Biology subjects

Li, D.

Publications and source records attributed to Li, D..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

A Pangenomic Method for Establishing a Somatic Variant Detection Resource in HapMap Mixtures

Somatic mosaicism is essential in human biology and disease, yet robust benchmarks are scarce. The SMaHT Consortium mixed six HapMap cell lines to create artificial somatic variants spanning 0.25% to 16.5% variant allele fractions. We developed a technology-agnostic method that builds pangenome graphs from individual assemblies to create unified benchmarking sets: > 6M single-nucleotide variants, 1.8M small insertions/deletions, 49K structural variations, and 10K mobile element insertions across autosomes, X, and mitochondrial chromosomes. We validated the variants using ultra-deep simulated reads and developed a binomial-based model to estimate coverage requirements for variant detection. Evaluating multiple callers showed CHM13 alignment improves structural variant detection and offers advantages in difficult-to-map regions compared to GRCh38. Systematic characterization showed regions with low detection rate are enriched in centromeres, satellite sequences, tandem repeats, and falsely duplicated genes. This accurate, versatile resource enables systematic evaluation of somatic variant detection technologies.

genomics↗

bioRxiv2025

Turning a new leaf: PhenoVision provides leaf phenology data at the global scale

Plant phenology dictates many aspects of community function and ecosystem dynamics. Yet, global phenology data are still limited, especially in areas lacking monitoring programs. Here we present a new data resource, PhenoVision-Leaf, which extends a computer-vision pipeline utilizing iNaturalist digital image vouchers to produce global-scale leaf phenophase data for deciduous, woody genera. We first discuss our implementation of a new human annotation framework for leaf phenology on iNaturalist, aligning with phenophase definitions used by the larger phenology community. We then showcase the use of 165,988 crowdsourced annotated records to train a Vision Transformer model with a two-stage regime to maximize accuracy across single- and multi-image records. This approach extends Phenovision from scoring individual images to aggregating at the iNaturalist record level, better aligning with human annotation processes. Post-hoc validation showed high performance for detecting present green and colored leaves (>98% accuracy), and reasonable accuracy for breaking leaf buds (>87% accuracy). Applying PhenoVision-Leaf to over 26 million iNaturalist records yielded 5.6 million record-level phenology observations across 6,500 species and 57 families, filling geographic and taxonomic gaps. These data, now accessible through the Phenobase portal, establish a foundation for near real-time monitoring of leaf phenology, supporting global-scale synthesis analyses.

ecology↗

bioRxiv2025

Deletion of Cd248 in Postn positive myofibroblast Fails to Attenuate Cardiac Remodeling and Fibrosis in a Murine Model of Pressure-Overload Cardiomyopathy

BackgroundCardiac fibrosis, driven by activated Periostin (Postn)-expressing myofibroblasts, is a key pathophysiological feature of heart failure induced by pressure overload. The clinic trial tested tumor vascular regulator CD248 has been validated as a potential anti-fibrotic target in ischemic heart injury. We therefore tested the hypothesis that targeting Cd248 specifically in Postn+ myofibroblasts ameliorates pressure-overload cardiomyopathy. MethodsSingle-cell RNA-sequencing data from murine hearts subjected to transverse aortic constriction (TAC) were analyzed to quantify the Cd248+/Postn+ fibroblast population. Subsequently, Postn+ myofibroblast-specific inducible Cd248 knockout mice were subjected to TAC. Gene deletion was induced with tamoxifen from the day of surgery. Cardiac remodeling and function were analyzed 8 weeks after TAC. ResultsAnalysis of scRNAseq data confirmed that about 1/3 of the fibroblasts in the 14-day TAC heart are Cd248+Postn+ fibroblasts. However, comprehensive echocardiographic analysis revealed no significant differences in key parameters of cardiac function or dimensions, including left ventricular ejection fraction, ventricular volumes, wall thickness and global muscle strain between PostnMCM+/-;Cd248fl/fl and PostnMCM+/- controls. Histological analysis at 8 weeks also showed no significant alteration in the extent of interstitial or perivascular fibrosis, myocardial vessel density, or cardiomyocyte hypertrophy. ConclusionThe specific deletion of Cd248 in Postn-expressing myofibroblasts does not mitigate pathological remodeling or fibrosis in response to chronic pressure overload. These findings suggest the role of Cd248 in this cell lineage is not a primary driver of pressure-overload pathology. Therefore, a better understanding of the context-dependent functions of CD248 is critical to successfully guide the clinical translation of CD248-based anti-fibrotic therapies.

