Nanoscale

Proteins in solution adsorb to the corona of nanoparticles such as single-walled carbon nanotubes (SWCNTs), but these interactions are difficult to predict and analyze due to ambiguities in the structure of the latter. In this work, we employ ss(GT)15-DNA wrapped SWCNTs, a commonly used fluorescent sensor construct, to examine protein adsorption by quantifying binding dissociation constants and characterizing the corresponding photophysical effects. A library of 20 proteins are used to evaluate adsorption-induced changes in photoluminescence (PL) intensity ({Delta}I/I0) and emission wavelength upon solution phase binding. We find that 15 proteins produce monotonic dose-response behavior well described using a single-site Langmuir model. Alternatively, five proteins exhibited more complex, non-monotonic behavior consistent with a two-step binding model representing protein-protein interactions coupled to adsorption. The study reveals that metalloproteins, which comprised 12 of the 20 proteins in the library, induced greater PL quenching compared with metal-free proteins for this system, with maximum binding-associated quenching ({Delta}I/I0) of 94% for metalloproteins versus 20% for metal-free proteins. For metalloproteins, we introduce a proximity-based quenching framework in which protein size provides a coarse proxy for cofactor-SWCNT separation, offering a mechanistic interpretation of the observed quenching variation across proteins. Together, these results establish the use of metal coordination sites, such as those in metalloproteins, to assist the transduction of certain nanoparticle fluorescent sensors, helping with sensor probe design and interpretation in biological environments.

It is an essential element of mechanobiology to measure the forces of biological cells. In microparticle traction force microscopy, they are inferred from the deformation of elastic microparticles. Two complementary variants have been introduced before: the volume method, which reconstructs surface stresses from the displacements of fiducial markers embedded inside the particles, and the surface method, which infers stresses directly from the deformation of the particle surface. However, a systematic comparison of the two methods has been lacking. Here, we quantitatively compare both approaches using simulated traction fields representing biologically relevant loading scenarios. We find that the surface method consistently reconstructs traction profiles with substantially lower errors than the volume method, which suffers from displacement tracking and stress calculation at the surface. At high noise levels, however, the performance gap becomes smaller. To compare the performance of the two methods in a realistic experimental setting, we developed DNA-based hydrogel microparticles equipped with both fluorescent surface labels and embedded fluorescent nanoparticles, enabling the direct comparison of the two methods within the same system. Compression experiments produced traction profiles consistent with Hertzian contact mechanics and confirmed the trends observed in the simulations. While our computational workflow establishes a framework to apply both methods, our experimental workflow establishes DNA microparticles as versatile and biocompatible probes for measuring cellular forces.

Cathodoluminescence (CL) microscopy has the potential to achieve a key goal in biological imaging: the simultaneous visualization of proteins and cellular ultrastructure. This goal can be attained by tagging proteins of interest with spectrally distinct cathodoluminescent probes for detection in electron microscopy. To this end, lanthanide nanoparticles (LNPs) are promising probe candidates due to their stability under the electron beam and their distinct ion-dependent emission spectra suitable for multiplexed detection. However, the hydrophobic surface chemistry of LNPs limits their use in biological samples and requires surface functionalization compatible with aqueous environments and EM sample preparation protocols. Here, we use a DNA-based ligand exchange strategy that renders cathodoluminescent LNPs hydrophilic and compatible with further functionalization for specific protein labeling. We characterize the CL emission of DNA-functionalized LNPs following aqueous transfer and common EM preparation steps, including osmium tetroxide staining and drying protocols based on hexamethyldisilazane and critical point drying, and show that LNPs retain their CL emission under all tested conditions. Finally, we demonstrate multicolor CL imaging of spectrally distinct, DNA-functionalized LNPs on the surface of mammalian cells, enabling simultaneous visualization of cellular ultrastructure via secondary electrons and LNPs via multiple CL color channels.

