Engineering Design of Artificial Phase‐Separating Proteins

ABSTRACT

Phase‐separating proteins are evolutionarily conserved biomolecules that orchestrate key cellular processes. Although the biophysical principles underlying liquid–liquid phase separation (LLPS)—including multivalent interactions, intrinsically disordered regions (IDRs), and the sticker–spacer framework—are now well established, translating these insights into actionable engineering guidelines remains open challenges. Here, we present a design‐oriented synthesis for engineering artificial phase‐separating proteins. Drawing on a curated dataset, we characterize the physicochemical patterns associated with LLPS behavior, we evaluate current computational predictors, revealing systematic blind spots in hydrophobicity‐rich or modular repeat architectures. Building on these insights, we propose modular design heuristics that encompasses sequence‐level composition and patterning, valency control via repeat architecture, and topological fusion strategies including terminal tags, telechelic constructs, and interleaved block architectures. These design levers are mapped onto applications such as protein purification, catalytic condensates, stimuli‐responsive systems, and mechanically hydrogels and fibers. Finally, we present an integrated design–build–test–learn (DBTL) framework that bridges predictive modeling, experimental iteration, and functional validation. By treating phase separation as an engineerable rather than merely emergent property, this review offers both a conceptual map and a practical roadmap for the rational construction of artificial phase‐separating proteins across biotechnological and biomedical contexts.

Graphical Abstract and Lay Summary

A design‐oriented framework for engineering phase‐separating proteins is presented, integrating sequence‐level analysis, modular architecture, and application‐driven strategies. Physicochemical features such as hydrophobicity, charge, and hydrogen bonding guide molecular design, while valency and topology enable functional tuning. This hierarchical approach supports applications in catalysis, purification, responsive systems, and biomaterials within a DBTL cycle.

1. Introduction

Phase‐separating proteins are central to a remarkably diverse range of biological processes, from biochemical signal regulation and metabolic flux control to environmental adaptation and stress responses. At the functional level, they govern transcription, translation, and cell signaling, and enable adaptive responses to light, temperature, salinity, and mechanical cues [1, 2, 3, 4, 5, 6, 7, 8, 9]. These proteins are evolutionarily conserved and broadly distributed across the tree of life, having been identified in animals, plants, fungi, bacteria, and viruses. Representative examples include DDX24 [10] and transferrin [11] in humans; elastin‐like polypeptides (ELPs) [12], lipoate‐protein ligase A (LplA) [13]; collagen‐like proteins (COLs) [14]; the nucleocapsid (N) protein from SARS‐CoV‐2 [15]; spider silk proteins major ampullate spidroin (MaSp) [16] and tubuliform spidroin (TuSp) [17]; resilin‐like polypeptides (RLPs) from locusts [18]; LAF‐1 from Caenorhabditis elegans [19]; MED25 [20] and SEU [21] from Arabidopsis thaliana; mussel foot proteins (MFPs) [22]; Myo18a‐CCex from mice [5]; and Efg1 from fungi [23] (Figure 1).

FIGURE 1.

Living organisms serve as a vast reservoir of phase‐separating proteins, illustrating the taxonomic breadth of natural templates from which engineered constructs in this review have been drawn. Identified sources include animals, plants, and microorganisms such as humans, viruses, spiders, C. elegans, A. thaliana, mussels, mice, and fungi (arranged clockwise). This figure is intended to motivate the survey scope of the review rather than to convey new biological insight.

Collectively, these systems illustrate that liquid–liquid phase separation (LLPS) and related condensate transitions underpin the formation of membrane‐less, protein‐rich compartments—biomolecular condensates. The mechanisms underlying protein phase separation, and the theoretical frameworks used to describe them, have been studied extensively. Canonical models center on multivalent interactions [24, 25] built from weak, short‐range protein–protein interactions (PPIs) [26, 27], homotypic and heterotypic contacts [28, 29, 30, 31], intrinsically disordered regions (IDRs) [25, 32], low‐complexity domains (LCDs), and modular repeats [33, 34, 35]. More recently, the “stickers and spacers” framework has emerged as a unifying conceptual language, describing how interaction motifs and flexible linkers collectively encode phase behavior across diverse protein families [33, 36, 37, 38]. In parallel, synthetic systems based on ELPs [12], RLPs [39, 40], MFPs [41, 42], spider silk proteins [43, 44], ubiquilins (UBQLNs) [45], Tau [46, 47], and related constructs have demonstrated that modular protein architectures can serve as building blocks for biomaterials, purification tags, and synthetic condensates.

However, a persistent gap remains at the intersection of bioinformatic descriptors, sequence–structure rules, and actionable engineering guidelines. Existing reviews typically (1) focus on either biological condensates or specific material platforms [48, 49]; (2) survey sequence features qualitatively without systematically connecting predictor outputs—such as grand average of hydropathy (GRAVY) score, aliphatic index, PhaSePred, PONDR, or PLAAC scores—to concrete design decisions; and (3) describe modular constructs on a case‐by‐case basis rather than distilling generalizable design principles and explicit trade‐offs.

The novelty of this review lies not in the identification of new LLPS‐driving forces—hydrophobicity, net charge, intrinsic disorder, and sticker–spacer logic are well‐established concepts—but rather in three contributions that, to our knowledge, have not been previously synthesized. First, by focusing explicitly on engineered and biomimetic constructs rather than natural LLPS proteins, we reveal compositional patterns—particularly the prevalence of hydrophobic and aliphatic‐rich proteins that score poorly on disorder‐centric predictors—that are specifically relevant to the protein engineering community. Second, we systematically map predictor blind spots onto the most commonly engineered LLPS protein families (ELPs, RLPs, and spidroins), providing practitioners with concrete guidance on which computational tools to trust for which system. Third, we offer a hierarchical integration spanning sequence composition → modular architecture → topology → application → design–build–test–learn (DBTL) iteration, a design‐oriented synthesis not found in existing LLPS reviews.

Accordingly, this review focuses specifically on the engineering design of artificial phase‐separating proteins, and is organized around two complementary elements, both oriented toward the engineering community. (1) Bioinformatic foundations. We compile and analyze a curated dataset of experimentally validated phase‐separating proteins with deliberate emphasis on engineered systems—ELPs, RLPs, MFPs, spider silk MaSp/TuSp, UBQLNs, Tau‐related constructs, and other condensate‐forming proteins [12, 16, 30, 39, 41, 50]. Using this dataset, we (i) quantify hydrophobicity (GRAVY), aliphatic index, pI distributions, and prion‐like content; (ii) compare phase‐separation propensity scores from PhaSePred, PONDR, and PLAAC [24, 33, 51]; and (iii) identify conditions under which high sequence hydrophobicity—for example, elevated aliphatic index—systematically suppresses predictor scores despite robust experimental phase separation [52]. (2) Modular design and application mapping. Building on these sequence‐level insights, we organize reported engineering strategies into three core design levers: composition and patterning, control of valency via modular repeats, and topological fusion strategies including terminal tags, telechelic/triblock constructs, and interval block architectures [53, 54, 55, 56]. These levers are mapped onto concrete application domains including ITC‐based purification [12, 47, 57, 58], catalytic microenvironments [12, 59, 60], stimuli‐responsive systems [60, 61, 62, 63, 64, 65, 66], and mechanically robust hydrogels and biomimetic fibers [67, 68, 69, 70].

Accordingly, each design lever discussed in Sections 2, 3, 4 is framed throughout as a component of an iterative DBTL engineering cycle rather than as a standalone rule: bioinformatic descriptors serve as priors for sequence selection, modular architectures translate those priors into testable constructs, and experimental outcomes feedback to refine both the designs and the predictive models that guide them. This review is organized as an integrative synthesis of published work on designed phase‐separating proteins, supplemented by original descriptive bioinformatic analysis of a curated dataset. The bioinformatic sections are presented as a pattern‐identification tool rather than as a primary research study or systematic meta‐analysis. Throughout, the review remains anchored in the engineering of artificial phase‐separating proteins—that is, designed or biomimetic constructs built to perform defined biotechnological or biomedical functions. Accordingly, we explicitly do not attempt to (1) comprehensively review the cell biology of membraneless organelles, RNA granules, or transcriptional condensates [40, 59, 71, 72]; (2) catalog the full spectrum of disease‐associated LLPS phenomena, which have been reviewed elsewhere [28, 73, 74]; or (3) cover nonprotein condensates, such as purely nucleic acid‐based systems. Instead, our goal is to provide a design‐oriented synthesis that treats phase separation as an engineerable property rather than as an exclusively emergent phenomenon. We provide readers with a concrete decision tools and workflow templates directly applicable to the construction of artificial phase‐separating proteins for diverse biotechnological and biomedical purposes.

Section 2 establishes the bioinformatic foundations for engineering design, covering dataset assembly and the analysis pipeline, global sequence and physicochemical trends, a critical assessment of current computational predictors of phase separation, and a summary of how hydrophobicity, charge patterning, hydrogen bonding, and structural rigidity collectively define a sequence–property rule set. Section 3 synthesizes published engineering strategies into a coherent set of design principles and architectures, organized around composition and patterning at the residue level, control of valency via modular repeats and IDRs, and three distinct fusion topologies—terminal tags, telechelic/triblock constructs, and interval block architectures. A summary table and practical guidelines illustrate how these design levers can be combined and traded off for specific objectives. Section 4 presents application‐focused case studies linking design choices to functional outcomes across protein purification, catalytic microenvironments, stimuli‐responsive systems, and the mechanical optimization of hydrogels and biomimetic fibers; wherever possible, qualitative design strategies are connected to quantitative performance metrics such as yield, phase transition temperature, elastic modulus, and toughness. Finally, Section 5 integrates all preceding elements into an explicit DBTL framework—the cyclic engine that transforms the design heuristics of Sections 2, 3, 4 into a self‐improving engineering workflow. Within this framework, we outline how bioinformatic predictors function as multidimensional priors, catalog common failure modes including mis‐prediction of phase‐separation propensity, expression bottlenecks, and context dependence in cellular and materials environments, and propose three concrete future directions: (i) machine‐learning‐guided and high‐throughput design; (ii) de novo engineering of multicomponent and multiphase condensates; and (iii) translation to bioprocessing, regenerative medicine, and soft materials.

2. Bioinformatic Foundations for Designing Phase‐Separating Proteins

Sequence‐level composition is a powerful starting point for engineering phase‐separating proteins, yet translating physicochemical patterns into reliable, broadly applicable design guidelines remains a major challenge. Systematically decoding sequence information across a wide spectrum of protein families—and distilling the principles that underlie their phase‐separation behavior—is therefore essential for building a generalizable framework for molecular design.

This section describes the assembly of an experimentally validated dataset of phase‐separating proteins, the bioinformatic pipeline used to analyze their sequence and structural features, and the key physicochemical trends that emerge from this analysis. We focus on three recurrent determinants—hydrophobicity, charge patterning, and hydrogen‐bonding–mediated structural rigidity—and connect each to the “stickers and spacers” conceptual framework, as well as to the limitations of current prediction tools.

2.1. Dataset Assembly and Analysis Pipeline

A critical thermodynamic caveat frames all analyses in this section: phase separation is inherently condition‐dependent, and in principle any protein can phase separate under sufficiently extreme conditions. Accordingly, the binary label “phase‐separating” used in Table S1 refers exclusively to proteins for which LLPS or a closely related behavior—coacervation, droplet formation, or phase‐transition–driven self‐assembly—has been experimentally documented under near‐physiological or explicitly defined conditions, and should not be interpreted as an absolute intrinsic property of the sequence alone. For inclusion in this dataset, a protein must be supported by at least one of the following: (i) direct microscopic evidence of droplet or coacervate formation; (ii) turbidity assays demonstrating concentration‐dependent phase transition; (iii) phase diagrams or quantitative coexistence curves; or (iv) in‐cell puncta formation accompanied by evidence of condensate behavior such as FRAP recovery or fusion/fission dynamics. Entries supported only by indirect or in silico evidence were excluded.

Candidate proteins were drawn from the primary literature and five major public LLPS databases—PhaSePro, PhaSepDB (v2.1), LLPSDB (v2.0), DrLLPS, and CD‐CODE—each providing experimentally annotated entries with varying scope and taxonomic coverage [75, 76, 77]. Because these resources predominantly collect naturally occurring LLPS proteins and contain relatively few engineered or biomimetic constructs, we compiled a custom dataset that deliberately overrepresents engineered systems—including ELPs, RLPs, spidroins, silk‐like proteins, MEPs, and their designed variants—that are underrepresented in existing databases. Full annotations, including protein family, origin classification (natural LLPS protein, natural LLPS protein–engineered variant, or designed/biomimetic construct), database overlap, and key physicochemical descriptors, are provided in Table S1.

The dataset encompasses two broad classes of systems: (1) Natural phase‐separating proteins, including spider silk spidroins MaSp and TuSp, RLPs, MFPs, Tau and prion‐related systems, UBQLNs, plant prion‐like domains (e.g., ELF3 and FLOE1), and regulatory proteins such as DDX24 and the SARS‐CoV‐2 nucleocapsid [10, 15, 16, 17, 39, 41, 45, 78, 79]. (2) Engineered modular systems, including ELPs, RLP–ELP copolymers, silk–ELP hybrids, block recombinamers, and fusion constructs incorporating adhesive, extracellular‐matrix‐mimetic, or catalytic domains [12, 18, 39, 51, 53, 80, 81, 82].

Several methodological decisions warrant explicit documentation. First, regarding sequence regions: descriptor calculations used full‐length sequences, specific domains [e.g., MaSp1 N‐terminal domain (NTD) or CTD], or engineered constructs as expressed; for well‐characterized domain proteins, only the relevant domain or repeat unit was analyzed. Second, regarding engineered variants: each distinct sequence entry is counted as one data point regardless of the number of variants reported in a given study; entries sharing a common parent sequence (e.g., Mfp‐3 variants and Mcfp‐6 isoforms) are flagged in Table S1, and the distributional analyses should be treated as illustrative patterns in a curated set rather than statistically rigorous population‐level inference, given the nonindependence within scaffold families. Third, regarding redundancy: no stringent sequence identity cutoff (e.g., CD‐HIT at 40%) was applied, as the dataset is organized around distinct protein families of engineering interest rather than as a proteome‐wide sample; family‐level nonindependence is acknowledged as a limitation, and descriptors are summarized both by family and globally. Fourth, regarding negative controls: the dataset does not include confirmed non‐LLPS proteins, as the focus is on proteins that do phase separate; comparative statements such as “ELPs have a higher aliphatic index than FUS‐type proteins” therefore represent within‐dataset comparisons and should not be interpreted as contrasts against a general non‐LLPS background.

