Biology subjects

Liu, Y.

Publications and source records attributed to Liu, Y..

At least 487 records · Page 27Linked to original sources

bioRxiv2025

From Hands to Feet: Experience-Driven Plasticity in Secondary Somatosensory Cortex in People Born without Hands

In congenital handlessness, where individuals rely on their feet for compensatory manual functions, reorganization in the primary somatosensory cortex (SI) appears limited to local shifts by adjacent body parts. It remains unclear how congenital handlessness and lifelong compensatory foot use shape the sensorimotor system beyond SI, and whether compensatory abilities can drive broader functional reorganization. Here, we used task-based and resting-state functional magnetic resonance imaging (fMRI) to investigate how compensatory foot use changes body representations in the secondary somatosensory cortex (SII) in people born without hands (individuals with upper-limb dysplasia, IDs). We found that IDs showed foot selectivity in the left hand-selective SII, with stronger foot activity than typically-developed (TD) control participants during action execution. Notably, this reorganization in the left SII hand area cannot simply be explained by this areas intrinsic organization - the typical secondary preference of this area in the TDs when hand responses are excluded, as this area in TDs shows shoulder - but not foot - selectivity when the hand activity is excluded. This indicates a functional reorganization dependent on the IDs sensorimotor experience. Resting-state results further revealed altered functional connectivity between SII and primary sensorimotor regions in IDs. Our findings suggest that SII exhibits flexible, experience-dependent plasticity, revealing a hierarchical principle of cortical plasticity, whereby reorganization in the SI is constrained by somatotopic principles while SII reflects functionally or experience-dependent plasticity.

neuroscience↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2025

An in situ generated CAR-M with IFNg and negative dominance Sirpa isoform augments hepatocellular carcinoma immunotherapy

Background and AimsChimeric antigen receptor (CAR) T cells have shown strong efficacy in hematological cancers but limited success in solid tumors. Macrophages, with their natural ability to infiltrate tumors, modulate immunity, and phagocytose cancer cells, offer a promising alternative when engineered with CARs. While studies have demonstrated the feasibility and anti-tumor activity of CAR macrophages (CAR-M), enhancing their persistence and phagocytic capacity remains a key challenge. Methods and ResultsBuilding on first-generation CD3{zeta}-based CAR-M, we developed a novel CAR targeting human GPC3, incorporating IFN-{gamma} and the extracellular domain of SIRP (SIRPECD) to improve CAR-M persistence and block the CD47-SIRP immune checkpoint. Following delivery via lipid nanoparticle-encapsulated mRNA (LNP-mRNA), the self-secreted IFN-{gamma} sustained M1 polarization through phospho-STAT1 activation. Meanwhile, the ectopically expressed SIRPECD competitively bound to CD47 on tumor cells, thereby blocking the endogenous SIRP-SHP2 interaction in a dominant-negative manner. This design enhanced pro-inflammatory activity and anti-tumor efficacy compared to CD3{zeta}-only CAR-M. Single-cell RNA sequencing and cellular analysis showed that in situ programmed CAR-M reprogrammed the tumor microenvironment toward inflammation in a murine HCC model. Moreover, CAR-M derived from human peripheral blood mononuclear cells (PBMCs) effectively phagocytosed human HCC organoids while sparing healthy tissues, indicating clinical potential. ConclusionsCollectively, our work presents a novel CAR design that enhances phagocytic function and sustains anti-tumor activity, offering a promising strategy for human solid tumor immunotherapy.

cancer biology↗

bioRxiv2025

IsoNet2 determines cellular structures at submolecular resolution without averaging

AbstractWe introduce IsoNet2, an end-to-end self-supervised deep-learning method that directly reconstructs high-quality 3D densities from cryogenic electron tomography. A unified network simultaneously performs denoising, contrast transfer function correction, and missing-wedge restoration, achieving [~]20 [A] resolution without averaging. A feature-rich GUI enables rapid, dataset-specific fine-tuning for end-users. IsoNet2 resolves domain organization in HIV capsid proteins, tRNA occupancy in individual ribosomes, and in situ architectures of mitochondrial respiration-related complexes, enabling atomic-level interpretation of cellular environments.

