Communications Chemistry

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.

Detergent-free extraction of membrane proteins using polymers directly into nanodiscs from the cell membrane has been used widely in recent years. Since the first use of poly(styrene-co-maleic acid) (SMA), numerous related polymers have been developed that differ in chemical architecture and nanodisc characteristics, each capable of influencing the structural and functional properties of the encapsulated membrane protein and its surrounding lipids. Identifying an optimal solubilising polymer, therefore, requires consideration not only of extraction efficiency but also compatibility with downstream applications and analyses. Polymer series in which a single parameter is systematically varied provide a valuable, nuanced tool for optimising nanodisc utility in downstream applications. This study utilises a chemically defined series of poly(styrene-co-maleic acid-co-(N-benzyl)maleimide) (BzAM) terpolymers that exhibit a stepwise, systematic increase in hydrophobicity. Using the human calcitonin gene-related peptide (CGRP) receptor as an exemplar class B1 G-protein-coupled receptor (GPCR), the ability of each BzAM terpolymer to solubilise the receptor from mammalian cell membranes was assessed. All members of the series successfully solubilised CGRP receptor, with solubilisation efficiency correlating positively with increasing hydrophobicity. Importantly, the receptor retained its characteristic high-affinity ligand-binding capability when encapsulated within the BzAM nanodisc, demonstrating that functional integrity is preserved following BzAM-mediated extraction and purification. These findings establish the BzAM terpolymer series as a systematic, tuneable, well-defined tool for the detergent-free solubilisation and functional investigation of GPCRs, and other membrane proteins, in near-native lipid environments. HIGHLIGHTSO_LIStepwise-tuned poly(styrene-co-maleic acid-co-(N-benzyl)maleimide) (BzAM) terpolymers provide a chemically defined, hydrophobicity-controlled platform for detergent-free membrane protein extraction. C_LIO_LIAll BzAM variants effectively solubilise the human calcitonin gene-related peptide (CGRP) receptor, with extraction efficiency increasing in line with terpolymer hydrophobicity. C_LIO_LICGRP receptor maintains high-affinity ligand binding in BzAM nanodiscs, demonstrating preservation of ligand-binding function after solubilisation. C_LIO_LIThe BzAM series provides a novel platform for studying G-protein-coupled receptors and other membrane proteins in near-native lipid environments, with the potential to deliver mechanistic insights and support future drug-discovery efforts. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/726474v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1cb167corg.highwire.dtl.DTLVardef@313e60org.highwire.dtl.DTLVardef@f64a2borg.highwire.dtl.DTLVardef@17f6629_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

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.

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

In this study, we demonstrate that urea enables reliable melting temperature (Tm) determination of hyperthermostable proteins by nano differential scanning fluorimetry (nanoDSF) Under native conditions, Pfu DNA polymerase and its Sso7d-fusion variant showed no detectable unfolding transitions, despite their Tm values falling within the instruments operational range, reflecting their extreme kinetic stability. In the presence of up to 7 M urea, intrinsic tyrosine and tryptophan fluorescence revealed clear unfolding transitions, yielding extrapolated Tm values of 104.8 {+/-} 0.09 {degrees}C for Pfu and 106.8 {+/-} 0.33 {degrees}C for its Sso7d-fusion variant. These results demonstrate that urea-gradient nanoDSF overcomes both instrumental and kinetic limitations, providing a simple and robust method for assessing the thermal stability of (hyper)thermostable proteins. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/717478v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@960d9org.highwire.dtl.DTLVardef@1b5613forg.highwire.dtl.DTLVardef@1039a08org.highwire.dtl.DTLVardef@1759841_HPS_FORMAT_FIGEXP M_FIG C_FIG

Lipid nanoparticles (LNPs) are the most successful drug delivery carrier to date, but optimizing lipid formulations to improve membrane fusion capabilities for effective drug release has been challenging due to lack of a quantitative measure for fusogenicity. Here we introduce a new framework based on small angle X-ray scattering to experimentally measure [Formula] for lipids used in LNP formulations such as glycerol monooleate (GMO) and ionizable lipids (SM-102 and ALC-0315). Q intrinsically captures spontaneous curvature (J0), which is traditionally used to assess fusogenicity. The change of cubic lattice parameters with temperature was measured for GMO-containing lipid mixtures, and the Q extracted quantitatively correlated with LNP fusogenicity power validated by fluorescence-based fusion assays and cryogenic electron microscopy. Fusogenicity of SM-102 and ALC-0315 was quantified by adding them to host membranes and assessing change in Q. This framework provides researchers with the ability to optimize the fusogenicity of LNP formulations for potent drug release and enhances understanding of parameters governing fusion in all biomembranes.