Non-invasive tracking of natural killer (NK) cells remains a major challenge in cancer immunotherapy, limiting our understanding of their in vivo migration and persistence. Magnetic particle imaging (MPI) offers a quantitative, real-time method for visualizing labeled cells, yet optimal labeling protocols for NK cells have not been established. Here, we evaluate commercially available iron oxide nanoparticles (IONPs) for MPI labeling of both NK92MI cells and primary human NK cells. Labeled cells retained viability and cytotoxicity, including activity against three-dimensional tumor spheroids, and were detectable by MPI. To further examine imaging performance in a biologically relevant context, we employed mouse phantoms that recapitulate organ-specific signal distributions, enabling evaluation of quantification and liver spillover effects. We identify key tradeoffs between particle colloidal stability and per-cell iron content: VivoTrax and VivoTrax Plus provided higher MPI signal but required post-labeling purification, reducing cell recovery, whereas Synomag-D and Perimag were more stable and preserved cell yield despite lower signal intensity per cell. These results provide a framework for selecting nanoparticles that balance detection sensitivity, cell viability, and workflow practicality, advancing non-invasive NK cell tracking.

Glioblastoma multiforme (GBM) is among the most aggressive malignant brain tumors originating from glial cells and characterized by severe infiltration into surrounding brain tissue, rendering early detection difficult with current diagnostic imaging methods. S100A4 has been identified as a biomarker protein associated with glioblastoma invasiveness due to its role in cell motility and tumor metastasis. Similarly, midkine (MDK) poses an optimal biomedical target for identifying GBM invasive phenotypes because of its connection to the tumor microenvironment and infiltrative proliferation. Both proteins notably possess a positive charge that interacts electrostatically with the negatively charged phosphate backbone of DNA. It has been established that early molecular detection remains a critical unmet need. This study investigates a promising strategy for GBM diagnosis based on how S100A4 and MDK can selectively bind with DNA tweezer nanostructures. Computationally predicting eight distinct nucleotide sequences yielded three-stranded, hinge-scaffolded tweezer conformations for each candidate. The target protein and DNA structures, derived from AlphaFold, were paired together by molecular docking simulations conducted with HDOCK. Docking analyses evaluated binding affinity, structural complementarity, and conformational stability of the complexes formed. Among the evaluated candidates, DT3_8 computationally established the most biochemically robust interaction with both biomarker proteins. Selectivity is especially important because many S100 proteins share similar electrostatic profiles, yet DT3_8 indicates stronger selectivity for S100A4 and MDK over other S100 family proteins. These findings establish a biomechanical basis for the development of nanoscale DNA biosensors, which suggests the potential for detecting invasive GBM phenotypes, preceding radiographic manifestation and pending experimental validation.

AO_SCPLOWBSTRACTC_SCPLOWSpatially resolved characterization of nanomaterial (NM) distribution within cellular ultrastructure is essential for understanding NM fate and activity in biological systems. Volume electron microscopy (vEM) is uniquely positioned to address this challenge, yet fully documented quantitative pipelines that simultaneously segment NMs and cellular structures remain scarce. Here, an end-to-end analytical pipeline is presented based on the example of serial block-face scanning electron microscopy (SBF-SEM) data of tumor spheroids containing nanoparticles (NPs). A hybrid segmentation strategy is adopted: a fine-tuned Cellpose-SAM model for cells and nuclei, and an empirical Bayes approach for AuNPs. The fine-tuned model outperforms both the pre-trained baseline and benchmark experiments in Amira, and shows good generalization to 2D EM datasets of varying sample types, suggesting potential as a general-purpose segmentation model for electron microscopy. Full 3D reconstruction of NP distributions reveals preferential clustering in the perinuclear region, with a median nucleus-to-NP distance of 2.57 {micro}m and NM uptake spanning several orders of magnitude across cells. Furthermore, morphological analysis of segmented cells and nuclei using 3D shape descriptors and local curvature metrics provides quantitative access to features inaccessible from single sections. Together, these results establish a reproducible, open framework for the joint quantitative analysis of NM distribution and cellular morphology in vEM data.