cell biology↗

bioRxiv2025

Unlocking karst biodiversity with eDNA: a validated qPCR toolkit for discovery and monitoring the worlds largest cavefish radiation (Cyprinidae: Sinocyclocheilus)

Environmental DNA (eDNA) enables non-invasive detection of aquatic species through trace genetic material, but uncertainties regarding DNA persistence, detection accuracy, and the limited taxonomic resolution of stygian fauna have constrained its use in subterranean aquatic environments. The species-rich cavefish genus Sinocyclocheilus, endemic to the karst landscapes of southwestern China, offers an excellent model for understanding these issues. With over eighty species, many of which are endemics, and many still being described, this lineage is taxonomically rich, poorly understood, and highly threatened. However, access to these deep habitats remains challenging, making eDNA detection one of the most practical methods for documenting and monitoring them. We developed and validated a qPCR assay targeting a hypervariable region of the mitochondrial 16S rRNA gene, allowing for genus-specific detection and species-level identification. Laboratory and field evaluations involving 47 representative species confirmed high specificity and a detection limit of around 20 DNA copies per reaction (R{superscript 2} = 0.995). Controlled degradation studies indicated that temperature and pH strongly affect DNA persistence. Field surveys across 47 karst sites (37 caves and 10 surface sites) detected Sinocyclocheilus eDNA in only 33 caves. Dideoxy sequencing of qPCR amplicons provided species-level identification and phylogenetic validation, including detection of a new species. At three cave sites yielding multiple qPCR detections, 16S metabarcoding enabled species discrimination. By integrating qPCR assay validation, eDNA degradation modelling, hierarchical genetic identification via dideoxy sequencing, and selective NGS metabarcoding, this study presents a strategic framework for applying eDNA methods in subterranean environments.

evolutionary biology↗

bioRxiv2025

Ecology of hydrocarbon degradation in widespread viruses

Hydrocarbons are vital for energy production and lead to serious environmental issues due to pollution. Microorganisms largely drive the production and degradation of hydrocarbons, yet little is known about viral contributions to hydrocarbon degradation. Here we identified 786 viral contigs from IMG/VR(v4), encoding five aerobic (alkB, ladA, almA, ndoB and dszC) and three anaerobic (bssA, ebdA and abcA) hydrocarbon-degrading genes (HDGs). vOTUs encoding HDGs span 26 distinct viral families, including 249 are mainly associated with host-associated environment and 463 are derived from aquatic habitat, respectively, implying that these viruses are broadly engaged in hydrocarbon-degradation processes across the entire biosphere. alkB (35%) and almA (28%) was tended to be encoded by Schizomimiviridae, bssA was tended to be encoded by T5-like bacteriophages (29%) and SPO1-like bacteriophages (22%), suggesting that the carriage of HDGs by viruses exhibits taxonomic specificity. With respect to host associations, Pseudomonadota and Bacillota constitute the two main potential host lineages, associated with 268 and 71 vOTUs, respectively. The profiling of ecological footprint reveals that viruses contribute a total of up to 30% gene abundance and 32% transcribing activity to hydrocarbon degradation in the global ocean. This study systematically revealed the distribution, diversity, virus-host correlation and activity of virus-encoded HDGs, underscoring the significant role of viruses in hydrocarbon metabolism on a global scale.

ecology↗

bioRxiv2025

Graded BMP signals modulate yellow and red color in fishes impacting adult pigment pattern and behavior

