Enhancing Protein Crystal Purity through Adaptive Kinetic Monte Carlo Modeling and Control of Surface Morphology

The escalating demand for producing highly purified proteins, free from contaminants, is paramount in preparative chromatography. To effectively mitigate crystal impurities and establish optimal operational strategies for crystallization processes, it becomes imperative to comprehensively understand the influence of surface morphology on protein crystals. In the present study, we introduce a novel microscopic kinetic Monte Carlo (kMC) model, specifically designed to account for various growth regimes by incorporating surface thermodynamic effects related to different growth mechanisms. This innovation centers on an adaptive adsorption rate that redefines the driving force behind crystallization. To ensure the model's precision and relevance, we validate it with experimental findings. Subsequently, the confirmed kMC model, which forecasts kink density changes, surface morphology, and crystal growth rates under disturbances, is incorporated into a model-based control strategy. This method calculates an optimal input trajectory that facilitates the achievement of the desired kink density, thereby significantly reducing impurities during the crystallization process. This research offers a novel approach for controlling the growth of protein crystals. Furthermore, the stochastic model presents a valuable framework for enhancing the purity of proteins through tunable kink density of crystals, critical for the biotechnology and pharmaceutical industries.