Multiscale simulations of protein landscapes: using coarse-grained models as reference potentials to full explicit...

Area of Science:

• Computational chemistry
• Biophysics
• Protein dynamics

Background:

• Protein free-energy landscape evaluation is computationally challenging due to landscape complexity and long simulation times.
• Simplified coarse-grained (CG) models aid landscape sampling but may lack accuracy regarding specific protein residues.
• Existing methods require refinement for precise electrostatic interactions in protein simulations.

Purpose of the Study:

• To refine and extend a CG model for accurate free-energy calculations of protein properties.
• To improve the electrostatic treatment within the CG model.
• To demonstrate the model's utility in key applications like mutation analysis and enzyme design.

Main Methods:

• Utilizing a CG model as a reference potential for free-energy calculations of an explicit protein model.
• Enhancing the electrostatic treatment for improved accuracy.
• Applying the refined model to diverse biological problems.

Main Results:

• The refined CG model provides accurate free-energy calculations, including folding energy changes upon mutations.
• The approach enables precise calculation of transition-state binding free energies for rational enzyme design.
• The model effectively evaluates catalytic landscapes and pH-dependent protein responses.

Conclusions:

• The enhanced CG approach overcomes limitations in simulating protein free-energy landscapes.
• This method offers a powerful tool for studying protein structure-function relationships.
• The refined model has broad applicability in computational biophysics and drug design.