The structural and functional characteristics of membrane proteins can be influenced by the composition of the membrane. Consequently, native membranes are most relevant for the study of receptors and other membrane proteins. In this study, we investigated two types of cell-derived vesicles: natively shed extracellular vesicles (EVs) and mechanically derived vesicles (MVs). To this end, we utilized the human breast cancer cell line SKBR3, which strongly overexpresses the receptor HER2. We designed a protocol based on designed ankyrin repeat proteins (DARPins) to purify EVs and MVs enriched in HER2, and to ensure the native orientation of the HER2 receptors within the vesicle. The isolated HER2-containing EVs and MVs were characterized by cryo-EM, cryo-electron tomography (cryo-ET) and mass spectrometry (MS), which revealed fundamental differences between the different vesicle types. Our study highlights the greater structural diversity of EVs over MVs. A single particle cryo-EM analysis and classification of all visible receptors on the vesicle surface yielded electron density consistent with HER2 at modest resolution. Taken together, our results suggest that MVs can serve better than EVs as a suitable platform for the structure determination of membrane proteins within their native membrane environments.

Human serum albumin (hSA) is the most abundant protein in human plasma, and its pharmacological properties, such as long plasma half-life mediated by the neonatal Fc receptor (FcRn) and its ability to bind endogenous and exogenous molecules, make it attractive for biotechnological applications. Currently, most wild type (WT) SAs are derived from human or bovine serum or produced in yeast and mammalian cells. Although well established, these methods are costly, difficult to reproduce, and not environmentally sustainable. Building on a previous study to design highly mutated hSA sequences, we extend the validation through an in-depth analysis of three engineered hSA variants; hSA1, hSA2, and hSA3, containing 16, 25, or 73 amino acid substitutions, respectively. These variants were designed for enhanced solubility, stability, and expression in Escherichia coli. All three variants showed low- micromolar affinities for hFcRn at pH 5.5, and negligible binding at pH 7.4. In a human endothelial cell-based recycling assay (HERA), the engineered hSA variants were recycled by hFcRn to the same extent as hSA isolated from serum. Exploring the properties of canonical drug-binding sites, warfarin affinity was comparable to WT hSA, whereas ibuprofen binding differed. Complementary cytotoxicity assays on human macrophages confirmed negligible toxicity and biocompatibility. A cryo-electron microscopy structure of hSA3 revealed that, despite extensive engineering, the native heart-shape of hSA, folding of domains, and its open conformation were preserved. These findings validate the structural integrity and functional adaptability of engineered hSA variants, underscoring their potential as versatile, animal-free solutions for next-generation therapeutics and biotechnological applications.

Coevolution proceeds through the evolution of traits that mediate ecological interactions and evolutionary outcomes. In the arms race between toxic Pacific newts (Taricha) and their garter snake predators (Thamnophis), this interface involves tetrodotoxin (TTX), an antipredator defense that inhibits nerve and muscle function by blocking voltage-gated sodium channels. In response, snakes have evolved TTX-resistant channels, in some cases leading to snake populations that are nearly invulnerable to TTX. For decades, newt TTX has been treated as a single defensive trait, yet TTX occurs as a family of structurally related analogs that may represent alternative defenses against snakes. Here, we characterize TTX analog diversity in all four species of Taricha and evaluate how these compounds interact with the sodium channels in coevolved garter snakes. Using LC-MS analysis of newt skin secretions, we detected a diverse suite of TTX analogs previously unrecognized in Pacific newts. We then used molecular docking models to evaluate interactions between various TTX analogs and variants of the skeletal muscle channel (Nav1.4) that span the range of TTX resistance in garter snakes. We found that some TTX analogs docked better than canonical TTX in resistant snake channels. Notably, we show that 11-deoxy-4-epi-TTX and 11-deoxy-TTX have favorable interactions with hydrophobic amino-acid substitutions in extremely resistant garter snake sodium channels, potentially circumventing predator resistance to canonical TTX. Our results suggest a complex arms race involving multiple newt TTX analogs and multiple snake sodium channel variants. As such, newts may keep pace with snakes by diversifying their arsenal of chemical weapons.

