Modeling Liquid-Liquid Phase Separations of Intrinsically Disordered Proteins on the Micrometer-Scale

Abstract

Intrinsically disordered proteins (IDPs) are known for their tendency to form aggregates and fibrils associated with neurodegenerative diseases. However, IDPs are also capable of undergoing a liquid-liquid phase separation (LLPS) resulting in micrometer-scale liquid droplets that can function as membraneless organelles in living cells. As a consequence, IDPs in solution exhibit a rich phase diagram that governs the stability of homogeneous solutions, phase-separated droplets and other aggregates, which is affected by the specific sequence of the IDPs, their concentration, and the solution temperature and electrolyte concentration. The ability to form and dissolve phase separated liquid droplets in solution by simply varying external parameters such as temperature has multiple interesting biotechnological applications. Computational predictions of the IDP phase behavior can provide guidance for the development of these technologies. However, the large numbers of interacting biomolecules required to model this process prohibits computational studies with atomistic models in explicit solvents. Even for coarse-grained polymer simulations in implicit solvents, which describe proteins as linear chains of beads representing distinct amino acids, computational modeling of droplet formation in three-dimensional systems remains a daunting task. Here, we introduce a multiscale simulation approach, which utilizes the sequence-specificity of coarse-grained polymer chain simulations to obtain effective pair-wise interactions between IDPs in solution. The latter are then used to model the behavior of micrometer-sized systems containing ∼105 interacting proteins in Monte Carlo simulations using algorithms designed for aggregating systems. Our results show that this procedure is able to capture temperature-dependent LLPS for intrinsically disordered proteins and our observations are compared to experimental light scattering data for a series for IDP sequences.