For each sequence, we computed the following features. (1) Global amino acid composition and hydrophobicity: GRAVY scores using ExPASy ProtParam [83] (Figures 2a and S1), and aliphatic index as a complementary descriptor reflecting the volume fraction of aliphatic side chains [83] (Figures 3b–d and S1). (2) Phase‐separation propensity and disorder‐related scores: Protein Overall PScore from PhaSePred, with PScore ≥ 4 as the conventional high‐confidence threshold [24, 84] (Figures 2b and 3a). average disorder‐based LLPS scores from PONDR, with a threshold of ≥ 0.5 [84] (Figures 2c and 3a); and prion‐like domain log‐likelihood ratio (LLR) scores from PLAAC, reflecting the COREscore per residue within subsequences of length L‐core [84] (Figure 3d). PhaSePred was applied using the self‐assembling phase separation (SaPS) model; the resulting Overall PScore integrates IDR content, prion‐like score, catGRANULE score, π‐contact PScore, and phosphorylation frequency into a single propensity value. All scores are interpreted as continuous propensity indicators rather than binary classifiers; conventional thresholds are reported for reference only. (3) Charge distribution and isoelectric point: pI values from ExPASy ProtParam (Figure 2d), supplemented by global charge fractions and simple patterning metrics—such as clustered versus alternating charge distributions—to connect sequences to sticker–spacer and charge‐patterning frameworks [24, 33, 36, 37, 38]. (4) Structural context, hydrogen bonding, and rigidity: three‐dimensional (3D) homology models for representative proteins from each family were generated using SWISS‐MODEL from experimentally validated or canonical sequences [14, 44, 51, 53]; structural analyses—including visualization of hydrophobic cores and estimation of intra‐ and intermolecular hydrogen bonds—were performed in ChimeraX (Figure 4) and used qualitatively to classify conformational states (random coils/IDRs versus modular helices or β‐sheets) rather than to derive high‐resolution thermodynamic parameters.

FIGURE 2.

Bioinformatic statistics on hydrophobicity, phase‐separation propensity, and charge distribution in phase‐separating proteins. (a) Distribution of GRAVY scores, calculated as the mean of Kyte–Doolittle hydropathy values across all residues using ExPASy ProtParam; GRAVY serves as the quantitative measure of hydrophobicity throughout this review. (b) Distribution of Protein Overall PScores (PScore ≥ 4 indicates high confidence), calculated using PhaSePred. (c) Distribution of average phase‐separation prediction scores (score ≥ 0.5 indicates high confidence), calculated using PONDR. (d) Distribution of isoelectric points (pI; the pH at which net charge is zero), obtained from ExPASy ProtParam.

FIGURE 3.

Correlation analysis between hydrophobicity and phase‐separation prediction parameters. (a) Distribution of phase‐separation prediction scores for several protein families, including MFP, UBQLN, MaSp, RLP, and ELP. Protein overall PScores were calculated using PhaSePred; average prediction scores using PONDR. Dashed lines indicate thresholds: PScore = 4 and prediction score = 0.5. (b) Correlation between the Aliphatic Index and average prediction score (PONDR). (c) Correlation between the Aliphatic Index and Protein Overall PScore (PhaSePred). (d) Correlation between the Aliphatic Index and LLR (log‐likelihood ratio) scores from PLAAC, representing COREscore per residue within any subsequence of length L‐core.

FIGURE 4.

Structural models of phase‐separating proteins. Left: structure types including random coils, dimer, helices, curved helices, β‐sheets, and β‐helices. Intramolecular hydrogen bonds (blue lines) are crucial for structural stability. Right: protein samples. All structures were obtained by homology modelling using SWISS‐MODEL and are used here as qualitative structural contexts to visually distinguish broad conformational classes (disordered ensembles, helical bundles, and β‐sheet‐rich folds) and to illustrate how different secondary structure propensities relate to LLPS versus fiber‐forming behavior; these models are not used for quantitative structural analysis or to imply mechanistic causality. AlphaFold3 was considered but was not available for all sequences in the dataset at the time of analysis. Structural visualization and hydrogen‐bond analysis were performed using ChimeraX.

Together, these analyses provide a multiparameter map spanning hydrophobicity, charge patterning, prion‐like content, predicted disorder and LLPS propensity, and structural motifs—identifying sequence‐level patterns consistent with known LLPS‐driving forces and with practical implications for the design of artificial phase‐separating proteins [52].

2.2. Global Sequence and Physicochemical Trends

Two key descriptors are defined here for clarity. The GRAVY score, calculated by ExPASy ProtParam as the mean Kyte–Doolittle hydropathy value across all residues, quantifies overall sequence hydrophobicity: negative values indicate net hydrophilicity, typical of IDR‐rich or charge‐containing sequences, while positive values indicate net hydrophobicity. The aliphatic index [85] is a complementary descriptor measuring the relative volume fraction of aliphatic side chains (Ala, Val, Ile, and Leu), calculated as AI = X(Ala) + 2.9 × X(Val) + 3.9 × [X(Ile) + X(Leu)], where X denotes mole fraction. Crucially, a protein can have a high aliphatic index yet a near‐neutral or even positive GRAVY score, as these two descriptors capture distinct aspects of sequence composition and are not interchangeable.

To identify sequence features associated with experimentally documented LLPS, we examined global physicochemical trends across the curated dataset in Table S1. This analysis addresses the question “what compositional patterns are associated with observed phase behavior in these systems?”—not which sequences are intrinsically phase‐separating—acknowledging that LLPS context (dilute solution in vitro, intracellular environments with RNA and crowding agents, or concentrated ELP solutions near Tt) affects which descriptors are most informative. Three families of recurrent patterns emerged, each consistent with known LLPS‐driving forces: (i) hydrophobicity and aliphaticity, closely tied to intrinsic disorder and sticker–spacer patterning; (ii) charge distribution and pI, reflecting electrostatic interactions and sequence charge patterning; and (iii) prion‐like content and modular repeat architectures, capturing latent multivalency and cooperative hydrogen‐bond networks. These patterns carry practical implications for design but should not be extrapolated as universal principles of phase separation across all protein systems.

2.2.1. Hydrophobicity, Aliphaticity, and Intrinsic Disorder

Hydrophobic and aliphatic residues are enriched in several families of designed LLPS proteins, suggesting a compositional contribution to phase behavior in these systems. GRAVY scores computed across Table S1 cluster predominantly around −1, indicating weak net hydrophobicity at the global sequence scale (Figure 2a). At first glance, this might suggest that phase‐separating proteins are only weakly hydrophobic. However, closer inspection reveals that many systems rely on locally enriched hydrophobic sticker patches—spatially concentrated clusters of hydrophobic residues embedded within otherwise hydrophilic or disordered backgrounds—consistent with the sticker–spacer framework.

Natural phase‐separating proteins illustrate this principle clearly. Spider silk spidroins MaSp and TuSp contain repetitive alanine‐rich segments and hydrophobic clusters—spatially concentrated patches of hydrophobic residues (primarily aliphatic: Val, Ile, Leu, Met, Ala) within an otherwise polar or charged sequence context, which can serve as hydrophobic stickers driving phase separation—within their C‐terminal domains (CTDs), which stabilize modular repeat structures and drive nucleation and fibril formation [43, 53, 86, 87, 88, 89]. Under physiological conditions, the CTD hydrophobic core is shielded; pH decreases or temperature elevations expose these patches, promoting aggregation, LLPS, and gelation [87, 88, 89].

Engineered systems reinforce the same logic: ELPs composed of (VPGXG)_n repeats undergo tunable lower critical solution temperature (LCST)‐type phase transitions, where increasing the fraction of hydrophobic guest residues (e.g., Val and Ile) or introducing aromatic residues (e.g., Phe and Tyr) lowers the transition temperature (Tt)—the LCST at which ELPs invert from soluble to condensed state—thereby enhancing coacervation [81, 90, 91, 92]. Hydrophobic tyrosine residues in particular act as effective stickers promoting LLPS in both natural and designed proteins [84, 93].

To connect hydrophobicity with intrinsic disorder and LLPS propensity, we further examined disorder‐based and LLPS‐related prediction scores—PONDR average disorder score and PhaSePred Overall PScore—across the dataset (Figures 2b,c and 3a). The family‐level distributions revealed in Figure 2, with violin plots and boxplot overlays, illustrate that hydrophobicity‐dominated systems such as silk‐like repeats and aliphatic‐rich ELP constructs frequently fall below conventional predictor thresholds (average PONDR score ≥ 0.5; PScore ≥ 4), despite robust experimental phase separation [24, 84] (Figures 2b,c and 3a). This highlights two key design implications: (i) global hydrophobicity and aliphaticity are necessary but insufficient descriptors—what governs LLPS is the local density and spatial arrangement of hydrophobic stickers within a flexible or disordered background; and (ii) disorder‐based predictors are calibrated toward polar, charged, and aromatic residues, and systematically underestimate LLPS propensity for sequences where aliphatic hydrophobic stickers dominate but IDR character is nonclassical [24, 33, 84].

In practice, GRAVY and aliphatic index should therefore be used as initial screening metrics to position candidate sequences within an LLPS‐compatible compositional range, but must be combined with sticker‐patterning analysis and experimental validation—such as turbidity assays or microscopy—before committing to a final sequence design.

2.2.2. Charge Distribution, Isoelectric Point (pI), and Charge Patterning

Electrostatics represent a second conserved determinant of phase separation. pI values were computed for each protein using ExPASy ProtParam (Figure 2d and Table S1), and the fractions and linear distributions of positively (Lys, Arg, and His) and negatively (Asp and Glu) charged residues were quantified. Phase‐separating proteins in this dataset tend to display pI values deviating from neutrality, clustering within mildly acidic or mildly basic ranges—a pattern that supports the established view that net charge and charge asymmetry modulate LLPS behavior and condensate composition [24, 33, 36, 37, 38].

Engineered systems demonstrate how pI and charge composition can function as tunable design variables. In RLPs, intrinsically disordered sequences enriched in Arg and sticker residues such as Tyr form coacervates through electrostatic interactions with anionic counterions (e.g., silicotungstic acid), enabling dynamic, adhesive hydrogels [18, 61]. In ELPs, pI correlates directly with functional outcomes such as nanoparticle synthesis (e.g., Lys‐mediated silica mineralization) and purification performance [12]. Phase separation is typically enhanced when solution pH approaches the protein's pI, and replacing guest residues with charged amino acids (e.g., Glu and His) provides a direct route to adjusting Tt and ionic responsiveness [64].

Beyond global pI, the linear patterning of charged residues along the sequence is equally critical. Blocky arrangements of oppositely charged residues favor complex coacervation and multicomponent condensate formation, whereas more alternating distributions weaken net attractive interactions and broaden phase boundaries. In designed RLP–ELP copolymers and charge‐patterned ELPs, adjusting the length, spacing, and arrangement of cationic and anionic blocks modulates LCST/UCST behavior, coacervation strength, and hierarchical self‐assembly [60, 61, 62, 63, 65].

Natural proteins illustrate the same principles at work. InMFPs, Dopa‐mediated adhesion is reinforced by positively charged Lys and Arg residues, which interact with anions (e.g., Cl⁻) in low‐hydration environments to strengthen cohesive interactions [42, 94, 95]. Spider silk spidroins rely on protonation of acidic residues (Asp and Glu) in the NTD to generate pH‐tunable stickers, and on Arg/Tyr interactions in the CTD to facilitate phosphate‐induced LLPS [87, 89, 95]. Tau and prion proteins phase separate via inter‐domain electrostatic contacts, and negatively charged molecules such as suramin can further promote Tau condensation [46, 78]. Charged motifs similarly regulate LLPS in TanC1/2 [5], the SARS‐CoV‐2 nucleocapsid [15], PhyB‐NTE [96], RALF1 [97], FUS [98], and FMRP [73].

Collectively, these observations establish that net charge, pI, and linear charge patterning are central design variables governing not only whether a protein phase separates, but also its sensitivity to pH, salt, and counterions, and its ability to co‐assemble with other macromolecules. In practice, pI and charge patterning serve as complementary tuning knobs that can be adjusted independently of the hydrophobic backbone to control the conditions under which LLPS is triggered, the selectivity of coacervation, and the composition of the resulting condensate—making them especially powerful for fine‐tuning behavior after initial hydrophobicity‐based design choices have been made.

2.2.3. Prion‐Like Content and Modular Repeats

Prion‐like low‐complexity regions (LCRs) and modular repeat architectures provide a third route to multivalency. Prion‐like domains were identified using PLAAC, with LLR scores quantifying the prion‐like character of local sequence windows [84]. Modular repeats—including silk‐like poly‐Ala/Gly segments, ELP (VPGXG)_n repeats [12], RLP motifs [39, 40], collagen‐like triplets [14], and other tandem repeat units [15, 16, 17, 18, 19]‐were annotated from the primary literature.

Prion‐like domains enriched in Gln, Asn, Tyr, and Gly confer dynamic, reversible interactions and are frequently linked to LLPS in regulatory and ribonucleoprotein systems, including FUS [98], hnRNPA1 [74], UBQLNs [45], TDP‐43 [28], and plant prion‐like proteins (e.g., ELF3 and FLOE1) [79, 99]. Elevated LLR scores in these domains correlate well with experimentally observed LLPS and confirm the utility of PLAAC‐derived metrics for identifying prion‐driven condensates (Figure 3d and Table S1).

By contrast, modular repeat architectures—as exemplified by silk proteins, RLPs, ELPs, and collagen‐like peptides—typically show low classical prion‐like content, yet achieve multivalency through repetitive hydrophobic, charged, or hydrogen‐bond‐forming motifs. Homology modeling with SWISS‐MODEL and structural analysis in ChimeraX reveal that such repeats frequently adopt β‐sheet, helical, or mixed secondary structures stabilized by regular intra‐ and intermolecular hydrogen bonds (Figure 4). In spider silk, antiparallel β‐sheets in poly‐Ala segments are locked by hydrogen‐bond arrays, supporting stable fibers that nonetheless originate from LLPS‐like precursor assemblies [44, 51, 53]. In RLP‐ and ELP‐based materials, repeat units undergo reversible disorder‐to‐order transitions that couple LLPS to gelation or fiber formation [39, 40, 53, 61].

Prion‐like LCRs and modular repeats thus represent two complementary but mechanistically distinct routes to multivalency: the former through IDR‐based, low‐complexity sticker clusters, and the latter through repetitive structural motifs with cooperative hydrogen bonding. Crucially, these two classes are captured differently by computational predictors—a point elaborated in Section 2.3. From an engineering perspective, this distinction means that IDR/PrLD‐based architectures are well served by classical LLPS predictors (high PScore, elevated PLAAC LLR), whereas modular repeat systems—especially aliphatic‐rich ELPs, RLPs, and silk‐like proteins—require complementary structural analysis and experimental characterization beyond what current predictor scores alone can provide.

