Biology subjects

Chen, M.

Publications and source records attributed to Chen, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Comparative analysis of microglia-targeted AAVs reveals capsid choice drives efficiency in vitro but has limited impact in vivo

Microglia play key roles in brain development, homeostasis, and neurodegeneration. Although multiple strategies for viral gene delivery to microglia have been reported, they have not been directly compared. Here, we developed microglia-targeting AAV capsids and benchmarked them against existing approaches. The novel capsids exhibit improved transduction efficiency in cultured mouse and human microglia, as well as neurons and astrocytes. However, when we compared microglial transduction efficiency of the novel capsids with published engineered and naturally occurring capsids after intracranial injection, all capsids achieved efficient and specific transduction when paired with a genome incorporating IBA1 promoter and miR-124 target sites. In contrast, CAG promoter did not support efficient microglial transduction. Moreover, blood-brain barrier- crossing capsids carrying IBA1 promoter and miR-124 target sites efficiently transduced microglia at high doses but exhibited off-target expression. Together, our work provides improved capsids for in vitro manipulation of microglia and establishes viral genome design, not capsid identity, as the principal determinant of efficient in vivo microglial targeting.

neuroscience↗

bioRxiv2026

Single-molecule nanopore-tweezers analysis of transcription elongation by intact and "stalk-less" yeast RNA polymerase II

Single-molecule picometer-resolution nanopore tweezers (SPRNT) enables monitoring of translocation of a nucleic-acid motor protein on a nucleic-acid track with sequence registration, sub-nucleotide spatial resolution, sub-millisecond temporal resolution, and the ability to apply forces that assist or oppose translocation. Recently, we used SPRNT to analyze the translocation of single molecules of Escherichia coli RNA polymerase relative to the DNA template strand during transcription elongation, and we directly detected sequence-dependent pausing and formation of a "half-translocated state" at the E. coli yrbL consensus pause element. Here, we apply SPRNT to analyze the translocation of single molecules of yeast RNA polymerase II (Pol II) relative to the DNA template strand during transcription elongation with single-nucleotide spatial resolution and millisecond-scale temporal resolution at biologically relevant, saturating substrate concentrations; we compare translocation by intact, 12-subunit Pol II to translocation by a 10-subunit Pol II sub-assembly lacking the dissociable Rpb4-Rpb7 Pol II "stalk"; and we assess possible pausing by intact Pol II and stalk-less Pol II at the E. coli yrbL consensus pause element. The results show that intact Pol II elongates more rapidly than stalk-less Pol II and show that neither intact Pol II nor stalk-less Pol II pauses at the E. coli yrbL consensus pause element. One-sentence summaryNanopore tweezers enable monitoring of translocation of RNA polymerase II relative to DNA in transcription elongation with single-nucleotide spatial resolution and millisecond-scale temporal resolution at biologically relevant, saturating substrate concentrations.

molecular biology↗

bioRxiv2026

UniFlow: Unifying protein conformational ensemble generation and machine-learned force fields with a scalable normalizing Flow

AO_SCPLOWBSTRACTC_SCPLOWMolecular dynamics (MD) provides a principled method for modeling equilibrium protein conformational energy landscapes, but its computational cost limits access to long timescales and larger protein systems. Recently, generative protein ensemble models and machine-learned coarse-grained force fields have emerged as complementary approaches for accelerating conformational sampling. However, they are typically developed separately despite modeling the same underlying equilibrium distribution. We introduce UniFlow, the first scalable generative model that unifies protein ensemble generation and machine-learned coarse-grained force fields for molecular dynamics simulation within a single framework. UniFlow employs an internal-coordinate normalizing flow that supports efficient i.i.d. sampling, exact likelihood evaluation, and differentiable energy and force computation. Across diverse protein systems, UniFlow generates ensembles that closely match reference MD simulations, generalizes to proteins beyond its training dataset, and samples substantially faster than diffusion-based ensemble-generation baselines. The same learned density further enables stable long-timescale molecular dynamics simulations. Together, UniFlow paves the way for a unified class of models that bridges generative ensemble modeling with physics-based molecular simulation. Codehttps://github.com/Harrydirk41/UniFlow.git

