Connecting coil-to-globule transitions to full phase diagrams for intrinsically disordered proteins

Abstract

Phase separation is thought to underlie spatial and temporal organization that is required for controlling biochemical reactions in cells. Multivalence of interaction motifs also known as stickers is a defining feature of proteins that drive phase separation. Intrinsically disordered proteins with stickers uniformly distributed along the linear sequence can serve as scaffold molecules that drive phase separation. The sequence-intrinsic contributions of disordered proteins to phase separation can be discerned by computing or measuring sequence-specific phase diagrams. These help to delineate the combinations of protein concentration and a suitable control parameter such as temperature that support phase separation. Here, we present an approach that combines detailed simulations with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of sequence-specific phase diagrams. Our approach leverages the known equivalence between the driving forces for single chain collapse in dilute solutions and the driving forces for phase separation in concentrated solutions. We demonstrate the application of the theory-aided computations through calculation of phase diagrams for a set of archetypal intrinsically disordered low complexity domains. STATEMENT OF SIGNIFICANCEIntrinsically disordered proteins that have the requisite valence of adhesive linear motifs can drive phase separation and give rise to membraneless biomolecular condensates. Knowledge of how phase diagrams vary with amino acid sequence and changes to solution conditions is essential for understanding how proteins contribute to condensate assembly and dissolution. In this work, we introduce a new two-pronged computational approach to predict sequence-specific phase diagrams. This approach starts by extracting key parameters from simulations of single-chain coilto-globule transitions. We use these parameters in our numerical implementation of the Gaussian cluster theory (GCT) for polymer solutions to construct sequences-specific phase diagrams. The method is efficient and demonstrably accurate and should pave the way for high-throughput assessments of phase behavior.

The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA

“…Regardless of which model is correct, N protein:RNA interaction is key for viral replication. As such, phase separation provides a macroscopic reporter on a nanoscopic phenomenon, in line with previous work 72,82,135,136 . In this sense, we believe the therapeutic implications of understanding and modulating phase separation here (and elsewhere in biology) are conveniently decoupled from the physiological relevance of actual, large phase separated "liquid droplets", but instead offer a window into the underlying physical interactions that lead to condensate formation 20 .…”

Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins

“…For the present work, we use domain and region interchangeably in this manner, though with a preference for using region. The goal of this work is to determine if IDRs that drive phase separation show differences in predicted hydrodynamic dimensions of their equilibrium conformational ensembles as compared to IDRs that do not (19, 24,25,31). Specifically, we hypothesize that when excised from the parent protein, compacted ensemble dimensions would indicate high LLPS potential for the disordered polypeptide, while elongated sizes would indicate low LLPS potential (Figure 1).…”

“…Because biomolecular LLPS includes the exchange of macromolecule-solvent interactions for macromolecule-macromolecule interactions (28)(29)(30), v could be a predictor of LLPS potential among heteropolymeric IDRs (19, 24,25,31). Numerous studies have already found that the hydrodynamic dimensions of some IDRs are correlated to the temperature dependence of the demixing transition (24,32,33).…”

“…We (14,22,35) and others (13,36) have developed sequence-based methods to predict the hydrodynamic dimensions of IDPs, allowing us to test whether an IDP's potential to phase separate can be predicted from its monomeric scaling exponent, v. We hypothesize that the same selfinteractions that facilitate LLPS will reduce the mean Rh (and thus v) for IDRs competent to phase separate into protein-rich droplets when compared to IDRs that are not (19, 24,25,31). Indeed, this trend is evident and shown schematically in Figure 1.…”

Ligand Effects on Phase Separation of Multivalent Macromolecules

“…In a binary mixture comprising macromolecules dissolved in an aqueous solvent, liquid-liquid phase separation gives rise to two coexisting phases -a dilute liquid phase that is deficient in macromolecules and a dense liquid phase that is enriched in macromolecules. The concentration threshold for phase separation, known as the saturation concentration, is set by the three-way interplay of inter-macromolecule, macromolecule-solvent, and solvent-solvent interactions (10,11).…”