Skip to content

Mini Review

Frontiers in Biophysics ·

Volume 4 - 2026 | https://doi.org/10.3389/frbis.2026.1795884

From the classical colloidal theory to biomolecular condensation: implications in health and disease

  • Noelia A. Melian1,2
  • Aldana Gomez2
  • Milagros B. Abate1,2
  • Pamela L. Toledo1,2
  • Mario R. Ermácora1,2,3*corresponding author
  • Diego S. Vazquez1,2,3*corresponding author
  1. 1Grupo de Biología Estructural y Biotecnología Vinculado al IMBICE (CONICET-CIC-UNLP), Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
  2. 2Laboratorio de Expresión y Plegado de Proteínas (LEPP), Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal, Argentina
  3. 3Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
Article metrics0 viewsView details

Abstract

Biomolecular phase separation and condensation are fundamental mechanisms of cellular compartmentalization that regulate biochemical processes in space and time. From a biophysical standpoint, biomolecular condensates can be considered biological colloids that share key properties with classical colloidal systems. In this article, we integrate classical colloidal theory with recent advances in biomolecular condensation to exemplify how physicochemical parameters, including pH, ionic strength, metal ions, and post-translational modifications, modulate the assembly dynamics and material properties of biomolecular condensates under physiological and pathological conditions. Additionally, we highlight the relationship between phase separation and amyloid formation, emphasizing recent evidence that amyloid-promoting proteins can exhibit catalytic activity in condensed or aggregated states. This perspective redefines biomolecular condensation and amyloid formation as highly versatile processes with significant implications for cell biology, disease mechanisms, and therapies.

Keywords

1 Introduction

In 1912, the biologist William Hardy stated: “A large number of observations upon the filterability and the optical properties of colloidal solutions prove that they are coarse grained and consist of particles dispersed throughout a continuous fluid phase. For this reason these solutions are commonly described as multiphase systems in which the dispersed particles form one of the phases” (Hardy, 1912). From the perspective of molecular biophysics, this quotation is akin to the modern definition of biomolecular condensates (herein biocondensates) formed via liquid-liquid phase separation (LLPS). Classical colloids and biocondensates share common structural features and properties. Prominent physicochemical features of colloids are the size scale, shared with biocondensates, ranging from nano-to hundreds of nanometers; the biphasic behavior; the capability to disperse light (Tyndall effect); the impossibility of separating the particles from the solvent by conventional filtration; the surface energy characterized by a high surface-to-volume ratio; and the high stability, explained by the Derjaguin, Landau, Verwey and Overbeek (DLVO) theory (Agmo Hernández, 2023), originated primarily by van der Waals attraction and electrostatic repulsion forces (Brangwynne et al., 2015). In fact, biocondensates are biological colloids that can phase-separate in the micron-scale (Shin and Brangwynne, 2017; Vazquez et al., 2022).

In the late 19th century, pioneering work by Selmi, Graham and Van Bemmelen, among others, paved the way for colloidal theory and shaped our understanding of dispersed systems. However, it had limitations when applied to the study of biomolecules (Lyklema, 2015). Its approach highlighted factors like Brownian motion, multivalency scaffolding, percolation, and viscoelastic properties, as well as the effects of particle size on diffusion (Brangwynne et al., 2015). As the study of biomolecules progressed, it became clear that colloidal foundational principles needed a revision to encompass the behaviour of complex biological systems: biomolecules exhibit distinctive properties that the original colloidal theory does not fully capture (Sarangapani et al., 2015). Its major limitation lies in the scale and complexity of biomolecular interactions; biomolecules are highly dynamic and heterogeneous macromolecules. In addition, biomolecules possess intricate three-dimensional domains, intrinsically disordered regions (IDRs), sequence complexity, and variable shapes that affect their interactions and functionalities. Most of the current knowledge of phase condensation has arisen from the analysis of large liquid-like condensates, like membraneless organelles (MLOs). These microscopically observable structures arise from smaller and less understood condensates measuring tens to hundreds of nanometres, known as nanocondensates (Toledo et al., 2023a). Nanocondensates are considered important precursors in several nucleation processes, such as fibrillation, irreversible aggregation, amorphous aggregation, and ordered crystallization. Nanocondensation could be widespread in the cell, as recent evidence indicates that nearly 20% of the proteome could potentially be organized in nanocondensates (Keber et al., 2024). New techniques are being developed to focus specifically on nanocondensates (Houx et al., 2026).

A generic phase diagram for proteins in some temperature-dependent parameter and concentration plane shows a binodal behavior (Figure 1) (Alberti et al., 2019; Soranno, 2020; Villois et al., 2022). In the single-phase regime, two regions are delimited by the saturation curve: the undersaturated and the supersaturated regions. The nanocondensate-nucleation processes emerge in the supersaturated (metastable) region below the solubility curve. Supersaturation leads to phase separation, and it is certainly the only stably way to increase protein concentration above solubility limits (Toledo et al., 2023a).

General schematic protein phase diagram in the modulating factor and concentration plane. The coexistence line separates the one-phase and two-phase regimes in a phase diagram of a temperature-dependent variable) versus protein concentration. In the two-liquid phase, the system consists of two coexisting phases, one dense protein droplet with a concentration C<sub>dense</sub> surrounded by a diluted phase of concentration C<sub>diluted</sub>.
Figure 1. General schematic protein phase diagram in the modulating factor and concentration plane. The coexistence line separates the one-phase and two-phase regimes in a phase diagram of a temperature-dependent variable) versus protein concentration. In the two-liquid phase, the system consists of two coexisting phases, one dense protein droplet with a concentration Cdense surrounded by a diluted phase of concentration Cdiluted.

2 The many roles of biocondensates in cellular processes

Classic organelles are compartments physically defined by a lipid bilayer membrane that separates their interior from the cytoplasm. The chemical composition of the organelle membrane regulates the entry and exit of most biological molecules through a specialised membrane-attached transport protein machinery. In contrast, biocondensates are MLOs that form through liquid-liquid phase separation and play a fundamental role in the organisation and compartmentalisation of cellular processes without a physical barrier (Figure 2). The liquid-like properties of biocondensates enable a dynamic exchange with the surrounding cytoplasm via a rapid assembling-disassembling mechanism. Starting with a uniform solution, the phenomenon of liquid-liquid phase separation depends on a demixing transition process that results in two coexisting phases. During this physicochemical process, macromolecules are drawn out from the initial solution, forming a liquid-like dense phase in swift exchange with a diluted phase (Vazquez et al., 2022).

