Chemical Science

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.

Reading both physical and chemical properties of a microenvironment from a single fluorophore remains a challenge. Here we demonstrate that two coexisting molecular equilibria within one near-infrared cyanine, CyC4, encode two mechanistically distinct ratiometric reporting channels. A meso-amino group and a pendant carboxylate form a tunable intramolecular hydrogen bond that toggles the dye between closed (700 nm) and open (780 nm) emissive conformers. Time-dependent density functional theory (TD-DFT) calculations show that the hydrogen bond raises the LUMO and blue-shifts the emission, establishing the 700/780 emission ratio as a local reporter of hydrogen bonding and polarity. Independently, the chromophore self-associates under crowding- and cosolvent-rich conditions into an aggregate with a blue-shifted, H-type absorption signature near 530-540 nm and a distinct emission near 610 nm upon 540 nm excitation. The intensity of this aggregate band relative to the monomer emission (Ra) serves as a ratiometric reporter of crowding and self-association. Because the two channels arise from distinct molecular equilibria (intramolecular hydrogen bonding vs. intermolecular self-association) they are largely decoupled: a glycerol titration series confirms that the self-association channel (Ra) can be moved while the hydrogen-bonding channel stays essentially fixed. Applied to protein-PEG biomolecular condensates, the two ratios move oppositely with increasing salt, showing that the interior's chemical (polarity, hydrogen bonding) and physical (packing, self-association) environments co-vary across the salt series; a single CyC4 measurement thereby maps this coupled microenvironment, providing a general strategy for multiparametric, ratiometric sensing of crowded microenvironments.

Carbapenems are the most potent {beta}-lactams, key antibiotics for healthcare-associated infections by Gram-negative bacteria and evade hydrolysis by most {beta}-lactamases, but are increasingly threatened by emergence of enzymes exhibiting hydrolytic activity towards them. Of the four recognised {beta}-lactamase subclasses, class A (active-site serine enzymes that hydrolyse {beta}-lactams via a covalent acylenzyme intermediate) is the most widely disseminated and, while the majority of such enzymes react with carbapenems to form long-lasting acylenzyme complexes, several possess carbapenem-hydrolyzing activity (carbapenemases). Here, we investigate the basis for these differences in a panel of class A {beta}-lactamases using molecular dynamics (MD) simulations of the respective acylenzyme complexes and tetrahedral intermediates (TI). The simulations reveal multiple features associated with catalytic activity across the spectrum of enzymes tested, including more extensive interactions of the carbapenem acylenzyme carbonyl and generally increased lifetimes of active site water molecules positioned for deacylation. Analysis of the dynamic trajectories shows carbapenemases to have reduced root mean-squared fluctuation (RMSF) differences between the acylenzyme and TI, that are not limited to the active site, indicating that the acylenzyme complex is pre-organised for reaction in carbapenemases but not in carbapenem-inhibited enzymes. Similarly, Principal Component Analysis (PCA) of acylenzyme and TI dynamics shows greater overlap between the two states in carbapenemases, providing further evidence for acylenzyme pre-organisation. Such simulations may represent an effective computational assay able to identify enzymes with carbapenemase activity at relatively modest computational cost.

Disulfide-rich peptides possess exceptional potency and selectivity but are often limited by the instability and synthetic challenges associated with native disulfide bonds. Here, we report the design, synthesis, pharmacological evaluation, and structural characterisation of triazole-based peptidomimetics of the -GI conotoxin, a selective antagonist of the muscle-type nicotinic acetylcholine receptor (nAChR). A series of 1,4- and 1,5-disubstituted triazole analogues were prepared entirely on resin using CuAAC and RuAAC chemistry to replace the native Cys3/13 disulfide bridge. Functional evaluation against human muscle nAChRs revealed that 1,5-triazole analogues retained low-nanomolar potency, with the lead mimetic exhibiting activity comparable to native -GI. Cryo-electron microscopy of the lead compound bound to the muscle-type nAChR provided the first structure of a disulfide-isostere peptidomimetic in complex with a membrane receptor. The structure demonstrates that the 1,5-triazole reproduces the native peptide fold with high fidelity while contributing receptor-facing interactions not available to the native disulfide bridge. Molecular dynamics simulations further revealed conserved hydration networks and similar conformational sampling between the native peptide and lead mimetic. Together, these findings establish triazoles as effective disulfide surrogates and provide a structural framework for the rational design of stabilised conotoxin therapeutics.