Among the most interesting adult traits are those with roles in animal communication. Yet developmental mechanisms by which genes drive cell behaviors in building the final forms of such traits are rarely known. In this context, pigmentation is useful because colors and patterns often provide signals in mate choice, predation avoidance and other behaviors and pigmentation is unusually accessible to observation and manipulation. Here we focus on some of the most prominent signaling colors--red, orange and yellow--and show how BMP signaling at the cellular level allows for a very different kind of signal at the organismal level. Using pearl danio, Danio albolineatus, we find that spatially and temporally graded BMP signals promote development of yellow/orange xanthophores over red erythrophores in the fin of this species and a distantly related minnow, Tanichthys albonubes, and that conserved mechanisms, involving BMP co-receptor Rgmb, regulate differentiation of other pigment cell types in corresponding locations of zebrafish, D. rerio. We further use mutants of D. albolineatus with more red or more yellow cells than wild-type to demonstrate female responsiveness to carotenoid-based color differences between males in shoaling preference assays, and we show the existence of polygenic standing variation for this pigmentary trait. Our findings illustrate a chain of function spanning hierarchical levels and provide a deeper understanding of pigmentary form and function and its evolution. HIGHLIGHTSO_LIFate specification of alternative red or yellow pigment cell types in the fin of a minnow, pearl danio, depends on thresholds and gradients in BMP signaling during fin outgrowth. C_LIO_LIGenetic loss of BMP signaling leads to production of red over yellow carotenoids with resulting "super-red" fish preferred by females in shoaling assays. C_LIO_LIBMP-dependence of pigmentary traits is conserved across species with standing, polygenic variation for fin pattern and color in pearl danio. C_LI

developmental biology↗

bioRxiv2025

AlphaFold3 for Structure-guided Ligand Discovery

Deep-learning methods for protein structure prediction, such as AlphaFold2 (AF2) and RosettaFold (RF), have transformed structural biology and accelerated downstream biological discovery. More recent models, including AlphaFold3 (AF3) and RosettaFold All-Atom (RFAA), extend this capability to protein-ligand co-folding, enabling direct prediction of bound complexes from sequence and ligand inputs. This advance has raised the possibility that such models could function not only as structure predictors but also as AI-based molecular docking engines for virtual screening. Yet their true impact on ligand discovery remains unclear and, in many cases, controversial. Here, we systematically assess AF3 co-folding across tasks central to small-molecule discovery compared to conventional molecular docking. First, retrospective enrichment of actives over decoys showed that AF3 outperforms the physics-based docking program DOCK3 across 43 drug targets in DUDE-Z; however, this advantage is largely driven by hidden ligand-only biases inherent to computational decoy sets. In contrast, in three large experimental datasets (the sigma-2 receptor ({sigma}2), the D4 dopamine receptor (D4), AmpC {beta}-lactamase (AmpC)) with over 2,500 tested molecules that lack such biases, DOCK3 achieved stronger overall enrichment, while AF3 contributed mainly to early enrichment. Second, out-of-sample pose reproduction on >8,000 protein-ligand complexes deposited after the AF3 training and validation cutoff showed that AF3 accuracy is strongly dependent on training-set similarity, indicating that the model memorizes atomic positions more than learning general principles of molecular recognition. Finally, in the first prospective head-to-head screen against the {sigma}2 receptor, novel to AF3s training set, AF3 achieved a 13% hit rate and identified a 13 nM binder directly from the screen. However, compared with the parallel DOCK3 campaign, AF3 delivered a two-fold lower hit rate, despite yielding a similar affinity distribution among the top five hits. A crystal structure of the {sigma}2 receptor bound to the most potent AF3-derived hit confirmed that AF3 produced a near-native ligand pose. AF3 therefore represents the beginning of deep-learning-based structure-guided ligand discovery: a complementary tool rather than a replacement for conventional docking, with practical applications both as a screening engine and as a post-docking filter that improves hit rates. More broadly, this work establishes a framework for evaluating next-generation deep-learning co-folding models and quantifying their impact on small-molecule discovery.

pharmacology and toxicology↗

bioRxiv2025

Enterococcus faecalis Influences the Transcription and Metabolism of Pathogenic Escherichia coli when Grown in Co-Culture