biochemistry↗

bioRxiv2026

Group Behaviors Foster Zebrafish Foraging Through Social Interaction

Group living enhances foraging efficiency, yet whether this benefit extends across developmental stages and how social coordination versus emotional buffering contribute remain unresolved. Using zebrafish (larvae 10 dpf, adults 80 dpf) in a Y-maze foraging task with high-resolution tracking and mirror experiments, we show that grouping reduces foraging latency in adults, but not larvae. Adults form coordinated groups (high polarization, small inter-individual distances), whereas larvae form loose aggregations. Adults, but not larvae, exhibit reduced freezing (social buffering) when grouped. Mirror-presented visual cues suffice to enhance foraging via emotional calming--reducing freezing and stabilizing speed--yet disrupt real-group coordination. Our results reveal a dual mechanism-- instrumental coordination and emotional buffering--by which group behavior promotes foraging. Both mechanisms are developmentally gated, emerging only after maturation, offering insights into the ontogeny of social competence and practical implications for aquaculture and conservation.

ecology↗

bioRxiv2026

Spatial mitochondrial lineage tracing uncovers a premetastatic niche and microenvironment programmed fate switching in osteosarcoma

Osteosarcoma progression involves complex spatiotemporal dynamics, yet the lineage relationships underlying tumor cell fate decisions remain poorly understood. Here we integrate single cell and spatial transcriptomics and mitochondrial variants-based lineage tracing to map the evolution of a late-stage, non-metastatic osteosarcoma. Single cell pseudotemporal reconstruction of malignant cells reveals a stepwise cascade of transcriptional programs that instructs bifurcation of COL3A1 progenitors into ALPL osteoblastic and THY1 mesenchymal lineages. Critically, we demonstrate that the metastatic THY1 mesenchymal cell fate is not governed by cell-autonomous transcriptional programs alone; this pro-tumorigenic state requires strict spatial licensing via direct co-localization with PLVAP dysfunctional endothelia to form a pro-metastatic niche (THY1_Endo) enriched for PTN/NOTCH signaling. To distinguish clonal ancestry from microenvironmental plasticity, we leveraged spatially resolved somatic mitochondrial variants as endogenous lineage tracers. A spatial autocorrelation filter confirmed significant clonal spatial coherence (Z = 10.07, p {approx} 4x10-{superscript 2}). Phylogenetic reconstruction using a Wright-Fisher drift model coupled with a hidden Markov tree (WF-HMT) revealed a striking directional transition rate from COL3A1 to THY1 tumor cells (724.08) versus the reverse (4.09)--a [~]177-fold asymmetry--whereas the COL3A1 to ALPL transition rate was lower (0.47). Notably, drift-agnostic coarse VAF clustering failed to resolve ALPL versus THY1 bifurcation, underscoring that explicit modeling of stochastic mitochondrial drift is essential for uncovering directional fate locking. We propose a "time-space-lineage" relay model where sequential microenvironmental signals program tumor cell fate and pre-metastatic niche assembly through irreversible clonal commitment. This multi-dimensional framework bridges single-cell states, spatial organization, temporal dynamics, and clonal ancestry --underscoring the power of somatic mitochondrial variants to resolve clonal history and intercept cancer evolution.

bioinformatics↗

bioRxiv2026

Organ-resolved endothelial regulatory programs link aging and metabolic overload to vascular immune remodeling

Systemic aging and metabolic overload remodel the vasculature; however, how endothelial cells integrate these stresses across organs remains unclear. Using multi-organ single-cell and spatial transcriptomics with functional validation, we mapped endothelial and hematopoietic responses in adipose tissue, skeletal muscle, liver, and heart. Organ-specific endothelial transcriptional features were relatively preserved, whereas chronic stress selectively reconfigured regulatory programs: aging induced a conserved Irf/Stat-centered endothelial program, while high-fat diet engaged organ-biased lipid and remodeling programs. Spatial analysis revealed perivascular niches centered on aging-associated interferon-stimulated endothelial activation, with neighboring immune and stromal cells expressing C3 and LRP1-associated signals. Rather than simply amplifying inflammation, these niches contained mechanisms that restrained IFN activation, as C3 depletion upregulated vascular IRF7 expression. In parallel, the IFN downstream effector BST2 promoted anti-inflammatory macrophage differentiation and suppressed atherosclerosis. These findings define vascular inflammaging as an organ-resolved niche process in which endothelial IFN activation is coupled to local inflammatory restraint. HighlightsO_LIAging induces a shared endothelial type I IFN program across organs. C_LIO_LIA high-fat diet triggers organ-biased endothelial remodeling programs. C_LIO_LIPerivascular interferon niches couple inflammation with local restraint. C_LIO_LIIFN-induced endothelial BST2 promotes CD200R-associated macrophage regulatory features. C_LI

