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.

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 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.

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

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.

Antibiotic resistance continues to rise, yet most new drug candidates act through long-established targets. Faster mode of action (MoA) assessment would enable more effective prioritization of screening hits and help identify compounds with novel mechanisms. In this study, we aimed to develop a scalable framework for MoA inference from antibiotic-induced cellular response profiles in Escherichia coli. We generated a multimodal dataset spanning more than 50 antibiotics, including proteome profiles, chemical structure descriptors, inhibitory concentrations and growth dynamics, and used it to build MAPPER (Mode of Action Prediction via Proteomics-Enhanced Representation), a framework comprising a fixed multimodal predictor and an uncertainty module. MAPPER accurately classified antibiotics across nine mechanistic classes, flagged compounds with likely novel mechanisms and retained predictive power in proteomics-only transfer experiments across mass spectrometry platforms and external data. Together, these results establish MAPPER as an innovative tool for MoA prediction and novelty detection, enabling prioritization of antibacterial candidates with distinct mechanisms.

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.

The glucagon-like peptide-1 receptor (GLP-1R) plays a central role in metabolic regulation and is a major therapeutic target for obesity and diabetes. Peptide agonists, like semaglutide, targeting the GLP-1R remain among the most effective regulators of glucose metabolism and appetite. Nonetheless, recent reports about weight regain have limited the effectiveness of GLP1R peptide agonists, sustaining the interest in expanding the chemical diversity of GLP-1R ligands through drug discovery strategies. However, the structural complexity and conformational plasticity of class B1 GPCRs make conventional single-method virtual screening approaches prone to bias and limited chemotype recovery. Using an integrated ligand- and structure-based virtual screening pipeline, explicitly combining complementary ligand-based descriptors, multi-fingerprint similarity, electrostatic similarity, pharmacophore modeling, and multi-conformation docking under a consensus-driven selection strategy, we were able to identify three chemically distinct classes of GLP-1R agonist candidates: GQB47810, a non-peptidic molecule; neuromedin C, a peptide, and 2,5-Pen-enkephalin (DPDPE), a small peptide. From all of them, DPDPE showed the greatest effectiveness, reaching values similar to those of GLP-1, although with lower potency. Further in vitro characterization confirmed that pen-enkephalin behaved as a full agonist and exhibited dual GLP-1R/GIPR agonistic activity. These findings establish a consensus-driven and transferable computational framework for chemotype-diverse agonist discovery at conformationally flexible GPCR targets, and revealed a pentapeptide with GLP-1-like efficacy as a promising lead for next-generation small peptide therapeutics.

Nanodiscs are nanoscale lipid bilayer membrane mimetics surrounded by two membrane scaffold proteins (MSP). They are widely used as soluble cassettes for membrane proteins and lipids in diverse applications. The original MSP1 was derived directly from human apolipoprotein A-1, and novel constructs have been adapted from this original design, including nanodiscs with larger sizes and covalent circularization. Here, we developed MSPs with a range of different fluorescent C-terminal protein tags, including a versatile HaloTag fusion. These fluorescent MSP were purified following typical MSP purification procedures with similar yield. Then, we demonstrate that fluorescent MSPs form nanodiscs with similar structure and stoichiometry to conventional MSP nanodiscs. These fluorescent MSP constructs enable a range of different applications and provide a versatile template for future design of nanodiscs with unique functions. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/716332v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@f85870org.highwire.dtl.DTLVardef@764055org.highwire.dtl.DTLVardef@179b7c5org.highwire.dtl.DTLVardef@ff6a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

