Communications Chemistry

Cyclic peptides are recognized as versatile scaffolds for therapeutic and functional applications due to their structural stability and resistance to degradation. Despite this promise, systematic analysis and prediction of their thermal stability remain limited by fragmented data resources, inadequate sequence comparison methods, and the lack of cyclicity-aware computational models. We provide a comprehensive, multi-scale computational framework to characterize cyclic peptides. First, we unified four fragmented public repositories of cyclic peptides into a single largest curated resource of 930 cyclic peptides, Cyclome930. This integrates cyclic topology, sequence, experimental structural coordinates, and source organism annotations into a consistently featurized dataset. Cyclome930 thus expands the dataset of annotated cyclic peptides by [~]3.4 fold (from 276 to 930). Second, we developed a novel cyclic sequence alignment algorithm that explicitly accounts for rotational symmetry and knot topology, enabling more accurate scoring of sequence similarity than conventional linear alignments. Third, we investigate the thermal stability of cyclic peptides using extensive all-atom replica-exchange molecular dynamics (100ns; REMD) simulations, allowing conformational sampling across 298 K - 400 K and track its stress tensors with increasing temperature. Finally, these simulation-derived thermo-stability metrics were used to train a machine learning model to predict cyclic peptide melting points from sequence and topology (STop2Melt). Crucially, the model introduces cyclicity-aware embeddings derived from ESMc representations coupled with cyclic offset vector, capturing the peptides knot topology. STop2Melt achieved strong predictive performance on held-out peptides and outperforms baseline methods that neglect cyclic structure. Finally, we scored Cyclome930 (cyclic ligands) for critical mineral metal binding using a multi-classifier model (CritiCL). To our knowledge, Cyclome930 represents the first effort in peptide literature to integrate physics-based temperature ramped simulations, cyclic sequence similarity scoring, machine learning for thermal stability prediction and scoring them for critical metal binding. Cyclicity-aware computational toolchains (cyclome930.studio/) provide a foundational resource for computational design of stable cyclic peptide prototype libraries thereby annotating and expanding genomic islands linked to critical mineral recovery.

Insulin detemir and insulin aspart are clinically complementary analogs engineered for distinct pharmacokinetic behavior, yet their comparative structural heterogeneity across temperature regimes remains insufficiently resolved. Here, we present a multi-scale crystallographic analysis integrating near-physiological serial femtosecond crystallography (SFX) with previously reported cryogenic and ambient multicrystal datasets for both analogs. Across conventional quality metrics, reciprocal-space intensity-field reconstructions, model-derived diffuse-scattering representations, Ramachandran stereochemical validation, solvent-accessibility coupling (SAArea-MSArea), and residue-level BDamage (a packing-normalized B-factor metric highlighting local mobility outliers) profiling, we identify a coherent ambient-versus-cryogenic contrast. Ambient datasets show broader reciprocal-space heterogeneity and more diffuse model-space distributions, consistent with increased conformational sampling outside cryogenic trapping. Despite this shared trend, disorder partitioning is analog-specific: detemir exhibits strong pseudo-translational signatures with moderate twinning, whereas aspart shows weak pseudo-translation but pronounced merohedral twinning approaching the theoretical twinned limit in ambient conditions. Importantly, backbone stereochemistry remains globally stable across all datasets, indicating that the observed differences reflect structured heterogeneity rather than model deterioration. Collectively, these findings support an ensemble-aware interpretation of insulin crystallography and provide transferable structural descriptors for analog comparison, stability assessment, and formulation-oriented design.