UTI involves bacterial growth in the disparate environments of the bladder and within uroepithelial cells. The bladder is a low-nutrient environment that nonetheless supports rapid growth. Phylogenetic group B2 Escherichia coli (Ec) is frequently isolated from urinary tract infection (UTI) patients. Non-B2 strains have also been isolated and are often co-isolated with Enterococcus faecalis (Ef). We characterized the interaction between three co-isolated Ec-Ef pairs and other Ec-Ef combinations in a nutrient-rich medium which was intended to emulate the rapid growth condition of the bladder. In this medium, Ef had little effect on Ec growth but resulted in major transcriptome differences. Ef affected the non-B2 and B2 strains differently. For the non-B2 Ec strains, Ef induced transcript for genes whose products degrade ornithine via putrescine to succinate which is subsequently metabolized by the TCA cycle. For a control B2 Ec strain, Ef induced transcripts for growth rate-associated genes of macromolecular synthesis, and for similar metabolic enzymes, except for those that degrade putrescine to succinate. Unexpectedly, Ef induced transcripts for glyoxylate shunt enzymes in both non-B2 and B2 Ec strains. The bacterial disparate and constantly changing environments during UTI suggest the potential for different types of Ec-Ef interactions. Our results provide evidence for nutrient cross-feeding as one type of Ec-Ef interaction.

microbiology↗

bioRxiv2025

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity, but its rich, complementary structure across cells and genes remains underexploited, especially in the presence of technical noise and sparsity. Effectively leveraging this multi-scale structure is essentially an information fusion problem that requires integrating heterogeneous graph-based views of cells and genes into robust low-dimensional representations. In this paper, we introduce GatorSC, a unified representation learning framework that models scRNA-seq data through multi-scale cell and gene graphs and fuses them with a Mixture-of-Experts architecture. GatorSC constructs a global cell-cell graph, a global gene-gene graph, and a local gene-gene graph derived from neighborhood-specific subgraphs, and learns graph neural network embeddings that are adaptively fused by a gating network. To learn noise-robust and structure-preserving embeddings without labels, we couple graph reconstruction and graph contrastive learning in a unified self-supervised objective applied to both cell- and gene-level graphs. We evaluate GatorSC on 19 publicly available scRNA-seq datasets covering diverse tissues, species, and sequencing platforms. Experiments showed that GatorSC consistently outperforms state-of-the-art deep generative, graph-based, and contrastive methods for cell clustering, gene expression imputation, and cell-type annotation. The learned embeddings are used for accurate trajectory inference, recovery of canonical marker gene programs, and cell-type-specific pathway signatures in an Alzheimers disease single-nucleus dataset. GatorSC provides a flexible foundation for comprehensive single-cell transcriptomic analysis and can be readily extended to multi-omic and spatial modalities.

bioinformatics↗

bioRxiv2025

MS4MS: LLMs-driven Multi-agent System for Small-molecule Identification via LC-MS/MS

Small molecule identification is central to research fields such as drug discovery, but in complex systems like Traditional Chinese Medicine (TCM), traditional mass spectrometry analysis methods remain constrained by bottlenecks including insufficient database coverage, fragmented analysis workflows, and poor result interpretability. To address these limitations, we developed MS4MS, a large language model-driven multi-agent system that enables an end-to-end automated pipeline from raw data to small molecule identification. Validation on a public benchmark demonstrates that MS4MS achieves state-of-the-art performance in molecular formula prediction. Furthermore, its innovative small molecule identification agent enables efficient and interpretable compound elucidation. Verification using herbal extracts indicates MS4MSs outstanding performance regarding analytical coverage and the discrimination of isomers. Consequently, MS4MS offers a novel, accurate, interpretable, and high-throughput end-to-end automated strategy for small molecule identification, overcoming the analytical bottlenecks of traditional mass spectrometry in natural products and complex TCM systems.

bioinformatics↗

bioRxiv2025

Comparative evaluation of reference genomes and cell-type annotation frameworks for single-nucleus transcriptomic analysis in apple