pathology↗

bioRxiv2025

Riemannian Diffusion Kernel-smoothed Continuous Structural Connectivity On Cortical Surface

Atlas-free continuous structural connectivity has garnered increasing attention due to the limitations of atlas-based approaches, including the arbitrary selection of brain atlases and potential information loss. Typically, continuous structural connectivity is represented by a probability density function, with kernel density estimation as a common estimation method. However, constructing an appropriate kernel function on the cortical surface poses significant challenges. Current methods often inflate the cortical surface into a sphere and apply the spherical heat kernel, introducing distortions to density estimation. In this study, we propose a novel approach using the Riemannian diffusion kernel derived from the Laplace-Beltrami operator on the cortical surface to smooth streamline endpoints into a continuous density. Our method inherently accounts for the complex geometry of the cortical surface and exhibits computational efficiency, even with dense tractography datasets. Additionally, we investigate the number of streamlines or fiber tracts required to achieve a reliable continuous representation of structural connectivity. Through simulations and analyses of data from the Adolescent Brain Cognitive Development (ABCD) Study, we demonstrate the potential of the Riemannian diffusion kernel in enhancing the estimation and analysis of continuous structural connectivity.

neuroscience↗

bioRxiv2025

A Spatially Resolved Atlas of Alternative Polyadenylation Across 18 Human Tissues and 76 Disease States

Alternative polyadenylation (APA) is a key regulator of gene expression and cellular dynamics, yet systematic investigations of spatially resolved APA across diverse human tissues remain limited. Here, we developed SpatialAPA (https://github.com/Omicslab-Zhang/spatialAPA), a framework that benchmarks multiple APA identification methods and integrates spatial APA data with gene expression and cellular dynamics at spatial resolution. Applying SpatialAPA to 363 spatial transcriptomic data from 56 projects across 18 human tissues and 76 diseases, we constructed a spatially resolved APA atlas comprising 346,932 APA events across 52,175 genes. This atlas reveals organ-specific APA patterns and provides new insights into how APA regulates tissue homeostasis and disease progression beyond transcriptional control. To ensure cross-sample comparability, we applied batch correction, while spatial cell deconvolution uncovered cell-type-specific dynamics and interactions. In triple-negative breast cancer, integrated spatial and single-cell analyses identified TSPAN8-positive epithelial subpopulations whose distinct APA regulation and transcriptional programs drive differentiation and malignant progression. To facilitate community access, we developed an online platform (http://www.biomedical-web.com/spatialAPAdb/home) for exploring APA, gene expression, and cellular dynamics in health and disease. Together, this study establishes the first comprehensive spatial APA atlas, providing a valuable resource and analytical framework for investigating molecular mechanisms and therapeutic targets.

bioinformatics↗

bioRxiv2025

Visual Regularities Underlie Hierarchical Object Representations in the Human Brain and Self-supervised DNN

NeuroAI develops the interplay of neuroscience and artificial intelligence, especially on visual processing. Human visual system organizes objects based on a representational hierarchy. However, it remains unclear whether this hierarchy arises from visual or semantic information. One hypothesis posits that the visual system is structured around statistical regularities of visual information. Here, we test this hypothesis using the THINGS datasets and pure-visual deep neural networks (DNN). We constructed a low-dimensional object space based on multiple abstract object properties, reflecting statistical patterns of visual regularities. By applying voxelwise encoding models, we identified clusters in the higher visual cortex based on their property tuning, and they were found to support specific object categories. These clusters serve as the middle level to reveal a property-cluster-object hierarchical organization. Subsequently, we investigated whether this hierarchical structure could be captured by a self-supervised DNN. Through activity similarity analysis, we mapped the brain clusters onto the DNN and independently found that the DNNs clusters exhibited distinct property tuning and influenced the classification accuracy of corresponding object categories, mirroring the effects observed in the human brain. Our results demonstrate similar hierarchical structures in the human brain and self-supervised DNN, suggesting that the visual regularities shape neural architecture of visual system. This study highlights the great potential of neural computational model in neuroscience study. Index Terms Visual Processing, Abstract Property, Hierarchical Representation, Self-supervised Visual DNN