While targeted protein degraders such as PROTACs are a clinically proven therapeutic strategy, the discovery of novel degraders remains hampered by trial-and-error process. To address this challenge, we developed the AIMS platform, which combines structural proteomics with AI models for rational PROTAC design. AIMS is an end-to-end toolkit for PROTAC optimization, encompassing structure solving using proteomics and AI, prediction of ADME and degradation properties, and prospective ranking of compound design ideas. Altogether, this integrated platform successfully enabled the multi-parameter optimization of a potent and bioavailable in vivo validated KAT6A degrader, establishing a versatile framework for PROTAC development across various targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/727609v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@13596d0org.highwire.dtl.DTLVardef@140500eorg.highwire.dtl.DTLVardef@147e585org.highwire.dtl.DTLVardef@12dbdfe_HPS_FORMAT_FIGEXP M_FIG C_FIG

Room temperature serial crystallography offers advantages over conventional cryo-crystallography, such as simplified crystal handling and the possibility to avoid potential artefacts associated with cryo-trapping. However, to be considered as an alternative for drug discovery, where compound availability may be limited and speed of structure delivery is a key factor, it suffers from several limitations. To address these challenges, we have optimized a serial crystallography workflow for ligand soaking, data collection and data processing, significantly reducing time and reagent consumption to make it a viable option for drug discovery applications, herein exemplified by crystallographic fragment screening. Our approach incorporates the use of dried-in fragment cocktails on fixed target supports, compatible with 96-well plates for crystal soaking, and an efficient data processing pipeline tailored for serial crystallography. To validate our workflow, we conducted an in-crystal fragment screen at room temperature on the protein soluble epoxide hydrolase. The screen comprised 384 compounds and resulted in identification of 40 fragment binders corresponding to a hit rate of 10.4 %. The resulting room-temperature structures are of high quality and reveal opportunities for specific interaction within the highly hydrophobic active site of soluble epoxide hydrolase. Finally, we discuss potential avenues for further workflow optimization, highlighting the future potential of this approach for drug discovery. SynopsisWe have developed a workflow that allowed us to efficiently conduct a fragment screen at room temperature using serial crystallography, of interest for future drug discovery campaigns.

Exploring fragment chemical space for ligand design remains a major challenge in early stage drug discovery. This task is particularly challenging due to the small size, low specificity, and weak binding affinities of low molecular weight (MW) fragments. We present FragmentScope, a computational pipeline that uses learned protein surface fingerprints to guide fragment placement and small molecule generation. By using a contrastive learning model trained on protein-ligand interactions, we built a database of surface-fragment pairs, which enables fast and accurate fragment placement in the target protein pocket. We demonstrate its effectiveness on benchmark datasets, achieving robust placement accuracy. FragmentScope also enables the design of small molecules based on the predicted fragments ensuring synthetic accessibility. We experimentally validated Fragmentscope across 5 different targets with binding assays and structural characterization. Our approach shows high success rates in fragment discovery and yielded promising leads for designed ligands. FragmentScope offers a scalable, structure-guided approach for narrowing chemical space and identifying prospective scaffolds, accelerating the early stages of small molecule design.

Biocompatible fluorosurfactants are essential for many droplet microfluidic workflows but are often obtained from commercial sources because published syntheses of perfluoropolyether (PFPE)-based surfactants typically require acid chloride intermediates and chemistry-oriented purification methods. These requirements can limit access for biology and clinical laboratories seeking low-cost or customizable surfactant systems. Here we describe a practical method for preparing functional PFPE-based fluorosurfactant materials by direct carbodiimide coupling of functionalized PFPE carboxylic acids(Krytox 157 FSH) to amine-containing head groups under laboratory-accessible conditions. Using this approach, we prepared a PFPE-polyethylene-glycol (PFPE-PEG) material from Jeffamine ED900 and a PFPE-Tris material from Tris base. Because these products were not fully structurally characterized, we present them as functional reaction products and evaluate them by use in biomicrofluidic workflows rather than by definitive compositional assignment. PFPE-Tris was useful for generating relatively uniform small droplets, whereas the PFPE-PEG preparation supported a broader range of biological applications. These materials were used in genomic library screening for {beta}-glucosidase activity, thermocycling-associated droplet workflows, and protein crystallization experiments. In addition, the PFPE-PEG preparation improved emulsion behavior in many protein crystallization screens that were unstable with a commercial droplet oil used in our laboratory. This method reduces the practical barrier to in-house fluorosurfactant preparation and allows biology-focused laboratories to explore head-group chemistry, oil composition, and operating conditions without complete reliance on commercial reagents. The results support this workflow as a useful entry point for biomicrofluidics laboratories, while also highlighting the need for careful interpretation of thermocycled droplet assays and for future analytical characterization of the resulting materials. Significance statementDroplet microfluidics relies on fluorosurfactants that are often costly and difficult to synthesize outside of chemistry-focused settings. We describe a simple, biology-laboratory-compatible approach for generating functional perfluoropolyether-based fluorosurfactant materials using direct carbodiimide coupling and straightforward cleanup. The resulting materials supported multiple biomicrofluidic workflows in our laboratory, including enzymatic screening and protein crystallization, and provide a practical route for groups seeking lower-cost and more customizable surfactant systems.

