Simulating biomolecules for physiological timescales.

Area of Science:

• Structural Biology
• Computational Biophysics
• Molecular Dynamics

Background:

• Advances in structural biology enable simulation of complex conformational motions in large-scale biological assemblies.
• All-atom and coarse-grained structure-based models are effective for studying collective rearrangements.
• Computational resource limitations can impact the scope of molecular simulations.

Purpose of the Study:

• To highlight recent applications of structure-based models in understanding long-timescale dynamics of large-scale biological processes.
• To demonstrate the utility of structure-based models (e.g., SMOG) in elucidating biological mechanisms.
• To showcase how computational simulations can predict energy landscapes and dynamics.

Main Methods:

• Utilizing structure-based models (e.g., SMOG) for simulating large-scale assemblies.
• Employing explicit-solvent simulations for precise calibration of energetics and kinetics.
• Performing long-timescale molecular dynamics simulations.

Main Results:

• Structure-based models successfully predict structural characteristics of energy landscapes.
• Explicit-solvent simulations enabled accurate calibration of model energetics and kinetics.
• Simulations provided insights into the dynamics of viral fusion proteins and the ribosome.

Conclusions:

• Structure-based models are powerful tools for investigating the long-timescale dynamics of complex biological systems.
• A balance between energetics and structural disorder is crucial for driving biological and disease processes.
• Computational modeling significantly advances our understanding of molecular mechanisms in health and disease.