The many roles of biocondensates in normal cellular processes and disease. The scheme illustrates some examples of MLOs in cells and common factors and compounds that modulate biocondensate behavior in both normal and disease conditions. Illustrations from NIAID-NIH BioArt source. Created in BioRender. Vazquez et al. (2022) [https://BioRender.com/h9fk1c1](https://biorender.com/h9fk1c1).
Figure 2. The many roles of biocondensates in normal cellular processes and disease. The scheme illustrates some examples of MLOs in cells and common factors and compounds that modulate biocondensate behavior in both normal and disease conditions. Illustrations from NIAID-NIH BioArt source. Created in BioRender. Vazquez et al. (2022) https://BioRender.com/h9fk1c1.

MLOs are involved in almost all biological processes, from signalling to protein storage and degradation (Hirose et al., 2023; Li Y. et al., 2024). Abnormal condensate properties can produce harmful effects leading to pathological conditions (Figure 2) (Zhang S. et al., 2023). Aberrant biocondensate-related diseases involve several mechanisms including loss of protein’s function or gain in protein’s toxicity, and RNA foci dysregulation. This makes them promising targets for exploring novel drug-based research and therapies.

From the view of a simplistic unified model, two main components are involved in the biocondensate: the scaffold and the client (Rana et al., 2024). Cellular biocondensates can contain hundreds to thousands of different components and, in most cases, only a few of these, the scaffold components, suffice to form a biocondensate. The scaffold components form a network of multivalent interactions constituting the core of the condensate. The client components are homo or heterotopically interacting biomolecules recruited to the preformed condensate, establishing multiple interactions with the multivalent scaffold. The client components typically undergo dynamic exchange with the surrounding environment, providing a regulatory mechanism.

In most cases, the scaffold components are proteins with low complexity sequences. Due to their inherent low complexity sequence, intrinsically disordered proteins (IDPs) have totally or in part, undefined tridimensional structures (Gonçalves-Kulik et al., 2022). Intrinsically disordered regions (IDRs) represent ∼14% of the proteome of Bacteria and Archaea and increase up to 44–54% in Eukaryotes (Perdigão et al., 2015). The lack of a well-defined structure gives IDPs the plasticity necessary to establish multiple and dynamic long-range weak interactions, providing a driving force for LLPS (Borcherds et al., 2021). Besides, several factors such as concentration, ionic strength, pH, osmolarity, crowding, temperature, hydrostatic pressure, and low molecular-weight molecules influence assembly-disassembly dynamics of biocondensates (Figure 2) (Alberti and Hyman, 2016). The following sections highlight the role of inorganic compounds and covalent modifications in biocondensate dynamics.

3 The importance of being inorganic: the role of protonation state

Water is the most abundant inorganic molecule, making up 70% or more of the total cell volume. Hydronium ion concentration, represented as the pH, exerts fundamental roles in biomolecular conformation, oligomeric state, catalytic activity, and protein-protein interactions via changes in the protonation state of titratable residues. In the same way, pH alters LLPS by influencing the electrostatic interacting network (Jin et al., 2022; Notarmuzi and Bianchi, 2024; Zhang et al., 2024).

Aging alters intracellular pH homeostasis (Kurz et al., 2004; Kenyon, 2010; Gems and Partridge, 2013). This phenomenon is well exemplified in yeast, where replicative aging is asymmetric: mother cells age, while daughter cells are rejuvenated (Henderson et al., 2014). This is due to the asymmetric distribution of the plasma membrane H+-ATPase (Pma1), the major regulator of cytosolic pH. Pma1 accumulates in aging mother cells, but it is largely absent in daughter cells. Consequently, vacuoles become less acidic in aging mother cells, which limits lifespan. Similarly, by analyzing a large sample cohort, Decker et al. found that the pH of the brain and cerebrospinal fluid decreases with aging (Decker et al., 2021). Following the same pattern, extracellular pH is further decreased in patients with Alzheimer’s disease (AD).

Several studies found amyloid deposits of the pathogenic low-complexity domain (LCD) of the transactive response DNA-binding protein (TDP-43) in patients with aging-related diseases like amyotrophic lateral sclerosis or AD (Kao et al., 2015; Josephs et al., 2017; Nemani et al., 2018). Under physiological conditions of pH, ionic strength and crowding, TDP-43 LCD can undergo LLPS at low concentration and form larger structures as protein concentration increases (Babinchak et al., 2019). However, a low decrease in pH can trigger the formation of amyloid fibrils even in the absence of liquid-like droplets (Babinchak et al., 2019), also showing hydrogel behavior in non-LLPS conditions (Lin et al., 2024). Moreover, the analysis of droplet formation in vitro through pH-jump from pH 11.0 to 7.5 shows an increase in the radius of the droplets over time, denoting its strong pH dependence (Van Lindt et al., 2021). This behavior is consistent with the formation of TDP-43 puncta observed in HEK239T cells at neutral pH (Al Ojaimi et al., 2024), the value where the maximum solubility was predicted (Pintado-Grima et al., 2022).

4 Enzymatic activity inside biocondensates

Catalytic biochemical reactions are known to be highly sensitive to temperature and pH, and most of them are performed by well-behaved folded proteins. Nevertheless, some IDPs exhibit enzymatic activity, often through a substrate-induced folding-upon-binding mechanism (Chakrabortee et al., 2010; Kim et al., 2018; Zhang Y. et al., 2023). While most enzymes have an optimal activity near neutral pH (Talley and Alexov, 2010), some evolved to function at extreme pH, like pepsin which has a narrow range of functionality at very low pH (pH 1.5–2.0).

As aforementioned, biocondensates usually present a dense phase characterized by a crowded environment, where the intrinsic dynamics of both globular proteins and IDPs can be altered (Candotti and Orozco, 2016; Bonucci et al., 2021; Das et al., 2024). To test the effect of biocondensation on enzyme and substrate dynamics, Saini and Mukherjee (Saini and Mukherjee, 2023) designed a highly efficient heterotypical biocondensate involving folded horseradish peroxidase and glucose oxidase that exhibited increased catalytic activity up to 205-fold. Surprisingly, this occurred at a more acidic pH than in a non-crowded environment (pH 6.0–6.5 versus pH 4.0) (Schomburg et al., 1994). Hence, biocondensates can be rationally designed as factories to enhance specific enzymatic activities.

The confinement within biocondensates can also protect enzymes from degradation or inhibition (Fiedler et al., 2010; Singha et al., 2019). This further optimises the catalytic efficiency, even for enzymes that depend on conformational rearrangements.

Protein intrinsic dynamics can also be modulated by changing the polarity or solvent-composition within the condensate displacing the equilibrium to the open or close conformation (Zhou and Pang, 2018; Vazquez et al., 2022). For instance, Stoffel and collaborators (Stoffel et al., 2025) conjugated lipase 2 from Bacillus thermocatenulatus with the LCD regions of DEAD-box protein Laf1, and the resulting low micrometer-size condensate recruited the chimera at millimolar concentrations in the dense phase. The chimera protein in the dense phase exhibited altered protonation state, expanding the pH range at which the protein shows high activity (Testa et al., 2021; Choi et al., 2023; Dindo et al., 2024). Notably, nanocondensates may exhibit a significant increase in enzymatic activity compared to larger condensates, as was recently shown by the same group (Gil-Garcia et al., 2024).