neuroscience↗

bioRxiv2025

Energy-Regularized Graph Learning for Multiscale Spatial Representation

Spatial omics map gene and protein expression in situ, demanding methods that recover cellular and tissue architecture from noisy, high-dimensional data. This need spans two scales: (i) subcellular, where high-resolution measurements must be grouped into coherent cells, and (ii) tissue-wide, where the goal is to recover spatial domains. Existing embedding approaches either ignore space or rely on static neighborhood graphs that over- or under-smooth local heterogeneity. We present Glimmer (Graph-Learned Inference of Multiscale Molecular Embedding for Spatial Representation), a unified framework that learns adaptive neighborhood graphs by minimizing Dirichlet energy under a log-barrier regularizer. Beginning from a k-nearest neighbor scaffold, Glimmer adaptively reweights edges to balance molecular similarity and spatial proximity, yielding interpretable, locally smoothed embeddings. These graphs enable segmentation-free reconstruction from transcript localizations or fine bins and support the discovery of tissue niches at large tissue scales. Across diverse datasets and modalities (Slide-tags, Slide-seq, MERFISH, Xenium, and CODEX), Glimmer surpasses kernel and graph neural network based methods in clustering accuracy and spatial conservation. At the subcellular level, Glimmer corrects transcript-to-cluster misassignments on lymph node slides, thereby improving gene specificity within biological clusters and addressing a key challenge in spatial transcriptomics. At the tissue-wide scale, Glimmer enables accurate region identification, as demonstrated in tonsil tissue by resolving germinal center subregions, which in turn facilitates niche-specific immune profiling. Glimmer thus offers a generalizable framework for spatial representation learning across scales and modalities, enabling comprehensive insights into tissue architecture and cellular ecosystems.

bioinformatics↗

bioRxiv2025

Arabidopsis Group I Pumilio RNA-binding factors are vital for embryo development and balancing between growth and stress resistance

Pumilio (PUM) RNA binding proteins are crucial for regulating gene expression by binding to a conserved motif in the 3'-untranslated region (3'-UTR). Despite their importance, the role of PUM in plants is largely unexplored. Here, we investigated the functions of Arabidopsis group I PUMs (APUM1-APUM6), which are ubiquitously expressed and localized in the cytosol. While single apum mutants exhibit no visible phenotypes, CRISPR/Cas9-generated apum1 apum2 apum3 triple mutants (apum1/2/3) display reduced growth in both vegetative and reproductive organs, alongside hypersensitivity to various stresses. Remarkably, apum1/2/3/4 quadruple mutants are embryonically lethal, highlighting their essential role in embryo development. Transcriptomic profiling revealed that differentially expressed genes (DEGs) upregulated in apum1/2/3 are enriched in pathways related to photosynthesis, stress responses and anthocyanin biosynthesis, while downregulated DEGs are associated with biotic stress defense and hydrogen peroxide metabolism. This suggests that APUM1/2/3 act as molecular hubs balancing plant growth and stress adaptation. Biochemical assays showed that recombinant APUM homologous domains bind to the 5-UGUGUAUA-3 core motif in the 3-UTR of the transcription factor Production of Anthocyanin Pigment1 (PAP1), crucial for anthocyanin biosynthesis. Notably, single nucleotide substitutions, except for the third U, do not affect binding, while multiple mutations disrupt interaction. Consistently, apum1/2/3 mutants accumulate significantly more anthocyanin than wild-type plants. Furthermore, we predicted 7053 potential target genes for APUM1/2/3, with 1609 present among the upregulated DEGs in apum1/2/3. Taken together, our findings demonstrate that group I APUMs are vital posttranscriptional regulators, providing a new perspective on the trade-offs between growth and stress resilience in plants.

plant biology↗

bioRxiv2025

Hugin-AstA circuitry is a novel central energy sensor that directly regulates sweet sensation in Drosophila and mouse

Taste sensation plays a crucial role in shaping feeding behavior and is intricately influenced by internal states like hunger or satiety. Despite the identification of numerous neural substrates regulating feeding behavior, the central neural substrate that linked energy-sensing and taste sensation remained elusive. Here, we identified a novel neural circuitry that could directly sense internal energy state and modulate sweet sensation in the Drosophila brain. Specifically, a subset of neuropeptidergic neurons expressing hugin directly detected elevated levels of circulating glucose via glucose transporter Glut1 and ATP-sensitive potassium channel. Upon activation, these neurons released hugin peptide and activated downstream Allatostatin A (AstA)+ neurons via its cognate receptor PK2-R1. Subsequently, the activation of AstA+ neurons then directly inhibited sweet sensation via AstA peptide and its cognate receptor AstA-R1 expressed in sweet-sensing Gr5a+ neurons. We also showed that neuromedin U (NMU), the mammalian homolog of fly hugin, served as an energy sensor to suppress sweet sensation. Therefore, these data identify hugin+ neuron as a glucose-responsive central energy-sensing module that modulates sweet sensation across species.