Cereblon (CRBN) is widely used in targeted protein degradation, but its ligand space has remained dominated by a narrow set of cyclic imide chemotypes. Here, we show that the accessible CRBN ligand space extends substantially beyond this canonical space. A high-throughput screen of > 40,000 compounds, followed by orthogonal biophysical validation, X-ray crystallography and SAR analyses, identified several chemically distinct ligand classes. These include linear acetyl-based motifs, a phthalide-derived scaffold, steroidal compounds, and a range of bicyclic ligands. They engage CRBN through distinct recognition modes, several of which deviate from the canonical hydrogen-bonding pattern. Steroidal scaffolds were particularly notable: cortisone binds the human CRBN thalidomide-binding domain with an affinity comparable to thalidomide, with its A-ring occupying the tri-tryptophan pocket in a glutarimide-like orientation despite lacking the canonical imide NH donor. SAR within this series showed substantial tolerance for chemical modification and scaffold simplification, raising the possibility that endogenous steroidal metabolites may contribute to the physiological ligand landscape of CRBN. Bicyclic lactams additionally provided synthetically accessible scaffolds with tunable affinity and promising sites for linker attachment. Across the identified ligand classes, none of the tested representatives induced detectable degradation of canonical CRBN neosubstrates, and several showed largely clean proteomic profiles. Together, these findings broaden the chemical, mechanistic and potential physiological landscape of CRBN recognition and provide diverse starting points for alternative, potentially neosubstrate-sparing CRBN recruiters.

Linker chemistry and conformation are central determinants of PROTAC activity, shaping ternary-complex geometry, cooperativity, target-lysine presentation and cellular permeability. Existing linker generators often lack explicit control over linker flexibility, require predefined attachment sites and linker lengths, or produce structures that demand substantial geometric correction, limiting their utility in practical PROTAC design. Here we introduce FlexiTAC, a Bayesian flow network that jointly generates linker atom types and coordinates from the warhead and E3-ligase-ligand contexts. We also assemble PROTAC-3D, a quality-controlled collection of 63,554 component-resolved PROTAC structures for model training, and PROTAC-Bench, which covers molecular quality, fragment preservation, geometric fidelity, conformational stability, fragment awareness, rediscovery and sampling efficiency. Compared to the best 3D baseline models, FlexiTAC improves validity by 12.0-12.7% and achieves the highest PoseBusters pass rate of 79.5%-80.0%. A differentiable guidance module shifted generated linkers along a conformational ensemble-derived rigidity axis without retraining the generator. In silico case studies further show that the model can accept crystal-derived, redocked or predicted structural inputs. Together, FlexiTAC, PROTAC-3D and PROTAC-Bench establish an integrated and reproducible framework for data-driven PROTAC linker design, combining controllable structure-conditioned generation with standardized training data and evaluation protocols. This framework expands the linker chemical and conformational space accessible to computational exploration, provides a foundation for future method development and enables the systematic generation of structure-conditioned linker designs with tunable conformational flexibility.

UCH-L1 is a monomeric deubiquitinating enzyme whose native structure embeds a shallow $5_2$ knot located near the N-terminus, placing the knotted topology in direct proximity to both the substrate-binding pocket and the catalytic site. While our previous work established that N-terminal integrity is critical for catalytic activity, the energetic cost of unknotting and its structural consequences remained unquantified. Here, we combine steered molecular dynamics with an umbrella sampling scheme to generate topologically modified variants of UCH-L1 and, for the first time, reconstruct the free-energy profile of UCH-L1 unknotting. The potential of mean force reveals a steep energetic barrier to knot disruption, consistent with knotting being a late, rate-limiting folding step that is effectively locked in once the native structure is established. Long unbiased MD simulations of fully unknotted variants in both apo and holo states show that knot removal increases local flexibility at the N-terminus without inducing significant global structural destabilization. Binding energy calculations indicate that the unknotted variant binds to ubiquitin less tightly than the wild-type ($\sim$-62~vs~$\sim$-76~kcal/mol), suggesting that topological integrity contributes to substrate affinity. Together, these results show that the $5_2$ knot in UCH-L1 is not a passive structural feature but a functional element that fine-tunes folding kinetics and contributes to substrate binding efficiency.