5 Catalytically active amyloids

Biomolecular condensates and amyloid formation are connected via a phase transition pathway from dynamic liquid states to solid structures (Figure 2). For instance, the amyloid-β peptide Aβ40 can undergo liquid-liquid phase separation to form an intermediate condensed state, in which high peptide concentrations accelerate nucleation and the rapid assembly of amyloid fibers prior to transitioning to a solid state (Morris et al., 2024). According to Vendruscolo and Fuxreiter (2022), there are two aggregation pathways: a deposition route involving increasingly organized disordered oligomers and a condensation route involving a liquid droplet state prior to forming solid amyloids. Sequence determinants for aggregation in condensates include droplet-forming propensity, intrinsic aggregation tendency, and binding entropy.

Amyloid protofibrils are soluble, high-molecular-weight intermediates in the aggregation process of proteins such as the amyloid-β. These represent a transitional state in the liquid-solid phase separation process between smaller oligomers and larger, insoluble mature fibrils. They can grow through a hierarchical colloidal assembly process in which pre-formed spherical oligomers act as “pearls”, aligning and fusing with each other (Xu et al., 2001; Modler et al., 2003). According to this model, amyloid formation is a colloidal phenomenon governed by physical forces, such as dipole moments and DLVO forces. These forces favor linear growth over amorphous precipitation.

Amyloids can be either functional, beneficial aggregates, like storage bodies, or unchain the formation of disease-linked amyloid fibers. Structural frustration—which relies on the degree of energetically unfavorable interactions in a protein conformation (Gianni et al., 2021) — emerged as a key concept and a hotspot to define the fate of these aggregates. Amyloid fibers are highly frustrated ensembles that can be regarded as the Last Universal Common Ancestor for protein folding, due to its repetitiveness, self-replication and information-transfer capability (Maury, 2018). It is generally accepted that amyloid fibrils are thermodynamically more stable and rigid structures than the classical native conformations, due to their tightly packed hydrophobic core (Buell, 2022). Nevertheless, they exhibit some degree of flexibility (Okumura and Itoh, 2016; Vugmeyster et al., 2016) — especially in the flanking regions which leads to conformational polymorphism—allowing the formation of well-defined tertiary functional binding sites lacking in monomeric counterparts (Bhopatkar and Kayed, 2023).

These supramolecular assemblies have garnered significant attention due to their catalytic activity (Marshall and Korendovych, 2021). In a proof-of-concept work, Rufo et al., designed a battery of simple minimalist heptapeptides that can coordinate with Zn2+ intermolecularly and form efficient catalysts for ester hydrolysis (Rufo et al., 2014). Thenceforth, an increasing number of physiological-relevant amyloids like Aβ42 (Arad et al., 2021); α-synuclein (Horvath and Wittung-Stafshede, 2023); the islet amyloid polypeptide (Sawazaki et al., 2024); and the glucose blood-level regulator glucagon (Arad et al., 2022; Nandi et al., 2024), tested positive for several catalytic activities, including hydrolysis, esterolysis, and dephosphorylation.

The catalytic efficiency and selectivity of the aggregates are not as high as those of optimized enzymes. However, their structural versatility and tunability make them exciting models for studying the evolution of catalysis within highly stable folds and adaptation to ancient physicochemical environments. Through this lens, frustration appears to be a feasible parameter for identifying potential fibrillation proneness via high-throughput proteomic and bioinformatic analysis and sparks the interest in exploring ways to design drugs to inhibit or enhance amyloid fibril activity.

6 Metal ions as low molecular-weight regulators of biocondensates

The metallome plays fundamental roles in cell metabolism (Aulakh et al., 2025) and also in biocondensate homeostasis (Figure 2). Two of the most important cations are magnesium and zinc. With a millimolar concentration in eukaryotic cells, magnesium ion plays a significant role in RNA-based biocondensates stabilizing RNA structure, folding, and promoting intermolecular interactions with other RNA molecules and proteins (Roden and Gladfelter, 2021). Phase separation of defined RNA structures, like riboswitches, can be promoted by Mg2+ ions using crowding agents such as polyethyleneglycol. Nevertheless, the presence of Mg2+ ions alone is enough to promote LLPS in the absence of a crowder, evidencing the importance of this cation in stabilizing RNA-based biocondensates (Poudyal et al., 2021). Also, Mg2+ ion diminishes the internal dynamic of heterotypical RNA-protein biocondensates, showing a liquid-gel transition that can be reversed in the presence of ATP (Yewdall et al., 2022), which acts as a chelator.

On the other hand, zinc has a major impact in liquid-to-gel transitions mostly in protein-based biocondensates (Shih et al., 2022). Particularly, Zn2+ ions are crucial for the correct biosynthesis, storage and release of insulin in pancreatic β-cells (Fukunaka and Fujitani, 2025). In connection with this, insulin biogenesis starts with the oxidative folding of its single-chain precursor proinsulin, and proinsulin can oligomerize into a R6 hexamer (Emdin et al., 1980; Kiselar et al., 2011). Further, our group recently demonstrated that proinsulin can self-populate nanocondensates in vitro, under conditions that mimic the environment of the Golgi apparatus and the immature secretory granules (Toledo et al., 2023b). Importantly, these nanocondensates can form microcondensates in presence of the secretory granule transmembrane protein ICA512. In this regard, Zn2+ ions may be important for the subsequent liquid-solid phase separation of insulin-Zn2+ crystals. Zinc ions can also bind to preformed insulin fibrils, altering its kinetics and morphology (Frankær et al., 2017). In this context, ion-induced aggregation of relevant therapeutic hormones for drug delivery and biomanufacturing is a promising strategy.

The accumulation of Fe3+, Mn2+, and Al3+ ions plays a significant role in the neuronal environment, leading to the formation of toxic amyloid species. In particular, iron promotes protein aggregation and the production of reactive oxygen species through Fenton-type chemistry, thereby exacerbating lipid peroxidation (Joppe et al., 2019; Xu et al., 2024). Exposure to manganese has been linked to the accelerated maturation of α-synuclein condensates (Xu et al., 2022). Trivalent cations, such as Al3+, exert a potent influence on the structural polymorphism of amyloid assemblies, often promoting the formation of insoluble fibrils over amorphous aggregates (Ricchelli et al., 2005).