Single-nucleus RNA sequencing (snRNA-seq) provides a powerful tool to profile cell-type-specific transcriptional programs in woody fruit trees. Yet its application in Malus domestica has been limited by heterogeneous reference genomes and the lack of validated marker genes. Here, we first established a comprehensive set of marker genes corresponding to 29 plant cell types and identified their orthologs in apple. Using these resources, we systematically evaluated six publicly available apple genome assemblies as references for snRNA-seq analysis. Our results demonstrate that reference genome selection has a major impact on mapping performance and downstream cell type annotation, with the GDDH13 assembly consistently producing the highest transcript counts per nucleus and the greatest number of identifiable transcriptional clusters (15 clusters representing 11 distinct cell types across 35,557 high-quality nuclei). To achieve robust cell type annotation, we integrated classical marker gene-based annotation with two cross-species computational frameworks -- Orthologous Marker Gene Groups (OMGs) and XSpeciesSpanner. These three approaches enabled confident identification of 11 major cell types in apple seedlings and provided cross-validation for ambiguous or previously unannotated clusters. Pseudotime analysis reconstructed the developmental trajectories of both procambial and stomatal lineages, revealing their progressive transcriptional transitions and enabling the delineation of key molecular programs underlying vascular differentiation and early guard-cell formation. Together, this work provides a reference-quality single-nucleus transcriptomic atlas for apple and underlined the critical impact of genome choice and multi-method cell-type annotation on single-cell studies in non-model crops.

bioinformatics↗

bioRxiv2025

Beyond Inheritance: De novo Fast Motion Computation in Primate Visual Cortex

Objects move through space and time, generating sequential visuotopic activations in all sighted animals leading to motion perception of velocity defined by direction and speed. Humans can effortlessly see motion with speeds ranging from 0.25 to 500{degrees}/s. However, direction-selective neurons in the primary visual cortex (V1)--from which all subsequent processing is presumed to derive-- only encode directionality at low speeds. To resolve this paradox, we recorded neuronal responses to moving dots, gratings, and movies across the LGN, V1, MT and MST of the macaque motion pathway. Regardless of cell type and motion stimuli, V1 neurons lost direction selectivity at [~]29{degrees}/s while MT and MST neurons maintained it up to [~]82{degrees}/s and [~]183{degrees}/s, respectively. A cascaded spatiotemporal integration model reveals that at each cortex direction-selective neurons can generate velocity selectivity de novo, by integrating sequential visuotopic activations from preceding areas, irrespective of speed and directionality. By computing velocity anew, the primate brain effectively uses the cortical hierarchy itself to shift gears to efficiently encode slow and fast motion. Thus, five visual areas from the retina into the brains processing hierarchy, external spatiotemporal information is being computed afresh, offering insights for motion processing in other species, modalities and machine vision.

neuroscience↗

bioRxiv2025

Alphaviral capsid proteins inhibit stress granule assembly via competitive RNA binding with G3BP1

Viral infection is one of the conditions that induces stress granule (SG) formation, a cellular defense mechanism that exerts antiviral effects. To counteract this host response, viruses have evolved a broad spectrum of strategies to inhibit SG formation. However, the molecular mechanisms underlying SG inhibition remain poorly understood. The nucleocapsid proteins play a critical role in virus replication and host interaction. Here, using Semliki Forest Virus (SFV) as a model, we uncover the function of the alphavirus nucleocapsid in SG inhibition. This inhibitory function depends on oligomerization mediated by an N-terminal -helix and with a positively charged intrinsically disordered region (IDR). We show that SFV capsid directly competes with G3BP1 for RNA binding, thereby disrupting G3BP1-RNA liquid-liquid phase separation (LLPS) in vitro and SG assembly in cells. This mechanism is conserved across the alphavirus family but is not shared by the nucleocapsid of SARS-CoV-2 or other endemic viruses examined. Notably, expression of a peptide from SFV capsid is sufficient to inhibit SG formation induced by Amyotrophic Lateral Sclerosis (ALS)-associated mutations, suggesting potential therapeutic applications. Our findings reveal mechanistic insight into SG modulation by the viral capsid protein and provide a possible bioengineering tool for probing SG dynamics in health and disease.

cell biology↗

bioRxiv2025

Artificial light at night and an invasive snail synergistically enhance the invasion of a non-native macrophyte