neuroscience↗

bioRxiv2025

rbio1-training scientific reasoning LLMs with biological world models as soft verifiers

Reasoning models are typically trained against verification mechanisms in formally specified systems such as code or symbolic math. In open domains like biology, however, we lack exact rules to enable large-scale formal verification and instead often rely on lab experiments to test predictions. Such experiments are slow, costly, and cannot scale with computation. In this work, we show that world models of biology or other prior knowledge can serve as approximate oracles for soft verification, allowing reasoning systems to be trained without additional experimental data. We present two paradigms of training models with approximate verifiers: RLEMF: reinforcement learning with experimental model feedback and RLPK: reinforcement learning from prior knowledge. Using these paradigms, we introduce rbio1, a reasoning model for biology post-trained from a pretrained LLM with reinforcement learning, using learned biological models for verification during training. We demonstrate that soft verification can distill biological world models into rbio1, enabling it to achieve state-of-the-art performance on perturbation prediction in the PerturbQA benchmark. We further show that composing multiple AI-verifiers improves performance and that models trained with soft biological rewards transfer zero-shot to cross-domain tasks such as disease-state prediction. We present rbio1 as a proof of concept that predictions from biological models can train powerful reasoning systems using simulations rather than experimental data, offering a new paradigm for model training.

bioinformatics↗

bioRxiv2025

2-Methoxyestradiol Treatment Prevents Graft-versus-Host Disease While Preserving Graft-versus-Leukemia Effect in Mice

2-Methoxyestradiol (2ME2, Panzem) is an endogenous metabolite that is well-tolerated in phase I/II clinical trials for variety of tumors. The plasma levels of 2ME2 may increase up to 1,000-fold during pregnancy and correlate temporally with the remission of rheumatoid arthritis (RA) and multiple sclerosis (MS) symptoms. The anti-inflammatory properties of 2ME2 were recently established in the mouse model of MS, and the mechanism of action is the ability of 2ME2 to inhibit lymphocyte proliferation, cytokine production and T cell polarization. Herein, we have demonstrated that 2ME2 treatment can significantly reduce the mortality and morbidity associated with graft-versus-host disease (GVHD). There is a lower number of donor-derived CD4+ and CD8+ T cells in the peripheral lymph node and Peyers patches of 2ME2-treated mice compared to control recipients. Moreover, 2ME2 exposure can significantly decrease the production of IFN-{gamma} and IL-2 in donor-derived CD4+ T cells and serum in GVHD mice. However, 2ME2 treatment has no effect on the differentiation of CD8+ effector T cells in vivo and their cytolytic activity remains intact. Furthermore, 2ME2 therapy is effective in preventing GVHD while preserving graft-versus leukemia (GVL) activity in mice. Our findings indicate that 2ME2 could be a novel and effective treatment for GVHD patients.

immunology↗

bioRxiv2025

Comprehensive Transcriptomic and Epigenomic Insights into Environmental Toxicant Exposures: The TaRGET II Resource

Environmental exposures to toxic chemicals can profoundly alter the transcriptome and epigenome in both humans and animals, contributing to disease development across the lifespan. To elucidate how early-life exposure to toxicants exerts such persistent effects, the Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription II (TaRGET II) Consortium generated a landmark resource comprising 2,564 epigenomes and 1,043 transcriptomes from longitudinal studies in mice. All data are publicly available through the TaRGET II data portal and the WashU Epigenome Browser. This resource from target (liver, brain, lung, heart) and surrogate (blood) tissues at weaning (3 weeks) and two adult time-points (5 and 10 months) characterized the molecular response to arsenic (As), lead (Pb), bisphenol-A (BPA), di-2-ethylhexyl phthalate(DEHP), tributyltin (TBT), tetrachlorodibenzo-p-dioxin (TCDD), and particulate matter with a diameter of <2.5m (PM2.5). The findings revealed persistent, toxicant-specific, sex-dependent epigenomic and transcriptomic perturbations, resulting in disrupted expression of 14,908 genes, altered chromatin accessibility at 87,409 regulatory elements, DNA methylation changes at 113,186 genomic regions, and chromatin state switching of histone modifications. The resulting high-resolution map of how environmental exposures reprogram the epigenome and transcriptome is broadly accessible via ToxiTaRGET database, offering unparalleled opportunities for the scientific community to investigate the molecular underpinnings of environmental toxicant exposures and their contributions to disease pathogenesis.