The interaction between Cu2+ ions and amyloidogenic proteins is governed by a bimodal regulatory mechanism that is strictly dependent on stoichiometric ratios and acts as either a kinetic catalyst or a structural inhibitor. At sub-stoichiometric concentrations, copper acts as a scaffold that neutralizes electrostatic repulsions primarily via histidine residues and stabilizes high-affinity binding sites, thereby accelerating the nucleation kinetics of the amyloid-β peptide (Sarell et al., 2010; Abelein et al., 2022). Conversely, supra-stoichiometric levels induce a rapid, disordered collapse of the protein chain. This favors the formation of amorphous aggregates or traps the system in non-fibrillar, curvilinear protofibrillar states (Tian et al., 2024). Recent literature highlighting the role of several other important metal ions of biological relevance can be found in references (Hong et al., 2020; Vazquez et al., 2022; Sołtys et al., 2023).

7 Chemical modification inside biocondensates: isopeptide bond formation

Post-translational modifications (PTMs) refers to the rearrangement or addition of covalent bonds in proteins, expanding their conformational landscape, localization, functionality, thermodynamic stability, net charge, and interactions with other biomolecules. The homeostasis of protein modifications is important for protein function and hence for human health (Figure 2). Abnormal regulation of PTMs leads to changes in protein properties and loss of functions, which is closely related to the development of several diseases (Zhong et al., 2023; Li W. et al., 2024; Ramazi et al., 2024).

Reversible protein phosphorylation, one of the best-known PRMs, can promote or repress condensate formation by changing net charge, protein conformation and/or modulating protein-protein or protein-RNA interactions (Snead and Gladfelter, 2019). Phosphorylation targets the hydroxyl groups of Ser, Thr and Tyr residues and the imidazole ring of histidine. Sridharan and collaborators (Sridharan et al., 2022) mapped numerous phosphorylated sites enriched in both soluble and condensate proteins in HeLa cell lysates, including a subset of phosphosites that modulate protein-RNA interactions. Notably, multi-phosphorylation of the key RNA-splicing factor heteronuclear ribonucleoprotein A1 reduces its ability to localize to nuclear clusters, suggesting a phosphorylation-induced alteration in RNA-protein condensates interactions. Conversely, multi-phosphorylation of nucleophosmin 1, an essential nucleolar protein, lowers its partitioning to the nucleolus, thereby decreasing protein-RNA interaction and increasing its retention in the nucleoplasm.

Phosphorylation of biocondensates can also regulate virus infection. Nuclear bodies (NBs) formed during the last stages of human adenovirus infection require the L1-52/55 (52k) protein for capsid assembly (Charman et al., 2023). Phosphorylation of the IDR region of 52k in Ser residues positively modulates liquid-like biocondensation and promotes viral particle assembly (Grams et al., 2024). In the same capacity, phosphorylation of a Ser/Arg-rich region of the nucleocapsid N protein of SARS-CoV-2 destabilises a distal Leu-rich helix (Favetta et al., 2024), promoting its partial unfolding, and reduces protein-RNA condensation, enhancing viral infection and evasion of the host immune system.

Although protein ubiquitination and related PTMs such as SUMOylation and neddylation have been known for more than 50 years, the understanding of how these PTMs influence phase separation is a recent development. An intricate crosstalk between protein degradation and function regulation takes place (Jin, 2019), upon which LLPS acts as a link. This is exemplified by the formation of nuclear bodies of the promyelocytic leukemia protein, where SUMOylated proteins are regulated by ubiquitination (Sha et al., 2019). Further research should focus on the fine-tuning properties of PTMs in biocondensate as cellular rheostats. Outstanding articles on the role of ADP-ribose addition to proteins (PARylation) in biocondensate organization can be found elsewhere (Alemasova and Lavrik, 2022; Rhine et al., 2023; Liu et al., 2025).

8 Conclusion and perspectives

Spontaneous biomolecules segregation into concentrated liquid-like droplets constitutes a new paradigm in protein biophysics, with deep implications for our understanding of normal and abnormal cellular biochemistry. Condensation implies a higher level of organization in the protein-energy landscape. The folding and function of single protein molecules, as described by the still-valid Anfinsen’s dogma, are now seen as part of a collective of molecules orchestrated to optimize physical and biological properties, allowing proteins to reach concentrations beyond their solubility limits (see Figure 1). The assembly-disassembly dynamics of biocondensates are tightly modulated by environmental factors like pH, temperature, cofactors (metal ions), ionic strength, PTMs, crowding and others. This provides a complex mechanism for regulating cellular processes adding a new dimension to the relationship between protein sequence, structure and cellular function.

Phase separation frequently represents the initial step in the protein aggregation pathway within the folding energy landscape and commonly results in the formation of amyloid fibrils. Importantly, the emergence of catalytic activities within these supramolecular scaffolds reframe amyloidogenic transitions as shifts toward non-canonical enzymatic states rather than only pathological misfolding. Consequently, the therapeutic frontier should evolve from inhibiting aggregation to embracing the concept of interfacial pharmacology (Kochman et al., 2025; Fleming and Schuijers, 2026). By targeting the rheological properties and thermodynamic stability of biocondensates, it becomes possible to move beyond aggregate dissolution and toward the deliberate reprogramming of the protein energy landscape.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited by: Diana Mitrea, DeMix Solutions, LLC, United States

Reviewed by: Eva Zerovnik, Institut Jožef Stefan (IJS), Slovenia

Hector Garcia Seisdedos, Spanish National Research Council (CSIC), Spain

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. DV and ME acknowledges financial support from the following grants: CONICET (PIP-1054/21), AGENCIA I + D + i (PICT-0465/21) and UNQ (PUNQ-1600/25).

Author contributions

NM: Conceptualization, Visualization, Writing – review and editing, Investigation. AG: Conceptualization, Visualization, Writing – review and editing, Investigation. MA: Conceptualization, Visualization, Writing – review and editing, Investigation. PT: Conceptualization, Visualization, Writing – review and editing, Investigation. ME: Conceptualization, Funding acquisition, Supervision, Writing – review and editing, Investigation. DV: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, Writing – review and editing.