Glucose is a central metabolite of living organisms, serving as the primary energy substrate produced predominantly by photosynthetic organisms. Under conditions of limited external glucose supply, organisms activate gluconeogenesis, an endogenous biosynthetic pathway that sustains essential glucose levels. Yet the mechanism of glucose generation, before the advent of photosynthesis and complex enzymatic systems, remains elusive. Recently, microdroplet chemistry has emerged as a novel approach for catalyst-free organic synthesis. Here, we report the non-enzymatic formation of glucose from a simpler organic precursor, pyruvate, in aqueous microdroplets without the aid of organic or inorganic catalysts. Our results show that glucose is generated via a reaction pathway analogous to canonical gluconeogenesis, proceeding through key intermediates including oxaloacetate, glycerate, and glyceraldehyde. Furthermore, thermodynamic analysis indicates that the free energy change associated with glucose formation is overcome in aqueous microdroplets at room temperature, without the need for external energy input or enzymatic catalysis. These findings indicate that aqueous microdroplets can non-enzymatically convert C3 compound, pyruvate, into the C6 sugar glucose, offering a plausible abiotic route for anabolic carbon transformations during abiogenesis.

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a central regulator of cellular antioxidant responses and a highly promising therapeutic target for a range of oxidative stress-related diseases. However, the clinical translation of Nrf2 activators has been hampered by significant off-target effects--notably unintended activation of the pregnane X receptor (PXR) and inhibition of cytochrome P450 2D6 (CYP2D6)--which can lead to dangerous drug-drug interactions and metabolic complications. To overcome this critical barrier, we conducted the first large-scale computational screening of 628,898 natural products from the COCONUT database, integrating molecular docking with a rigorous three-tier selectivity strategy designed to prioritize compounds that strongly bind KEAP1 (the primary Nrf2 repressor) while minimizing interactions with PXR and CYP2D6. Our innovative approach identified 10 ultraselective candidates that demonstrate potent KEAP1 affinity, negligible PXR engagement, and only moderate CYP2D6 binding--achieving up to 12.29-fold selectivity for Nrf2 pathway activation. These top hits are structurally novel, enriched in lipid-like and nucleoside-inspired scaffolds, and exhibit promising drug-like properties. By providing both a curated set of chemically diverse, selectivity-optimized leads and a publicly accessible screening dataset, this work establishes a new foundation for the rational development of safer, more precise Nrf2-targeted therapies, bridging a crucial gap between target potential and clinical viability. By prioritizing compounds with minimal off-target effects on PXR and CYP2D6, our approach offers a scalable template for reducing drug development failures and advancing safer therapeutics for oxidative stress-related diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/718057v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@13c6c79org.highwire.dtl.DTLVardef@1f5a078org.highwire.dtl.DTLVardef@fa4f4borg.highwire.dtl.DTLVardef@16bc881_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