pharmacology and toxicology↗

bioRxiv2025

ToxiTaRGET: a multi-omics resource for toxicant-responsive molecular targets

Environmental toxicant exposures can induce widespread alterations in both the transcriptome and epigenome of mammals, and directly contribute to the increased risk of various diseases, including cardiovascular disorders, cancer, and neurological disorders. To evaluate how early-life toxicants produce long-term impacts on the transcriptome and epigenome in mice, the Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription II (TaRGET II) Consortium generated a landmark resource comprising 3,607 multi-omics from longitudinal studies in mice. The molecular changes in responding to distinct environmental toxicants, including arsenic (As), lead (Pb), bisphenol A (BPA), tributyltin (TBT), di-2-ethylhexyl phthalate (DEHP), dioxin (TCDD), and fine particulate matter (PM2.5), were systematically identified and visualized on an integrative platform, ToxiTaRGET, to allow quickly search and browse by researchers. ToxiTaRGET houses a rich repository of molecular signatures, including gene expression, chromatin accessibility, and DNA methylation profiles, in response to early-life toxicant exposures. These molecular signatures span multiple biologically important tissues in both male and female mice at three distinct life stages, offering a valuable resource for the environmental health and toxicogenomic research communities.

bioinformatics↗

bioRxiv2025

Profiling and Functional Analysis of Urinary Exosomal MicroRNAs in Pregnant Women with Systemic Lupus Erythematosus

BackgroundPregnancy in Systemic Lupus Erythematosus (pSLE) is high-risk, necessitating non-invasive biomarkers for monitoring and predicting complications. Urinary exosomes, containing miRNAs, offer a promising source reflecting systemic and renal states, yet their profile in late gestation pSLE is less studied. ObjectiveThis study aimed to investigate the profile of urinary exosomal miRNAs in pregnant women with SLE during late gestation compared to healthy pregnant controls and to explore their potential biological roles and pathways. MethodsUrinary exosomes were isolated from 6 pSLE patients and 5 controls. Exosomes were characterized, and miRNAs were sequenced. Bioinformatics analyses, including target gene prediction and functional enrichment (GO, KEGG), were performed. ResultsExosomes were successfully isolated. Sequencing identified 29 significantly downregulated miRNAs in pSLE exosomes. Enrichment analysis of these miRNAs and their target genes indicated involvement in metabolic and immune pathways, virus infection, and cellular senescence. Target genes also enriched in protein stability and transport, and high-level analysis showed broader roles in metabolic, immune, and cellular maintenance/stress processes. Targets of top differentially expressed miRNAs linked to mRNA regulation, AMPK, and senescence pathways. ConclusionOur findings demonstrate that urinary exosomal miRNAs are significantly altered in pregnant women with SLE during late gestation. The functional enrichment analysis suggests their target genes are involved in critical biological processes relevant to pSLE pathophysiology and pregnancy complications, including metabolism, immunity, viral response, senescence, and cellular maintenance. This highlights the potential of urinary exosomal miRNAs as non-invasive biomarkers for monitoring and predicting risks in this high-risk population.

immunology↗

bioRxiv2025

Phasor diagrams clock oscillatory hemodynamic switching between overt speech production and micro resting states

This study investigates the intricate interplay between the task-positive network and the default mode network (DMN) during transitions between overt language tasks and brief resting periods. While previous research suggests that these networks are not invariably anticorrelated, the precise timing of transitions has remained elusive. We employed rapid phase-encoded fMRI to decode brain dynamics with ultimate precision, capturing these transitions in real time. By utilizing phasor diagrams to represent the oscillatory activities, we examined the amplitudes and phases of hemodynamic fluctuations within the language network and DMN. Our findings align with existing empirical and theoretical perspectives on DMN functions and cognitive task performance, affirming the validity of our approach. We identified heterogeneous micro resting states interwoven with periods of overt speech production. Notably, various core regions of the DMN exhibited task-dependent amplitude and phase modulations, with activation strength and delay rising in line with increasing task complexity, ranging from comprehension to immediate and delayed speech production. This study sheds light on the dynamic engagement of the DMN during overt speech production, providing precise timing data of transitions between the DMN and language network. It demonstrates that rapid phase-encoded fMRI and phasor diagrams are powerful tools for measuring the switching between active tasks and micro resting states with subsecond accuracy, while also elucidating task load-dependent changes in the DMN. By accurately measuring the timing of these transitions, we gain insights into cognitive flexibility, attention, and the efficiency of information processing.