Intratumoral (i.t.) delivery of nanoparticles (NPs) is widely used to achieve high local NP concentrations. However, the temporal fate of i.t.-injected NPs remains poorly understood. We present a quantitative approach using whole-body magnetic particle imaging (MPI) to track magnetic NPs (MNPs) following i.t. injection. Using fiducial-calibrated imaging, we quantified MNP mass over time in subcutaneous 4T1 breast tumors. Longitudinal imaging revealed progressive loss of i.t. MNP content and heterogeneous systemic redistribution across animals despite standardized delivery conditions. Ex vivo MPI confirmed off-target accumulation primarily in the liver and spleen, consistent with reticuloendothelial clearance pathways. Histological analysis demonstrated spatially heterogeneous i.t. MNP deposition, potentially associated with local vascular features and tumor microenvironmental heterogeneity that may influence i.t. MNP retention or MNP clearance from the tumor. These findings highlight the importance of quantitative longitudinal whole-body MPI for understanding the fate of MNPs for informing localized nanotherapy.

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.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global health challenge. Currently treatment of drug-sensitive TB, involves a six-month regimen consisting of a combination of four anti-TB drugs, with drug-resistant TB requiring over two years of treatment and additional drugs. As toxicity of anti-TB drugs often leads to poor compliance, disease relapse and the emergence of drug-resistant strains, new strategies to reduce drug toxicity and shorten treatment duration are critical. We report nanocarrier-based drug delivery systems targeting macrophages, which primarily support replication and survival of Mtb. We have developed mannose-functionalized nanoparticles that bind to mannose receptors on macrophages and feature a pH-sensitive core which releases an encapsulated drug in the acidic lysosomal environment of macrophages. Rifampicin (RIF), a main anti-TB drug currently in use clinically, was encapsulated within the nanoparticles. We demonstrate that antibiotic-containing nanocarriers efficiently accumulated in macrophages without causing toxicity. Encapsulated RIF showed enhanced efficacy against both BCG and Mtb in primary macrophages. Biodistribution studies in mice revealed that the nanoparticles have extended circulation time and do not induce toxicity. In addition, the encapsulated RIF showed better targeting of mycobacteria when compared to free RIF in a murine model of mycobacterial infection. Such an enhanced bacterial killing using mannose-functionalised nanocarriers loaded with the key anti-TB drug rifampicin offers excellent potential for TB therapy.

Dragonfly and cicada wing-inspired titanium nanopillar surfaces show promising bactericidal properties for antibacterial medical implant applications, but the precise mechanisms of bacteria-nanopillar interactions under hydrated conditions remain unclear. Cryo-electron tomography (cryo-ET) enables the visualisation of cellular organelles within their native hydrated cellular environment at molecular resolution. Visualising the bacteria-material interface on nanostructured surfaces by cryo transmission electron microscopy (cryo-TEM) requires the preparation of thin lamellae. Obtaining lamellae of bacteria directly on metal substrates while in a non-fixed and hydrated state requires cryo-focused ion beam (cryo-FIB) milling to isolate the targeted bacteria from the bulk sample. This approach faces additional challenges compared to tissues or cells on TEM grids, as titanium samples require a simultaneous cross-section of soft and hard materials at the same position and require vitrification, which embeds the sample in a thick layer of ice. Nonetheless, we demonstrate how to target a specific bacterium interacting with a titanium nanopillar surface using correlative cryo-fluorescence imaging, and how lamellae can still be prepared from vitrified samples by extracting the targeted bacterium and its surrounding as a small volume and transferring it to a receptor grid for thin lamella preparation, called targeted cryo-lift-out. Here, we outline the workflows and discuss their advantages and limitations for producing lamellae through lift-out techniques under cryogenic conditions, using methods that do not involve gas injection systems (GIS) for the lift-out transfer. These advances enhance cryo-ET applications, enabling in situ investigations of the interface between bacteria and nanopillars to effectively study the bactericidal mechanisms of biomimetic nature-inspired nanotopographies in a hydrated environment.