References

  1. Abelein A., Ciofi-Baffoni S., Mörman C., Kumar R., Giachetti A., Piccioli M. (2022). Molecular structure of Cu(II)-Bound Amyloid-β monomer implicated in inhibition of peptide self-assembly in alzheimer’s disease. JACS Au 2, 2571 – 2584. doi:10.1021/jacsau.2c00438
  2. Agmo Hernández V. (2023). An overview of surface forces and the DLVO theory. ChemTexts 9, 10. doi:10.1007/s40828-023-00182-9
  3. Al Ojaimi Y., Slek C., Osman S., Alarcan H., Marouillat S., Corcia P. (2024). The effect of pH alterations on TDP-43 in a cellular model of amyotrophic lateral sclerosis. Biochem. Biophys. Rep. 38, 101664. doi:10.1016/j.bbrep.2024.101664
  4. Alberti S., Hyman A. A. (2016). Are aberrant phase transitions a driver of cellular aging? BioEssays 38, 959 – 968. doi:10.1002/bies.201600042
  5. Alberti S., Gladfelter A., Mittag T. (2019). Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell. 176, 419 – 434. doi:10.1016/j.cell.2018.12.035
  6. Alemasova E. E., Lavrik O. I. (2022). A sePARate phase? Poly(ADP-ribose) versus RNA in the organization of biomolecular condensates. Nucleic Acids Res. 50, 10817 – 10838. doi:10.1093/nar/gkac866
  7. Arad E., Baruch Leshem A., Rapaport H., Jelinek R. (2021). β-Amyloid fibrils catalyze neurotransmitter degradation. Chem. Catal. 1, 908 – 922. doi:10.1016/j.checat.2021.07.005
  8. Arad E., Yosefi G., Kolusheva S., Bitton R., Rapaport H., Jelinek R. (2022). Native glucagon amyloids catalyze key metabolic reactions. ACS Nano 16, 12889 – 12899. doi:10.1021/acsnano.2c05166
  9. Aulakh S. K., Lemke O., Szyrwiel L., Kamrad S., Chen Y., Hartl J. (2025). The molecular landscape of cellular metal ion biology. Cell. Syst. 16, 101319. doi:10.1016/j.cels.2025.101319
  10. Babinchak W. M., Haider R., Dumm B. K., Sarkar P., Surewicz K., Choi J.-K. (2019). The role of liquid–liquid phase separation in aggregation of the TDP-43 low-complexity domain. J. Biol. Chem. 294, 6306 – 6317. doi:10.1074/jbc.ra118.007222
  11. Bhopatkar A. A., Kayed R. (2023). Flanking regions, amyloid cores, and polymorphism: the potential interplay underlying structural diversity. J. Biol. Chem. 299, 105122. doi:10.1016/j.jbc.2023.105122
  12. Bonucci A., Palomino-Schätzlein M., Malo De Molina P., Arbe A., Pierattelli R., Rizzuti B. (2021). Crowding effects on the structure and dynamics of the intrinsically disordered nuclear chromatin protein NUPR1. Front. Mol. Biosci. 8, 684622. doi:10.3389/fmolb.2021.684622
  13. Borcherds W., Bremer A., Borgia M. B., Mittag T. (2021). How do intrinsically disordered protein regions encode a driving force for liquid–liquid phase separation? Curr. Opin. Struct. Biol. 67, 41 – 50. doi:10.1016/j.sbi.2020.09.004
  14. Brangwynne C. P., Tompa P., Pappu R. V. (2015). Polymer physics of intracellular phase transitions. Nat. Phys. 11, 899 – 904. doi:10.1038/nphys3532
  15. Buell A. K. (2022). Stability matters, too – the thermodynamics of amyloid fibril formation. Chem. Sci. 13, 10177 – 10192. doi:10.1039/D1SC06782F
  16. Candotti M., Orozco M. (2016). The differential response of proteins to macromolecular crowding. PLOS Comput. Biol. 12, e1005040. doi:10.1371/journal.pcbi.1005040
  17. Chakrabortee S., Meersman F., Kaminski Schierle G. S., Bertoncini C. W., McGee B., Kaminski C. F. (2010). Catalytic and chaperone-like functions in an intrinsically disordered protein associated with desiccation tolerance. Proc. Natl. Acad. Sci. 107, 16084 – 16089. doi:10.1073/pnas.1006276107
  18. Charman M., Grams N., Kumar N., Halko E., Dybas J. M., Abbott A. (2023). A viral biomolecular condensate coordinates assembly of progeny particles. Nature 616, 332 – 338. doi:10.1038/s41586-023-05887-y
  19. Choi S., Knoerdel A. R., Sing C. E., Keating C. D. (2023). Effect of polypeptide complex coacervate microenvironment on protonation of a guest molecule. J. Phys. Chem. B 127, 5978 – 5991. doi:10.1021/acs.jpcb.3c02098
  20. Das N., Khan T., Halder B., Ghosh S., Sen P. (2024). Macromolecular crowding effects on protein dynamics. Int. J. Biol. Macromol. 281, 136248. doi:10.1016/j.ijbiomac.2024.136248
  21. Decker Y., Németh E., Schomburg R., Chemla A., Fülöp L., Menger M. D. (2021). Decreased pH in the aging brain and Alzheimer’s disease. Neurobiol. Aging 101, 40 – 49. doi:10.1016/j.neurobiolaging.2020.12.007
  22. Dindo M., Bevilacqua A., Soligo G., Calabrese V., Monti A., Shen A. Q. (2024). Chemotactic interactions drive migration of membraneless active droplets. J. Am. Chem. Soc. 146, 15965 – 15976. doi:10.1021/jacs.4c02823
  23. Emdin S. O., Dodson G. G., Cutfield J. M., Cutfield S. M. (1980). Role of zinc in insulin biosynthesis: some possible zinc-insulin interactions in the pancreatic B-cell. Diabetologia 19, 174 – 182. doi:10.1007/BF00275265
  24. Favetta B., Wang H., Cubuk J., Singh A., Barai M., Ramirez C. (2024). Phosphorylation toggles the SARS-CoV-2 nucleocapsid protein between two membrane-associated condensate states. doi:10.1101/2024.10.17.618867
  25. Fiedler J. D., Brown S. D., Lau J. L., Finn M. G. (2010). RNA‐Directed packaging of enzymes within virus‐like particles. Angew. Chem. Int. Ed. 49, 9648 – 9651. doi:10.1002/anie.201005243
  26. Fleming C., Schuijers J. (2026). Targeting biomolecular condensates: beyond dissolution. BMC Biol. 24, 59. doi:10.1186/s12915-026-02538-2
  27. Frankær C. G., Sønderby P., Bang M. B., Mateiu R. V., Groenning M., Bukrinski J. (2017). Insulin fibrillation: the influence and coordination of Zn 2+. J. Struct. Biol. 199, 27 – 38. doi:10.1016/j.jsb.2017.05.006
  28. Fukunaka A., Fujitani Y. (2025). “ Role of zinc homeostasis in the pathophysiology of diabetes,” in Zinc in biology. Editors Yasui H., Kambe T. (London: Royal Society of Chemistry), 214 – 228. doi:10.1039/9781837676583-00214
