A Probabilistic Model for Crystal Growth Applied toProtein Deposition at the Microscale

Area of Science:

• Biophysics
• Crystallography
• Computational Biology

Background:

• Protein crystallization is crucial for structural biology.
• Accurate modeling of crystal growth is essential for optimizing experimental conditions.

Purpose of the Study:

• To develop a versatile probabilistic discrete model for 2D protein crystal growth.
• To validate the model against experimental data and explore simulation parameters.

Main Methods:

• A probabilistic discrete model incorporating available space and flexible parameters was developed.
• Simulations were validated using experimental recrystallization data of bacterial SbpA protein.

Main Results:

• The model accurately simulates 2D protein crystal growth.
• High agreement was observed between simulation results and experimental images of SbpA protein crystal growth.
• The study highlights the impact of interface regularity on simulation evolution.

Conclusions:

• The proposed probabilistic model offers a flexible and accurate approach to simulating 2D protein crystal growth.
• The model's accuracy is confirmed by experimental validation.
• Interface regularity is a key factor influencing crystal growth simulations.