Herbivory shapes plant invasion outcomes, yet its role in aquatic plant invasions under changing environmental conditions, such as artificial light at night (ALAN), remains poorly understood. We conducted three experiments using invasive macrophyte Myriophyllum aquaticum, a native macrophyte community (Vallisneria natans, Hydrilla verticillata, M. spicatum), invasive snail Pomacea canaliculata, and native snail Cipangopaludina chinensis to test the combined effects of ALAN and herbivory on non-native macrophyte invasions. ALAN increased M. aquaticum height and total biomass, but had no effect on native species growth. In feeding assays, P. canaliculata consumed all three native species but consistently avoided M. aquaticum under both light treatments. C. chinensis showed no feeding in no-choice assays, but in choice assays, consumed H. verticillata and M. spicatum under No-ALAN and only M. spicatum under ALAN. In community mesocosms, P. canaliculata reduced native macrophyte biomass by 48.0% under No-ALAN and 87.2% under ALAN without affecting M. aquaticum. This selective feeding increased M. aquaticums proportional biomass, with a stronger effect under ALAN than No-ALAN. These results suggest that ALAN can indirectly facilitate non-native macrophyte invasion by amplifying their relative biomass within native communities, particularly in the presence of invasive herbivores, and may promote invasional meltdown through altered feeding preferences.

ecology↗

bioRxiv2025

Alphaviral nonstructural protein-host RBP co-condensation as a mechanism to sustain virus replication

It has long been recognized that the intracellular replication of alphaviruses critically relies on several key host RBPs, including G3BP1/2 and FXR1/FXR2/FMR1, but how these RBPs modulate alphaviral replication and whether it would be possible to target these RBPs for antiviral treatment are less explored. Here, using SFV as a model, we report that SFV nsP3 exploits G3BP for its condensation and transforms antiviral stress granules into proviral nsP3-G3BP co-condensates. The gel-like co-condensates of nsP3 and G3BP enrich and protect viral genomic RNAs from host RNase degradation and serve as viral translational hubs to promote viral replication. The mode of nsP3-RBP co-condensation is prevalent across alphaviruses, and disruption of nsP3 condensates is an efficient antiviral approach. Thus, these findings uncover a general anti-alphavirus strategy based on the conserved reliance of nsP3-RBP co-condensation.

cell biology↗

bioRxiv2025

Epitope and functional classification of human neutralizing antibodies against SFTSV Gn

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a lethal bunyavirus lacking approved countermeasures. From SFTS survivors, we isolate 84 human monoclonal antibodies (mAbs) against the viral glycoproteins Gn and Gc. Gn-specific mAbs demonstrate superior neutralization breadth and potency compared to the restricted neutralizing activity observed with Gc. Using a high-throughput yeast display deep mutational scanning (DMS) platform, we classify Gn-head mAbs into eight epitope groups, among which four groups (IA, ID, IIIA, IIIB) conferring neutralization. Notably, mAbs BD70-4003 (group IA) and BD70-4017 (group IIIA) exhibit broad neutralization and provide 100% protection in a lethal mouse model. Cryo-EM structural analysis of these mAbs in complex with the Gn-head reveal their binding interfaces, directly validating the epitope residues identified by DMS. Our study delineates the antigenic landscape of SFTSV Gn, identifies potent therapeutic candidates, and establishes DMS coupled with structural validation as a powerful framework for antibody discovery against bunyavirus.

immunology↗

bioRxiv2025

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores In Single-Molecule Sensing

Protein nanopores are essential molecular gateways in biology and have inspired transformative technologies in biosensing and single-molecule sequencing. While this technology has transformed genomics and biosensing, the discovery of novel nanopore scaffolds remains limited due to the scarcity of experimentally resolved pore structures. Here, we present NanoporeDB, an open-access structural resource comprising over 6,600 high-confidence multimeric models across four representative pore types. Candidate proteins were systematically mined from large-scale datasets, including the AlphaFold Protein Structure Database, UniRef90, and MGnify90, and assembled using AlphaFold-Multimer and AlphaFold3. We performed membrane embedding, pore axis annotation, and constriction profiling to enable functional interpretation. NanoporeDB features an interactive web interface with 3D visualization and quantitative metrics such as insertion depth, tilt angle, and pore geometry. This resource provides a structural foundation for advancing nanopore-based molecular sensing, precision diagnostics, and synthetic biology.

bioinformatics↗