Analytical Solution to the Flory-Huggins Model

Harnessing Competitive Interactions to Regulate Supramolecular "Micelle-Droplet-Fiber" Transition and Reversibility in Water

The supramolecular assembly of proteins into irreversible fibrils is often associated with diseases in which aberrant phase transitions occur. Due to the complexity of biological systems and their surrounding environments, the mechanism underlying phase separation-mediated supramolecular assembly is poorly understood, making the reversal of so-called irreversible fibrillization a significant challenge. Therefore, it is crucial to develop simple model systems that provide insights into the mechanistic process of monomers to phase-separated droplets and ordered supramolecular assemblies. Such models can help in investigating strategies to either reverse or modulate these states. Herein, we present a simple synthetic model system composed of three components, including a benzene-1,3,5-tricarboxamide-based supramolecular monomer, a surfactant, and water, to mimic the condensate pathway observed in biological systems. This highly dynamic system can undergo "micelle-droplet-fiber" transition over time and space with a concentration gradient field, regulated by competitive interactions. Importantly, manipulating these competitive interactions through guest molecules, temperature changes, and cosolvents can reverse ordered fibers into a disordered liquid or micellar state. Our model system provides new insights into the critical balance between various interactions among the three components that determine the pathway and reversibility of the process. Extending this "competitive interaction" approach from a simple model system to complex macromolecules, e.g., proteins, could open new avenues for biomedical applications, such as condensate-modifying therapeutics.

Reentrant Condensation of Polyelectrolytes Induced by Diluted Multivalent Salts: The Role of Electrostatic Gluonic Effects

We explore the reentrant condensation of polyelectrolytes triggered by multivalent salts, whose phase-transition mechanism remains under debate. We propose a theory to study the reentrant condensation, which separates the electrostatic effect into two parts: a short-range electrostatic gluonic effect because of sharing of multivalent ions by ionic monomers and a long-range electrostatic correlation effect from all ions. The theory suggests that the electrostatic gluonic effect governs reentrant condensation, requiring a minimum coupling energy to initiate the phase transition. This explains why diluted salts with selective multivalency trigger a polyelectrolyte phase transition. The theory also uncovers that strong adsorption of multivalent ions onto ionic monomers causes low-salt concentrations to induce both collapse and reentry transitions. Additionally, we highlight how the incompatibility of uncharged polyelectrolyte moieties with water affects the polyelectrolyte phase behaviors. The obtained results will contribute to the understanding of biological phase separations if multivalent ions bound to biopolyelectrolytes play an essential role.

Differential Effects of Sequence-Local versus Nonlocal Charge Patterns on Phase Separation and Conformational Dimensions of Polyampholytes as Model Intrinsically Disordered Proteins

Conformational properties of intrinsically disordered proteins (IDPs) are governed by a sequence-ensemble relationship. To differentiate the impact of sequence-local versus sequence-nonlocal features of an IDP's charge pattern on its conformational dimensions and its phase-separation propensity, the charge "blockiness" κ and the nonlocality-weighted sequence charge decoration (SCD) parameters are compared for their correlations with isolated-chain radii of gyration (Rgs) and upper critical solution temperatures (UCSTs) of polyampholytes modeled by random phase approximation, field-theoretic simulation, and coarse-grained molecular dynamics. SCD is superior to κ in predicting Rg because SCD accounts for effects of contact order, i.e., nonlocality, on dimensions of isolated chains. In contrast, κ and SCD are comparably good, though nonideal, predictors of UCST because frequencies of interchain contacts in the multiple-chain condensed phase are less sensitive to sequence positions than frequencies of intrachain contacts of an isolated chain, as reflected by κ correlating better with condensed-phase interaction energy than SCD.

Dominance analysis to assess solute contributions to multicomponent phase equilibria

Phase separation in aqueous solutions of macromolecules underlies the generation of biomolecular condensates in cells. Condensates are membraneless bodies, representing dense, macromolecule-rich phases that coexist with the dilute, macromolecule-deficient phases. In cells, condensates comprise hundreds of different macromolecular and small molecule solutes. How do different solutes contribute to the driving forces for phase separation? To answer this question, we introduce a formalism we term energy dominance analysis. This approach rests on analysis of shapes of the dilute phase boundaries, slopes of tie lines, and changes to dilute phase concentrations in response to perturbations of concentrations of different solutes. The framework is based solely on conditions for phase equilibria in systems with arbitrary numbers of macromolecules and solution components. Its practical application relies on being able to measure dilute phase concentrations of the components of interest. The dominance framework is both theoretically facile and experimentally applicable. We present the formalism that underlies dominance analysis and establish its accuracy and flexibility by deploying it to analyze phase diagrams probed in simulations and in experiments.