PTCH1, the receptor for the Sonic Hedgehog morphogen, mediates cholesterol transport across the plasma membrane by harnessing the proton motive force. In cancer, PTCH1 is frequently overexpressed and promotes chemoresistance by transporting drugs such as doxorubicin (dxr) out of cells. Among the inhibitors identified, PAH stands out for its ability to significantly enhance the efficacy of several chemotherapeutic drugs on melanoma and breast cancer cells. To investigate PTCH1s structure in complex with its inhibitor PAH, we overexpressed a construct spanning residues 1-619 and 720-1305 in HEK293 cells. The protein localized to the membrane, and transfected cells exhibited reduced sensitivity to dxr compared to control cells. Additionally, we observed a pH-dependent efflux of dxr, which was reversed by PAH, confirming that the PTCH1 construct used in this study functions as an active drug-efflux pump. In the structure of PTCH1 bound to PAH determined using cryo-electron microscopy, PAH occupies a hydrophobic cavity in an extracellular domain which is normally occupied by cholesterol in other PTCH1 structures, and engages in a key hydrogen bond via one of its hydroxyl groups, a feature previously established as essential for its inhibitory function. These findings not only clarify the molecular basis of PAHs action but also provide a structural roadmap for rational drug design, enabling the development of next-generation inhibitors with enhanced potency.

Multidrug resistant (MDR) bacterial wound infections are an increasing clinical challenge and require alternatives to conventional antibiotics. Although antimicrobial proteins offer promise, their therapeutic use is limited by poor stability, proteolytic degradation, reduced activity under physiological conditions, and potential toxicity. This work reports pH-sensitive lipid nanocarriers composed of granulysin (GNLY) and oleic acid (OA) for antimicrobial delivery to infected tissues. At neutral pH, GNLY is retained within OA-based nanocarriers and protected from proteolytic degradation. At pH 5.0, such as in infected wounds, the carriers undergo structural reorganization and release GNLY, restoring antimicrobial activity. OAGNLY (32 {micro}g/mL) achieved >3-log reductions in Staphylococcus aureus and Escherichia coli within 1 hour, and up to 4-log reductions in Pseudomonas aeruginosa and Acinetobacter baumannii, at physiological salt concentrations where free GNLY was largely inactive. Minimum inhibitory concentrations were 16 {micro}g/mL for MRSA and 32 {micro}g/mL for colistin-resistant E. coli. Ultrastructural analysis using transmission electron microscopy revealed disruptions of bacterial membranes and intracellular structures following OAGNLY treatment. In a murine surgical wound infection model, topical application of OAGNLY for 4 hours reduced bacterial burden by >5 logs and significantly decreased inflammation, as confirmed by histological analysis. In parallel, OAGNLY demonstrated minimal cytotoxicity to mammalian cells at active concentrations. These findings identify OAGNLY nanocarriers as a promising platform for pH-responsive delivery of GNLY and highlight their potential application for treating MDR skin and soft tissue infections..

Cholesterol is a key component of cellular membranes, regulating membrane organization, fluidity, and signaling. However, cholesterol analysis remains technically challenging, as no single method currently allows both accurate quantification and spatially resolved visualization. Biochemical assays provide accurate quantification but lack spatial resolution, whereas imaging strategies can perturb membrane organization or cholesterol accessibility. Here, we describe optimized protocols using fluorescent D4 probes derived from the cholesterol-binding domain of perfringolysin O (D4-mCherry and D4-GFP) to detect, visualize, and quantify cholesterol in biological samples. We detail procedures for probe production, purification, and application, and establish conditions that ensure robust and reproducible labeling of membrane-accessible cholesterol. By combining fluorescence-based imaging with quantitative analysis, this approach enables the assessment of cholesterol distribution while preserving its native membrane environment. The proposed methodology provides a versatile and reliable framework for studying cholesterol in a wide range of experimental systems.

