Protein unfolding thermodynamics predict multicomponent phase behavior

AbstractAn increasing number of proteins are known to undergo liquid-liquid phase separation (LLPS), with or without nucleic acids or partner proteins, forming dense liquid-like phases termed biomolecular condensates. This physical phenomenon has been implicated in the existence of cellular membraneless organelles as well as in the formation of pathological protein aggregates in several human diseases. While common structural features of proteins with a propensity to undergo LLPS have been well documented, currently there is no thermodynamic framework capable of predicting the phase behavior of native proteins. Here, we show that two fundamental thermodynamic properties associated with the unfolding of a native protein, change in heat capacity (ΔCunfold) and change in Gibbs free energy (ΔGunfold), are sufficient to predict the formation of multicomponent biomolecular condensates. We find that proteins with smallΔCunfoldandΔGunfoldvalues, which indicate a native state that is thermodynamically similar to the fully unfolded state, promote LLPS. In contrast, proteins with largeΔCunfoldandΔGunfoldvalues promote aggregation. We also demonstrate that the stability of the liquid-like condensate can be predicted from the proximity of a protein’s thermodynamic variable values to the phase boundary. This work elucidates a deep connection between single-protein thermodynamics and multicomponent phase behavior, and provides an avenue for predicting pathological droplet-aggregate transition.Significance StatementBiomolecular phase transitions, such as LLPS of proteins and nucleic acids, is emerging as an important concept in understanding the link between dysregulation of membraneless compartmentalization in cells and several human diseases. Currently, analysis of structural features such as intrinsic disorder and charged residue content is the “rule-of-thumb” to predict a protein’s propensity to undergo LLPS. There are several instances where this qualitative approach fails, potentially due to not accounting for a protein’s native structure. We demonstrate an empirical correlation between the unfolding thermodynamics of native proteins and their phase behavior, which enables a quantitative prediction of multicomponent LLPS. This novel approach, based on single-protein thermodynamics, has the potential to quantitatively predict pathological phase transitions of proteins in degenerative human diseases.