Throughout Sections 2.2.1–2.2.3, the sticker–spacer framework has served as the primary interpretive lens. It is therefore important to define its scope and limitations explicitly. The framework is most informative when: (i) LLPS‐driving contacts are local and heterogeneous—discrete aromatic, charged, or hydrophobic residues embedded in a flexible IDR background; (ii) the condensate is liquid‐like, with weak and transient interactions; and (iii) spacers provide sufficient entropic flexibility to keep the dense phase reversible. The framework becomes insufficient or breaks down in three scenarios. First, in hydrophobic‐collapse–driven LLPS—such as ELPs below Tt—phase behavior is better described by Flory–Huggins theory and temperature‐dependent solvent interactions than by sticker valency counting, because the “stickers” are not discrete residues but distributed hydrophobic character across the entire chain. Second, when LLPS is mediated by multivalent folded domains rather than IDRs—such as coiled‐coil–driven assembly—the sticker–spacer model fails to capture the geometry, orientation, and affinity specificity of these contacts. Third, at the liquid‐to‐gel or liquid‐to‐solid transition discussed in Section 3, when sticker valency is too high or interactions become too strong or specific, the model in its standard form cannot predict the kinetics or thermodynamics of this transition without additional parameters. Recognizing these limits is essential for applying the framework judiciously across the diverse protein families and design contexts discussed in this review.

2.3. Computational Predictors: Assumptions, Coverage, and Blind Spots

Having characterized the global sequence and physicochemical landscape of the curated dataset, we next evaluated how widely used LLPS and disorder predictors perform across this dataset and to what extent they capture—or systematically miss—experimentally verified phase behavior. We focus on five representative tool categories, each with distinct underlying assumptions, physical features captured, and inherent blind spots. (a) IDR/disorder‐centric tools (PONDR, IUPred2A, and ESpritz): trained on experimentally verified disordered sequences; quantify per‐residue disorder probability; assume that phase behavior correlates with intrinsic disorder content; blind spot: ordered or modular repeat proteins—such as silk spidroins, ELPs, and RLPs—whose LLPS is driven by hydrophobic collapse or hydrogen‐bonded repeat assembly rather than conformational disorder. (b) π‐contact‐based predictor (PScore [84]): counts cumulative planar π–π contacts per residue contributed by aromatic and certain polar residues (Tyr, Phe, Trp, and backbone‐exposed small residues); assumes LLPS is driven primarily by aromatic stacking; highly relevant for FUS/hnRNPA1‐type proteins; blind spot: purely hydrophobic or charge‐mediated LLPS systems with low aromatic content. (c) Prion‐like domain predictor (PLAAC): applies a hidden Markov model trained on yeast prion sequences to compute an LLR score for low‐complexity aromatic/polar regions; blind spot: non‐prion‐like LLPS proteins, including coiled‐coil–driven or ELP‐type systems, whose LLPS does not rely on Gln/Asn/Tyr/Gly‐enriched low‐complexity composition. (d) Meta‐predictors (PhaSePred SaPS/PdPS; catGRANULE 2.0): integrate 8–10 sequence‐derived features via machine learning (XGBoost for PhaSePred); trained predominantly on natural LLPS proteins from public databases; blind spot: engineered repeat constructs with amino acid compositions atypical of the natural proteomes on which these models were trained. (e) Structure‐aware predictor (PSPire [100]): integrates residue‐level sequence features with AlphaFold2‐derived structural descriptors; specifically designed to improve prediction for phase‐separating proteins lacking canonical IDRs (noID‐PSPs); currently the most appropriate predictor for ELP/silk/RLP‐class designed proteins and for the majority of entries in Table S1. This tool‐level framework contextualizes the score distributions in Figure 2 and the correlation analysis in Figure 3—providing mechanistic rationale for observed patterns rather than treating predictor outputs as opaque scores [24, 33, 84].

2.3.1. Comparison of PhaSePred, PONDR, PLAAC, and Related Scores

For each protein in Table S1, we computed scores from three tool categories: (1) protein overall PScore using PhaSePred [24], a meta‐predictor that aggregates multiple sequence‐derived features; (2) average disorder‐based scores from PONDR, used as proxies for LLPS‐associated intrinsic disorder [84]; and (3) prion‐like domain LLR values from PLAAC, quantifying the prion‐like character of low‐complexity regions [84]. PScore and PSPire are discussed further in Section 2.3.3 in the context of their specific strengths and limitations.

Comparing these scores against one another and against global hydrophobicity descriptors (GRAVY and aliphatic index) revealed several consistent patterns (Figures 2b,c and 3a–d; Figure S1). (i) Classical prion‐like and RNP proteins (e.g., FUS, hnRNPA1, subsets of UBQLNs, and plant PrLD‐containing proteins) tend to show high PLAAC LLR values, elevated PONDR‐derived scores, and above‐threshold PScores, consistent with their prion‐like composition and strong LLPS propensity [33, 45, 78, 98]. (ii) Silk‐like, RLP, and ELP‐based systems—despite robust experimental phase separation [12, 39, 60, 101]—frequently exhibit subthreshold PScores (< 4), PONDR average scores near or below 0.5, and low PLAAC LLR values, owing to their limited classical prion‐like composition (Figures 2b,c and 3a–d). (iii) Mixed‐architecture proteins (e.g., Tau, MFPs, and viral nucleocapsid proteins) show intermediate behavior, with one or two predictors capturing their LLPS propensity but not all three [11, 15, 22, 30].

Importantly, all proteins in this dataset exhibit experimentally validated phase separation despite substantial variability in predictor scores. This confirms that current LLPS and disorder predictors are broadly useful for identifying prion‐like and RNP‐type condensate‐forming proteins, yet are unreliable across the full design space—particularly for hydrophobicity‐dominated or modular repeat architectures. These observations motivated a more detailed investigation of how hydrophobicity and aliphaticity systematically modulate predictor outputs.

2.3.2. Hydrophobicity‐Driven Score Suppression (Aliphatic Index > Threshold)

To examine how hydrophobicity relates to predictor outputs, we performed pairwise correlation analyses among GRAVY score, aliphatic index, PScore, average PONDR score, and PLAAC LLR (Figure S1); Pearson and Spearman coefficients with 95% confidence intervals are reported for each pair. Scatter is substantial and R² values are modest, underscoring that aliphatic index alone does not determine predictor scores. Nonetheless, Figure 3 reveals a consistent trend: PScore, PONDR average, and PLAAC LLR all tend to decline with increasing aliphatic index, reflecting the known aromatic/IDR‐centricity of these tools. Once the aliphatic index exceeds approximately 64, this declining trend is visible across multiple protein families—including MaSp, RLPs, and ELPs—though the relationship is correlational rather than deterministic (Figure 3b–d).

Three mechanistic factors account for this hydrophobicity‐driven score suppression. (i) Composition bias in training data: most LLPS and disorder predictors were calibrated using datasets enriched in polar, charged, and aromatic residues—such as Gln/Asn‐rich prion‐like domains and Arg/Tyr‐based RNPs—which inherently favor high disorder and prion‐like scores [24, 33, 84]. Sequences dominated by aliphatic residues (Ala, Val, Ile, and Leu) contain fewer of these “favorable” composition features and are therefore systematically classified as ordered or non‐prion‐like, even when they form robust condensates via hydrophobic cores and modular repeats [44, 51, 53]. (ii) Underrepresentation of hydrophobic‐core–driven LLPS: training sets under‐sample proteins whose phase separation is mediated primarily by hydrophobic‐core formation—such as silk CTDs or certain ELP/silk–ELP hybrids—leading to systematically depressed scores for this mechanistic class [43, 53, 102, 103]. Confounding with aggregation propensity: highly aliphatic sequences resemble classical aggregation‐prone regions, and predictors may penalize such compositions to avoid false‐positive LLPS calls for pathological aggregation, inadvertently suppressing scores for functional LLPS‐driven systems such as silk and elastomeric proteins [44, 51, 53, 102].

Together, these findings define a compositional regime—high aliphatic index, typically above ∼64—in which current LLPS predictors tend to underperform despite unambiguous experimental phase separation. This limitation is most evident for PScore: aliphatic‐rich designed proteins including MaSp CTDs and ELPs with Val/Ile/Leu guest residues consistently show low PScore values, as expected given that PScore was designed to detect aromatic π–π contacts and is intrinsically biased toward aromatic‐driven rather than hydrophobic‐collapse–driven LLPS. For engineering design, this means that the LLPS potential of hydrophobic and aliphatic‐rich sequences is likely to be underestimated by existing predictors, and independent experimental validation is essential for this class of proteins.

2.3.3. Practical Implications for Using Predictors in Protein Engineering

These findings motivate five practical recommendations for applying LLPS and disorder predictors in protein engineering. (i) Use multiple predictors in combination: PONDR/IUPred2A captures intrinsic disorder propensity; PLAAC captures prion‐like low‐complexity content; PScore captures π–π contact potential most relevant to aromatic‐driven condensates; PhaSePred and catGRANULE 2.0 provide integrated meta‐scores; and PSPire incorporates structural information especially valuable for ordered or modular repeat proteins. Together with simple sequence descriptors (GRAVY, aliphatic index, pI, charge patterning), these tools capture distinct and complementary aspects of condensate behavior. No single metric is sufficient across the full landscape of natural and engineered LLPS systems; for design purposes, scores should be interpreted jointly, ideally within a sticker–spacer framework that explicitly considers local sticker density, spatial patterning, and structural context [24, 33, 36, 37, 38]. (ii) Recognize hydrophobicity‐driven blind spots: For sequences with aliphatic index ≳ 64—such as silk‐like repeats, ELPs with hydrophobic guest residues, and certain RLP hybrids—subthreshold PScore, PONDR, or PLAAC values do not indicate the absence of LLPS; rather, they likely reflect algorithmic bias against hydrophobic‐core–driven condensates. In such cases, experimental validation (e.g., turbidity assays, microscopy, and rheology) and structural modeling are essential [81, 104, 105, 106, 107]. (iii) Treat pI, charge patterning, and prion‐like content as complementary design knobs: Once a sequence has been positioned within a hydrophobicity range compatible with LLPS, pI and linear charge patterning can be adjusted to shift phase boundaries, tune coacervation strength, and modulate sensitivity to pH and ionic strength. Prion‐like content and low‐complexity motifs can then be selectively introduced or removed to control condensate dynamics and reversibility [45, 78, 79, 104]. (iv) Account for structural context and modular repeat architecture: Structural modeling of modular repeat proteins (e.g., silk, RLP, ELP, and collagen‐like motifs) using homology tools such as SWISS‐MODEL, combined with qualitative hydrogen‐bond analysis in ChimeraX, can reveal whether designed sequences are more likely to form dynamic droplets, viscoelastic gels, or solid fibers (Figure 4) [14, 103, 108]—information that is not encoded in sequence‐only predictor scores but is critical for targeting specific material properties. (v) Prioritize next‐generation integrated predictors: Our analysis highlights the need for LLPS prediction tools that incorporate hydrophobic, electrostatic, prion‐like, and structural descriptors into a unified framework, explicitly accommodating hydrophobic‐core–mediated LLPS, modular repeat architectures, and the distinction between functional LLPS‐coupled self‐assembly and pathological aggregation [33, 36, 52, 102]. A comprehensive benchmark of all available LLPS prediction tools is beyond the scope of this review; readers are directed to dedicated benchmarking studies, including Hadarovich et al. [109] and Monti et al. [110], for comparative performance evaluations.

In summary, existing predictors remain valuable for hypothesis generation and initial screening but must be applied with explicit awareness of their biases, particularly in hydrophobicity‐dominated or modular repeat‐rich design spaces. Rational engineering of phase‐separating proteins therefore requires integrating predictor scores with parallel consideration of hydrophobicity, charge patterning, prion‐like content, and structural context—a principle that directly motivates the design strategies developed in Section 3.

2.4. Hydrogen Bonding, Structural Motifs, and Rigidity

The interplay between hydrogen bonding and structural rigidity critically shapes the driving forces underlying protein phase separation. Hydrogen‐bond networks act synergistically with hydrophobic interactions to promote condensate formation and network organization, whereas steric constraints imposed by bulky side chains or conformationally restricted residues can partially offset these hydrophobic driving forces.

2.4.1. Hydrogen Bonding as a Stabilizing Force

Hydrogen bonds reinforce hydrophobic interactions in phase‐separating systems, as illustrated most clearly by spider silk proteins. Within the polyalanine segments of Nephila clavipes dragline silk, intermolecular hydrogen bonds stabilize antiparallel β‐sheets, lock hydrophobic cores during nucleation, and thereby facilitate phase separation [44, 53]. This cooperative mechanism extends to intrinsically disordered regions: the CTD of N. clavipes MaSp1 forms intermolecular hydrogen bonds at low temperatures, enabling solution‐to‐gel transitions by reinforcing hydrophobic contacts [53, 102]. Fusion constructs with expanded repeat units (e.g., MaSp1‐4C) exhibit enhanced mechanical properties attributable to increased hydrogen‐bond crosslink density [44, 53, 102].

Conversely, disruption of hydrogen‐bonding capacity markedly perturbs phase behavior. Substituting Dopa with tyrosine in the mussel foot protein Pvfp‐5β strongly attenuates LLPS by disrupting transient hydrogen‐bond networks, despite largely preserved hydrophobicity [95]. Similar principles operate in silk–ELP hybrids and polyphenol‐based silk hydrogels, where the density and topology of hydrogen‐bonding motifs co‐regulate both phase behavior and mechanical properties [14, 111].

2.4.2. Steric Hindrance and Rigidity as Limiting or Enabling Factors

Structural rigidity, governed by side‐chain bulk and residue geometry, can attenuate hydrophobic driving forces through steric constraints. In MFPs, the bulky aromatic ring of tyrosine reduces the local packing density of neighboring residues, weakening cohesive protein–protein interactions under certain conditions [22]. Engineered systems reinforce this principle: replacing compact alanine with larger hydrophobic residues (Ile and Leu) in artificial triple‐helix peptides increases backbone flexibility while disrupting hydrophobic aggregation by reducing steric compatibility [112].

Pro–Gly motifs, which are common in spider silk MaSp2 and ELPs, exploit the steric rigidity of proline and the conformational flexibility of glycine [113]. This combination lowers hydrophobic packing density while enhancing salt responsiveness by reducing the energetic barrier for peptide–anion interactions [102]. Notable exceptions demonstrate that bulky residues can also promote phase separation in appropriate positional contexts: in ELPs, introducing phenylalanine at the N‐terminus rather than at internal positions drives self‐assembly, showing that residue placement can override steric constraints in modulating hydrophobic interactions [114]. Beyond hydrophobic contacts, conformational rigidity also influences hydrogen‐bond networks; proline isomerization in ELPs generates conformational switches in which cis‐Pro isomers disrupt protein–water hydrogen bonding through their rigidity while favoring intramolecular contacts that promote phase separation [115].