  29. Gems D., Partridge L. (2013). Genetics of longevity in model organisms: debates and paradigm shifts. Annu. Rev. Physiol. 75, 621 – 644. doi:10.1146/annurev-physiol-030212-183712
  30. Gianni S., Freiberger M. I., Jemth P., Ferreiro D. U., Wolynes P. G., Fuxreiter M. (2021). Fuzziness and frustration in the energy landscape of protein folding, function, and assembly. Acc. Chem. Res. 54, 1251 – 1259. doi:10.1021/acs.accounts.0c00813
  31. Gil-Garcia M., Benítez-Mateos A. I., Papp M., Stoffel F., Morelli C., Normak K. (2024). Local environment in biomolecular condensates modulates enzymatic activity across length scales. Nat. Commun. 15, 3322. doi:10.1038/s41467-024-47435-w
  32. Gonçalves-Kulik M., Mier P., Kastano K., Cortés J., Bernadó P., Schmid F. (2022). Low complexity induces structure in protein regions predicted as intrinsically disordered. Biomolecules 12, 1098. doi:10.3390/biom12081098
  33. Grams N., Charman M., Halko E., Lauman R., Garcia B. A., Weitzman M. D. (2024). Phosphorylation regulates viral biomolecular condensates to promote infectious progeny production. EMBO J. 43, 277 – 303. doi:10.1038/s44318-023-00021-0
  34. Hardy W. B. (1912). The general theory of colloidal solutions. Proc. R. Soc. Lond. Ser. Contain. Pap. Math. Phys. Character 86, 601 – 610. doi:10.1098/rspa.1912.0052
  35. Henderson K. A., Hughes A. L., Gottschling D. E. (2014). Mother-daughter asymmetry of pH underlies aging and rejuvenation in yeast. eLife 3, e03504. doi:10.7554/elife.03504
  36. Hirose T., Ninomiya K., Nakagawa S., Yamazaki T. (2023). A guide to membraneless organelles and their various roles in gene regulation. Nat. Rev. Mol. Cell. Biol. 24, 288 – 304. doi:10.1038/s41580-022-00558-8
  37. Hong K., Song D., Jung Y. (2020). Behavior control of membrane-less protein liquid condensates with metal ion-induced phase separation. Nat. Commun. 11, 5554. doi:10.1038/s41467-020-19391-8
  38. Horvath I., Wittung-Stafshede P. (2023). Amyloid fibers of α-Synuclein catalyze chemical reactions. ACS Chem. Neurosci. 14, 603 – 608. doi:10.1021/acschemneuro.2c00799
  39. Houx J., Cussac J., Copie T., Gambin Y., Sierecki E. (2026). Direct observation and quantification of single nanocondensates of the low complexity domain of TDP-43. Nat. Commun. doi:10.1038/s41467-026-69024-9
  40. Jin J. (2019). Interplay between ubiquitylation and SUMOylation: empowered by phase separation. J. Biol. Chem. 294, 15235 – 15236. doi:10.1074/jbc.H119.011037
  41. Jin X., Zhou M., Chen S., Li D., Cao X., Liu B. (2022). Effects of pH alterations on stress- and aging-induced protein phase separation. Cell. Mol. Life Sci. 79, 380. doi:10.1007/s00018-022-04393-0
  42. Joppe K., Roser A.-E., Maass F., Lingor P. (2019). The contribution of iron to protein aggregation disorders in the central nervous system. Front. Neurosci. 13, 15. doi:10.3389/fnins.2019.00015
  43. Josephs K. A., Dickson D. W., Tosakulwong N., Weigand S. D., Murray M. E., Petrucelli L. (2017). Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer’s disease: a longitudinal retrospective study. Lancet Neurol. 16, 917 – 924. doi:10.1016/s1474-4422(17)30284-3
  44. Kao P. F., Chen Y., Liu X., DeCarli C., Seeley W. W., Jin L. (2015). Detection of TDP‐43 oligomers in frontotemporal lobar degeneration–TDP. Ann. Neurol. 78, 211 – 221. doi:10.1002/ana.24431
  45. Keber F. C., Nguyen T., Mariossi A., Brangwynne C. P., Wühr M. (2024). Evidence for widespread cytoplasmic structuring into mesoscale condensates. Nat. Cell. Biol. 26, 346 – 352. doi:10.1038/s41556-024-01363-5
  46. Kenyon C. J. (2010). The genetics of ageing. Nature 464, 504 – 512. doi:10.1038/nature08980
  47. Kim Y., Furman C. M., Manhart C. M., Alani E., Finkelstein I. J. (2018). Intrinsically disordered regions regulate both catalytic and non-catalytic activities of the MutLα mismatch repair complex. Nucleic Acids Res. 47, 1823 – 1835. doi:10.1093/nar/gky1244
  48. Kiselar J. G., Datt M., Chance M. R., Weiss M. A. (2011). Structural analysis of proinsulin hexamer assembly by hydroxyl radical footprinting and computational modeling. J. Biol. Chem. 286, 43710 – 43716. doi:10.1074/jbc.M111.297853
  49. Kochman U., Sitka H., Kuźniar J., Czaja M., Kozubek P., Beszłej J. A. (2025). Neuronal death and biomolecular condensates: are there any new treatment options for alzheimer’s disease? Cells 14, 1356. doi:10.3390/cells14171356
  50. Kurz D. J., Decary S., Hong Y., Trivier E., Akhmedov A., Erusalimsky J. D. (2004). Chronic oxidative stress compromises telomere integrity and accelerates the onset of senescence in human endothelial cells. J. Cell. Sci. 117, 2417 – 2426. doi:10.1242/jcs.01097
  51. Li W., Li H.-L., Wang J.-Z., Liu R., Wang X. (2024a). Abnormal protein post-translational modifications induces aggregation and abnormal deposition of protein, mediating neurodegenerative diseases. Cell. Biosci. 14, 22. doi:10.1186/s13578-023-01189-y
  52. Li Y., Liu Y., Yu X.-Y., Xu Y., Pan X., Sun Y. (2024b). Membraneless organelles in health and disease: exploring the molecular basis, physiological roles and pathological implications. Signal Transduct. Target. Ther. 9, 305. doi:10.1038/s41392-024-02013-w
  53. Lin P.-H., Wu G.-W., Lin Y.-H., Huang J.-R., Jeng U.-S., Liu W.-M. (2024). TDP-43 amyloid fibril formation via phase separation-related and -Unrelated pathways. ACS Chem. Neurosci. 15, 3767 – 3775. doi:10.1021/acschemneuro.4c00503
  54. Liu H., Pillai M., Leung A. K. L. (2025). PARPs and ADP-ribosylation-mediated biomolecular condensates: determinants, dynamics, and disease implications. Trends biochem. Sci. 50, 224 – 241. doi:10.1016/j.tibs.2024.12.013