Cell-free expression systems (CFES) are increasingly used alongside conventional biotechnological approaches to accelerate early-stage prototyping and are particularly valuable in point-of-use settings. However, their broader adoption remains limited by time- and cost-intensive preparation, as well as stringent cryogenic storage requirements. To address this, several studies have explored lyophilization with protective additives to generate stable, solid-state CFES. These approaches had to balance the protection gained with a loss of activity due to the additives. In this study, we present a CFES that contains a tardigrade-derived Cytosolic-Abundant Heat-Soluble (CAHS) protein to protect the biosynthetic machinery in lysates from damages during drying. We show that the CAHS protein, without any other additives, preserves protein synthesis activity during low-cost room temperature desiccation, while unprotected lysates are affected in mRNA synthesis kinetics and translation yields. The diversity of tardigrade-derived protective proteins is a treasure trove for cell-free synthetic biology, in particular for making CFES more accessible and portable. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/715078v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@8ecc2eorg.highwire.dtl.DTLVardef@ff0432org.highwire.dtl.DTLVardef@6c940eorg.highwire.dtl.DTLVardef@6c5390_HPS_FORMAT_FIGEXP M_FIG C_FIG

Phycocyanin (PC) is the principal natural blue pigment used in functional beverages, but it rapidly loses color and aggregates under acidic conditions (pH {approx} 3). Experimental screening of stabilizers is costly and combinatorially intractable. Here we develop a chemoinformatics framework -- descriptor-based QSPR, a chemistry-prior heuristic, and virtual screening -- that learns from three rounds of commissioned screening (48 compounds, 6% hit rate) to predict stabilizer efficacy directly from molecular structure. In this genuinely small-data regime (3 positives), a LightGBM classifier built from 10 RDKit descriptors and 11 domain-expert charge/polymer features attained a leave-one-out AUC of 0.73, only marginally above a single-feature charge-density baseline (AUC 0.67); LOO sensitivity was 1/3 at threshold 0.5. A complementary chemistry-prior heuristic encoding anion-type priors substantially outperformed both, reaching AUC 0.95, indicating that explicit chemical knowledge captures information that descriptor-based ML cannot readily recover at this dataset size. SHAP analysis of the QSPR identified effective negative-charge density per unit, log molecular weight, polyphosphate identity, and functional-group density as the dominant features (jointly {approx}97% of mean |SHAP|), recovering the electrostatic-complexation mechanism without it being supplied as a prior. Virtual screening of 30 generally recognized as safe (GRAS) food additives nominated the pyrophosphate family -- led by sodium pyrophosphate decahydrate and sodium hexametaphosphate (SHMP), both at P {approx} 0.99 -- and the heuristic additionally flagged sodium phytate (IP{square}), which the descriptor model under-ranked at P = 0.045. Experimental validation at pH 3 and 46 {degrees}C for 7 days confirmed SHMP 2:1 (78.1 {+/-} 11.3% color retention), TSPP 2:1 (54.1 {+/-} 10.6%) and IP{square} 1:1 (52.7 {+/-} 9.0%), while a ternary IP{square} + STPP combination reached 83.8 {+/-} 11.9%, surpassing all single-component formulations. {zeta}-Potential measurements indicated a predominantly electrostatic origin for the protection (Pearson r = -0.82 between {zeta} and CR{square} {square} {square}; n = 24; p = 1 x 10{square} {square}). The framework, dataset and code are released to accelerate stabilizer discovery for other acid-sensitive food colorants and to provide a candid small-data benchmark.

The functional role of biomolecular condensates is shaped by the composition of constituent proteins, nucleic acids, ions, and small molecules. Selective partitioning of small molecules into condensates has therefore emerged as a potential route to condensate-specific chemical probes and therapeutics. Although partitioning is influenced by differences in solvation environments between coexisting dense and dilute phases, a molecular framework connecting small-molecule structure to condensate-specific enrichment remains lacking. Here, we use existing experimental partitioning data for a library of FDA-approved drugs and metabolites across four biomolecular condensates to develop an interpretable graph-based model of small-molecule partitioning. By combining multitask pretraining, condensate-specific fine-tuning, evidential uncertainty quantification, and atom-level attribution analysis, our model predicts continuous partition coefficients with improved accuracy over descriptor-based approaches. Atom-level attributions reveal that condensate partitioning is not governed by a universal chemical rule: the same molecular scaffold can be read differently by distinct condensate environments, with local atomic context and connectivity determining whether specific atoms promote or suppress enrichment. We further apply the trained model to ~1.7 million drug-like molecules from ChEMBL, identifying a chemically diverse space of predicted condensate-selective partitioners and mapping regions where predictions are confident versus where new measurements would be most informative. Together, this work establishes condensate partitioning as a chemically learnable property shaped by the interplay between small-molecule structure and condensate-specific microenvironments, providing an interpretable and uncertainty-aware framework for defining molecular determinants of partitioning and guiding the discovery of condensate-selective small molecules.