Exact analytical solution of the Flory-Huggins model and extensions to multicomponent systems

The Flory-Huggins theory describes the phase separation of solutions containing polymers. Although it finds widespread application from polymer physics to materials science to biology, the concentrations that coexist in separate phases at equilibrium have not been determined analytically, and numerical techniques are required that restrict the theory's ease of application. In this work, we derive an implicit analytical solution to the Flory-Huggins theory of one polymer in a solvent by applying a procedure that we call the implicit substitution method. While the solutions are implicit and in the form of composite variables, they can be mapped explicitly to a phase diagram in composition space. We apply the same formalism to multicomponent polymeric systems, where we find analytical solutions for polydisperse mixtures of polymers of one type. Finally, while complete analytical solutions are not possible for arbitrary mixtures, we propose computationally efficient strategies to map out coexistence curves for systems with many components of different polymer types.

Fundamental Aspects of Phase-Separated Biomolecular Condensates

Biomolecular condensates, formed through phase separation, are upending our understanding in much of molecular, cell, and developmental biology. There is an urgent need to elucidate the physicochemical foundations of the behaviors and properties of biomolecular condensates. Here we aim to fill this need by writing a comprehensive, critical, and accessible review on the fundamental aspects of phase-separated biomolecular condensates. We introduce the relevant theoretical background, present the theoretical basis for the computation and experimental measurement of condensate properties, and give mechanistic interpretations of condensate behaviors and properties in terms of interactions at the molecular and residue levels.

Direct prediction of intermolecular interactions driven by disordered regions

Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.

Solutes unmask differences in clustering versus phase separation of FET proteins

Phase separation and percolation contribute to phase transitions of multivalent macromolecules. Contributions of percolation are evident through the viscoelasticity of condensates and through the formation of heterogeneous distributions of nano- and mesoscale pre-percolation clusters in sub-saturated solutions. Here, we show that clusters formed in sub-saturated solutions of FET (FUS-EWSR1-TAF15) proteins are affected differently by glutamate versus chloride. These differences on the nanoscale, gleaned using a suite of methods deployed across a wide range of protein concentrations, are prevalent and can be unmasked even though the driving forces for phase separation remain unchanged in glutamate versus chloride. Strikingly, differences in anion-mediated interactions that drive clustering saturate on the micron-scale. Beyond this length scale the system separates into coexisting phases. Overall, we find that sequence-encoded interactions, mediated by solution components, make synergistic and distinct contributions to the formation of pre-percolation clusters in sub-saturated solutions, and to the driving forces for phase separation.

Dominance Analysis: A formalism to uncover dominant energetic contributions to biomolecular condensate formation in multicomponent systems

Phase separation in aqueous solutions of macromolecules is thought to underlie the generation of biomolecular condensates in cells. Condensates are membraneless bodies, representing dense, macromolecule-rich phases that coexist with the dilute, macromolecule-deficient phase. In cells, condensates comprise hundreds of different macromolecular and small molecule solutes. Do all components contribute equally or very differently to the driving forces for phase separation? Currently, we lack a coherent formalism to answer this question, a gap we remedy in this work through the introduction of a formalism we term energy dominance analysis. This approach rests on model-free analysis of shapes of the dilute arms of phase boundaries, slopes of tie lines, and changes to dilute phase concentrations in response to perturbations of concentrations of different solutes. We present the formalism that underlies dominance analysis, and establish its accuracy and flexibility by deploying it to analyse phase spaces probed in silico, in vitro , and in cellulo .

Emerging experimental methods to study the thermodynamics of biomolecular condensate formation

The formation of biomolecular condensates in vivo is increasingly recognized to underlie a multitude of crucial cellular functions. Furthermore, the evolution of highly dynamic protein condensates into progressively less reversible assemblies is thought to be involved in a variety of disorders, from cancer over neurodegeneration to rare genetic disorders. There is an increasing need for efficient experimental methods to characterize the thermodynamics of condensate formation and that can be used in screening campaigns to identify and rationally design condensate modifying compounds. Theoretical advances in the field are also identifying the key parameters that need to be measured in order to obtain a comprehensive understanding of the underlying interactions and driving forces. Here, we review recent progress in the development of efficient and quantitative experimental methods to study the driving forces behind and the temporal evolution of biomolecular condensates.