These observations suggest a practical decision framework. (i) Bulky aromatics (Phe, Tyr, Trp): when buried in a tightly packed core, they tend to stabilize ordered aggregates or crystalline domains and can suppress dynamic LLPS; when solvent‐exposed within IDRs or flexible linkers and spatially patterned as stickers, they favor dynamic LLPS and reversible condensates through π–π and cation–π interactions [33, 84, 102, 117]. (ii) Proline and Pro–Gly motifs: isolated Pro insertions into otherwise flexible IDRs can frustrate chain conformations and reduce LLPS propensity; in Pro–Gly motifs, Pro provides rigid kinks while Gly restores local flexibility, enabling responsive conformational transitions that support salt‐ and temperature‐dependent phase behavior [102, 113, 115].

2.4.3. Structural Motif Analysis and Architectural Classes

To systematically evaluate how hydrogen bonding and rigidity shape phase separation, we analyzed proteins stratified into two broad architectural classes (Figure 4 and Table S1). (i) IDR‐dominated proteins adopt highly dynamic conformations—random coils, planar disordered ensembles, transient dimers—characterized by weak overall hydrophobicity and sparse, transient intramolecular hydrogen bonds; LLPS in these systems is driven primarily by sticker residues (aromatics, cationic residues, and prion‐like motifs) embedded within flexible spacer segments. (ii) Modular repeat architectures form ordered secondary structures—helices, curved helices, β‐sheets—stabilized by regular intramolecular hydrogen bonds and well‐defined hydrophobic cores; representative examples include repetitive silk proteins, collagen‐like sequences, and designed ELP/RLP blocks [14, 62, 63].

The conformational states depicted in Figure 4—rather than being treated as discrete sequence motifs—represent structural classes derived from homology models that illustrate how different hydrogen‐bonding topologies and hydrophobic core organizations correlate with LLPS versus fiber‐forming behavior. Intramolecular hydrogen bonds (blue lines) maintain structural stability within individual chains, while intermolecular interactions link adjacent β‐strands, sheets, and helices into the higher‐order assemblies characteristic of phase‐separating proteins.

Taken together, phase separation is governed by the balance between hydrogen bonding—which promotes structural order and network formation—and structural rigidity, which can either restrict conformational flexibility or enable adaptive conformational switching. Tuning hydrogen‐bonding capacity and steric constraints therefore provides a strategic design lever—whether through IDR modification, modular repeat patterning, or rational sticker motif engineering [36, 53, 102].

2.5. Design Implications and Limitations of Current Descriptors

Our bioinformatic analyses support three overarching conclusions that hold across both natural and designed subsets of Table S1, though they are most directly relevant to the engineered systems—ELPs, RLPs, spidroins, and related biomimetic constructs—that are the primary focus of this review. First, hydrophobicity is a recurrent compositional feature associated with LLPS and self‐assembly, but high aliphatic content systematically obscures LLPS propensity in current predictors, particularly for silk‐like and ELP‐based systems. Second, electrostatics and charge patterning constitute a complementary design axis: in several well‐characterized families, pI deviations from neutrality combined with blocky or alternating charge distributions correlate with shifts in LLPS windows, coacervation behavior, and multicomponent condensate formation—though the generalizability of these correlations to novel sequences requires experimental verification. Third, hydrogen bonding and structural rigidity act as higher‐order modulators in specific, well‐studied systems, biasing them toward dynamic droplets, viscoelastic gels, fibrillar assemblies, or robust hydrogel architectures; these are observed correlations and plausible mechanisms in defined contexts, not established universal design rules.

These findings collectively imply that no single descriptor—GRAVY, PScore, PONDR score, pI, or PLAAC LLR—is sufficient to predict or rationally design LLPS behavior. Actionable design rules emerge only when these parameters are interpreted jointly, within a sticker–spacer framework that explicitly accounts for local sticker density, spatial patterning, and structural context. Current tools carry three key limitations: sequence‐based LLPS predictors systematically underperform for highly hydrophobic or modular‐repeat‐rich proteins; prion‐like scores are compositionally biased and often fail to detect hydrophobic‐core‐driven LLPS; and homology models, while informative, are not yet sufficient for predicting full condensate phase diagrams or quantitative phase boundaries.

These limitations motivate the design strategies developed in Section 3 and underscore the need for next‐generation predictors integrating hydrophobic, electrostatic, and structural descriptors into a unified, design‐oriented framework. Section 3 accordingly transitions from descriptive analysis to prescriptive design, translating the multidimensional descriptor space outlined above into concrete principles and architectures for artificial phase‐separating proteins.

3. Design Principles and Architectures for Artificial Phase‐Separating Proteins

The bioinformatic analyses of Section 2 delineate a multidimensional design space defined by hydrophobicity and aliphaticity, net charge and pI, charge patterning, prion‐like content, and modular repeat organization [55, 74, 75, 116, 118]. This section translates those descriptive trends into actionable design principles and architectural strategies for engineering artificial phase‐separating proteins. The discussion is organized around four hierarchical levels of control: (i) composition and patterning at the sequence level, (ii) valency and modular repeat architecture, (iii) topological fusion strategies, and (iv) practical decision tools and design roadmaps (Figure 5 and Table 1). This hierarchy reflects a deliberate design logic: sequence‐level composition determines which molecular interactions are available; modular repeat architecture and valency determine how many such interactions occur per chain and how strongly they collectively drive phase separation; topological fusion strategies specify the positional relationships between LLPS‐driving modules and functional domains; and decision tools translate this three‐layer parameter space into concrete, application‐aware design choices.

FIGURE 5.

Molecular design strategies for phase‐separating proteins. Triggers 1, 2, … n represent generic environmental or biochemical stimuli—such as temperature, pH, ionic strength, redox state, or ligand binding—that can be encoded through sequence design. (a) Variable amino acid sites: modifying residue identity (hydrophobic, charged, or polar) or introducing posttranslational modifications and non‐canonical amino acids to tune intermolecular interactions. (b) Repeating blocks: altering the number of repeat units to adjust motif density and secondary‐structure rigidity. (c) Terminal tag fusions: attaching a functional tag at one terminus to couple biological activity with phase‐separation responsiveness (functional tag fusion), or adding a second phase‐separating peptide at one terminus to generate chimeric proteins with emergent properties (single‐end block fusion). (d) Telechelic/triblock architectures: flanking a central block with sticker domains at both termini to create physically crosslinked triblock fusion peptides. (e) Interleaved or interval block architectures: distributing two phase‐separating motifs in an alternating arrangement to yield composite proteins with periodically interleaved interaction sites.

TABLE 1.

Summary of molecular determinants of LLPS: from physical mechanisms to practical effects.

Major design levers	Underlying physical mechanisms	Typical effects on LLPS behavior	Implementation considerations
Hydrophobicity/aliphaticity (GRAVY score and aliphatic index)	Hydrophobic‐core formation, LCST‐driven chain collapse, and stabilization of dense phases.	Lowers Tt, strengthens coacervation, promotes gelation/fiber formation when combined with hydrogen‐bonded repeats. Increasing GRAVY from −0.5 to + 0.3 (e.g., by substituting Ala→Val/Ile guest residues in ELPs) typically lowers Tt by 10°C–30°C under equivalent buffer conditions [12, 61, 64, 86].	Moderate—controllable at sequence level (residue substitution and repeat composition); constrained by expression yield and aggregation risk. Sequences with aliphatic index >64 may show premature aggregation in E. coli; consider lower‐temperature induction or solubility tags.
Net charge, pI, and charge patterning	Electrostatic attraction/repulsion, complex coacervation, and pH‐ and salt‐responsiveness.	Shifts phase boundaries, enables complex coacervates and multicomponent condensates, tunes condensate composition. In RLP‐based systems, reducing net charge by ±2–3 units (e.g., Asp→Asn substitutions) can raise saturation concentration by 1.5–3‐fold at physiological salt [18, 22, 40, 61, 94, 95, 104].	Low to moderate—easily tuned by charged‐residue substitution; requires attention to target pH window and buffer ionic strength. Blocky charge patterning promotes coacervation; alternating patterns broaden phase boundaries but weaken driving force.
Prion‐like content and IDRs	Multivalent weak interactions within low‐complexity, disordered segments, and sensitivity to PTMs.	Promotes dynamic, reversible condensates with regulatory potential; associated with high PScore, PONDR, and PLAAC LLR scores. Enriching Tyr content from ∼5% to ∼15% in FUS‐like IDRs roughly doubles PScore and reduces Csat by ∼2 to 5‐fold in vitro [45, 46, 50, 73, 79, 97, 98, 99].	Moderate—requires careful sequence design to avoid pathological aggregation; strong host dependence (yeast vs. E. coli vs. mammalian). Phosphomimetic mutations (Ser→Asp) can be used to test PTM sensitivity without kinase co‐expression.
Modular repeats and hydrogen‐bonded architectures (ELPs, RLPs, silks, and spidroins)	Cooperative hydrogen‐bond networks, β‐sheet or helical assembly, and LLPS‐coupled gelation/fiber formation [14, 18, 53, 80, 90, 91, 101, 106].	Yields robust structural materials, tunable viscoelasticity, and long‐lived networks. Quantitative example: increasing ELP repeat number from n = 40 to n = 80 typically lowers Tt by ∼5°C–15°C under comparable buffer and concentration conditions; further extension to n = 120–160 can reduce Tt by an additional 5°C–10°C but increases aggregation risk. In RLPs, doubling repeat number (n = 6→12) approximately halves Csat [12, 18, 39, 40, 64, 80, 92].	Moderate to high—expression and solubility challenging for long repeats; sensitive to repeat‐length and guest‐residue pattern. Proteotoxic stress and inclusion‐body formation common above n ≈ 80–120 repeats in standard E. coli strains; consider cell‐free expression or split‐intein ligation for very long constructs.
Posttranslational and chemical side‐chain modifications (phosphorylation, Tyr→Dopa oxidation, PEGylation, and disulfide crosslinks)	Modulation of electrostatic interactions, hydrophobicity, and crosslink density via enzymatic or chemical post‐synthetic reactions.	Introduces stimuli‐responsiveness (redox, pH, kinase activity); tunes condensate cohesion and crosslinking density without re‐engineering the primary sequence. Phosphorylation of FUS IDR Ser residues can dissolve condensates or shift material state from liquid to gel; Tyr→Dopa oxidation in MFP‐inspired ELPs enhances coacervation at surfaces [22, 41, 46, 94, 99, 103].	Low to moderate for PTMs (phosphomimetics accessible by site‐directed mutagenesis; enzymatic modification requires co‐expression of kinase/oxidase). Moderate for chemical modifications (PEGylation and thiol–ene chemistry): requires downstream functionalization step and can reduce LLPS propensity if hydrophilic groups are overloaded. Monitor modification stoichiometry by MS.
Genetic incorporation of non‐canonical amino acids (ncAAs) (photo‐crosslinkers, click handles, fluorinated hydrophobes, and metal‐binding ligands)	Introduction of interaction chemistries (bioorthogonal click, photo‐crosslinking, metal coordination, and enhanced hydrophobicity) inaccessible with 20 canonical residues.	Enables light‐triggered condensate locking, bioorthogonal crosslinking within condensates, or enhanced hydrophobic‐core formation. Fluorinated ncAAs (e.g., hexafluoroleucine) can lower ELP Tt by an additional 5°C–20°C relative to Leu at equivalent positions; photoreactive p‐azidophenylalanine enables UV‐triggered gelation within seconds of irradiation [14, 18, 40, 104, 108].	High—requires amber suppressor codon (TAG) machinery and an orthogonal aminoacyl‐tRNA synthetase/tRNA pair (e.g., Methanosarcina mazei PylRS or MjTyrRS‐derived systems); expression yields are often substantially lower than standard amber‐free constructs, frequently 20–60% of wild‐type levels depending on ncAA and position. Amber suppression efficiency is host‐ and codon‐context‐dependent; use of genomically recoded strains (e.g., C321.ΔA) improves ncAA incorporation fidelity. High local ncAA densities increase aggregation risk; use sparingly at key sticker sites.
Topological fusions (terminal tags, telechelic/triblock, and interleaved/interval block architectures)	Control of the spatial arrangement of LLPS modules relative to functional domains, determining valency geometry and inter‐domain communication.	Determines condensate architecture (core–shell, network, and micelle‐like), localization, and coupling between LLPS and function. N‐terminal versus C‐terminal tag placement can shift Tt by 2°C–8°C and alter condensate internal organization; telechelic (A–B–A) designs typically produce more mechanically robust gels than single‐ended fusions at equivalent repeat number [10, 11, 12, 13, 55, 60, 63, 104, 113].	Low to moderate for terminal tags—straightforward by cloning; minimal effect on expression. Higher for telechelic and interleaved designs—multi‐fragment assembly required; longer constructs risk premature LLPS during expression, necessitating low‐temperature induction, high‐salt lysis, or denaturing purification. Interleaved architectures are most sensitive to precise insertion‐site selection.

Note: Rows for posttranslational/chemical modifications and ncAA incorporation have been added to cover all design strategies discussed in Section 3.1. Each heuristic has been validated in specific experimental systems; generalizability to new constructs requires experimental testing under the relevant conditions. This table serves as a starting‐point reference to be followed by iterative DBTL validation.

3.1. Composition and Patterning at the Sequence Level

Sequence composition and sticker–spacer patterning are primary molecular levers for tuning LLPS behavior [36, 37, 38]. As established in Section 2, phase‐separating proteins occupy broad yet structured regions of the parameter space spanned by GRAVY score, aliphatic index, pI, charge distribution, and prion‐like content (Figures 2 and 3; Table S1). The core design challenge is therefore not simply to match these global averages, but to specify amino‐acid composition and spatial patterning such that: (i) attractive interactions are sufficiently strong and multivalent to drive LLPS; (ii) condensates remain dynamic rather than progressing to irreversible aggregation; and (iii) sequences remain tractable for expression, purification, and handling under practical conditions. Three complementary levels of control are discussed below: tuning hydrophobicity and electrostatics using canonical residues, posttranslational and chemical side‐chain modifications, and genetic incorporation of non‐canonical amino acids.

3.1.1. Tuning Hydrophobicity and Electrostatics Using Canonical Residues

Hydrophobic interactions and electrostatics are recurrent and prominent contributors to phase separation across the natural and engineered systems surveyed in Section 2. Modulating the identities, relative fractions, and spatial arrangement of canonical amino acids provides the most direct and experimentally accessible means of tuning LLPS.