Biomolecular condensates organize cellular biochemistry, yet the principles governing their internal solvent architectures remain poorly understood. Most current models focus on macromolecular scaffolds while treating the solvent as a passive, spatially uniform background. Here, we introduce Condensate Spatial Topography via Emission Lifetimes (ConSTEL) to map the continuous solvent polarity landscape inside biomolecular condensates. Using PopZ as a model system, we show that the condensate interior contains a persistent, tunable mosaic of aqueous environments whose apparent polarity, reported by Nile Red fluorescence lifetimes, is organized by thermodynamic state and chemical cues. This microphase-separated solvent architecture defines distinct mesoscale rheological regimes, with intermediate aqueous niches supporting fast, confined tracer motion and highly polar or non-polar extremes forming a slower, viscoelastic mesh. We further demonstrate that drug-like small molecules partition non-uniformly across this landscape according to their physicochemical properties, and that exceeding local solubility limits drives "reciprocal sculpting", in which mismatched guests remodel the host solvent architecture. Together, these results highlight internal solvent organization as an active, tunable determinant of condensate material properties, molecular transport, and partitioning, and suggest that predictive models of condensate function and pharmacology would benefit from incorporating the spatial arrangement of solvent environments alongside bulk composition.

Biomolecular condensates have so far been studied in terms of their structural, compositional, and functional properties. However, condensate enzymatic activity --a key aspect of cellular metabolism-- remains unexplored due to the complexity of the system. In this study, using a combination of experimental, computational and theoretical techniques, we have discovered that the non-equilibrium activity which originates from catalytic reactions couples with the environment through various feedback mechanisms across five orders of magnitude of length scales. We observe that condensed enzymes catalyse more rapidly in the presence of crowding proteins and show that the increased enzymatic activity within these droplets stems from the emergence of lower-energy protein conformations induced by the highly crowded environment. Despite the crowding in the environment of the droplet, which might suggest an effective increase in its overall viscosity, we find that it becomes more agile, as evidenced by the observation of enhanced diffusion and macroscopic flow, due to the enzymatic activity. These findings shed new light on the dynamic interplay between enzymatic activity, composition and crowding in condensates, and their roles on the mobility and accessibility of various functional units in these environments, offering a novel perspective on liquidliquid phase separation in metabolically active conditions.

Nucleotide-derived cofactors could function as a missing link between the informational and the metabolic part at lifes emergence. One well-known example is nicotinamide dinucleotide (NAD), one of the evolutionarily most conserved redox cofactors found in metabolism. Here, we propose that the role of these cofactors could even extend to missing links between geo- and biochemistry. We show NAD+ can be reduced under close-to nature conditions with nickel-iron-alloys found in water-rock-interaction settings rich in hydrogen (serpentinizing systems) and that nicotinamide mononucleotide (NMN), a precursor molecule to NAD, has different properties regarding reduction specificity and sensitivity than NAD. The additional adenosine monophosphate (AMP) "tail" of the dinucleotide, a shared trait between many organic cofactors, seems to play a crucial mechanistic role in preventing overreduction of the nicotinamide-bearing nucleotide. This specificity is also connected to the used transition metals. While the combination of nickel and iron promotes the reduction of NAD+ to 1,4-NADH most efficiently, in the case of NMN, the presence of nickel leads to the accumulation of overreduction products. Testing the reducing abilities of both NADH and NMNH under abiotic conditions showed that both molecules act as equally effective, soluble hydride donors in non-enzymatic, proto-metabolic stages of lifes emergence.

