Multiscale frameworks for exploring protein energy landscapes: advances in theory and simulation

Area of Science:

• Biophysics
• Computational Biology
• Theoretical Chemistry

Background:

• Protein energy landscapes are central to understanding protein structure, dynamics, and function.
• Directly accessing these landscapes is challenging; they are represented by various theoretical models.
• Each model involves reducing degrees of freedom and transforming the landscape.

Purpose of the Study:

• To review how protein energy landscape representations change with successive approximations.
• To emphasize the emergence, interpretation, and robustness of landscape concepts across different scales.
• To clarify principles of coarse-graining, solvent modeling, and multilevel simulations.

Main Methods:

• Examination of theoretical models from quantum mechanics to coarse-grained potentials.
• Analysis of landscape transformations under systematic approximations to the molecular Hamiltonian.
• Review of coarse-graining, solvent modeling, and multilevel simulation strategies.

Main Results:

• Key topological features (funnels, barriers, basins) are preserved under projection, explaining model success.
• Simplified models are predictive due to the robustness of these essential landscape features.
• Energy landscape theory remains predictive even with reduced chemical resolution.

Conclusions:

• Energy landscape theory provides a unifying framework for biomolecular exploration.
• The preservation of topological features is crucial for the predictive power of simplified models.
• Understanding landscape transformations is key to leveraging theoretical models in biophysics.