Surface functionalization of inorganic quantum dot nanoparticles is of great interest in the application of these materials toward a wide range of biological applications where membrane interactions are critical. The use of amphiphilic lipids to functionalize the surfaces of quantum dots represents a promising alternative to produce water-soluble and membrane-active materials with facile tuning of the quantum dots surface properties. Here, we demonstrate an experimental approach that yields lipid-coated quantum dots with highly tunable surface charge by controlling the concentration of cationic lipids during preparation. Through fluorescence-activated cell sorting assays, we show that these cationic lipid-coated quantum dots can enhance membrane interactions and increase membrane labeling density in live HEK293 cells. We further employed coarse-grained molecular dynamics simulations to model the lipid self-assembly process using an implicit solvent force field and subsequently model the adsorption of lipid-coated quantum dots to model membranes. Our simulations show that we can control the effective surface charge of lipid-coated quantum dots and influence the strength of adsorption to oppositely charged lipid membranes, a process that is mediated by the release of counterions at the quantum dot-membrane interface. This work supports the future development of biocompatible and water-soluble inorganic nanoparticles with highly tunable surfaces, and provides mechanistic insight into how different lipids can influence nanoparticle-membrane interactions at a molecular scale.

Fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) centers are promising quantum sensors for intracellular measurements, yet nuclear applications remained out of reach because optically detected magnetic resonance (ODMR) signals are weak and capillary delivery is inefficient. This study addresses both constraints by optimizing the electron irradiation dose to balance NV creation and charge-state stability, and by grafting hyperbranched polyglycerol with terminal carboxyl groups (HPGCOOH) to suppress aggregation and prevent needle clogging. The optimized dose yields strong ODMR contrast while preserving fluorescence suitable for microscopy. HPGCOOH surfaces enable smooth and reproducible microinjection through fine capillaries. Using this strategy, the microinjection of ODMR-active FNDs into the nuclei of living COS7 cells is achieved, and clear intranuclear spectra comparable to cytoplasmic readouts are obtained. Furthermore, field-of-view temperature sensing across multiple cell nuclei is demonstrated, enabling quantitative and spatially resolved thermal mapping within the genomic environment. This methodology provides a practical route to nuclear quantum sensing and opens opportunities for nanoscale physicochemical measurements within the genomic environment.

Preparing electron-transparent cryo-lamellae is inherently a serial and low-throughput process. Once the lamellae are milled, these thin structures endure both mechanical and thermal stress, and as a result many valuable lamellae crack or even disintegrate entirely. This loss is often regarded as a "lamella tax", i.e. an unavoidable cost of working with such fragile specimens. In this work, we introduce two modifications to the standard lamella-preparation workflow aimed at improving lamella mechanical resistance to crack formation and external stress. The first modification involves milling arrays of perforations directly within the lamella body. These perforations are designed to function as crack-arrest holes, intercepting cracks as they appear and preventing, or at least delaying their further propagation. By slowing crack growth, these features increase the likelihood that the lamella remains intact long enough to complete cryo-TEM imaging. The second modification replaces the conventional rigid attachment of the lamella to the surrounding cellular bulk material with a softer suspension using ring-shaped springs formed by ion beam milling. Mounting the lamella on smooth annular springs provides mechanical compliance both across and along the lamella axis, as well as at intermediate angles and in the out-of-plane direction. This flexibility allows the lamella to accommodate larger stresses and deformations without reaching its mechanical failure threshold. We fabricated a series of test lamellae incorporating different crack-arrest hole geometries, as well as lamellae suspended on soft annular springs. We performed high-resolution cryo-TEM imaging to characterise the perforations themselves and characterised the captured crack geometry within the lamellae at the highest level of detail achieved to date. TEM imaging shows crack interception and guided, non-catastrophic failure paths, while simulations confirm lowered stress in suspended lamellae.