G protein-coupled receptors (GPCRs) are critical regulators of human physiology and major drug targets. Although structural studies have provided valuable insights, determining GPCR structures remains challenging, especially for inactive state receptors. Recent advances in cryo-electron microscopy (cryo-EM) have enabled structural determination of small GPCRs by using fusion partner proteins and binders to increase molecular weight. However, current methods require extensive experimental screening of fusion constructs. Widely adopted strategies, such as BRIL-Fab complexes, also face limitations due to inherent flexibility. Here, we introduce a streamlined and universal pipeline that integrates an in silico fusion construct screening program, NOAH (NOAH: NOn-experimental, AI-assisted High-throughput construct screening), with a de novo designed fusion protein called ARK1 (ARtificially-designed fiducial marKer). We validate the efficacy of NOAH by determining the structures of the vasopressin V2 receptor (V2R) bound to the clinical antagonist tolvaptan and the partial agonist OPC51803, as well as the bradykinin B2 receptor (B2R) bound to the clinical antagonist icatibant, thereby elucidating their activation and deactivation mechanisms. Furthermore, we demonstrate the capability of NOAH-ARK1 by solving the tolvaptan-bound V2R structure at higher resolution and showcase the methods versatility by determining the structure of lysophosphatidic acid receptor 2 (LPA2) bound to the antagonist Ki16425. This approach eliminates the need for time-consuming and labor-intensive construct optimization, providing a rapid and widely applicable solution for high-resolution GPCR structure determination and drug discovery.

Despite rapid progress in AI agents for computer-aided drug design (CADD), protein-ligand simulation workflows remain fragmented across disparate tools, creating a major bottleneck for scalable candidate evaluation. Here, we present PRISM (Protein-Receptor Interaction Simulation Modeler), a Python platform built on GROMACS that unifies ligand parameterization across multiple force fields, automated system construction, enhanced sampling, multi-tier binding free energy estimation, and trajectory analysis within a single workflow. Through the Model Context Protocol (MCP), PRISM further serves as the computational infrastructure for CADD-Agent, an expert-workflow-driven AI agent designed to orchestrate hierarchical drug screening pipelines. As a pilot application, we applied PRISM to riboflavin synthase and demonstrated end-to-end automation from candidate library assembly to binding pocket characterization, identifying a potential allosteric inhibition site at the oligomerization interface. Together, these results establish PRISM as a high-throughput simulation infrastructure for agent-enabled CADD.

Limited proteolysis coupled to mass spectrometry (LiP-MS) is a peptide-centric conformational proteomics approach during which a brief incubation with a non-specific protease (e.g., proteinase K) under native conditions generates structural fingerprints that report on treatment-induced conformational changes, which is followed by a tryptic digest under denaturing conditions allowing to read out these fingerprints 1. In contrast, the recently introduced peptide-centric local stability assay (PELSA) uses a high trypsin-to-substrate ratio under native conditions to release fully tryptic peptides that reflect structural stability upon ligand binding 2. In their paper, Li et al. compared PELSA and LiP-MS across several benchmarks and reported that PELSA exhibited quantitative sensitivity comparable to or exceeding LiP-MS. Notably, PELSA quantified a 21-fold greater rapamycin-induced change for FKBP1A compared to LiP-MS. Because such claims influence method selection for conformational proteomics, we reanalyzed the publicly deposited datasets underlying these comparisons and assessed the experimental and analytical choices that contributed to the reported effect sizes. Our evaluation indicates that the reported 21-fold difference arises from non-matched experimental conditions and undisclosed data imputation, and that conclusions regarding quantitative superiority or biological interpretability should therefore be treated with caution.