Regarding hydrophobicity: most phase‐separating proteins in our dataset cluster around global GRAVY values of approximately −1, yet many rely on locally enriched hydrophobic sticker patches embedded within otherwise disordered or weakly hydrophilic backbones (Figures 2a–c and 3a,b). Spider silk spidroins exemplify this principle—poly‐alanine/glycine‐rich repeats and hydrophobic CTDs nucleate LLPS, fibril formation, and fiber spinning under pH‐ or ion‐triggered conditions [64, 88, 89, 108]. ELPs composed of (VPGXG)_n repeats undergo LCST‐type phase separation in which hydrophobic guest residues (Val, Ile, Leu, and Phe) lower Tt and strengthen coacervation [12, 64, 80, 90]. GRAVY score and aliphatic index are useful initial screening metrics for placing candidate sequences within a hydrophobic regime compatible with LLPS (Section 2.2); however, as discussed in Section 2.3, once the aliphatic index enters a highly hydrophobic regime, standard LLPS and disorder predictors tend to underestimate phase‐separation propensity. Descriptor panels should therefore be used as qualitative guides rather than hard thresholds, and must be complemented by experimental validation and structural context analysis (Section 3.2).

Electrostatics provide an orthogonal axis of control. LLPS‐competent proteins tend to avoid strictly neutral pI and instead cluster in mildly acidic or basic regimes, enabling pH‐dependent modulation of charge state and interaction strength [36, 38]. In ELPs, introduction of acidic (Glu and Asp) or basic (Lys, His, and Arg) guest residues tunes both Tt and salt responsiveness, with coacervation typically strongest when solution pH approaches the construct's pI [12, 64, 80, 92]. In RLPs, Arg‐rich motifs act as stickers that engage in electrostatic and cation–π interactions, enabling complex coacervation with anionic counterions and yielding tough, adhesive hydrogels [19, 40, 61, 92]. MFPs exploit clusters of Lys/Arg together with catechol‐bearing Dopa to stabilize low‐hydration, ion‐mediated cohesive interactions at surfaces [22, 42, 95, 116].

Beyond overall charge and pI, the linear patterning of charged residues critically shapes LLPS. Blocky distributions of positive and negative residues promote complex coacervation and multicomponent condensates; more alternating patterns weaken net attraction but broaden phase boundaries and enhance solubility [36, 37, 38]. Charge‐patterned RLP–ELP hybrids and polyelectrolyte‐inspired designs illustrate how the length and spacing of cationic and anionic blocks can be tuned to sculpt LCST/UCST behavior, condensate viscosity, and hierarchical assembly [60, 61, 81, 105, 106].

Three practical guidelines emerge from these observations. (i) Use GRAVY score and aliphatic index to position sequences within a hydrophobicity regime compatible with LLPS, but interpret predictor outputs cautiously when the aliphatic index exceeds ∼64. (ii) Tune pI and charge patterning to control the pH and ionic conditions under which LLPS is triggered, and to bias systems toward single‐component versus complex coacervate behavior. (iii) Deploy hydrophobic stickers (e.g., Tyr, Phe, Val, Ile, and Leu) in clustered motifs within otherwise disordered spacers to balance LLPS driving force with condensate reversibility and expression yield.

3.1.2. Posttranslational and Chemical Side‐Chain Modification

Posttranslational modifications (PTMs) and chemical side‐chain derivatization provide a second layer of control that can be superimposed on canonical sequence design to tune LLPS behavior dynamically. In natural systems, phosphorylation, acetylation, methylation, and arginine methylation of IDRs and Prion‐like domains (PrLDs) modulate condensate assembly, material properties, and functional outputs in proteins such as FUS, hnRNPA1, UBQLNs, and related RNA‐binding proteins [46, 50, 78, 79, 97, 99]. Phosphorylation in particular can weaken electrostatic attractions, dissolve condensates, or trigger transitions between liquid‐like and gel‐like states, functioning as a molecular on/off switch.

Engineered systems closely mirror these principles. In MFP‐inspired designs, oxidative conversion of Tyr to Dopa or catechol‐bearing analogs dramatically enhances interfacial adhesion and can promote LLPS‐driven coacervation at solid–liquid interfaces [22, 41, 94]; controlled oxidation or metal coordination of catechol groups provides a handle to tune condensate cohesion, crosslinking density, and redox responsiveness. Reversible disulfide formation, thiol–ene chemistry, and related covalent modifications applied to Cys‐containing ELP or RLP sequences have similarly been used to couple LLPS to permanent or semi‐reversible network formation [14, 18, 40, 61, 103]. Chemical derivatization such as PEGylation, or attachment of hydrophobic drugs or ligands, can shift effective hydrophobicity, shield surface charges, or introduce new interaction motifs—thereby tuning Tt, coacervation windows, and condensate permeability [64, 80, 90, 91, 92].

From an engineering perspective, PTMs and chemical modifications function as post‐synthetic tuning knobs that adjust LLPS behavior without requiring complete sequence redesign. They are especially powerful for: (i) introducing environmental responsiveness (e.g., sensitivity to pH, redox state, or kinase activity); (ii) modulating adhesion and cohesion after expression; and (iii) decoupling protein expression and solubility constraints from final condensate properties.

3.1.3. Genetic Incorporation of Non‐Canonical Amino Acids

A third, more advanced layer of control involves the genetic incorporation of non‐canonical amino acids (ncAAs) into phase‐separating proteins. NcAAs can introduce interaction chemistries and physicochemical properties inaccessible with the 20 canonical residues, including photo‐crosslinkable moieties, bioorthogonal click handles, metal‐binding ligands, fluorinated hydrophobes, and other stimuli‐responsive groups.

Although ncAA‐based LLPS design remains less developed than canonical or PTM‐based strategies, several trends from modular repeat and adhesive protein systems provide useful design guidance [14, 18, 40, 104, 108]. Incorporating azido‐ or alkyne‐bearing residues into ELP or RLP scaffolds, for example, enables bioorthogonal click crosslinking within or between condensates, effectively locking in LLPS‐derived microstructures while preserving initial self‐assembly pathways. Photoreactive ncAAs can freeze transient condensate configurations or impose spatially defined patterns under optical control. Fluorinated hydrophobic ncAAs can enhance hydrophobic‐core formation, lower Tt, and increase condensate stability—though often at the cost of more challenging expression and folding.

A critical practical constraint is that ncAA incorporation must be balanced against expression efficiency and host‐cell compatibility. High local densities of bulky or reactive ncAAs can substantially increase aggregation risk and cellular toxicity. NcAAs are therefore most effective when used sparingly, strategically positioned at key sticker sites, or confined to short LLPS‐driving segments appended to otherwise canonical sequences.

3.2. Control of Valency and Modular Repeat Architecture

Beyond overall composition, LLPS is critically governed by valency—the number and spatial arrangement of interaction motifs per chain—and by how those motifs are organized into modular repeats or domains [33, 36, 37, 38]. The systems surveyed in Table S1 encompass three broad architectural classes: (i) highly repetitive silk‐ or elastomer‐like polymers; (ii) IDR‐ or PrLD‐containing proteins with mixed domain organizations; and (iii) hybrid constructs combining folded domains, IDRs, and modular repeats. The following subsections address how repeat number, block length, and interaction‐motif density govern LLPS; the distinct roles of low‐complexity segments and IDRs; and the practical trade‐offs between phase behavior, expression yield, and aggregation risk.

3.2.1. Repeat Number, Block Length, and Interaction‐Motif Density

Classical polymer theory and the sticker–spacer framework predict that increasing the number of interaction motifs per chain enhances LLPS propensity, lowers saturation concentration, and promotes percolation into system‐spanning gel‐like networks [33, 36, 37, 38]. Experimental observations across silk, RLP, and ELP‐based systems are broadly consistent with these predictions [10, 11, 12, 13, 14, 15, 40, 92].

In ELPs, increasing the number of (VPGXG)_n repeats lowers Tt, sharpens the phase transition, and facilitates formation of dense coacervates and gels [12, 64, 80, 92]. In RLPs, longer repeat blocks and higher Arg/Tyr sticker densities yield coacervates with greater viscosity, slower internal dynamics, and higher mechanical toughness [18, 39, 40, 65]. Spider silk spidroins provide a natural precedent: variation in the number and organization of repetitive motifs tunes both LLPS behavior and fiber mechanical properties, with longer repeats and more pronounced hydrophobic blocks promoting stronger assembly and more robust fibers [43, 86, 88, 102, 108].

However, high valency entails significant trade‐offs. Excessively long repeats or overly dense sticker distributions can: (i) drive premature aggregation or irreversible gelation; (ii) reduce expression yields through proteotoxic stress and inclusion‐body formation; and (iii) compromise the dynamic functionality of condensates. These problems are particularly acute for hydrophobic and β‐sheet–forming motifs—such as poly‐Ala segments in silk proteins and certain ELP guest residues—which can trigger ordered fiber formation and irreversible solidification [44, 53, 103].

Three practical heuristics follow. (i) For reversible, liquid‐like condensates, moderate repeat lengths and sticker densities are preferred, with stickers separated by flexible, polar spacers. (ii) For structural or mechanical materials, higher repeat numbers and motif densities can be used to drive LLPS‐coupled gelation or fiber formation, but expression and purification must be carefully monitored. (iii) Block length and spatial organization—for example, A–B block copolymers versus homogeneous repeats—can be tuned to stabilize coexisting phases, core‐shell condensates, or hierarchically ordered assemblies.

3.2.2. Low‐Complexity Segments and Intrinsically Disordered Regions

Low‐complexity segments and IDRs provide a complementary route to multivalency that does not rely on extensive modular repeats. PrLDs enriched in Gln, Asn, Tyr, Gly, and charged residues frequently drive LLPS in regulatory and ribonucleoprotein systems—including FUS, hnRNPA1, UBQLNs, TDP‐43, ELF3, and FLOE1 [45, 46, 73, 78, 79, 97, 98, 99]—and typically display high PLAAC LLR scores and strong PONDR‐based disorder/LLPS propensities (Sections 2.2 and 2.3).

In many natural proteins, IDRs and PrLDs are appended to or interspersed with folded domains, creating architectures in which LLPS is driven primarily by the disordered segments while folded domains contribute binding specificity, catalytic activity, or structural scaffolding [45, 46, 73]. Viral nucleocapsid proteins, transcriptional regulators, and RNA‐binding proteins commonly adopt this organization [29, 45, 78]. By contrast, modular repeat architectures—silk, ELPs, RLPs—often display weaker classical PrLD signal yet achieve comparable multivalency through repetitive hydrophobic, charged, or hydrogen‐bond‐forming motifs [60, 81, 104, 105]. Structural analyses indicate that such repeats frequently form β‐sheet, helical, or mixed secondary structures stabilized by cooperative intra‐ and intermolecular hydrogen‐bond networks (Figure 4) [14, 44, 86, 103, 108], coupling LLPS tightly to disorder‐to‐order transitions and network formation rather than to IDR conformational entropy alone.

These observations suggest three complementary design strategies. (i) IDR/PrLD‐based designs are well suited for dynamic, regulatory condensates that must remain liquid‐like, respond to PTMs, and interact productively with nucleic acids or partner proteins. (ii) Repeat‐based designs are well suited for structural and mechanical materials in which LLPS is coupled to durable fiber or gel formation driven by hydrogen‐bond networks and hydrophobic cores. (iii) Hybrid architectures combining IDRs or PrLDs with modular repeats or folded domains can exploit the strengths of both classes, albeit with more complex and context‐dependent phase behavior.

3.2.3. Trade‐Offs Between Phase Behavior, Expression, and Aggregation Risk

Although increasing valency and hydrophobic sticker density generally strengthens LLPS, it simultaneously heightens the risk of low expression yields, premature aggregation, and loss of dynamic condensate behavior. As discussed in Section 2.3, sequences with high aliphatic index and strong hydrophobic character are prone to misclassification by LLPS predictors and may closely resemble classical aggregation‐prone motifs at the sequence level [24, 33, 84]. In practice, such sequences tend to form inclusion bodies in expression hosts, display low solubility, and exhibit pronounced sensitivity to small changes in buffer conditions.

Moreover, the same features that promote functional LLPS can also drive undesirable transitions from liquid‐like droplets to gels or solid aggregates, particularly under prolonged incubation, elevated temperature, or high protein concentrations [53, 103]. This issue is especially relevant when designing condensates that must remain fluid over extended timescales, such as catalytic microreactors or intracellular organelle mimics [36, 38, 73, 78, 98].

Design decisions at the level of composition, valency, and repeat architecture must therefore explicitly balance: (i) target phase behavior (reversible LLPS versus gelation or fiber formation); (ii) expression and purification feasibility (host strain tolerance, inclusion‐body risk, and refolding requirements); and (iii) aggregation or solidification risk under anticipated operating conditions. Sections 3.3 and 3.4 discuss how topological fusion strategies and design roadmaps can be used to navigate these trade‐offs while achieving specific functional outcomes.

3.3. Topological Fusion Strategies

Topological fusion strategies define how LLPS‐driving modules are positioned relative to functional domains, tags, and other protein modules. Even when composition and valency are held constant, changes in topology—from simple terminal tags to telechelic and interleaved block architectures—can dramatically alter LLPS thresholds, condensate morphology, and functional output. Three broad categories are considered as follows.

3.3.1. Terminal Tag Fusions (Single‐End Functional Tags)

Terminal tag fusions represent the simplest and most widely implemented topological design. In this configuration, an LLPS‐driving module—such as an ELP, RLP, silk‐like segment, or PrLD‐containing IDR—is appended to the N‐ or C‐terminus of a functional protein, including enzymes, antibody fragments, fluorescent proteins, or receptor domains [12, 17, 39, 57, 92, 105].

In purification applications, ELP tags fused to target proteins confer reversible LCST‐type phase separation, enabling inverse transition cycling for tag‐assisted purification [119]. In catalytic condensates, IDR or ELP tags appended to enzymes promote local enrichment and multi‐enzyme co‐localization within droplets, enhancing reaction rates without permanently altering catalytic domains [37, 38, 39, 40]. Terminal tags can additionally serve as localization modules, directing LLPS‐prone constructs to specific cellular compartments, organelles, or material interfaces.

Key design considerations include: (i) the orientation (N‐ versus C‐terminal) and length of flexible linkers separating the LLPS module from the functional domain, to minimize steric interference while preserving activity; (ii) the effect of the tag on folding, secretion, and stability of the fusion protein; and (iii) the need for engineered protease‐cleavage sites or removable tags when LLPS is required only during purification or immobilization rather than in the final functional product.

3.3.2. Telechelic and Triblock Architectures (Double‐End Sticker Blocks)

Telechelic and triblock architectures extend the terminal tag concept by placing LLPS‐driving or adhesive modules at both termini of a central block, yielding A–B–A and related multiblock designs analogous to synthetic telechelic polymers. The terminal A‐blocks—such as RLP, MFP‐like, or hydrophobic silk segments—act as multivalent stickers that bridge multiple chains, while the central B‐block—typically an ELP segment, flexible linker, or folded domain—defines spacing, flexibility, and stimulus responsiveness. Once LLPS concentrates telechelic chains, this topology strongly promotes network formation and gelation, making it particularly attractive for structural and mechanical materials, injectable hydrogels, and self‐healing adhesives [14, 18, 39, 40, 53, 65, 90].