Post-translational modifications (PTMs) of the tau protein are increasingly recognized as pivotal regulators in the onset and progression of tauopathies, such as Alzheimers disease (AD). To systematically evaluate the structural and functional consequences of specific PTMs, we generated and analyzed seven distinctly modified variants of the tau-K32 construct. These included phosphorylation at Ser202/Thr205, phosphorylation at Ser258/Ser262/Ser356, full phosphorylation at all reported Ser/Thr sites, acetylation at Lys274/Lys281, acetylation at Lys280, full acetylation at all sites, and an unmodified control. Selection of PTM sites was guided by prior experimental literature. By incorporating fully modified tau models, we assessed the global impact of widespread modifications on structural properties and aggregation behavior. Our findings establish a comparative framework for understanding how discrete and cumulative PTMs modulate tau aggregation and provide mechanistic insight into PTM-induced tau dysfunction relevant to neurodegenerative diseases.

Affinity purification is a essential technique for isolating highly purified proteins; however, generating affinity ligands require significant time and financial investment. To address these limitations, this study proposes a novel affinity chromatography method utilizing in silico-designed cyclic peptides as ligands. Targeting Complement C1q (C1q), a plasma protein that plays crucial roles in classical complement pathway, we employed the biomolecular structure prediction model, AlphaFold2, to design specific binding cyclic peptides. Based on these designs, we synthesized lariat-type cyclic peptides characterized by disulfide cyclization and biotinylation, which were subsequently immobilized on streptavidin carriers. Performance tests confirmed that the resulting column specifically captured C1q, allowing for elution via a standard NaCl concentration gradient. Notably, high selectivity was preserved even in the presence of plasma, underscoring the ligands practical robustness. By overcoming traditional constraints through (1) rapid and simple design, (2) high specificity, and (3) universal versatility without genetic modification, this de novo design strategy represents a potential breakthrough in protein purification technologies. HighlightsO_LIAI-driven de novo design generated a specific cyclic peptide ligand for Complement C1q C_LIO_LIThe synthetic ligand enabled one-step purification of Complement C1q directly from human plasma C_LIO_LIMild elution conditions preserved the targets oligomeric structure and native interactome C_LIO_LIThis label-free strategy offers a rapid, low-cost alternative to antibody-based chromatography C_LI

Aedes aegypti remains a major arboviral vector, making larval control a critical strategy to reduce mosquito populations. However, resistance to commercial larvicides has reduced the long-term effectiveness of current interventions, reinforcing the need for new compounds with improved potency and selectivity. Here, we present an instance-wise contrastive graph neural network (GNN) framework to accelerate the discovery of novel larvicidal compounds. The model was trained on a curated dataset of 556 organic compounds organized into LC50-derived multitask classification thresholds and integrated Transformer-inspired graph learning with whole-molecule and fragment-level contrastive regularization. This model achieved strong held-out performance, with global AUC = 0.95 {+/-} 0.01, PR-AUC = 0.93 {+/-} 0.01, and MCC = 0.77 {+/-} 0.03, outperforming conventional machine learning and graph-based baselines. Predictive uncertainty analysis and counterfactual maps further supported the interpretation of threshold-sensitive predictions and substructural contribution patterns. The model was applied to screen 1.3 million compounds, resulting in 10 candidates for experimental validation. Three compounds showed measurable larvicidal activity against A. aegypti larvae. Among them, LC-79 emerged as the most promising hit, with 2-day and 5-day LC50 values of 0.24 {micro}g/mL (0.66 {micro}M) and 0.05 {micro}g/mL (0.13 {micro}M), respectively, an IE50 of 0.06 {micro}g/mL (0.16 {micro}M), and rapid larval mortality (LT50 = 1.10 days at 1 {micro}g/mL). LC-79 also showed no measurable acute toxicity to Daphnia magna at the highest tested concentration [EC50-48h >43 {micro}g/mL (>119 {micro}M)], resulting in selectivity indices >180 and >860 relative to its 2-day and 5-day LC50 values. Overall, this study demonstrates that contrastive graph learning can move beyond retrospective larvicide modeling to experimentally validated hit discovery, identifying LC-79 as a potent and preliminarily selective acylthiourea larvicide candidate for further mechanism-of-action, resistance, and semi-field evaluation.