Surface tension measurement and calculation of model biomolecular condensates

The surface tension of liquid-like protein-rich biomolecular condensates is an emerging physical principle governing the mesoscopic interior organisation of biological cells. In this study, we present a method to evaluate the surface tension of model biomolecular condensates, through straighforward sessile drop measurements of capillary lengths and condensate densities. Our approach bypasses the need for characterizing condensate viscosities, which was required in previously reported techniques. We demonstrate this method using model condensates comprising two mutants of the intrinsically disordered protein Ddx4N. Notably, we uncover a detrimental impact of increased protein net charge on the surface tension of Ddx4N condensates. Furthermore, we explore the application of Scheutjens-Fleer theory, calculating condensate surface tensions through a self-consistent mean-field framework using Flory-Huggins interaction parameters. This relatively simple theory provides semi-quantitative accuracy in predicting Ddx4N condensate surface tensions and enables the evaluation of molecular organisation at condensate surfaces. Our findings shed light on the molecular details of fluid-fluid interfaces in biomolecular condensates.

Mass photometric detection and quantification of nanoscale α-synuclein phase separation

Protein liquid-liquid phase separation can lead to disease-related amyloid fibril formation. The mechanisms of conversion of monomeric protein into condensate droplets and of the latter into fibrils remain elusive. Here, using mass photometry, we demonstrate that the Parkinson's disease-related protein, α-synuclein, can form dynamic nanoscale clusters at physiologically relevant, sub-saturated concentrations. Nanoclusters nucleate in bulk solution and promote amyloid fibril formation of the dilute-phase monomers upon ageing. Their formation is instantaneous, even under conditions where macroscopic assemblies appear only after several days. The slow growth of the nanoclusters can be attributed to a kinetic barrier, probably due to an interfacial penalty from the charged C terminus of α-synuclein. Our findings reveal that α-synuclein phase separation occurs at much wider ranges of solution conditions than reported so far. Importantly, we establish mass photometry as a promising methodology to detect and quantify nanoscale precursors of phase separation. We also demonstrate its general applicability by probing the existence of nanoclusters of a non-amyloidogenic protein, Ddx4n1.

Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.

FIREBALL: A tool to fit protein phase diagrams based on mean-field theories for polymer solutions

Biomolecular condensates form via phase transitions of condensate-specific biomacromolecules. Intrinsically disordered regions featuring the appropriate sequence grammars can contribute via homotypic and heterotypic interactions to the driving forces for phase separation of multivalent proteins. Experiments and computations have matured to the point where the concentrations of coexisting dense and dilute phases can be measured or computed for individual intrinsically disordered regions in complex milieus. For a macromolecule such as a disordered protein in a solvent, the locus of points that connects concentrations of the two coexisting phases defines a phase boundary, or binodal. Often, only a few points along the binodal are accessible via measurements. In such cases, and for quantitative and comparative analysis of parameters that describe the driving forces for phase separation, it is useful to fit measured or computed binodals to mean-field free energies for polymer solutions. The nonlinearity of the underlying free energy functions makes it challenging to put mean-field theories into practice. Here, we present FIREBALL, a suite of computational tools designed to enable efficient construction, analysis, and fitting to experimental or computed data of binodals. We show that depending on the theory being used, one can also extract information regarding coil-to-globule transitions of individual macromolecules.

Theory and Simulation of Multiphase Coexistence in Biomolecular Mixtures

Biomolecular condensates constitute a newly recognized form of spatial organization in living cells. Although many condensates are believed to form as a result of phase separation, the physicochemical properties that determine the phase behavior of heterogeneous biomolecular mixtures are only beginning to be explored. Theory and simulation provide invaluable tools for probing the relationship between molecular determinants, such as protein and RNA sequences, and the emergence of phase-separated condensates in such complex environments. This review covers recent advances in the prediction and computational design of biomolecular mixtures that phase-separate into many coexisting phases. First, we review efforts to understand the phase behavior of mixtures with hundreds or thousands of species using theoretical models and statistical approaches. We then describe progress in developing analytical theories and coarse-grained simulation models to predict multiphase condensates with the molecular detail required to make contact with biophysical experiments. We conclude by summarizing the challenges ahead for modeling the inhomogeneous spatial organization of biomolecular mixtures in living cells.