Amyloid fibrils are highly ordered protein aggregates characterized by a conserved cross-{beta}-sheet architecture despite originating from structurally diverse precursor proteins. Growing evidence suggests that interactions between different amyloidogenic proteins can modulate aggregation pathways through heterologous cross-seeding; however, the influence of seed polymorphism on the structure and biological properties of cross-seeded fibrils remains poorly understood. Here, we investigated the cross-seeding of native human insulin by two structurally distinct polymorphs of hen egg-white lysozyme (HEWL): flexible fibrils (FFs) and rigid fibrils (RFs). Native insulin remained stable under physiological conditions and underwent spontaneous fibrillation only under acidic conditions. In contrast, both HEWL polymorphs efficiently induced insulin aggregation at physiological pH, bypassing the nucleation barrier. Thioflavin T fluorescence, circular dichroism spectroscopy, and transmission electron microscopy revealed that lysozyme FFs templated the formation of insulin flexible fibrils (IFFs), whereas lysozyme RFs produced insulin rigid fibrils (IRFs), demonstrating that the structural characteristics of the parental HEWL polymorphs were propagated during heterologous cross-seeding. The toxicity of the resulting insulin fibrils was evaluated in SH-SY5Y neuronal cells and CCF-STTG1 astrocytes. IFFs exhibited minimal cytotoxicity and only subtle morphological alterations, whereas IRFs caused modest reductions in cell viability accompanied by more pronounced cellular damage. These findings demonstrate that the structural polymorphism of HEWL fibrils governs both the architecture and biological activity of cross-seeded insulin fibrils, highlighting amyloid polymorphism as an important determinant of heterologous amyloid propagation and a potential design principle for engineering functional amyloid-based biomaterials and protein delivery platforms.

Protein synthesis supports most biological processes. In the brain in particular, protein synthesis plays a critical role in physiological and pathological states. Here, we describe Tellurophene-Alkyne Cycloaddition-mediated Amino acid Tagging (TeACAT), a versatile strategy for fast, facile, and flexible tagging of newly synthesized proteins in mice. TeACAT is based on metabolic incorporation of the non-canonical amino acid TePhe into proteins by the endogenous protein synthesis machinery. Due to their high similarity, TePhe can efficiently replace canonical Phe without dietary or genetic manipulation. The subsequent bio-orthogonal reaction of TePhe with either fluorescent dyes or affinity handles enables both visualization and affinity enrichment of proteins synthesized during TePhe exposure. TeACAT is compatible with immunofluorescence for cell-type specific visualization of protein synthesis with subcellular resolution and can be used in conjunction with routine proteomics to identify and quantify newly synthesized proteins. Robust incorporation into the mouse proteome was observed on the scale of hours to days, allowing the interrogation of various biological processes. In summary, TeACAT enables the visualization and quantification of protein synthesis with minimal perturbation for biological discoveries.

The navigational prowess of migratory birds is thought to arise from light-dependent radical-pair chemistry in cryptochrome 4 (CRY4), yet the slow structural transitions that couple photochemistry to signaling remain elusive. Here, we combine temperature-controlled steady-state UV-visible spectroscopy and hydrogen-deuterium exchange mass spectrometry (HDX-MS) to elucidate the photochemical and conformational dynamics of pigeon CRY4 (ClCRY4). Steady-state measurements at 5-25 {degrees}C reveal that lower temperatures slow FAD photoreduction and prolong the FAD neutral semiquinone signaling state. This occurs without a solvent kinetic isotope effect, implicating a conformational change rather than proton transfer as the rate determining step in FAD neutral semiquinone formation. Simultaneous HDX-MS under blue-light exposure identifies protection near the FAD-binding site and C-terminal region. To enhance sensitivity, we developed a pump-probe HDX-MS approach at 10 {degrees}C. This reveals eight peptides (within the phosphate-binding loop, protrusion motif, electron-transfer-chain loops and C-terminal tail) that exhibit rapid ([≤]10 s) and sustained light-induced protection, delineating early conformational rearrangements as a prerequisite for FAD neutral semiquinone accumulation. The findings of slower onset HDX protection as well as a bimodal pattern of deuterium uptake in the phosphate-binding loop further identify a local redistribution of conformational substates on the time scale of the accumulation of the signaling species. Site specific mutagenesis within the CTT supports the findings, which lead to a model in which blue light triggers rapid clamping down of protein near the two regions of spin pair separation, followed by a rate limiting closure of a surface loop. The resolution of time-dependent structural transitions that follow photoactivation of CRY4 resolves the interface between quantum radical-pair formation and classical conformational changes, while providing an enhanced structural framework for the molecular events that underlie avian magnetoreception.

