An efficient computational method for predicting rotational diffusion tensors of globular proteins using an ellipsoid...

Area of Science:

• Computational Biology
• Biophysics
• Structural Biology

Background:

• Predicting protein rotational diffusion is crucial for understanding molecular dynamics.
• Existing computational methods can be computationally intensive and time-consuming.

Purpose of the Study:

• To introduce a novel, computationally efficient method for predicting protein rotational diffusion properties.
• To assess the accuracy and speed of the new method compared to existing approaches and experimental data.

Main Methods:

• Representing the protein surface as an ellipsoid shell.
• Utilizing principal component analysis (PCA) of protein surface coordinates for enhanced efficiency.
• Investigating the effects of hydration layers and surface topography on rotational diffusion.

Main Results:

• The proposed method achieves accuracy comparable to existing approaches but is approximately 500 times faster.
• A hydration layer was found to significantly affect rotational diffusion, effectively doubling tumbling time.
• Analysis of 841 protein structures indicates that anisotropic rotational diffusion models are generally necessary for NMR relaxation data.

Conclusions:

• The new PCA-based ellipsoid shell method offers a significant advancement in computational efficiency for predicting protein rotational diffusion.
• Hydration and surface topography play critical roles in protein tumbling dynamics.
• Anisotropic models are often required for accurate NMR relaxation data analysis in single-domain proteins.