neuroscience↗

bioRxiv2025

A comprehensive data repository of environmental toxicants exposed mouse epigenomes from the TaRGET II Consortium

The Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET) is a multiphase program that aims to understand how environmental factors contribute to disease susceptibility using toxicant-exposed mouse models. Here, we introduce the TaRGET II Data Portal (https://data.targetepigenomics.org/), a repository of exposomes from mice exposed to environmental toxicants, including arsenic (As), lead (Pb), bisphenol A (BPA), tributyltin (TBT), di(2-ethylhexyl) phthalate (DEHP), tetrachlorodibenzo-p-dioxin (TCDD), and air pollution (PM2.5). Sequencing assays capturing changes in chromatin accessibility, DNA methylation, gene expression, and post-translational histone modifications from multiple centers were quality-controlled and uniformly processed. The datasets cover multiple tissues collected at four time points: 3 weeks, 5 weeks, 20 weeks, and 40 weeks. The TaRGET II Data Portal offers an efficient way to browse, search, visualize, and download relevant datasets and associated metadata, serving as a key resource for studying the impact of environmental toxicant exposures on disease susceptibility for the broader scientific community.

genomics↗

bioRxiv2025

Coiled-coil-mediated phase separation of Spef1 for non-centrosomal microtubule organization and function

Central-pair microtubules (CP-MTs) are non-centrosomal MTs essential for planar beat pattern of cilia. The CP-MT formation requires the MT-associated protein Spef1, but the underlying molecular mechanism remains unclear. Here, we show that Spef1 undergoes liquid-liquid phase separation (LLPS) to facilitate non-centrosomal MT assembly by enriching tubulins. The LLPS of Spef1 is mediated by its C-terminal coiled-coil (CC) domain. Crystallography reveals that the Spef1-CC domain forms a parallel CC dimer with a unique charge distribution pattern on the surface. The dimerization capacity and charge distribution of Spef1-CC are both critical for controlling in-vitro LLPS. Disruption of the dimerization capacity abolishes ciliary functions of Spef1. In contrast, a charge-changing mutant with attenuated LLPS still supports the CP-MT formation but results in cilia with abnormal beat pattern. Thus, the CC-mediated LLPS of Spef1 provides a mechanistic explanation for its prominent role in controlling non-centrosomal CP-MT organization and function in the axoneme. Significance statementThe MT-associated protein Spef1 is a new essential player for the non-centrosomal CP-MT formation in motile cilia and flagella. This study reveals the unexpected LLPS feature of Spef1, leading to forming biomolecular condensates that enrich tubulins to facilitate non-centrosomal MT assembly. Spef1-CC contains a unique charge-distribution pattern, together with its dimerization capacity, contributing to multivalent interactions for initiating LLPS. The LLPS property of Spef1 is important for CP-MT formation and Spef1-mediated ciliary function. The formation of Spef1-LLPS condensates indicates that they work as MT nucleation centers and tubulin sources for the continuous growth of CP-MTs or repairing CP-MTs damaged during ciliary beating, and suggests that LLPS may be a common process for generating and organizing non-centrosomal CP-MTs in the axoneme.

cell biology↗

bioRxiv2025

PInteract: Detecting Aromatic-Involving Motifs in Proteins and Protein-Nucleic Acid Complexes

With the recent development of accurate protein structure prediction tools, virtually all protein sequences now have an experimental or a modeled structure. It has therefore become essential to develop fast algorithms capable of detecting non-covalent interactions not only within proteins but also in protein-protein, protein-DNA, protein-RNA, and protein-ligand complexes. Interactions involving aromatic compounds, particularly their{pi} molecular orbitals, hold unique significance among molecular interactions due to the electron density delocalization, which is known to play a key role in processes such as protein aggregation. In this paper, we present PInteract, an algorithm that detects{pi} -involving interactions in input structures based on geometric criteria, including{pi} -{pi}, cation-{pi}, amino-{pi}, His-{pi}, and sulfur-{pi} interactions. In addition, it is capable of detecting chains and clusters of{pi} interactions as well as particular recurrent motifs at protein-DNA and protein-RNA interfaces, called stair motifs, consisting of a particular combination of{pi} -{pi} stacking, cation/amino/His-{pi} and H-bond interactions. PInteract is freely available for download at https://github.com/3BioCompBio/PInteract.

bioinformatics↗