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

Tumour biomarkers such as CA125, CA15-3, CA19-9, AFP and CEA are routinely used in the oncology clinic to diagnose cancer, monitor response to therapy, and detect relapse. However, their quantification depends on immunoassay-based methods that are time-consuming, reagent-dependent, and poorly suited to resource-limited settings. Here, we present a machine learning-assisted ATR-FTIR spectroscopy approach for label-free tumour biomarker analysis to enable simple and rapid quantification at the bedside. Using principal component analysis (PCA), we first demonstrate that these five clinically relevant biomarkers are spectrally separable, with the protein-associated region (1200-1700 cm-1) providing the greatest discriminative information. We then develop partial least squares regression (PLSR) models to quantify CA125 in phosphate-buffered saline (R2 = 0.95) and in human serum across a clinically relevant concentration range, achieving reliable predictions at and above the clinical decision threshold of 35 U/mL. A semi-quantitative classification model further demonstrated robust identification of elevated CA125, with a macro-average sensitivity of 0.86 and specificity of 0.92. These results support ATR-FTIR spectroscopy as a rapid, reagent-free platform for cancer biomarker monitoring, with potential utility in resource-limited settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/714840v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1be9c03org.highwire.dtl.DTLVardef@f49e5eorg.highwire.dtl.DTLVardef@1c93e39org.highwire.dtl.DTLVardef@1141e6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Inositols are a family of cyclic sugar alcohols comprising nine stereoisomers. Myo-inositol is the most abundant isomer found in humans and has been studied most extensively. It plays an important role in osmoregulation and is incorporated into membrane-anchored phosphatidylinositols. Scyllo-inositol is the second most abundant inositol isomer in the human brain and aberrant concentrations are associated with various diseases; however, its biological functions remain poorly understood. Here, the development and application of [13C6]scyllo-inositol as an isotopic tracer to study its metabolism is reported. A concise and robust synthetic route was established to obtain [13C6]scyllo-inositol from [13C6]myo-inositol in good yield. The uptake of [13C6]scyllo-inositol and responses of endogenous inositol isomers were measured in multiple cell lines by HILIC-MS/MS, showcasing the advantages of isotopic tracing. [13C6]scyllo-inositol proved to be a versatile isotopic tracer, when coupled with MS-based lipidomics and 2D NMR experiments. These experiments provide evidence that scyllo-inositol is incorporated into phosphatidylinositols in different cell lines. The results suggest a previously underappreciated role of scyllo-inositol in mammalian cells. The utilization of [13C6]scyllo-inositol will help to elucidate the role of scyllo-inositol metabolism in healthy and diseased states. SignificanceScyllo-inositol is a cyclic sugar alcohol found predominantly in the human brain. Changes in its concentration are associated with different diseases, and scyllo-inositol has been investigated as a potential drug against Alzheimers disease in clinical trials. However, its metabolic fate in mammalian cells is not well understood. We report here a synthetic strategy to obtain [13C6]scyllo-inositol and demonstrate, through isotopic tracing, its incorporation into phosphatidylinositols in different human-derived cell lines. This new stable isotopic tracer enables the investigation of the biological role of scyllo-inositol in mammals and beyond. HighlightsO_LIConcise synthesis of [13C6]scyllo-inositol C_LIO_LI[13C6]scyllo-inositol uptake and response of endogenous inositol isomers studied in multiple cell lines C_LIO_LIUse of [13C6]scyllo-inositol as an isotopic tracer in metabolomics and lipidomics experiments C_LIO_LIEvidence for scyllo-inositol incorporation into phosphatidylinositol in mammalian cells C_LI

Biomolecular condensates form through phase separation driven by multivalent interactions in eukaryotic cells, yet the factors that control small molecule partitioning remain incompletely understood. Building on previous evidence linking hydrophobicity and solubility to condensate affinity, we applied machine learning models to evaluate the role of ionization in this process. Using RDKit molecular descriptors, we trained regularized XGBoost regressors and classifiers across four representative condensates: cGAS-DNA, SUMO-SIM, SH3-PRM, and DHH1. Inclusion of logD, a pH dependent distribution coefficient that reflects effective lipophilicity, consistently improved predictive performance compared to models using only logP or logS. SHAP analysis identified logD as the dominant contributor to model predictions, suggesting that ionization coupled partitioning governs molecular localization within condensates. The addition of three-dimensional descriptors provided no further benefit, indicating that two dimensional physicochemical features and logD are sufficient to capture the main determinants of phase separation behavior. These findings establish logD as a mechanistic link connecting ionization, hydrophobicity, and small molecule partitioning in condensates, and offer a predictive framework for understanding small molecule behavior in these dynamic environments.