cell biology↗

bioRxiv2026

Abbapolin inhibitors of the PLK1 PBD as Prostate Cancer Therapeutics, in vivo activity and synergy with androgen therapy

Polo-like kinase 1 (PLK1) is an established therapeutic target in cancer; however, ATP-competitive kinase inhibitors have shown limited clinical success because of toxicity, acquired resistance, and incomplete inhibition of non-catalytic PLK1 functions. Targeting the Polo-box domain (PBD), which regulates PLK1 localization and substrate recognition, represents an alternative therapeutic strategy but has been hindered by the lack of selective, cell-active small molecules. Here, the optimization and biological characterization of abbapolins, a series of non-peptidic PLK1 PBD inhibitors developed using the REPLACE strategy are described. Structure-guided optimization and screening across the NCI-60 cancer cell panel identified compounds with preferential activity against prostate cancer cells. Proteomic analyses demonstrated that cellular sensitivity correlated with PLK1 protein abundance, supporting an on-target mechanism of action. Abbapolins directly engaged PLK1 in cells, induced selective degradation of endogenous PLK1, and suppressed long-term clonogenic growth. Lead compounds demonstrated favorable pharmacokinetic properties and significantly inhibited prostate tumor growth in xenograft models without detectable systemic toxicity. PLK1 abundance was significantly reduced in treated tumors and correlated with tumor response, identifying PLK1 degradation as a potential pharmacodynamic biomarker. Abbapolins also synergized with enzalutamide in castration- resistant prostate cancer cells, supporting their potential as combination therapies for advanced disease. Collectively, these studies establish selective inhibition of the PLK1 Polo-box domain as a viable therapeutic strategy, provide in vivo proof-of-concept for the REPLACE approach, and identify abbapolins as promising leads for advanced prostate cancer.

cancer biology↗

bioRxiv2026

mirCCC: Repression-aware graph learning for miRNA-mediated cell-cell communication inference

Cell-cell communication analyses usually focus on protein ligands and receptors and therefore miss extracellular vesicle transfer of microRNAs, an important route of signalling in cancer. Here we show that microRNA-mediated communication can be inferred from standard single-cell RNA sequencing by detecting coordinated decreases in the expression of validated miRNA target genes. We developed mirCCC, a computational framework that estimates cell-specific microRNA activity, models cellular sending and receiving capacity for extracellular vesicle transfer, and learns microRNA-resolved communication graphs from transcriptomic data. In synthetic benchmarks with strong confounding signals, mirCCC improved whereas all comparison methods declined. Applied to a human colorectal cancer atlas, mirCCC recovered known colorectal cancer-associated microRNAs and identified stromal- and myeloid-to-epithelial communication converging on a plasticity program linked to TGF-{beta} and Wnt/{beta}-catenin signalling. These results provide a practical route for studying extracellular vesicle-mediated communication in existing single-cell atlases. Author summaryCell-cell communication plays important roles across a wide range of biological processes. Most studies of cell-cell communication focus on interactions between ligands and receptors. However, cells can also use extracellular vesicles to deliver microRNAs (miRNAs), which regulate gene activity in receiving cells and contribute to cancer progression, immune regulation, and changes in the tissue environment. Since standard single-cell sequencing does not directly capture miRNAs, this mode of communication remains largely unexplored. Here we developed mirCCC, a computational method for inferring miRNA-mediated communication from standard single-cell transcriptomic data. Instead of detecting miRNAs directly, mirCCC examines the effects they leave in receiving cells. Active miRNAs often cause coordinated suppression of their target genes. mirCCC uses these coordinated reductions in the expression of known target genes to estimate miRNA activity and combines them with the ability of sending cells to package and release miRNAs and of receiving cells to process them. It can therefore reconstruct directional communication networks for individual miRNAs. Our evaluations show that mirCCC remains reliable in the presence of misleading signals and reveals biologically meaningful regulatory patterns in human colorectal cancer data. Overall, mirCCC provides a practical way to study miRNA-mediated cell-cell communication using existing single-cell transcriptomics datasets.