  55. Lyklema J. (2015). Colloidal models. A bit of history. J. Colloid Interface Sci. 446, 308 – 316. doi:10.1016/j.jcis.2014.10.052
  56. Marshall L. R., Korendovych I. V. (2021). Catalytic amyloids: is misfolding folding? Curr. Opin. Chem. Biol. 64, 145 – 153. doi:10.1016/j.cbpa.2021.06.010
  57. Maury C. P. J. (2018). Amyloid and the origin of life: self-replicating catalytic amyloids as prebiotic informational and protometabolic entities. Cell. Mol. Life Sci. 75, 1499 – 1507. doi:10.1007/s00018-018-2797-9
  58. Modler A. J., Gast K., Lutsch G., Damaschun G. (2003). Assembly of amyloid protofibrils via critical oligomers—A novel pathway of amyloid formation. J. Mol. Biol. 325, 135 – 148. doi:10.1016/S0022-2836(02)01175-0
  59. Morris O. M., Toprakcioglu Z., Röntgen A., Cali M., Knowles T. P. J., Vendruscolo M. (2024). Aggregation of the amyloid-β peptide (Aβ40) within condensates generated through liquid–liquid phase separation. Sci. Rep. 14, 22633. doi:10.1038/s41598-024-72265-7
  60. Nandi A., Zhang A., Arad E., Jelinek R., Warshel A. (2024). Assessing the catalytic role of native glucagon amyloid fibrils. ACS Catal. 14, 4656 – 4664. doi:10.1021/acscatal.4c00452
  61. Nemani S. K., Notari S., Cali I., Alvarez V. E., Kofskey D., Cohen M. (2018). Co-occurrence of chronic traumatic encephalopathy and prion disease. Acta Neuropathol. Commun. 6, 140. doi:10.1186/s40478-018-0643-9
  62. Notarmuzi D., Bianchi E. (2024). Liquid-liquid phase separation driven by charge heterogeneity. Commun. Phys. 7, 412. doi:10.1038/s42005-024-01875-4
  63. Okumura H., Itoh S. G. (2016). Structural and fluctuational difference between two ends of Aβ amyloid fibril: MD simulations predict only one end has open conformations. Sci. Rep. 6, 38422. doi:10.1038/srep38422
  64. Perdigão N., Heinrich J., Stolte C., Sabir K. S., Buckley M. J., Tabor B. (2015). Unexpected features of the dark proteome. Proc. Natl. Acad. Sci. 112, 15898 – 15903. doi:10.1073/pnas.1508380112
  65. Pintado-Grima C., Bárcenas O., Ventura S. (2022). In-Silico analysis of pH-Dependent liquid-liquid phase separation in intrinsically disordered proteins. Biomolecules 12, 974. doi:10.3390/biom12070974
  66. Poudyal R. R., Sieg J. P., Portz B., Keating C. D., Bevilacqua P. C. (2021). RNA sequence and structure control assembly and function of RNA condensates. RNA 27, 1589 – 1601. doi:10.1261/rna.078875.121
  67. Ramazi S., Dadzadi M., Darvazi M., Seddigh N., Allahverdi A. (2024). Protein modification in neurodegenerative diseases. MedComm 5, e674. doi:10.1002/mco2.674
  68. Rana U., Wingreen N. S., Brangwynne C. P., Panagiotopoulos A. Z. (2024). Interfacial exchange dynamics of biomolecular condensates are highly sensitive to client interactions. J. Chem. Phys. 160, 145102. doi:10.1063/5.0188461
  69. Rhine K., Odeh H. M., Shorter J., Myong S. (2023). Regulation of biomolecular condensates by Poly(ADP-ribose). Chem. Rev. 123, 9065 – 9093. doi:10.1021/acs.chemrev.2c00851
  70. Ricchelli F., Drago D., Filippi B., Tognon G., Zatta P. (2005). Aluminum-triggered structural modifications and aggregation of β-amyloids. Cell. Mol. Life Sci. 62, 1724 – 1733. doi:10.1007/s00018-005-5141-0
  71. Roden C., Gladfelter A. S. (2021). RNA contributions to the form and function of biomolecular condensates. Nat. Rev. Mol. Cell. Biol. 22, 183 – 195. doi:10.1038/s41580-020-0264-6
  72. Rufo C. M., Moroz Y. S., Moroz O. V., Stöhr J., Smith T. A., Hu X. (2014). Short peptides self-assemble to produce catalytic amyloids. Nat. Chem. 6, 303 – 309. doi:10.1038/nchem.1894
  73. Saini B., Mukherjee T. K. (2023). Biomolecular condensates regulate enzymatic activity under a crowded milieu: synchronization of liquid–liquid phase separation and enzymatic transformation. J. Phys. Chem. B 127, 180 – 193. doi:10.1021/acs.jpcb.2c07684
  74. Sarangapani P. S., Hudson S. D., Jones R. L., Douglas J. F., Pathak J. A. (2015). Critical examination of the colloidal particle model of globular proteins. Biophys. J. 108, 724 – 737. doi:10.1016/j.bpj.2014.11.3483
  75. Sarell C. J., Wilkinson S. R., Viles J. H. (2010). Substoichiometric levels of Cu2+ ions accelerate the kinetics of fiber formation and promote cell toxicity of Amyloid-β from alzheimer disease. J. Biol. Chem. 285, 41533 – 41540. doi:10.1074/jbc.M110.171355
  76. Sawazaki T., Sasaki D., Sohma Y. (2024). Catalysis driven by an amyloid–substrate complex. Proc. Natl. Acad. Sci. 121, e2314704121. doi:10.1073/pnas.2314704121
  77. Schomburg D., Salzmann M., Stephan D. (1994). Enzyme handbook 7 (Berlin, Heidelberg: Springer Berlin Heidelberg). doi:10.1007/978-3-642-78521-4
  78. Sha Z., Blyszcz T., González-Prieto R., Vertegaal A. C. O., Goldberg A. L. (2019). Inhibiting ubiquitination causes an accumulation of SUMOylated newly synthesized nuclear proteins at PML bodies. J. Biol. Chem. 294, 15218 – 15234. doi:10.1074/jbc.RA119.009147
  79. Shih P.-Y., Fang Y.-L., Shankar S., Lee S.-P., Hu H.-T., Chen H. (2022). Phase separation and zinc-induced transition modulate synaptic distribution and association of autism-linked CTTNBP2 and SHANK3. Nat. Commun. 13, 2664. doi:10.1038/s41467-022-30353-0
  80. Shin Y., Brangwynne C. P. (2017). Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382. doi:10.1126/science.aaf4382
  81. Singha N., Srivastava A., Pramanik B., Ahmed S., Dowari P., Chowdhuri S. (2019). Unusual confinement properties of a water insoluble small peptide hydrogel. Chem. Sci. 10, 5920 – 5928. doi:10.1039/C9SC01754B
