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Realistic biological conditions are characterized by high concentrations of biomolecular solutes. Protein conformations and protein-protein interactions can be affected by crowding. The inclusion of a high number of proteins to model such environments necessitates the use of computationally inexpensive methods, such as rigid-body Brownian dynamics or Monte Carlo (MC) simulations. However, the rigid body representation of many protein systems gives rise to artifacts in protein-protein interactions. Presented here is the multi-conformational Monte Carlo (mcMC) method that avoids such artifacts by incorporating molecular flexibility at a low computational cost. We employ it to study the interaction of eye lens proteins, crystallins. In a healthy eye lens crystallins are the structure proteins. Their solubility at concentrations exceeding 400 mg/mL ensures lens transparency. The aggregation of crystallins leads to lens opacification, called cataract. We study how known point mutations associated with cataract formation lead to altered protein-protein interaction and creation of large aggregates.

We present a novel multi-conformation Monte Carlo simulation method that enables the modeling of protein-protein interactions and aggregation in crowded protein solutions. This approach is relevant to a molecular-scale description of realistic biological environments, including the cytoplasm and the extracellular matrix, that are characterized by high concentrations of biomolecular solutes (e.g., 300-400 mg/mL for proteins and nucleic acids in the cytoplasm of Escherichia coli). Simulation of such environments necessitates the inclusion of a large number of protein molecules. Therefore, computationally inexpensive methods, such as rigid-body Brownian dynamics or Monte Carlo simulations, can be particularly useful. However, as we demonstrate herein, the rigid-body representation typically employed in simulations of many-protein systems gives rise to certain artifacts in protein-protein interactions. Our approach allows us to incorporate molecular flexibility in Monte Carlo simulations at low computational cost, thereby eliminating ambiguities arising from structure selection in rigid-body simulations. We benchmark and validate the methodology using simulations of hen egg white lysozyme in solution, a well-studied system for which extensive experimental data, including osmotic second virial coefficients, small-angle scattering structure factors, and multiple structures determined by x-ray and neutron crystallography and solution NMR, as well as rigid-body BD simulation results, are available for comparison.