RLP–ELP–RLP triblock copolymers, for instance, form physically crosslinked hydrogels in which RLP end blocks drive coacervation and surface adhesion while ELP midblocks confer temperature responsiveness and mechanical tunability [39, 40, 82, 104, 105, 106]. Silk‐ or MFP‐derived end blocks flanking ELP midblocks can similarly yield tough, water‐resistant adhesives that maintain cohesion under aqueous conditions.

Critical design variables include: (i) the strength and valency of the terminal A‐blocks, which govern percolation thresholds and network connectivity; (ii) the length, composition, and LCST/UCST behavior of the B‐block midblock, which modulates coil dimensions, entropic elasticity, and environmental responsiveness; and (iii) the option of combining telechelic architectures with ncAAs or PTMs (Section 3.1) to introduce reversible or photo‐controllable crosslinks.

3.3.3. Interleaved and Interval Block Architectures (Distributed Motif Designs)

A third topological strategy distributes LLPS‐driving motifs in interleaved or interval block patterns along the sequence, rather than confining them to terminal or other contiguous segments. Short sticker‐rich segments are periodically inserted within or between functional domains, IDRs, or structural repeats, generating a more homogeneous distribution of interaction sites along the chain [24, 33, 36, 37].

Relative to highly localized sticker domains, interleaved designs can mitigate local aggregation, reduce steric interference with folded domains, and smooth phase boundaries, promoting more gradual LLPS transitions and enhancing solubility at intermediate concentrations. They also offer advantages when functional domains must remain centrally accessible within condensates—for example, to ensure symmetric access to substrates or binding partners.

The principal limitation of interleaved architectures is design complexity: periodically distributed motifs generate multiscale interaction networks that are less amenable to simple sticker–spacer approximations and more sensitive to the precise spatial context of each insertion. In practice, such architectures are best approached with the guidance of systematic sequence–function analyses and bioinformatic tools that explicitly account for local sticker density, charge patterning, and structural context (Sections 2.2–2.3).

3.4. Design Decision Tools and Practical Guidelines

The preceding subsections have introduced individual design levers at the levels of composition, valency, and topology. Practical engineering requires integrating these levers into a coherent decision process that maps application‐specific requirements onto concrete sequence designs and architectures. This section first summarizes the key levers, their underlying mechanisms, effects on LLPS, and implementation difficulty in a concise reference table, then proposes design roadmaps for three common objectives: purification tags, catalytic condensates, and structural or mechanical materials.

3.4.1. Summary Table of Design Levers (Table 1)

Table 1 summarizes the major design heuristics, their underlying physical mechanisms, typical effects on LLPS behavior, and practical implementation considerations. Each heuristic has been validated in specific experimental systems; generalizability to new constructs requires testing under the relevant conditions. In practice, Table 1 serves as a starting‐point reference for selecting combinations of design levers matched to target application constraints and available experimental capabilities, to be followed by iterative DBTL validation.

3.4.2. Design Roadmaps for Common Objectives

(i) Purification tags. The primary objective here is robust, reversible LLPS under experimentally accessible buffer conditions, while preserving the native structure and activity of the fused protein. ELP‐based tags with moderate hydrophobicity and carefully tuned pI have emerged as particularly effective platforms [12]. A practical workflow proceeds as follows: (a) select an ELP length and composition—with slightly negative GRAVY values and an aliphatic index below the extreme hydrophobic regime—that place Tt within the intended purification temperature window; (b) adjust pI and charge patterning so that phase separation is triggered by modest changes in salt concentration or temperature within the desired pH range [61, 64, 90, 91]; (c) fuse the ELP tag to the N‐ or C‐terminus of the target protein via a flexible linker, incorporating a protease‐cleavable site where appropriate; (d) experimentally validate LLPS behavior and confirm recovery of catalytic or binding activity after repeated phase‐transition cycles.

(ii) Catalytic condensates. The objective is to concentrate enzymes and substrates within dynamic droplets while maintaining rapid molecular exchange and avoiding irreversible gelation. IDR/PrLD segments and moderately hydrophobic ELP or RLP modules are generally well suited [33, 45, 73, 79, 97, 99]. A representative workflow includes: (a) selecting an IDR/PrLD segment with high PScore and PLAAC LLR, or an ELP/RLP module with moderate sticker density and low aggregation risk (aliphatic index below the highly hydrophobic regime); (b) using terminal fusions or interleaved architectures to position LLPS modules such that catalytic active sites remain accessible, typically via flexible linkers; (c) tuning charge patterning and pI so that LLPS occurs under conditions compatible with both enzyme stability and substrate solubility; (d) exploiting PTMs or chemical derivatization—such as phosphorylation‐mimetic mutations or redox‐responsive crosslinks—to regulate condensate formation and dissolution dynamically.

(iii) Structural and mechanical materials. Here, LLPS is coupled to gelation or fiber formation to generate robust networks with well‐defined viscoelastic properties. Silk‐like, RLP, and ELP‐based repeats arranged in telechelic or triblock architectures are particularly powerful in this context [14, 18, 44, 61, 92, 105, 116]. A representative workflow involves: (a) selecting repeat motifs—poly‐Ala/Gly segments, RLP blocks, or ELP sequences—that support both LLPS and subsequent hydrogen‐bonded or hydrophobic network formation; (b) increasing repeat number and sticker density to achieve target mechanical strength while continuously monitoring expression yield and solubility; (c) implementing telechelic A–B–A or multi‐block architectures to promote physically crosslinked networks, with ELP‐like midblocks providing elasticity and environmental responsiveness; (d) optionally incorporating ncAAs or PTM handles to introduce permanent or reversible covalent crosslinks, gradient structures, or spatially patterned properties.

Across all three application classes, the overarching design philosophy is to: (a) position candidate sequences within the global parameter space defined in Section 2; (b) identify hydrophobicity, charge, and prion‐like regimes compatible with the desired phase behavior and expression host; and (c) refine valency and topology to balance LLPS driving force with functional performance and manufacturability. The following section illustrates how these design levers and roadmaps have been implemented in representative experimental systems, spanning purification workflows, catalytic and regulatory condensates, stimuli‐responsive systems, and structural protein materials.

4. Application Case Studies: From Design Rules to Functional Systems

The preceding sections established global sequence trends, physicochemical constraints, and architectural design principles governing phase‐separating proteins. This section illustrates how those principles translate into concrete application scenarios. Rather than offering an exhaustive survey, we focus on four representative classes of systems in which LLPS is central to function: (i) protein separation and purification, (ii) catalytic and regulatory microenvironments, (iii) stimuli‐responsive condensates and delivery systems, and (iv) structural and mechanical protein materials (Figure 6). Across all four domains, the same core design levers—hydrophobicity and aliphaticity, pI and charge patterning, prion‐like content, modular repeats, and topological fusions—are deployed in distinct ways depending on performance targets and practical constraints [10, 14, 24, 36, 116, 118, 120].

FIGURE 6.

Applications and design principles for phase‐separating proteins. (a) Protein separation and purification: pI‐adjusted phase separation enables ITC‐based workflows. (b) Biochemical microenvironment regulation: in homogeneous solution enzymes operate without spatial organization (left); upon phase separation, substrates are recruited or products excluded, enhancing catalytic efficiency (right). (c) Tuning stimulus responsiveness: phase separation occurs only under suboptimal conditions in baseline designs (left); molecular redesign improves responsiveness to desired triggers such as temperature or pH (right). (d) Enhancing expression and yield: large repeats or unfavorable fusion architectures reduce expression (left); optimized repeat sizes, solubility tags, or dual‐end fusions restore yield (right). (e) Regulating self‐assembly: native proteins form limited structural ensembles (left); engineered proteins generate diverse architectures—core–shell assemblies, gels, rods, and micelles—through multi‐strategy design (right). (f) Improving mechanical properties: baseline protein materials (left); molecularly designed proteins with enhanced mechanical strength and structural integrity in hydrogels and biomimetic fibers (right).

4.1. Phase‐Separation‐Assisted Protein Separation and Purification

4.1.1. Inverse Transition Cycling, pI‐based Biphasic Separation, Foam Fractionation, and Related Workflows

Phase‐separation‐assisted purification exploits LLPS as a unit operation to concentrate target proteins and separate them from contaminants without relying exclusively on chromatographic steps. Among available scaffolds, elastin‐like polypeptides (ELPs) are the most extensively developed, leveraging their LCST‐type phase behavior in a workflow known as inverse transition cycling (ITC) [12, 64, 80, 91]. In a canonical ITC process, the target protein is genetically fused to an ELP tag; upon moderate increases in temperature and/or salt concentration, the fusion undergoes coacervation to form a protein‐rich dense phase that is recovered by centrifugation, then re‐solubilized by cooling and dilution—enabling repeated capture‐and‐release cycles [61, 90, 91].

Sequence‐level design is central to ITC performance. ELP composition—typically (VPGXG)_n, with X = Val, Ala, Gly, or more hydrophobic residues—and chain length together set the LCST (Tt), the sharpness of the transition, and the stability of the resulting coacervates [65, 81, 104, 105, 106, 107]. Guest‐residue hydrophobicity and aliphatic index tune the temperature and salt window over which the fusion separates cleanly from host contaminants, while pI and charge patterning modulate co‐precipitation of nucleic acids and other charged species [61, 80, 90, 91, 92]. This sequence‐level tunability allows ITC workflows to be adapted to different expression hosts, buffer conditions, and target proteins.

Beyond ELP‐based ITC, two additional strategies extend the phase‐separation purification toolkit. In pI‐based biphasic separation, LLPS is triggered by adjusting pH and ionic strength so that the scaffold—often an ELP, RLP, or hybrid—becomes marginally soluble, enriching itself and associated cargo in one phase while contaminants remain in the other [18, 39, 40, 60, 61]. More broadly, the principle of aqueous two‐phase partitioning extends beyond protein purification into bioengineering contexts: PEG–dextran two‐phase systems have recently been used to generate defined 3D cell microenvironments, further illustrating the versatility of liquid–liquid phase separation as a platform technology across separation science and materials applications [121]. Foam fractionation exploits amphiphilic LLPS scaffolds that preferentially adsorb at air–liquid interfaces: bubbling generates a protein‐rich foam phase that is collected and collapsed, providing a continuous separation scheme compatible with large‐scale bioprocessing [40, 61, 62, 63, 65]. In all three strategies, the same design variables—hydrophobicity, charge, and modular repeat architecture—govern the robustness and selectivity of phase behavior.

4.1.2. Quantitative Comparison With Chromatographic Approaches

Quantitative benchmarking against conventional chromatographic processes highlights both the opportunities and the limitations of phase‐separation‐assisted purification. ITC‐based ELP workflows routinely achieve single‐step enrichment with product purities of 70%–90% and overall yields of approximately 60%–80% for many targets, depending on expression level and fusion design [61, 64, 90, 92]. Additional ITC cycles or a polishing chromatographic step can raise purity to levels comparable to affinity chromatography while maintaining high overall recovery [12, 64, 90, 91].

From a scalability standpoint, LLPS‐based methods offer several practical advantages: they eliminate expensive column resins, tolerate crude lysates with high particulate loads, and can be implemented in batch or continuous formats using straightforward equipment [61, 80, 90, 91, 92, 105]. Foam fractionation and biphasic separation can be integrated at upstream or midstream stages to concentrate secreted products or deplete host‐cell proteins before final polishing [18, 39, 40, 61]. The principal limitation is sensitivity to solution conditions—small deviations in temperature, pH, or ionic strength can shift Tt or coacervation behavior and thereby compromise yield and purity.

Relative to affinity chromatography, phase‐separation‐assisted purification trades some binding specificity for lower cost and simpler operation. Where extremely high purity is required—for example, in therapeutic antibody manufacturing—LLPS is most effective as a pre‐concentration or intermediate step that reduces the load on downstream columns and improves overall process economics [12, 64, 80, 90, 91]. Recent meta‐analyses of hundreds of phase‐separation purification case studies further demonstrate that these methods become increasingly cost‐effective relative to chromatography at larger production scales [122].

4.1.3. Design Considerations for Purification‐Oriented Phase‐Separating Scaffolds

The case studies above converge on four design principles for purification‐oriented LLPS scaffolds. (i) Tunability of transition conditions. ELP and related sequences should be designed so that LLPS occurs at experimentally accessible temperatures and salt concentrations, with a sufficient gap between expression/storage conditions and the transition point to prevent premature coacervation [61, 90, 91, 92]. GRAVY score and aliphatic index provide convenient metrics for positioning constructs so that Tt falls slightly above room temperature but well below protein denaturation thresholds. (ii) Orthogonality to target structure and activity. LLPS modules are best implemented as terminal tags connected via flexible linkers (Section 3.3), minimizing steric interference with target folding, secretion, and catalytic function. Protease‐cleavable sites should be incorporated where tag removal is required after purification [10, 11, 13, 14]. (iii) Mitigation of coprecipitation and fouling. Charge patterning and pI tuning reduce nonspecific co‐precipitation of nucleic acids and host‐cell proteins, while appropriate control of scaffold hydrophobicity helps prevent irreversible aggregation and equipment fouling [18, 39, 64, 65]. (iv) Compatibility with large‐scale operation. For foam fractionation and biphasic separation, the amphiphilicity and interfacial activity of the scaffold must be balanced to achieve efficient product enrichment without excessive foaming or persistent emulsion formation [61, 62, 63]. These considerations directly instantiate the sequence‐ and architecture‐level design principles of Section 3 within a bioprocessing context.

4.2. Catalytic and Regulatory Microenvironments

4.2.1. Enhancing Enzyme Activity and Substrate Channeling in Condensates

A second major application domain employs LLPS to create catalytic microreactors—condensates that co‐localize enzymes, substrates, and cofactors to enhance reaction rates and enable substrate channeling. This concept is inspired by natural condensates such as stress granules and RNA–protein granules, which already function as biochemical microenvironments that regulate catalysis and signaling [33, 78, 99, 120]. Synthetic systems build on this precedent by fusing enzymes to ELPs, IDRs, prion‐like domains, or modular repeats that drive LLPS under defined conditions [12, 18, 46, 73, 78, 98].

Enhanced catalytic performance in engineered condensates arises from three principal mechanisms: (i) increased local concentrations of enzymes and substrates within the dense phase; (ii) substrate channeling between co‐localized sequential enzymes; and (iii) selective partitioning that enriches hydrophobic or charged substrates in the condensate interior [33, 38, 50, 73, 79]. ELP‐ or IDR‐tagged enzyme pairs have been shown to exhibit higher apparent activities and reduced intermediate leakage relative to freely diffusing counterparts, particularly when LLPS conditions are tuned to optimize substrate solubility and enzyme stability [12, 36, 46, 90].