Deep learning has accelerated drug discovery, yet most existing models are trained using in vitro affinity datasets and consequently remain disconnected from the cellular context in which functional ligand-protein interactions occur. This limitation hinders the ability to reflect the complexity of native interactomes and characterize biological responses to molecular perturbation. Here we introduce C-PLANK (Chemi-Proteome Language Attention NetworK), a deep learning framework trained on fragment-protein interactions profiled directly in living cells using fully functionalized fragment (FFF) chemoproteomics. C-PLANK combines physicochemical embeddings with a bilinear attention network (BAN) to model both global cellular context and local residue-atom interactions, generating interpretable interaction fingerprints. Particularly, C-PLANK incorporates Cellular Interaction State Index (CISI), a systems-level evidential metric that contextualizes the biological plausibility of each predicted interaction against the global cellular interaction landscape. Across 431 ligand interactomes curated from eight independent chemoproteomic studies, C-PLANK consistently outperformed current state-of-the-art interaction prediction frameworks under both random and cold-protein evaluation settings. The inferred interaction fingerprints aligned with orthogonal evidence from structure-based pocket predictions, co-crystal structures, and cellular binding-site annotations. C-PLANK further generalized to unseen ligands. In a cellular target-focused discovery campaign, C-PLANK identified a previously unrecognized ligand that was subsequently advanced into an active chemical probe acting as a SIRT3 agonist in cellular assays. By learning directly from cellular chemoproteomics, C-PLANK moves beyond isolated interaction prediction toward cellular interaction-state modelling, establishing a computational foundation for future digital-twin frameworks in drug discovery.

Protein aggregation is a context-dependent process in which the molecular environment can influence the properties of the resulting assemblies. In biological systems, these interactions can occur sequentially, as aggregates formed in one cellular or tissue context may encounter different molecular partners and act as seeds in subsequent aggregation events. Here, we used sequential seeding as a controlled experimental model of this temporal and contextual complexity to investigate how prion-like sequences from the gut microbiome modulate amyloid-{beta} aggregation across successive aggregation cycles. Combining kinetic, biophysical, conformational, and toxicity analyses, we show that early interactions with exogenous peptides modify the properties of first-generation A{beta}40- and A{beta}42-derived seeds, resulting in propagated A{beta}42 assemblies with distinct molecular and functional properties. These findings support an Interaction History model in which exogenous sequences bias the emergence of aggregate populations whose properties and subsequent propagation depend on the molecular contexts experienced during earlier aggregation events. Overall, our results present A{beta} aggregation as a history-dependent process and suggest that single-step assays may fail to capture aggregate diversity that emerges across successive aggregation cycles.

Lysine acylations, including lactylation (Klac), are pivotal regulators of cellular physiology. However, their analysis is currently bottlenecked by antibody enrichment strategies that suffer from sequence bias and require milligram-scale protein inputs, severely precluding the profiling of scarce clinical biopsies and rare cell populations. Here we present ChemIntelligence, an acyl-NHS chemistry-empowered derivatization strategy that rapidly generates unprecedented acylation-specific spectral libraries, exemplified by over 2.5x10^9 human Klac peptides, enabling cross-species reference atlases. Integrated with Prosit-based rescoring, these libraries substantially increase Klac identifications across diverse proteomic datasets. Leveraging this spectral resource, we devised ChemIntelligence Scope, a reproducible, multiplexed parallel reaction monitoring (PRM) platform that quantifies hundreds of Klac peptides per injection from as little as ~200 ng of cell lysates, clinical biopsies, and even true single cells - revealing functional Klac signatures inaccessible to conventional methods. The ChemIntelligence pipeline also extends seamlessly to lysine nicotinylation, underscoring its broad adaptability for discovering and profiling new acylations. Together, these chemical and computational advances establish a scalable, antibody-free framework for acyl-proteome mapping that overcomes input constraints and enables deep functional insights from otherwise intractable biological samples.