Injectable biomaterials with aligned microstructures play a critical role in tissue engineering and drug-delivery applications where control over the position and orientation of cells and nano/micron-scale architectures enhance intervention efficacy. Patients are often subject to MRI scans; for patient safety and treatment efficacy, we investigated the effects of MRI on a biomaterial treatment consisting of aligned magnetic microstructures being developed for guiding cell growth. Under MRI exposure, potential movement of aligned structures could be detrimental to nearby cells, and potential MRI-induced heating could adversely affect traumatized tissue. In this work, the alignment state and heat conduction of such a treatment were studied using a 9.4 T preclinical MRI. The treatment comprises short magnetic rod-shaped polycaprolactone fibers (rods) with embedded magnetic nanoparticles in a surrounding hydrogel (gelatin methacrylate), with rod alignment observed before and after a 45-minute MRI scan. No change in rod alignment state was observed, and no heat generation was measured. A theoretical framework was developed which supports the experimental observation that the biomaterial is stable under MRI. This work can be extended to other biomaterial systems with aligned architectures used in tissue engineering applications such as spinal cord, muscle and tendon.

Tetrahedral DNA nanostructures (TDNs) are promising nanocarriers due to their structural precision, biocompatibility, and efficient cellular uptake. However, their stability under physiological conditions remains a key challenge. In this study, TDNs were synthesized via a one-pot thermal annealing method and characterized using native PAGE, dynamic light scattering (DLS), and zeta potential analysis, confirming uniform size ([~]13 nm) and negative surface charge. Their stability was systematically evaluated across different biological media (DMEM complete, serum-free DMEM, and E3), temperatures (4 {degrees}C, 25 {degrees}C, and 37 {degrees}C), and pH conditions (4.0, 7.0, and 8.5) over 24 h. Results revealed rapid degradation in serum-containing medium, increased instability at higher temperatures, and reduced stability under acidic conditions, while serum-free, lower-temperature, and neutral to mildly basic environments enhanced structural integrity. These findings highlight the strong environmental dependence of TDN stability and provide insights for optimizing their design for biomedical applications.

Curcumin-functionalized gold nanoclusters are promising platforms for catalysis and drug delivery, yet the molecular determinants of their stability, morphology, and solvent response remain unclear. Here, microsecond all-atom molecular dynamics simulations are employed to investigate a 2 nm gold nanoparticle noncovalently coated with different curcumin forms, including neutral enol and trans-keto tautomers, the deprotonated enolate, and their mixtures in water-ethanol and water-methanol solvents. Layer-resolved analyses of radius of gyration, density profiles, and surface coverage reveal that neutral enol and trans forms generate compact assemblies with near-complete surface coverage, whereas enolate-rich systems adopt more expanded conformations with solvent-exposed molecules. Mixed systems preserve these intrinsic packing characteristics while improving overall coverage. Solvent substitution from ethanol to methanol reduces {pi}-{pi} stacking, strengthens Au-curcumin interactions, and increases surface coverage, yielding more compact nanostructures. Free energy and potential of mean force calculations indicate that deprotonated curcumin most effectively screens Au-Au interactions and stabilizes dispersed nanoparticles, while neutral tautomers provide moderate stabilization. Curcumin also enhances the loading of anticancer drug doxorubicin (DOX) onto Au nanoparticles, improving biocompatibility. Enolate(An)-containing systems produce extended structures with weaker membrane interactions, whereas neutral curcumin complexes form compact, positively charged assemblies that strongly bind to negatively charged cancer cell membranes. These findings clarify how tautomeric state and solvent environment cooperatively govern interfacial organization and colloidal stability, establish design guidelines for curcumin-based gold nanocarriers in catalysis, sensing, and drug delivery applications.

The optimization of mRNA-lipid nanoparticles (mRNA-LNPs) for therapeutic applications is limited in part by the inadequate characterization of mRNA payload heterogeneity. One current challenge is accurately measuring the number of mRNA copies within individual LNPs, where the standard method of intensity-based mRNA number determination is sensitive to fluorescent dye-dye interactions and heterogeneity of mRNA labeling. Here we present a single-particle microscopy method that combines direct counting of the mRNA copies per LNP with LNP size measurements. While confined in microwells, individual mRNA-LNPs are lysed to release their cargo and stained with a dye such that the number of mRNA molecules in each well can be directly counted using fluorescence microscopy. Since the method stains the mRNA cargo in situ, it enables characterization of LNPs formulated with therapeutic grade (e.g., unlabeled) mRNA. We applied this approach to two Onpattro(R)-based LNP formulations prepared using different formulation buffers, where the two formulations had different average mRNA copy number, particle size, and fraction of LNPs lacking mRNA. The ability to directly count the number of mRNA molecules in LNPs establishes a complimentary method to intensity-based mRNA number determination and supports the characterization and screening of clinically relevant LNP formulations.