Molecular similarity searching is a workhorse of cheminformatics, but the dominant Tanimoto/topological-fingerprint paradigm has well-known blind spots. It is highly sensitive to molecular size, suffers from steep activity cliffs, and frequently fails to retrieve scaffold-hopping bioisosteres. A complementary descriptor that has received comparatively little attention is global elemental composition. Despite the conceptual simplicity of comparing molecules by their elemental ratios, no widely deployed method exists for the statistically rigorous identification of "chemical twins" defined by stoichiometric proximity. We address this gap with TwinSAR (Stoichiometric Analysis and Retrieval), an adaptive kernel-based algorithm that combines three methodological innovations: (i) binary fingerprint blocking that partitions molecule by element-presence patterns and bounds the cost of all-pairs comparison from O(NM) to O({sum}nimi) enabling million/billion-scale searches; (ii) a per-block adaptive radial basis function (RBF) kernel whose precision parameter is calibrated independently for each fingerprint block via the median heuristic, providing fair similarity comparison across chemical sub-spaces of vastly different density; and (iii) a logit-transformed Z-score filter that maps bounded RBF scores onto an unbounded scale, allowing high-similarity pairs to be prioritized relative to the empirical score distribution of their own fingerprint block. TwinSAR is offered in two operating modes: (i) a deterministic BULK mode for exact reproducibility; and (ii) a stochastic FAST mode that achieved a 3.29x wall-clock speed-up in the present benchmark while preserving the similar unique-query and unique-target coverage. Statistical validation showed that detected twin pairs are 12.7x more similar in absolute ratio space than block-matched random pairs (p < 0.001), while a column-permutation negative control returned a median of zero spurious twins across three independent permutations. A controlled benchmark further established that an 8-element representation (single-element heavy-atom ratios) is sensitivity-equivalent to a comprehensive 254-element representation while running 3.55x faster. As a case study, TwinSAR was deployed in an end-to-end virtual screening pipeline against the BCL-2 target protein, where it reduced a 327,071-compound commercial library to a 390 focused candidate panel. The chemical interpretability of the retrieved twins is illustrated by their structural diversity around conserved heavy-atom skeletons. TwinSAR therefore provides a fast, conformation-free, and statistically principled prefilter that is fully orthogonal to topological fingerprints.

Recombinant human lactopontin (rhLPN), an equivalent of human milk lactopontin, is of increasing interest for human nutrition applications due to its roles in mineral binding, gastrointestinal function and immune modulation. These properties depend strongly on post-translational modifications, particularly phosphorylation and glycosylation. Here, we report the production of rhLPN in Kluyveromyces lactis at laboratory and pilot scale and present a comprehensive molecular comparison with native human lactopontin (nhLPN) isolated from human milk. Mass spectrometry-based peptide mapping confirmed the primary structure and identified extensive phosphorylation, consistent with the native protein. Middle-up analyses demonstrated closely matched phosphoform distributions between rhLPN and nhLPN, while glycosylation profiling revealed a defined population of low-complexity O-glycoforms localized to the N-terminus. Functional assessment demonstrated substantially greater iron binding by phosphorylated rhLPN compared with dephosphorylated and non-phosphorylated forms. Similar phosphorylation-dependent behaviour was observed for bovine lactopontin, supporting a conserved role for phosphorylation in mineral interaction. Across five 750 L pilot scale batches, both phosphorylation and glycoform distributions were highly consistent, indicating robust process reproducibility. Together, these results demonstrate that rhLPN produced in K. lactis recapitulates key structural and functional attributes of nhLPN, supporting its suitability as a scalable ingredient for nutrition applications.

De novo protein design often produces thermostable proteins that denature above 100 {degrees}C, which complicates the analysis of their stability. Thermostable proteins can be unfolded by combined chemical and thermal denaturation followed by global analysis of multiple melting curves. Here, we have developed CheMelt, a new online tool for global analysis of unfolding data via an intuitive graphical user interface. We use nanoscale differential scanning fluorimetry followed by CheMelt data analysis to dissect the combined thermal and chemical denaturation of thirty-five de novo designed protein binders. Fifteen present sufficient fluorescence changes to extract thermodynamic parameters of unfolding. These de novo designed proteins have systematically lower {Delta}Cp and m-values than comparable natural proteins, which implies that they expose fewer hydrophobic residues upon unfolding. We show that a high thermostability of a designed protein does not necessarily imply a high equilibrium stability; and demonstrate the potential of CheMelt in dissecting thermodynamic properties for protein design and engineering.