Small interfering RNA (siRNA) enables precise post-transcriptional gene silencing for refractory diseases, yet its clinical translation remains limited by the lack of safe and efficient delivery vectors. Inspired by the dissymmetric alkyl chain architecture of natural membrane phospholipids, we designed and synthesized 34 novel ionizable lipids with dissymmetric hydrophobic tails and formulated them into lipid nanoparticles (LNPs). Through systematic physicochemical and biological assessments, we established clear structure-activity relationships and identified two lead LNPs (O14-LNP, H18a-LNP) with superior endosomal escape capacity, enhanced in vivo gene silencing potency, and favorable biosafety relative to the clinical benchmark MC3-LNP. In both streptozotocin-induced and spontaneous db/db type 2 diabetes (T2D) mouse models, lead LNPs delivering ferroptosis-related siRNAs effectively ameliorated glucose and lipid metabolic disorders, restored islet function, and alleviated hepatic steatosis. This study not only lays a theoretical foundation for the rational design of novel ionizable lipids, but also validates the therapeutic potential of siRNA therapy targeting ferroptosis, providing a versatile delivery platform and targeted therapeutic strategy for the treatment of T2D.

Proteins are dynamic molecules existing in diverse conformational states underlying their biological functions. Although recent approaches have enabled diverse conformational sampling by emulating molecular dynamics simulations, perturbing evolutionary information, or steering internal mechanisms of structure prediction models, predicting conformations resulting from major domain motions or motions that occur over long timescales still remains a challenge. To this end, we introduce GNMCADS, a conformational sampling strategy that enhances the diversity of protein diffusion models by selectively annealing the conditioning signal guided by the intrinsic dynamical organization of the sampled protein. Further, we implement GNMCADS in the diffusion module of AlphaFold3, enabling the generation of diverse protein conformations. When benchmarked across 92 proteins that include 54 class A GPCRs, 15 transporters, and 23 proteins with major domain movements, GNMCADS exhibits improved sampling diversity compared to other current conformational sampling methods.

Many biological processes rely on mechanical forces, with protein molecules acting as key mediators. Understanding how proteins respond to mechanical stress is essential for conditions including cardiomyopathy and muscular dystrophy. Natural proteins such as dystrophin and utrophin are composed of heterogeneous folding domains with distinct mechanical properties; deciphering domain-level behavior provides insights into disease mechanisms and informs therapeutic strategies. Single-molecule force spectroscopy (SMFS) enables probing the mechanical properties of entire proteins, yet current approaches struggle to identify heterogeneous folding domains, particularly without prior knowledge. Here, we present the first automated framework to identify heterogeneous folding domains in SMFS data, applying both existing clustering methods and a novel physics-aware deep clustering architecture, LatentUnfold. LatentUnfold learns complementary latent representations from force magnitude and the force-extension physical relationship through dual autoencoders, jointly optimized for clustering assignments. We apply our framework to experimental SMFS data collected from a synthetic two-domain protein (ddFLN4-Titin I27) as well as natural protein constructs of dystrophin and utrophin, with Monte Carlo simulated datasets serving as controlled validation. For the synthetic protein, we recover mechanical properties consistent with previously reported values for each domain. For the natural proteins, we uncover two mechanically distinct domain populations - corresponding to the N-terminal domain and spectrin-like repeats - with differences in both unfolding force and contour length increase, and reveal different unfolding order between them for the first time. This work enables domain-level biological inference, overcoming prior limitations that relied on averaging and overlooked heterogeneity, thus advancing the understanding of mechanical behavior in protein unfolding.

Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native states, remains a significant challenge. Here, we present PathFold, a deep learning framework that predicts protein folding pathways directly from sequence information. PathFold leverages an AlphaFold-based module to extract structural information from the sequence and generates a progressive folding trajectory from an extended conformation using a diffusion model. By modeling the full trajectory, it enables prediction of folding intermediates and transition pathways, analogous to those observed in steered molecular dynamics (SMD) simulations. The predicted pathways reveal well-defined intermediates and sequential folding events, and show agreement with experimental folding data, including measured {Phi}-values.

