A physics-based model for predicting temperature-dependent phase behavior of proteins

When certain proteins inside the cell are subject to temperature perturbations, they phase separate to form condensates, such as stress granules and P-bodies. Some proteins exhibit upper critical solution temperature (UCST) behavior, forming condensates at low temperatures, whereas others exhibit lower critical solution (LCST) behavior, condensing at higher temperatures. While experiments are able to capture this behavior at a microscopically visible level, elucidating the full biophysics of temperature-driven condensate formation requires sub-molecular insight. Computational approaches, specifically coarse-grained molecular dynamics simulations have proven useful in understanding molecular underpinnings of condensates. However, while some approaches have recapitulated UCST behavior, capturing LCST behavior quantitatively has been a challenge, as this requires models to carefully account for differences in solvation with temperature. We have developed a quantitatively accurate residue-level coarse-grained model to predict temperature-driven protein condensation. By parametrizing our model with atomistic simulations and validating with experimental data, we are able to capture full phase behavior of disordered proteins. This work has direct implications for uncovering biophysics of temperature-dependent phase separation in disordered proteins. Furthermore, our model expands the set of tools that can be used to engineer thermoresponsive biopolymers applications in smart nanomaterials.