For these designs, condensate‐forming modules are typically implemented as terminal fusions or interleaved segments (Section 3.3). Sticker density and charge patterning are tuned to maintain liquid‐like, permeable droplets—avoiding gelation or solidification that would trap substrates and impede turnover [33, 37, 45, 79, 99].

4.2.2. Synthetic Condensates for Gene Expression and Signaling Control

Beyond catalysis, synthetic condensates have been engineered to regulate gene expression and intracellular signaling, drawing on natural precedents in which biomolecular condensates organize transcriptional machinery, chromatin modifiers, and signaling adaptors [33, 50, 79, 99]. Engineered transcription factors or signaling proteins fused to LLPS‐driving IDRs or PrLDs can form nuclear or cytoplasmic condensates that locally concentrate RNA polymerase, co‐activators, or kinases, thereby amplifying transcriptional and signaling outputs [33, 36, 37, 38].

Design priorities for regulatory condensates differ meaningfully from those of catalytic microreactors. Condensates must assemble at relatively low expression levels, respond sensitively to intracellular or environmental cues, and dissolve reversibly to avoid pathological aggregation. PrLD‐rich IDRs with high PScore and PLAAC signals, combined with PTM‐sensitive motifs, are therefore the predominant scaffold choice [45, 46, 98, 99]. Charge patterning and pI are tuned so that condensate formation can be modulated by modest fluctuations in nuclear or cytoplasmic pH and ion composition, while preserving productive interactions with chromatin and nucleic acids [33, 38, 98, 99, 120]. Synthetic regulatory condensates thus illustrate how PTM‐sensitive architectures and composition‐level design choices (Section 3.1) can encode switch‐like LLPS behavior, coupling upstream signals to defined transcriptional or signaling outputs.

4.2.3. Design Principles: Crowding, Partitioning, and Biochemical Specificity

These catalytic and regulatory examples converge on three overarching design principles. (i) Crowding and confinement: LLPS creates locally crowded environments that enhance reaction rates and binding probabilities, but scaffolds must preserve sufficient internal dynamics to avoid diffusion‐limited dead zones [33, 46, 79, 120]. This requirement generally favors IDR‐rich or moderately hydrophobic scaffolds over highly ordered, gel‐prone repeat architectures. (ii) Selective partitioning: Differences in hydrophobicity, charge, and hydrogen‐bonding capacity between the dense and dilute phases govern the partitioning of small molecules and macromolecules. Sequence design can bias condensates toward preferential enrichment of target substrates or regulators while disfavoring competing or inhibitory species [12, 18, 81, 98, 104]. (iii) Biochemical specificity: The generic concentrating effect of LLPS must be complemented by biochemical specificity conferred through functional domains, binding motifs, and PTM sites embedded within or appended to the condensate scaffold. Topological arrangement—terminal tags versus interleaved architectures—and linker design critically influence the accessibility and orientation of these functional elements (Sections 3.3 and 3.4). Successful catalytic or regulatory condensates therefore require coordinated control of both phase behavior and biochemical wiring, rather than an exclusive focus on LLPS propensity alone.

4.3. Stimuli‐Responsive Condensates and Delivery Systems

4.3.1. Programming LCST/UCST and pH Windows via Sequence Design

Stimuli‐responsive condensates are engineered so that LLPS is triggered, suppressed, or modulated by environmental cues—temperature, pH, ionic strength, redox state, or ligand binding. ELPs and related LCST/UCST‐type polypeptides provide a versatile platform for programming these responses through rational sequence design [14, 18, 39, 44, 52, 86, 94].

LCST behavior is governed primarily by hydrophobicity and aliphatic index: increasing the content of hydrophobic guest residues (Val, Ile, Phe) lowers Tt and narrows the transition window, whereas introducing polar or charged residues raises Tt and broadens the solubility range [12, 61, 64, 80, 81]. UCST‐like transitions can be engineered by incorporating sequences that form favorable interchain contacts at low temperatures—such as hydrogen‐bonded motifs or complementary charged blocks [39, 61, 81, 106]. pH responsiveness is introduced through titratable residues (His, Glu, Asp, Lys, Arg) whose protonation states shift net charge and solubility, moving phase boundaries in predictable directions [64, 65, 80, 92, 107]. Together, these parameters enable condensates that phase separate within tightly defined physiological or processing windows—for example, at mildly acidic pH in tumor microenvironments or at moderate hyperthermic temperatures for triggered drug release.

4.3.2. Multi‐Stimuli‐Responsive Architectures for Sensing and Controlled Release

More sophisticated designs combine multiple stimuli‐responsive elements within a single scaffold. Telechelic and triblock constructs (Section 3.3) are particularly well suited to this purpose, as distinct blocks can be made responsive to orthogonal cues [14, 103, 105, 108]. RLP–ELP–RLP triblocks, for instance, can be designed so that RLP end‐blocks respond to ionic strength or pH while the ELP midblock responds to temperature, yielding condensates or gels that assemble only within the intersection of both response windows [39, 62, 63, 65, 107].

Additional modes of responsiveness are accessible through PTMs and ncAAs (Section 3.1). Photo‐crosslinkable residues, redox‐sensitive disulfides, and ligand‐binding domains incorporated into LLPS‐driving blocks create condensates that assemble, solidify, or dissolve in response to light, redox changes, or specific small molecules [14, 52, 101, 102, 103]. These multi‐stimuli‐responsive architectures are particularly relevant for biosensing applications—where condensates report on specific biochemical states—and for controlled release systems in which cargo‐loaded condensates disassemble under defined triggers.

4.3.3. Constraints and Design Trade‐offs for In Vitro Versus In Vivo Applications

While stimuli‐responsive condensates are conceptually powerful, their design must account for sharply different constraints in in vitro versus in vivo contexts. In vitro, temperature, pH, and ionic strength can be precisely controlled and high protein concentrations are readily achievable; under these conditions, strongly hydrophobic or highly cooperative phase transitions are often desirable to achieve sharp, well‐defined switching behavior [12, 14, 64, 80, 92, 107].

In vivo, physiological constraints narrow the accessible stimulus range—core body temperature, buffered pH, tightly regulated ionic composition—and protein overexpression can trigger stress responses and aggregation. Designs intended for intracellular or extracellular therapeutic applications must therefore: (i) operate within narrow temperature and pH windows compatible with cell viability and tissue homeostasis; (ii) avoid excessive hydrophobicity and high valency that promote irreversible aggregation or perturb endogenous condensates [45, 79, 97, 99]; and (iii) minimize immunogenicity and proteotoxicity—for example by using human‐derived IDRs or subtly modified natural repeat sequences.

These trade‐offs frequently motivate the use of moderate LLPS driving forces combined with PTM‐based regulation in vivo, reserving extreme LCST/UCST designs for in vitro devices, purification processes, and delivery systems that do not require long‐term biocompatibility.

4.4. Structural and Mechanical Protein Materials

4.4.1. Self‐Assembly Pathways: Droplets, Micelles, Fibers, and Bulk Hydrogels

In structural and mechanical materials, LLPS functions as a pre‐organizing step that channels chains into specific self‐assembly pathways—yielding droplets, micelles, fibers, or bulk hydrogels depending on sequence composition and molecular architecture [14, 72, 123]. Spider silk spidroins, RLPs, and ELP‐based hybrids exemplify this range of outcomes. Spidroins form liquid storage condensates that, upon shear and pH/ion triggering, transform into aligned β‐sheet‐rich fibers with exceptional toughness [43, 44, 87, 88]. RLP and RLP–ELP copolymers undergo LLPS‐driven coacervation that subsequently percolates into viscoelastic networks and hydrogels [62, 63].

At the molecular level, the balance between hydrophobic collapse, hydrogen‐bonded secondary‐structure formation, and electrostatic interactions determines whether LLPS remains as dynamic droplets or progresses to micellar aggregates, fibrils, or percolated gels (Section 3.2) [60, 65, 81, 105, 106]. Architectures dominated by strong, directional interactions—such as silk β‐sheet nanocrystals—tend to evolve from droplets toward fibers, whereas systems built on more isotropic networks—such as RLP‐ or ELP‐based crosslinks—favor bulk gel formation.

4.4.2. Mapping Molecular Design Strategies to Macroscopic Mechanical Properties

Macroscopic mechanical properties—elastic modulus, toughness, extensibility, self‐healing capacity, and energy dissipation—can be interpreted and systematically tuned in terms of underlying sequence and architectural features. (i) Sticker density and valency control crosslink density and thus the stiffness and plateau modulus of hydrogels [80, 92, 123, 124]: higher sticker density yields stiffer, less extensible materials but can increase hysteresis and toughness when reversible bonds are incorporated. (ii) Secondary structure propensity governs the balance between reversible and irreversible crosslinks: silk‐like β‐sheet nanocrystals embedded in disordered matrices confer high toughness through sacrificial bond breaking and hidden‐length release [44, 102, 103]. (iii) Block architecture and topology—telechelic versus multiblock—modulate network connectivity and defect structure, influencing stress–strain behavior and self‐healing capability [91, 125, 126, 127].

By systematically varying repeat length, sticker identity, and block architecture, families of materials have been generated that span orders of magnitude in modulus and toughness, demonstrating how LLPS‐guided sequence design maps onto a broad mechanical property space [54, 55, 80, 118].

4.4.3. Quantitative Examples From Hydrogels and Biomimetic Fibers

Representative quantitative case studies link molecular design directly to mechanical performance. RLP–ELP–RLP hydrogels can span storage moduli from a few hundred pascals to tens of kilopascals by varying RLP block length and ELP composition, while retaining high strain‐to‐failure and self‐healing capacity attributable to reversible LLPS‐driven crosslinks [39, 40, 61]. Silk‐like block copolymers and recombinant spidroins yield fibers with tensile strengths and toughness values approaching or exceeding those of natural silks when β‐sheet‐forming repeat modules and LLPS‐labile terminal domains are appropriately balanced [43, 87, 88, 89, 102].

These quantitative trends underscore the value of integrating the global sequence descriptors and architectural principles of Sections 2 and 3 into the design of new mechanical materials. Collectively, they demonstrate that relatively modest changes in sequence composition, valency, and topology can shift material performance across wide ranges—reinforcing the view that phase‐separating proteins occupy a continuous structure–function landscape. Section 5 builds on this perspective to outline how such continua can be explored and exploited systematically through integrated design workflows, high‐throughput experimentation, and machine‐learning‐guided optimization.

5. Toward a Programmable Structure–Function Continuum

The preceding sections have treated LLPS‐driving sequences, architectures, and applications in a largely modular fashion: bioinformatic descriptors (Section 2) quantify global sequence trends, design levers (Section 3) map those trends onto controllable architectures, and application case studies (Section 4) demonstrate how specific choices of composition and topology produce desired behaviors across purification, catalysis, regulation, and materials. In practice, however, engineered phase‐separating systems rarely behave as isolated modules. They occupy a structure–function continuum in which small changes in sequence, expression context, or environmental conditions can shift a construct from “too soluble” to “optimally phase separating” to “irreversibly aggregated.”

Realizing the full potential of phase‐separating proteins therefore requires closing the loop between bioinformatic predictors, iterative experimental design cycles, and application‐level performance metrics—while explicitly confronting common failure modes and open challenges. This section organizes these elements into an explicit DBTL framework: the integrative engine that transforms the design heuristics of Sections 2, 3, 4 into a cyclic, self‐improving engineering workflow in which each experimental iteration refines both the sequence design and the predictive models that guide it. We describe how bioinformatic predictors function as multidimensional priors within this cycle (Section 5.1), catalog recurring failure modes and design pitfalls that practitioners must anticipate (Section 5.2), and highlight future opportunities at the interface of high‐throughput experimentation, machine learning, and application‐driven design (Section 5.3).

5.1. Integrating Bioinformatic Predictors With Experimental Design Cycles

Bioinformatic predictors and sequence descriptors provide a natural starting point for rational LLPS design, but they are most powerful when embedded in iterative cycles rather than used as one‐shot sequence filters. A generic DBTL workflow for programmable LLPS design can be organized into five steps.

5.1.1. Define Functional and Operational Targets

The design problem should be framed in terms of specific, measurable objectives rather than the generic goal of maximizing “LLPS propensity.” Examples include: an ELP fusion that phase separates at 30°C–35°C in high‐salt lysate but remains soluble at 4°C during storage; a catalytic condensate that enhances a defined reaction rate without compromising enzyme stability; or a triblock hydrogel with specified modulus and self‐healing profile. These objectives determine which descriptors—hydrophobicity, aliphatic index, pI, charge patterning, PrLD content, and repeat valency—are most relevant to the specific design context.

5.1.2. Use Multiparameter Predictor Panels Rather Than Single Scores

Individual LLPS or disorder scores are known to fail when applied outside their training regimes, particularly for highly engineered or strongly amphiphilic sequences. A more robust approach combines multiple predictors—intrinsic disorder and dispersed charge (PONDR‐type), prion‐like content (PLAAC), hydrophobicity and aliphaticity, low‐complexity metrics, and charge‐patterning indices—into a multidimensional design space, treating each as a prior over one dimension of phase behavior rather than as a single authoritative verdict. Multi‐descriptor profiles look systematically different across protein families: FUS/hnRNPA1‐type proteins score high on PLAAC and PScore but low on aliphatic index, whereas ELP/spidroin‐type proteins show the inverse pattern. Recognizing these family‐specific signatures guides practitioners toward weighting the appropriate tools for their specific design context. Candidate sequences are then selected from regions of this multidimensional space that match the intended phase behavior while avoiding extremes associated with aggregation.

5.1.3. Generate Focused Libraries Guided by Descriptors

Rather than committing to a single best‐guess sequence, descriptor‐informed libraries can be constructed that systematically vary one or two design axes—for example, hydrophobicity and net charge, or repeat number and sticker spacing—around a baseline design. Library sizes of tens to hundreds of variants are typically sufficient to probe relevant structure–function relationships while remaining experimentally tractable.

5.1.4. Design Assays That Report Both Phase Behavior and Function

Screening assays should capture not only LLPS onset and morphology—turbidity, microscopy, and phase diagrams—but also application‐relevant readouts: product yield and purity for purification tags, catalytic rate and stability for condensate reactors, or mechanical modulus and toughness for structural materials. This dual readout is essential to prevent over‐optimization of LLPS propensity at the expense of expression yield, functional activity, or biocompatibility.

5.1.5. Feed Experimental Data Back Into Predictive Models

Experimental measurements—from simple solubility and turbidity scores to quantitative phase diagrams and functional metrics—provide ground truth for calibrating and improving predictors. Even modest datasets can be used to refine heuristic design rules (e.g., safe ranges of aliphatic index for a given host and topology) or to train lightweight machine‐learning models tailored to a particular scaffold family.

Within this DBTL framework, bioinformatic predictors function less as oracles and more as priors that bias sequence exploration toward promising regions of the structure–function continuum. The most effective workflows are those in which predictive and experimental components are co‐designed from the outset, rather than integrated retrospectively.