Designing enzymes from functional descriptions remains challenging because catalytic activity is governed by sequence-structure-function relationships. Here we present EnzymeArt, a function-conditioned enzyme-design framework centred on a generative sequence model. EnzymeArt couples function-conditioned sequence generation with structure-guided refinement, annotation checks and substrate-aware computational prioritization to select candidates for synthesis and biochemical testing. Across alcohol dehydrogenase (ADH), malate dehydrogenase (MDH) and triacylglycerol lipase design campaigns, 57 of 60 synthesized designs showed crude-lysate activity above matched background controls. Purified representatives further showed quantitative steady-state catalytic activity. The best designed ADH reached kcat = 223.7/s and exceeded a wild-type reference under matched conditions, an MDH reached kcat = 267.57/s despite having only 33% sequence identity to its closest BLASTP hit, and a designed lipase hydrolysed both short- and long-chain triglycerides with apparent activity modestly above that of a commercial lipase reference. Together, these results establish a route for converting functional descriptions into experimentally validated enzyme designs with quantitative steady-state kinetic activity.

The internal dynamics of liquid-liquid phase-separated systems are governed primarily by polymer packing, excluded-volume effect, and interactions between polymers and encapsulated macro-molecules. Although one immediate effect of such a constrained microenvironment is diffusion limitation, it remains unclear whether encapsulated macromolecules can also exhibit phase composition-specific functional behaviour that is not observable in a well-mixed aqueous environment. In this regard, different phases in a phase-separated environment can be accessed via a phase diagram that demarcates the region between two-phase (droplets) and one-phase (polymer-rich, no droplets) regimes. While the two-phase region is heterogeneous, most previous work on encapsulating functional macromolecules in phase-separated droplets uses a single point from the phase diagram. This leaves a clear gap in understanding on how the function scales across this landscape of droplets and identifying regions advantageous for the encapsulated macromolecule and its function. Here, using the Spinach light-up RNA aptamer, we show that RNA function does not scale uniformly across the phase diagram. We show that RNA can exhibit phase composition-specific functional behaviour due to constraints imposed by the internal microenvironment of phase-separated droplets. Furthermore, using variants of the Spinach aptamer, we show that fluorescence activity differences among the variants vary differently with phase-separation regimes across the phase map, suggesting that some regions of the phase diagram can confer a selective advantage. Our results highlight the potential of liquid-liquid phase-separated internal microenvironments in guiding the differentiation of functional RNA variants, which could serve as a physical selection pressure in pre-cellular evolution.

Protein aggregation is of broad importance in biotechnology and disease, yet the structural heterogeneity of cellular aggregates has confounded high-resolution structural analysis. Inclusion bodies (IBs) formed in Escherichia coli are an attractive, controllable system for unravelling the complexities of protein aggregation in a cellular context. Here, a multimodal analysis integrating residue-resolved quenched amide hydrogen-deuterium exchange (qHDX), proteolysis, FTIR, Congo red binding, and chemical denaturation is applied to IBs formed by proteins encompassing stable {beta}- and -globular folds, a partially structured protein fragment, and intrinsically disordered low complexity domains (LCDs). Remarkable conformational diversity is observed: IBs formed by well-folded proteins are extensively structured and include substantial local native-like features, whereas proteins with decreased access to stable native conformations form more heterogeneous and dynamic aggregates increasingly shaped by intrinsic sequence features. Strikingly, qHDX protection of TDP-43 LCD IBs strongly aligns with the core of cryo-EM structures of ex vivo pathological fibrils; however, peripheral regions that appear fully hydrogen-bonded in the cryo-EM structures exhibit little protection. The results reveal that individual protein IBs contain distinct mixtures of native-like, disordered, and amyloid-like conformers, informing the prediction and control of cellular aggregate structure and stability.