWNM's rough energy landscapes are essential for accurately characterizing protein conformational transition mechanisms.
• This approach complements elastic-network models by capturing essential features of transition pathways.