An effective continuum approach for modeling non-equilibrium structural evolution of protein nanofiber networks

Area of Science:

• Biophysics
• Materials Science
• Computational Biology

Background:

• Protein self-assembly into nanofibers is crucial for biological functions and biomaterials.
• Understanding the kinetics and evolution of protein nanofiber formation is essential for controlling their properties.

Purpose of the Study:

• To develop and validate a novel phase field modeling framework for quantifying protein nanofiber formation and evolution.
• To compare simulation results with experimental data, including transmission electron microscopy (TEM) and time-dependent growth measurements.

Main Methods:

• Employed a phase field modeling framework using effective continuum equations and nanoscale forces.
• Utilized a nonlinear finite element approach to couple homogenized protein molecular structure with chemical potential relations.
• Incorporated anisotropic molecular and chemical flux relations critical for nanofiber growth.

Main Results:

• The model predicts both sigmoidal and first-order growth kinetics for protein nanofibers (unseeded and seeded, respectively).
• Simulations qualitatively match TEM measurements of RADA16-I protein and growth rates of amyloid nanofibers.
• Quantitative predictions include characteristic length and time scales of protein self-assembly (∼2.4 nm and ∼9.1 h).

Conclusions:

• The developed phase field model provides a new computational tool for studying protein nanofiber self-assembly.
• The framework successfully couples underlying monomer structure with emergent nanofiber behavior.
• The model is compatible with various external loadings and chemical environments, enabling further research.