FIREBALL: A tool to fit protein phase diagrams based on mean-field theories for polymer solutions

Biomolecular condensates form via phase transitions of condensate-specific biomacromolecules. Intrinsically disordered regions (IDRs) featuring the appropriate sequence grammar can contribute homotypic and heterotypic interactions to the driving forces for phase separation of multivalent proteins. At this juncture, experiments and computations have matured to the point where the concentrations of coexisting dense and dilute phases can be quantified for individual IDRs in complex milieus both in vitro and in vivo . For a macromolecule such as a disordered protein in a solvent, the locus of points that connects concentrations of the two coexisting phases defines a phase boundary or binodal. Often, only a few points along the binodal, especially in the dense phase, are accessible for measurement. In such cases and for quantitative and comparative analysis of parameters that describe the driving forces for phase separation, it is useful to fit measured or computed binodals to well-known mean-field free energies for polymer solutions. Unfortunately, the non-linearity of the underlying free energy functions makes it challenging to put mean-field theories into practice. Here, we present FIREBALL, a suite of computational tools designed to enable efficient construction, analysis, and fitting to experimental or computed data of binodals. We show that depending on the theory being used, one can also extract information regarding coil-to-globule transitions of individual macromolecules. Here, we emphasize the ease-of-use and utility of FIREBALL using examples based on data for two different IDRs.

Statement of Significance

Macromolecular phase separation drives the assembly of membraneless bodies known as biomolecular condensates. Measurements and computer simulations can now be brought to bear to quantify how the concentrations of macromolecules in coexisting dilute and dense phases vary with changes to solution conditions. These mappings can be fit to analytical expressions for free energies of solution to extract information regarding parameters that enable comparative assessments of the balance of macromolecule-solvent interactions across different systems. However, the underlying free energies are non-linear and fitting them to actual data is non-trivial. To enable comparative numerical analyses, we introduce FIREBALL, a user-friendly suite of computational tools that allows one to generate, analyze, and fit phase diagrams and coil-to-globule transitions using well-known theories.

LLPS vs. LLCPS: analogies and differences

We compare the process of Liquid-Liquid Phase Separation (LLPS) of flexible macromolecular solutions, with the Liquid-Liquid Crystalline Phase Separation (LLCPS) of semiflexible polymers and rigid filamentous colloids, which involves the formation of a liquid phase that possesses a directional alignment. Although the observed phase separation follows a similar dynamic path, namely nucleation and growth or spinodal decomposition separating two phases of dilute and concentrated compositions, the underlying physics that defines the theoretical framework of LLCPS is completely different from the one of LLPS. We review the main theories that describe the phase separation processes and relying on thermodynamics and dynamical arguments, we highlight the differences and analogies between these two phase separation phenomena, attempting to clarify the inner mechanisms that regulate those two processes. A particular focus is given to metastable phases, as these intermediate states represent a key element in understanding how phase separation works.

Analytical Formulation and Field-Theoretic Simulation of Sequence-Specific Phase Separation of Protein-Like Heteropolymers with Short- and Long-Spatial-Range Interactions

A theory for sequence-dependent liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) in the study of biomolecular condensates is formulated by extending the random phase approximation (RPA) and field-theoretic simulation (FTS) of heteropolymers with spatially long-range Coulomb interactions to include the fundamental effects of short-range, hydrophobic-like interactions between amino acid residues. To this end, short-range effects are modeled by Yukawa interactions between multiple nonelectrostatic charges derived from an eigenvalue decomposition of pairwise residue-residue contact energies. Chain excluded volume is afforded by incompressibility constraints. A mean-field approximation leads to an effective Flory-Huggins χ parameter, which, in conjunction with RPA, accounts for the contact-interaction effects of amino acid composition and the sequence-pattern effects of long-range electrostatics in IDP LLPS, whereas FTS based on the formulation provides full sequence dependence for both short- and long-range interactions. This general approach is illustrated here by applications to variants of a natural IDP in the context of several different amino-acid interaction schemes as well as a set of different model hydrophobic-polar sequences sharing the same composition. Effectiveness of the methodology is verified by coarse-grained explicit-chain molecular dynamics simulations.