To achieve a therapeutic effect, nanoparticles delivering nucleic acids must facilitate endosomal escape (EE) of their cargo. Despite extensive research, the mechanisms that lead to an effective EE are not sufficiently understood. Herein, we utilized Molecular Dynamics (MD) simulations in All Atom (AA) and Coarse Grained (CG) resolutions to differentiate the interaction of four polymeric formulations (polyplexes) and one lipid nanoparticle (LNP) with endosomal membranes. On the one hand, the results emphasize the benefit of hydrophobic residues in the nanoparticles. On the other hand, the role of anionic lipids in the biological membranes is demonstrated. Furthermore, the identified interaction patterns were successfully correlated to the in vitro performance of the formulations. For the first time, different EE mechanisms of polyplex formulations are visualized in simulation and therefore distinguishable from one another. Hence, this work highlights the power of MD simulations for taking a big step towards better understanding EE efficiency. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/711661v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@abba74org.highwire.dtl.DTLVardef@5e2b8eorg.highwire.dtl.DTLVardef@7db144org.highwire.dtl.DTLVardef@1034e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Atomic force microscopy (AFM) enables nanometre-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited compared to other bioimaging modalities. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artefacts, data formats, and topographical outputs. Here, we present the latest version of TopoStats, an open-source Python package for automated and quantitative AFM image analysis, developed as a deep-learning enabled advancement of our original TopoStats software to support more complex samples and richer molecular characterisation. The pipeline integrates all key processing stages, including image flattening and noise correction, object detection and segmentation, morphometric feature extraction, and strand tracing with topological classification. Designed for accessibility and reproducibility, TopoStats adheres to the FAIR for Research Software (FAIR4RS) principles and provides configurable workflows adaptable to diverse biological samples. Combining high-resolution AFM and our analysis pipeline allows the quantification of subtle structural changes within a heterogeneous sample set, revealing properties not accessible with other structural biology techniques. We demonstrate the effectiveness of our pipeline to differentiate between plasmids with both different topology and sequence, by extracting meaningful quantitative descriptors that distinguish the samples with statistical significance. Collectively, these developments establish TopoStats as a versatile framework for high-throughput, quantitative AFM analysis, advancing AFM from a fundamentally qualitative visualisation technique toward a quantitative analytical tool.

Polypeptoids (poly-N-substituted glycines) are synthetic peptidomimetic polymers with their sidechains attached to the backbone amide nitrogen rather than the -carbon in natural peptides. Peptoids display pronounced sequence-dependent conformational flexibility arising from the absence of backbone hydrogen bonding and slow cis/trans {omega}-dihedral isomerization. Despite growing interest in peptoid-based biomaterials, a coarse-grained (CG) model compatible with the modern MARTINI 3 framework is not yet available, limiting mesoscale simulation of peptoid structure and self-assembly. In this work, we develop the first MARTINI 3 compatible peptoid CG forcefield, covering 19 commonly used residue types. Extensive all-atom reference simulations employing parallel bias metadynamics (PBMetaD) were performed to ensure converged sampling of {omega}-dihedral transitions. Bonded parameters were derived from atomistic distribution functions via direct Boltzmann inversion (DBI), while nonbonded interactions were primarily adopted from the standard MARTINI 3 parameter library. The resulting CG model reproduces structural and thermodynamic properties in close agreement with all-atom simulations, while providing up to 57-fold enhanced computational efficiency. To facilitate its adoption by the research community, we have integrated all parameters and workflows to the MARTINI-based martinize2 tool, enabling automated generation of MARTINI 3 peptoid structures and topologies. This work establishes a transferable and computational efficient framework for simulating large-scale peptoid confirmations, assemblies, membrane interactions, and nanostructure formation, and supports the rational design of next-generation sequence-specific functional peptoid-based materials.