  82. Snead W. T., Gladfelter A. S. (2019). The control centers of biomolecular phase separation: how membrane surfaces, PTMs, and active processes regulate condensation. Mol. Cell. 76, 295 – 305. doi:10.1016/j.molcel.2019.09.016
  83. Sołtys K., Tarczewska A., Bystranowska D. (2023). Modulation of biomolecular phase behavior by metal ions. Biochim. Biophys. Acta BBA - Mol. Cell. Res. 1870, 119567. doi:10.1016/j.bbamcr.2023.119567
  84. Soranno A. (2020). Physical basis of the disorder-order transition. Arch. Biochem. Biophys. 685, 108305. doi:10.1016/j.abb.2020.108305
  85. Sridharan S., Hernandez-Armendariz A., Kurzawa N., Potel C. M., Memon D., Beltrao P. (2022). Systematic discovery of biomolecular condensate-specific protein phosphorylation. Nat. Chem. Biol. 18, 1104 – 1114. doi:10.1038/s41589-022-01062-y
  86. Stoffel F., Papp M., Gil-Garcia M., Küffner A. M., Benítez-Mateos A. I., Jacquat R. P. B. (2025). Enhancement of enzymatic activity by biomolecular condensates through pH buffering. Nat. Commun. 16, 6368. doi:10.1038/s41467-025-61013-8
  87. Talley K., Alexov E. (2010). On the ph‐optimum of activity and stability of proteins. Proteins Struct. Funct. Bioinforma. 78, 2699 – 2706. doi:10.1002/prot.22786
  88. Testa A., Dindo M., Rebane A. A., Nasouri B., Style R. W., Golestanian R. (2021). Sustained enzymatic activity and flow in crowded protein droplets. Nat. Commun. 12, 6293. doi:10.1038/s41467-021-26532-0
  89. Tian Y., Shang Q., Liang R., Viles J. H. (2024). Copper(II) can kinetically trap arctic and Italian Amyloid-β 40 as toxic oligomers, mimicking Cu(II) binding to wild-type Amyloid-β 42: implications for familial alzheimer’s disease. JACS Au 4, 578 – 591. doi:10.1021/jacsau.3c00687
  90. Toledo P. L., Gianotti A. R., Vazquez D. S., Ermácora M. R. (2023a). Protein nanocondensates: the next frontier. Biophys. Rev. 15, 515 – 530. doi:10.1007/s12551-023-01105-1
  91. Toledo P. L., Vazquez D. S., Gianotti A. R., Abate M. B., Wegbrod C., Torkko J. M. (2023b). Condensation of the β‐cell secretory granule luminal cargoes pro/insulin and ICA512 RESP18 homology domain. Protein Sci. 32, e4649. doi:10.1002/pro.4649
  92. Van Lindt J., Bratek-Skicki A., Nguyen P. N., Pakravan D., Durán-Armenta L. F., Tantos A. (2021). A generic approach to study the kinetics of liquid–liquid phase separation under near-native conditions. Commun. Biol. 4, 77. doi:10.1038/s42003-020-01596-8
  93. Vazquez D. S., Toledo P. L., Gianotti A. R., Ermácora M. R. (2022). Protein conformation and biomolecular condensates. Curr. Res. Struct. Biol. 4, 285 – 307. doi:10.1016/j.crstbi.2022.09.004
  94. Vendruscolo M., Fuxreiter M. (2022). Sequence determinants of the aggregation of proteins within condensates generated by liquid-liquid phase separation. J. Mol. Biol. 434, 167201. doi:10.1016/j.jmb.2021.167201
  95. Villois A., Capasso Palmiero U., Mathur P., Perone G., Schneider T., Li L. (2022). Droplet microfluidics for the label‐free extraction of complete phase diagrams and kinetics of liquid–liquid phase separation in finite volumes. Small 18, 2202606. doi:10.1002/smll.202202606
  96. Vugmeyster L., Clark M. A., Falconer I. B., Ostrovsky D., Gantz D., Qiang W. (2016). Flexibility and solvation of Amyloid-β hydrophobic core. J. Biol. Chem. 291, 18484 – 18495. doi:10.1074/jbc.M116.740530
  97. Xu S., Bevis B., Arnsdorf M. F. (2001). The assembly of amyloidogenic yeast Sup35 as assessed by scanning (atomic) force microscopy: an analogy to linear colloidal aggregation? Biophys. J. 81, 446 – 454. doi:10.1016/S0006-3495(01)75712-8
  98. Xu B., Huang S., Liu Y., Wan C., Gu Y., Wang D. (2022). Manganese promotes α-synuclein amyloid aggregation through the induction of protein phase transition. J. Biol. Chem. 298, 101469. doi:10.1016/j.jbc.2021.101469
  99. Xu X., Xu X.-D., Ma M.-Q., Liang Y., Cai Y.-B., Zhu Z.-X. (2024). The mechanisms of ferroptosis and its role in atherosclerosis. Biomed. Pharmacother. 171, 116112. doi:10.1016/j.biopha.2023.116112
  100. Yewdall N. A., André A. A. M., Van Haren M. H. I., Nelissen F. H. T., Jonker A., Spruijt E. (2022). ATP:Mg2+ shapes material properties of protein-RNA condensates and their partitioning of clients. Biophys. J. 121, 3962 – 3974. doi:10.1016/j.bpj.2022.08.025
  101. Zhang S., Pei G., Li B., Li P., Lin Y. (2023a). Abnormal phase separation of biomacromolecules in human diseases. Acta Biochim. Biophys. Sin. 55, 1133 – 1152. doi:10.3724/abbs.2023139
  102. Zhang Y., Liu X., Chen J. (2023b). Coupled binding and folding of disordered SPIN N-terminal region in myeloperoxidase inhibition. Front. Mol. Biosci. 10, 1130189. doi:10.3389/fmolb.2023.1130189
  103. Zhang H., Guo W., Xu W., Li A., Jiang L., Li L. (2024). Electrostatic interactions in nucleosome and higher-order structures are regulated by protonation state of histone ionizable residue. doi:10.7554/elife.100738.1
  104. Zhong Q., Xiao X., Qiu Y., Xu Z., Chen C., Chong B. (2023). Protein posttranslational modifications in health and diseases: functions, regulatory mechanisms, and therapeutic implications. MedComm 4, e261. doi:10.1002/mco2.261
  105. Zhou H.-X., Pang X. (2018). Electrostatic interactions in protein structure, folding, binding, and condensation. Chem. Rev. 118, 1691 – 1741. doi:10.1021/acs.chemrev.7b00305

Citation

Melian, N. A., et al. (2026). From the classical colloidal theory to biomolecular condensation: implications in health and disease. Frontiers in Biophysics, Volume 4. https://doi.org/
Received
Accepted
Published
Volume
4 - 2026

Copyright

Copyright © 2026 Melian, Gomez, Abate, Toledo, Ermácora and Vazquez.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Mario R. Ermácora; Diego S. Vazquez

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0