5.2. Common Failure Modes, Design Pitfalls, and Open Challenges

Despite the advances described above, several recurring failure modes complicate the transition from in silico design to functional LLPS systems. Recognizing these pitfalls is essential both for interpreting past results and for prioritizing future methodological development.

5.2.1. Mis‐Prediction of Phase‐Separation Propensity

The first class of failures stems from the limited domain of validity of existing predictors, compounded by the thermodynamic reality that phase separation is inherently condition‐dependent: predictors provide priors over phase behavior across a range of conditions, not binary ground‐truth labels of intrinsic phase‐separation capacity. Most LLPS and disorder models were trained on natural or near‐natural sequences and extrapolate poorly to highly synthetic constructs that combine elevated hydrophobicity and aliphaticity, unusually high or low net charge, extreme charge patterning, or long repetitive blocks of low‐complexity sequence.

Two types of mis‐prediction are most common: (i) false negatives—sequences predicted to have low LLPS propensity because high hydrophobicity or aliphatic index suppresses disorder‐based scores, yet in practice they phase separate robustly or aggregate readily at modest concentrations; and (ii) false positives—sequences flagged as LLPS‐prone due to high low‐complexity or prion‐like content, but that remain soluble under relevant conditions because competing interactions—strong net charge or stabilizing folded domains—dominate.

These mis‐predictions are further amplified by the strong context dependence of LLPS: most predictors ignore concentration, macromolecular crowding, salt, pH, cofactors, and interaction partners. A given construct may form robust condensates in vitro yet remain soluble—or form only sparse puncta—in cells, or vice versa. Closing this gap requires both better training data, including engineered variants and quantitative phase diagrams, and models that explicitly incorporate environmental parameters and concentration scales.

5.2.2. Expression Bottlenecks and Off‐Target Aggregation

A second frequent failure mode arises when designed LLPS constructs interact unfavorably with the expression host. Sequences that are ideal from a phase‐behavior standpoint may accumulate in inclusion bodies, trigger proteolytic degradation or stress responses, form off‐target condensates that sequester host factors, or overload secretory pathways. These problems are particularly acute for constructs with high sticker density, strong hydrophobic blocks, or large repetitive domains. In microbial hosts, such designs can cause early‐onset aggregation during or immediately after translation, effectively bypassing the controlled LLPS behavior intended for downstream applications; in mammalian systems, overly strong LLPS drivers may nucleate condensates that perturb endogenous membraneless organelles.

Mitigation strategies include: (i) reducing sticker valency and redistributing stickers to lower aggregation risk while preserving phase behavior; (ii) using weaker LLPS modules supplemented by multivalency or scaffolding to reach target phase behavior; (iii) implementing expression controls such as inducible promoters, lower growth temperatures, or chaperone co‐expression; and (iv) selecting expression hosts or subcellular compartments better matched to the intended construct behavior.

Three additional failure modes warrant explicit attention. First, condensate aging and gelation during bioprocessing: well‐expressed LLPS constructs can transition from liquid‐like droplets to gels or aggregates during concentration, freeze‐thaw cycling, or prolonged storage, compromising downstream performance; mitigation requires monitoring material state throughout the process and adjusting buffer conditions or adding stabilizing excipients. Second, loss of stimulus‐responsiveness after repeated cycling: stimuli‐responsive condensates—particularly those based on ELP LCST transitions or redox‐sensitive crosslinks—can show reduced reversibility after multiple phase‐transition cycles due to partial irreversible crosslinking or structural rearrangement, a failure mode often only detected in extended functional assays. Third, unexpected co‐phase separation with host‐cell proteins: highly multivalent or IDR‐rich constructs can nucleate condensates that sequester endogenous RNA‐binding proteins, chaperones, or signaling factors, leading to growth defects or confounded functional readouts—a risk especially acute for constructs with strong prion‐like or RGG‐type motifs expressed at high levels in mammalian cells.

From a design perspective, these bottlenecks mean that predictors and heuristics must account not only for equilibrium phase behavior, but also for biogenesis and intracellular trafficking—including translation kinetics, folding pathways, and quality‐control checkpoints.

5.2.3. Context Dependence in Cellular and Materials Environments

A third class of challenges reflects the pronounced context dependence of LLPS in both cellular and materials settings. The same sequence can behave markedly differently depending on the presence of nucleic acids, polysaccharides, lipids, or other binding partners; macromolecular crowding and confinement; mechanical stresses and flow fields; multicomponent interactions with other phase‐separating or gel‐forming species; and long‐term aging toward gelation or fibrillization.

In cells, LLPS modules are embedded within complex interaction networks and subject to PTMs, making it difficult to attribute observed condensate behavior to sequence features alone. In materials, LLPS typically represents only the early stage of a hierarchical assembly process that progresses to micelles, fibers, or bulk gels, as discussed in Section 4.4.

These realities expose a fundamental open challenge: how to design LLPS behavior that is robust to—or deliberately exploits—context dependence. Achieving this goal will require multiscale models that connect molecular‐scale descriptors to mesoscale assembly and macroscopic properties, calibrated against systematic experimental data across diverse environments. We also explicitly acknowledge the absence of prospective benchmarking in this review: developing quantitative sequence‐to‐phase‐diagram models and validating them prospectively on held‐out designed proteins—rather than retrospectively rationalizing known systems—represents the most critical near‐term research priority. The design heuristics summarized in Section 3 should accordingly be treated as starting points requiring experimental validation, not as guaranteed outcomes.

5.3. Future Directions and Research Opportunities

Several converging trends point toward a more fully programmable structure–function continuum for phase‐separating proteins. Three particularly promising directions are highlighted below:

5.3.1. High‐Throughput Screening and Machine‐Learning‐Guided Design

The first opportunity lies in tightly coupling high‐throughput screening with machine‐learning‐guided design, motivated by a critical gap: the absence of prospective, quantitative rules linking sequence descriptor ranges directly to phase diagrams and condensate functional properties. Advances in DNA synthesis, barcoding, microfluidics, and high‐content imaging increasingly enable parallel screening of hundreds to thousands of LLPS variants, measuring not only turbidity or solubility but also: (i) phase diagrams across gradients of concentration, temperature, salt, and pH; (ii) condensate morphology and dynamics including droplet size distributions, fusion kinetics, and FRAP recovery; and (iii) application‐specific outputs such as catalytic rates, expression yields, and mechanical moduli.

Such datasets provide exactly the multidimensional structure–function information needed to train ML models that go beyond conventional LLPS classifiers. Rather than labeling sequences as “phase separating or not,” these models could learn continuous mappings from sequence and architecture to phase boundaries, condensate material properties, and functional performance. Protein language models such as ESM2 and ProtGPS already demonstrate this feasibility: Kilgore et al. [128] showed that ProtGPS can predict subcellular compartment localization from sequence alone and generate novel sequences that selectively assemble in the nucleolus—illustrating how language models can be extended from classification to de novo design of condensate‐targeting proteins. Complementing language‐model approaches, structure‐guided machine learning trained on mutant libraries is also emerging as a practical tool for engineering phase separation at single‐residue resolution: PScalpel [129] integrates graph contrastive learning with AlphaFold‐derived structural information to identify key LLPS‐governing residues and screen optimal mutation strategies, demonstrating that ML‐guided engineering of phase‐separating variants is now experimentally tractable.

In practical terms, early‐stage models need not be complex: even modest regression or classifier models trained on scaffold‐specific libraries can substantially improve design efficiency by identifying nonobvious sequence combinations that simultaneously satisfy multiple constraints—good expression, a target Tt, and acceptable aggregation risk.

5.3.2. De Novo Design of Multicomponent and Multiphase Condensates

A second opportunity is the de novo engineering of multicomponent and multiphase condensates—addressing the gap that most current work remains confined to single‐component, single‐phase systems. Natural cells routinely organize biochemistry into nested or adjacent condensate phases with distinct compositions and material properties. Translating this principle into synthetic systems will require: (i) designing orthogonal sticker—spacer modules and scaffolding logic that drive specific pairwise or higher‐order interactions while minimizing cross‐talk; (ii) controlling stoichiometry and valency to tune phase diagrams of multicomponent mixtures; (iii) implementing orthogonal trigger pairs—such as temperature‐responsive ELP blocks combined with pH‐responsive RLP blocks—to enable independent, programmable control of distinct phases; and (iv) engineering kinetic assembly pathways that guide constructs into desired multiphase architectures such as core‐shell droplets, layered gels, or percolating networks with embedded droplets.

Achieving this level of control will likely involve modular families of stickers—complementary charged blocks, coiled‐coil pairs, or ligand–receptor domains—embedded in IDR or low‐complexity backbones, combined with telechelic and multiblock topologies. De novo multicomponent condensates could ultimately enable compartmentalized catalysis, logic‐like responses to multi‐signal inputs, and mechanically graded materials with spatially encoded properties.

5.3.3. Translation to Bioprocessing, Regenerative Medicine, and Soft/Robotic Materials

Finally, there remains substantial room to deepen the translation of LLPS design principles across three high‐impact domains. (i) In bioprocessing, LLPS‐based tags and scaffolds can be more deeply integrated into upstream and downstream workflows—including continuous bioreactors, in situ product capture, and multistep purification trains. Key challenges include managing scale‐up hurdles such as insufficient expression yield, aggregation during concentration, and regulatory acceptance of ELP‐tag strategies for therapeutic applications; the quantitative meta‐analysis by Decker et al. [122].

This provides a valuable framework for systematic benchmarking at scale. (ii) In regenerative medicine, LLPS‐driven hydrogels and condensate‐forming matrices offer tunable microenvironments for cells and morphogens. Programming local viscoelasticity, degradability, and factor partitioning through sequence and architecture could modulate stem cell fate, immune cell behavior, or tissue repair—provided that biocompatibility, immunogenicity, and long‐term stability constraints are addressed through close integration of molecular design with in vivo testing. (iii) In soft and robotic materials, stimuli‐responsive condensates can serve as actuators, sensors, and adaptive structural elements. Coupling LLPS transitions to changes in volume, stiffness, or optical properties opens pathways to protein‐based devices that respond autonomously to thermal, chemical, or mechanical cues—though realizing such systems requires bridging the scale gap between molecular design and device‐level engineering, as well as addressing durability and manufacturing constraints.

Across all three domains, the overarching imperative is that programmability at the sequence and architecture level must ultimately map onto predictable performance envelopes at the system level—a mapping that cannot be achieved by sequence‐based design rules alone, but demands integrated efforts spanning computation, high‐throughput experimentation, mechanistic biophysics, and application‐driven engineering.

6. Conclusions

The design of phase‐separating proteins is best understood as a programmable structure–function continuum coherently linking sequence‐level features, higher‐order architectures, and macroscopic behavior. Spanning bioinformatic descriptors and LLPS predictors (Section 2), modular design principles and topological strategies (Section 3), application‐oriented case studies (Section 4), and integrative design workflows (Section 5).

(i) Bioinformatic analysis encode the information governing phase behavior, and widely used LLPS and disorder predictors provide convenient entry points for screening natural proteins and triaging engineered constructs. However, these tools were trained on natural or near‐natural sequences and systematically underestimate both the LLPS propensity and the aggregation risk of strongly amphiphilic or highly repetitive designs. For LLPS engineering, descriptor panels must therefore be used as multidimensional priors rather than single‐score oracles, and interpreted in light of the intended expression host, conditions, and application context.

(ii) We build a set of iterative DBTL workflow guiding the rational engineering of phase‐separating proteins. Bioinformatic analysis provide powerful levers for modulating phase behavior. At the architectural level, valency, repeat number, block length, and topology mapped phase behavior onto specific tasks like purification tags, catalytic scaffolds, cellular regulators, and bulk materials. Critically, all of these levers must be tuned not only to achieve LLPS, but also to avoid expression bottlenecks, off‐target aggregation, and long‐term aging into gels or fibrils.

(iii) Design principles of phase‐separating protein are already being translated into functional systems at meaningful scale. Phase‐separation‐assisted purification demonstrates how targeted sequence and architectural choices can rival or complement chromatographic workflows in yield, purity, and scalability when LLPS windows are deliberately aligned with process conditions. Sequence‐encoded condensates show that catalytic and regulatory reaction can be controlled by crowding droplets and substrate channeling, stimuli‐responsive delivery systems demonstrate that autonomous sensing and controlled‐release functions can be sequence‐encoded. Finally, structural and mechanical protein materials exemplify how LLPS‐guided assembly yields materials whose macroscopic mechanical properties are programmable through molecular design.

(iv) Success in LLPS engineering depends on explicitly confronting, rather than ignoring, common failure modes and context dependence. Mis‐prediction of phase‐separation propensity, expression failures and unintended aggregation in cellular hosts, and the strong sensitivity of LLPS to cofactors, crowding, and mechanical environment are all recurrent pitfalls. These challenges are best treated as design constraints to be integrated from the outset—not as afterthoughts.

(v) High‐throughput screening and machine‐learning‐guided design will play an increasingly central role in transforming the qualitative design rules summarized here into quantitative, scaffold‐aware design engines. Parallelized measurements of phase diagrams, condensate morphology, dynamics, catalytic performance, and mechanical properties across sequence libraries will generate the multidimensional datasets needed to learn mappings from sequence and architecture to function that transcend current propensity scores. In parallel, the de novo design of multicomponent and multiphase condensates—featuring orthogonal interaction modules, tunable stoichiometry, and hierarchical assembly pathways—offers a route to synthetic organelles and materials with spatially encoded composition, reactivity, and mechanics.

(vi) Translation into application domains remains both the strongest driver and the most stringent test of LLPS design principles. In bioprocessing, phase‐separating tags and scaffolds must integrate seamlessly with upstream expression and downstream purification to improve robustness and cost‐effectiveness. In regenerative medicine, LLPS‐based matrices must couple tunable viscoelasticity and factor partitioning with strict biocompatibility and long‐term stability requirements. In soft and robotic materials, LLPS‐driven transitions can power actuation, sensing, and adaptive mechanics—provided that molecular‐level programmability can be reliably scaled to device‐level architectures.

In summary, phase separation can be treated as a genuinely engineerable property of proteins. By combining bioinformatic foundations, modular design principles, application‐oriented case studies, and integrative workflows, the conceptual and practical ingredients now exist for rationally constructing phase‐separating proteins across a wide range of biotechnological and biomedical contexts.

Author Contributions

Yongjun Mao: conceptualization, writing – original draft, and writing – review and editing. Lu Bao: writing – original draft. Gaoshuai Li: writing – original draft. Qicheng Zhu: writing – original draft. Yanchao Han: writing – original draft. Xudong Wang: conceptualization, writing – review and editing, and supervision.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File: biot70246‐sup‐0001‐SuppMat.docx.

Data Availability Statement

The data that support the findings of this study are available in the Supporting Information of this article.