Modeling the Early Stages of Phase Separation in Disordered Elastin-like Proteins

Elastin-like proteins (ELPs) are known to undergo liquid-liquid phase separation reversibly above a concentration-dependent transition temperature. Previous studies suggested that, as temperature increases, ELPs experience an increased propensity for type II β-turns. However, how the ELPs behave below the phase transition temperature itself is still elusive. Here, we investigate the importance of β-turn formation during the early stages of ELP self-association. We examined the behavior of two ELPs, a 150-repeat construct that had been investigated previously (ELP[V₅G₃A₂-150] as well as a new 40-repeat construct (ELP40) suitable for nuclear magnetic resonance measurements. Structural analysis of ELP40 reveals a disordered conformation, and chemical shifts throughout the sequence are insensitive to changes in temperature over 20°C. However, a low population of β-turn conformation cannot be ruled out based on chemical shifts alone. To examine the structural consequences of β-turns in ELPs, a series of structural ensembles of ELP[V₅G₃A₂-150] were generated, incorporating differing amounts of β-turn bias throughout the chain. To mimic the early stages of the phase change, two monomers were paired, assuming preferential interaction at β-turn regions. This approach was justified by the observation that buried hydrophobic turns are commonly observed to interact in the Protein Data Bank. After dimerization, the ensemble-averaged hydrodynamic properties were calculated for each degree of β-turn bias, and the results were compared with analytical ultracentrifugation experiments at various temperatures. We find that the temperature dependence of the sedimentation coefficient (s_20,w^o) can be reproduced by increasing the β-turn content in the structural ensemble. This analysis allows us to estimate the presence of β-turns and weak associations under experimental conditions. Because disordered proteins frequently exhibit weak biases in secondary structure propensity, these experimentally-driven ensemble calculations may complement existing methods for modeling disordered proteins generally.