The binding affinities of a chimeric analog of a riboflavin derivative linked to biotin, (6- (7,8-dimethyl-2,4-dioxo-3,4-dihydrobenzo[g]pteridin-10(2H)-yl)hexyl 5-((3aS,4S,6aR)-2- oxohexahydro-1H-thieno[3,4-d]imidazol-4-yl)pentanoate), referred to as C6-Rf-biotin-tag, to the riboflavin binding retain or streptavidin are in the M range, 1.29 {+/-} 0.277 and 3.00 {+/-} 0.459, respectively. These values suggest that C6-Rf-biotin-tag has potential applications in diagnostic assay and labelling target flavin binding proteins. The C6-Rf-biotin-tag which was characterized with respect to physical and biochemical properties retains UV/Vis spectroscopic and fluorescence behavior similar to riboflavin.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by progressive motor neuron degeneration. Mutations in the SOD1 gene represent the second most common genetic cause of ALS (ALS), and distinct SOD1 missense variants present with markedly different clinical profiles. A4V leads to an aggressive form of the disease (median survival [~]1y), H46R confers a mild, slowly progressive course and I113T exhibits an intermediate phenotype. The molecular basis by which these mutations produce divergent clinical outcomes remains poorly understood. We performed extensive classical molecular dynamics simulations of wild-type SOD1 and the three ALS-associated variants in the apo monomeric state to attempt to investigate the mechanisms behind such phenotypic differences. Structural stability, global compactness, and conformational flexibility, as well as analysis of collective motions between residues and estimation of free energy, were assessed. The H46R, A4V, and I113T variants exhibited distinct dynamic behaviours, highlighting differences in structural stability, local flexibility, and intramolecular interactions. These findings suggest that specific structural regions may contribute differently to protein dysfunction and could represent key elements for understanding the relationship between molecular dynamic properties and the differing clinical severity associated with these variants. Most strikingly, H46R exhibited exceptional structural stability across every analytical level, the lowest global deviation, most attenuated local flexibility, strongest internal dynamic coordination, and the deepest, most confined free energy basins of any system examined. This convergent multi-layered evidence of structural restraint provides a compelling mechanistic basis for the mild and slowly progressive clinical course of H46R ALS, suggesting that enhanced conformational rigidity, rather than bulk destabilisation, is the defining biophysical feature of this variant, and that its pathogenic mechanism operates through a route fundamentally decoupled from the aggregation-driven toxicity that characterises the more aggressive SOD1-ALS mutations.

Online structural characterization during microfluidic lipid self-assembly is important for understanding and controlling the formation of nonlamellar liquid crystalline nanodispersions. Here, we report a 3D-printed, X-ray-compatible hydrodynamic flow-focusing microfluidic chip with variable channel dimensions, integrated with synchrotron small-angle X-ray scattering (SAXS), for position-resolved SAXS-on-chip monitoring of Ca2+-triggered hexosome formation. Hexosomes were produced under continuous flow by mixing ethanolic solutions of docosahexaenoic acid monoglyceride (MAG-DHA), the negatively charged phosphatidylglycerol DOPG, and -tocopherol with Ca2+-containing PIPES buffer. Online SAXS-on-chip measurements detected three Bragg reflections characteristic of the internal inverse hexagonal (H2) phase on a tens-of-milliseconds residence-time scale, revealing rapid structural evolution during microfluidic mixing. Complementary ex situ SAXS identified the DOPG/Ca2+ molar ratio as a key parameter modulating the direct vesicle-to-hexosome transformation and the compactness of the internal H2 nanostructures. Dynamic light scattering showed that the flow-rate ratio modulated nanoparticle size, yielding hexosomes with mean hydrodynamic diameters in the range of approximately 120-175 nm and polydispersity index values down to 0.14 at a total flow rate of 200 {micro}L min-1. Cryo-TEM revealed coexistence of hexosomes and vesicular nanostructures, highlighting morphological heterogeneity, while Coarse-Grained Molecular Dynamics simulations supported a central role of Ca2+-DOPG association in promoting a direct lamellar-H2 phase transition. Overall, this work shows that 3D-printed SAXS-compatible microfluidics can integrate continuous production with online structural characterization, providing a basis for future formulation and process optimization of drug-loaded cubosomes, hexosomes, and related nonlamellar liquid crystalline nanodispersions.