Efficient method to characterize the context-dependent hydrophobicity of proteins

Area of Science:

• Biophysics
• Computational Chemistry
• Protein Science

Background:

• Protein surface hydrophobicity is crucial for understanding molecular interactions.
• Traditional methods for mapping context-dependent hydrophobicity at the nanoscale are computationally intensive.
• The free energy of cavity formation, μ(v)(ex), is a key measure of hydrophobicity but difficult to estimate.

Purpose of the Study:

• To present a novel, highly efficient computational method for estimating context-dependent hydrophobicity.
• To generate a nanoscale hydrophobicity map of a protein surface.
• To investigate the influence of cavity size and shape on hydrophobicity measurements.

Main Methods:

• Developed a new method to estimate the free energy of cavity formation (μ(v)(ex)) by calculating the average number of water molecules in a volume under an external potential.
• The method reduces computational cost by two orders of magnitude compared to conventional techniques.
• Applied the method to map the hydrophobicity of the hydrophobin II protein surface using short molecular dynamics simulations.

Main Results:

• The new method accurately estimates μ(v)(ex) using short simulations (50-100 ps).
• Generated a detailed hydrophobicity map of the hydrophobin II surface, considering topographical and chemical context.
• Demonstrated that the hydrophobicity map is dependent on the shape and size of the probed cavity (observer context).

Conclusions:

• The developed method offers a computationally efficient way to characterize nanoscale protein surface hydrophobicity.
• The findings highlight the importance of context (topography, chemistry, and probe size) in defining protein hydrophobicity.
• This approach enables more accurate predictions of protein-ligand and protein-protein interactions.