Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation

Area of Science:

• Computational biophysics
• Protein aggregation
• Colloidal science

Background:

• Reversible protein cluster formation is crucial for protein aggregation.
• Atomistic simulations are limited by long time and length scales.
• Understanding protein interactions requires robust modeling approaches.

Purpose of the Study:

• To develop a coarse-grained (CG) model for protein oligomerization.
• To characterize the thermodynamics and free energy landscape of protein clustering.
• To correlate model predictions with experimental measures like the osmotic second virial coefficient (B22).

Main Methods:

• Developed a CG model representing proteins as rigid bodies with amino acid-specific parameters (size, hydrophobicity, charge).
• Parametrized the model using experimental B22 data for α-chymotrypsinogen A and γD-Crystallin across varying ionic strengths.
• Applied the CG model to study pairwise interactions and dimerization of γD-Crystallin under different conditions (temperature, concentration, ionic strength).

Main Results:

• Entropic contributions dominate the free energy of protein cluster formation at experimentally relevant conditions.
• Electrostatic interactions modulate protein pair configurations, while short-range attractions dictate relative orientations.
• Principal Component Analysis identified known and novel aggregation-prone sites on γD-Crystallin.

Conclusions:

• The CG model provides a semiquantitative tool for studying protein oligomerization thermodynamics.
• Protein interactions and cluster formation are significantly influenced by entropy, not solely energetics.
• The model successfully predicts and explains experimental observations of protein interactions and aggregation sites.