The voltage-gated potassium channel hERG (Kv11.1) plays a central role in cardiac repolarisation by mediating the rapid delayed rectifier K+ current (IKr). Blockage of hERG by small molecules can lead to delayed repolarisation, QT interval prolongation, and potentially fatal arrhythmias, making the channel a critical focus in drug safety screening. Despite extensive pharmacological and electrophysiological characterisation, a complete structural understanding of hERG gating remains limited by the absence of an experimentally determined closed-state structure. Here, we use AI-based structural modelling to predict and compare candidate closed conformations of hERG. Building on recent work in which AlphaFold2 (AF2) predictions guided by engineered structural templates captured closed and inactivated states, we applied the emerging protein structure predictor, Chai-1, which employs a single-sequence, language model-based approach independent of multiple-sequence alignments. The resulting Chai-1 hERG model was compared with the AF2-derived closed structure, a homology model based on the Rattus norvegicus EAG channel, and an experimentally resolved open-state cryo-EM structure. We assessed these models using a combination of all-atom and coarse-grained molecular dynamics simulations, analysing protein dynamics, pore geometry, gating residue orientation, hydration, and lipid interactions. The Chai-1 and AF2 models displayed strong structural and dynamic agreement, both adopting compact, non-conductive conformations consistent with a physiologically closed state. Our data reveal insights into VSD dynamics, as well as suggesting a state dependence for ceramide binding at the previously identified M651 residue. Our findings support the validity of AI-derived closed-state hERG models and underscore the growing potential of deep learning-based protein structure prediction to identify previously uncharacterised, pharmacologically relevant conformations of membrane proteins. Further, our Chai-1 derived closed state model expands our structural insights into hERG gating and may have utility for investigation of drug-hERG interactions.

The importance of human aldehyde oxidase (hAOX1) has increased over the last decades due to its involvement in drug metabolism. Inhibition studies concerning hAOX1 are extensive and a common reducing agent, dithiothreitol (DTT), was recently found to inactivate the enzyme. However, in previous crystallographic studies of hAOX1, DTT was found to be essential for crystallization. To surpass this concern another reducing agent used in crystallization trials. Using tris(2-carboxyethyl)phosphine (TCEP), a sulphur-free reducing agent, it was possible to obtain well-ordered crystals from hAOX1 wild type and variant, hAOX1_6A, which diffracted beyond 2.3 [A]. Instead of the typical star-shaped crystals of hAOX1, at pH 4.7, plates are obtained in the orthorhombic space group (P22121) with two molecules in the asymmetric unit. Activity assays with the enzyme incubated with both reducing agents show that contrary to DTT, TCEP does not lead to irreversible inactivation of the enzyme. The replacement of DTT with TCEP in crystallization of hAOX1 provides a strategy to circumvent enzyme inactivation during crystallographic studies, allowing future applications of new assays, such as time-resolved crystallography.

Saponin-phospholipid bicelles have emerged as promising membrane-mimetic systems for anisotropy-based NMR studies, but their utility depends on their ability to accommodate physiologically relevant lipid compositions and ionic environments. Here, we systematically investigated how saponin chemistry governs three key properties of magnetically alignable bicelles: tolerance to anionic lipid incorporation, responsiveness to lanthanide-induced alignment reorientation, and stability in the presence of divalent metal ions. Using 31P NMR spectroscopy as a sensitive probe of phase behavior and alignment, we compared bicelles formed with glycyrrhizic acid (GA), hederacoside C (HC), and crude Quillaja saponins (CQS). While all saponins effectively solubilized the anionic lipid DMPG, only HC supported magnetically aligned bicelles with high anionic lipid fractions (up to 70%), whereas GA was limited to low incorporation (10%). Both GA- and HC-based bicelles underwent lanthanide-induced alignment flipping, though with significant spectral broadening and intermediate alignment states indicative of increased heterogeneity. Divalent cation effects were strongly ion- and saponin-dependent; HC bicelles were robust to the presence of Ca2+ but were destabilized by Mg2+, while GA bicelles were disrupted by both ions at low concentrations. Together, these results demonstrate that saponin identity critically determines bicelle compatibility with charged lipids and ionic conditions, establishing design principles for tailoring saponin-based bicelles as versatile, biomimetic alignment media for membrane-protein structural studies.

