Patchy Particle Models to Understand Protein Phase Behavior.

Area of Science:

• Computational physics
• Biomolecular modeling
• Statistical mechanics

Background:

• Understanding protein phase behavior is crucial for various applications, including drug design and protein crystallization.
• Coarse-grained models offer a computationally efficient way to study large biomolecular systems.
• Anisotropic interactions, like those in patchy models, are essential for capturing protein-specific interactions.

Purpose of the Study:

• To present numerical procedures for evaluating the phase behavior of coarse-grained protein models.
• To focus on hard-sphere models with anisotropic interactions mimicking protein surfaces.
• To introduce simulation techniques for predicting critical points, coexistence curves, and phase diagrams.

Main Methods:

• Grand canonical Monte Carlo (GCMC) simulations accounting for translational and rotational moves.
• Techniques for estimating fluid-fluid critical points and coexistence curves.
• Successive umbrella sampling for efficient phase diagram evaluation.

Main Results:

• Demonstration of numerical procedures for phase behavior analysis of coarse-grained protein models.
• Application of GCMC simulations and successive umbrella sampling to predict phase diagrams.
• Successful estimation of fluid-fluid critical points, coexistence curves, and fluid-crystal boundaries.

Conclusions:

• The described numerical methods provide a robust framework for studying protein phase behavior.
• These techniques are applicable to both single protein components and binary mixtures.
• The study facilitates the prediction of complex phase diagrams for coarse-grained protein models.