Biomolecular hydration: from water dynamics to hydrodynamics.

Area of Science:

• Biophysics
• Computational Biology
• Physical Chemistry

Background:

• Protein diffusion is governed by frictional coupling with the solvent environment.
• Predicting this coupling requires understanding water dynamics near biomolecules, a significant challenge.
• Existing models often assume static, rigidly bound water, contradicting experimental and simulation data.

Purpose of the Study:

• To resolve the paradox between static hydration models and dynamic experimental observations of water molecules.
• To develop a dynamic hydration model linking protein hydrodynamics with hydration dynamics.
• To enable accurate, structure-based predictions of biomolecular diffusion coefficients.

Main Methods:

• Developed a dynamic hydration model integrating protein hydrodynamics and hydration dynamics.
• Applied the model to predict rotational diffusion coefficients for a set of 16 proteins.
• Validated predictions against experimentally determined diffusion coefficients.

Main Results:

• The dynamic hydration model successfully reconciles protein hydrodynamics with fast water dynamics at the interface.
• Structure-based predictions of rotational diffusion coefficients were achieved with high accuracy.
• Demonstrated the model's capability using 16 diverse protein examples with available experimental data.

Conclusions:

• A dynamic hydration model provides a more accurate framework for understanding and predicting biomolecular diffusion.
• This approach overcomes limitations of static hydration models, offering a pathway for bona fide structure-based predictions.
• The developed model has significant implications for biophysics and computational biology research.