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

Liquid-liquid phase separation (LLPS) of biomacromolecules is a key mechanism driving the formation of membraneless organelles (MLOs) within cells, playing a crucial role in fundamental biological processes such as cell proliferation and stress response. Accurately understanding and predicting the phase separation propensity of proteins is essential for unraveling the assembly mechanisms of MLOs and their functions under both physiological and pathological conditions. Traditional research methods primarily rely on biochemical experiments, which are limited by low throughput, high cost, and difficulty in systematically exploring sequence-phase transition relationships. This study proposes and implements a novel three-stage, iterative paradigm based on artificial intelligence (AI) to propel phase separation research towards systematization, predictability, and mechanistic understanding. O_LIBenchmark Model Construction: A preliminary predictive model was established based on a Multilayer Perceptron (MLP) neural network, and the driving effect of phenylalanine/tyrosine (F/Y) residue-mediated {pi}-{pi} interactions on LLPS was validated. C_LIO_LIModel Robustness Enhancement: The model was optimized through adversarial training strategies, which effectively identified and eliminated misclassifications of "highly disordered non-phase-separating" trap sequences. This significantly improved the models generalization capability and reliability when handling complex, real-world sequences. C_LIO_LIPhysical Mechanism Integration and Functional Expansion: Incorporating the Uniform Manifold Approximation and Projection (UMAP) manifold learning method and constraints from non-equilibrium thermodynamics, a "fingerprint space" capable of characterizing the thermodynamic behavior of phase separation was constructed. This space enables cluster analysis of different MLO types, and the model can output a thermodynamic stability score for protein phase separation. Based on this score, we identified 10 high-confidence candidate proteins with the potential to form novel MLOs. The paradigm established in this study upgrades phase separation prediction from the traditional "binary classification" approach to a novel research framework characterized by "physical mechanism analysis + novel MLO discovery." It provides the phase separation field with a computational tool that combines high accuracy, strong robustness, and good physical interpretability. C_LI

We developed AlphaUnfold, an automated pipeline that couples AF3 predictions with short-time (5 ns) high-pressure Molecular Dynamics (MD) using NAMD3. By subjecting models to baric stress, AlphaUnfold acts as a dynamic "stress-test" to identify structural fragility and potential unfolding. Testing a diverse set of proteins revealed a significant inverse correlation between average pLDDT and Root Mean Square Deviation (RMSD) after MD, indicating that lower confidence translates to rapid structural drift. Furthermore, domains with low local pLDDT consistently exhibited high Root Mean Square Fluctuation (RMSF), a behavior also observed in 200 ns simulations under standard pressure, pinpointing specific metastable areas. AlphaUnfold thus provides a viable, computationally efficient framework for assessing the biophysical robustness of AI-generated models, offering an "experimental-like" validation that ensures more reliable downstream applications in structural biology. MotivationAlphaFold3 (AF3) provides high-accuracy protein models characterized by the Predicted Local Distance Difference Test (pLDDT). However, these static predictions may harbor "not well-forged" regions lacking thermodynamic resilience. There is a critical need for rapid computational protocols to validate structural integrity beyond static confidence scores. AvailabilityGitHub: https://github.com/pegados/pipeline_AlphaUnfold Supplementary informationSupplementary data are available at http://biodados.icb.ufmg.br/alphaunfold Contacte-mail fabio, silva-jrp.miguel@ufmg.br

Sirtuins (SIRTs), which remove protein lysine acyl modifications, play crucial roles in diverse cellular processes, including metabolism, gene transcription, DNA damage repair, cell survival, and stress response. Several sirtuins are considered non-oncogene addiction of cancer cells and promising targets for anticancer drug development. High-throughput screening (HTS) methods for sirtuins are critical for the development of potent and isoform-selective sirtuin inhibitors, which are needed to validate the therapeutic potential. Herein, we designed and synthesized a fluorescent polarization (FP) tracer, KP-SC-1. Using this high-affinity tracer, we developed a robust, high-throughput FP competition assay for screening SIRT1-3 inhibitors. The assay was validated by testing known SIRT1-3 inhibitors. The assay can detect NAD+-independent SIRT1-3 inhibitors, as well as NAD+-dependent inhibitors, such as Ex-527 and TM. Finally, our assay showed satisfactory stability and outstanding performance in a pilot library screening. Compared to previous assays, the FP assay uses much less SIRT1-3 enzymes, a feature important for high-throughput library screening. We believe that the FP assay developed here will accelerate the discovery and development of SIRT1-3 inhibitors.