bioinformatics↗

bioRxiv2026

Enrichments along gradients resolve eco-evolutionary forces on subsurface microbiomes

How a single gram of soil harbors billions of microorganisms, each with distinct genomic variants that collectively maintain coherent ecological function(s), is one of microbiologys grand unsolved problems. A key obstacle is determining which variants contribute to individual- and community-level fitness, in which contexts, and how co-occurring ecotypes interact to divide niche space. Here, using nitrate (NO3-)-contaminated subsurface sediment as inoculum, we have performed high throughput enrichments in laboratory media of defined carbon source compositions across ecologically relevant gradients of pH and NO3-. Long-read metagenomics and link-community decomposition of co-occurrence networks of taxa across these enrichments has revealed context-specific functional interactions among dominant generalist and lower-abundance specialist denitrifier ecotypes that comprise 53 distinct enriched communities (EnComs) across 288 enrichments derived from a single sediment sample. We identified a single enzymatic difference of alternative NO3- reductases (NapAB vs. NarGHI) with differing substrate affinities that provided a mechanistic explanation for competitive niche partitioning between the two dominant taxa, Neorhizobium spp. and Allorhizobium spp., along the NO3- gradient. Genome-wide polymorphism ratios (pN/pS) revealed that selective pressures vary systematically with carbon source availability and gradients of pH and NO3-, which helps explain the natural biodiversity and functional interactions of ecotypes within denitrifying communities in the subsurface sediment. Our findings show that controlled enrichments along ecological gradients can thus uncover eco-evolutionary forces of selection, drift, and diversification that sculpt the biodiversity of microbial populations in the natural environment.

microbiology↗

bioRxiv2026

Origin of Schooling and Collective Environmental Adaptation in Zebrafish

Zebrafish exhibit intricate schooling behaviors when swimming as a group. Such collective motion serves profound ecological functions and continuously inspires the design of highly coordinated artificial systems. Although the features and functions of schooling have been extensively studied, how this behavior originates over the course of individual development remains unknown, limiting a comprehensive understanding of its biological consequences. To address this gap, we developed a cross-scale, multi-modal experimental platform to capture zebrafish schooling and integrated AI-based algorithms to track fine-scale body posture and eye movements. We find that schooling emerges within a discrete developmental window, coinciding with coordinated changes in locomotor architecture and visual perceptual capacity. Specifically, structural remodeling of the caudal fin, enhanced muscle bundling, and an expanded visual perceptual range together provide the physical and sensory basis for the stabilization of polarized group movement. Network analyses under different representational frameworks reveal that the biological function of schooling is a collective group strategy for adapting to the external geometric environment. Our work provides a fundamental explanation of zebrafish schooling from a developmental perspective and elucidates a collective strategy that could inform the design of underwater robot arrays.

ecology↗

bioRxiv2026

Reverse genetics in humanized mice reveals CARD8-mediated pyroptosis causing pancytopenia in human DPP9 deficiency

Loss of function mutation in the human DPP9 gene causes Hatipoglu syndrome leading to severe inflammasomopathy. A key feature of the disease is pancytopenia and patients require bone marrow transplantation, but the mechanism of cell loss is unclear since Dpp9 mutant mice have normal hematopoiesis, suggesting that a distinct mechanism of disease occurs in humans. Here, we present a model of human DPP9 deficiency leveraging reverse genetics in the MISTRG6 humanized mice. We found that CRISPR editing of human CD34+ hematopoietic stem and progenitor cells (HSPCs) led to very efficient and persistent gene deletion in vivo. Human DPP9 deletion recapitulated cytopenia in peripheral blood and in the bone marrow, and cell loss was cell intrinsic. However, DPP9 deletion led to little transcriptional changes suggesting post-transcriptional regulation in human HSPCs. Mechanistically, DPP9 deficiency led to the activation of the CARD8 inflammasome resulting in HSPC pyroptosis, whereas NLRP1 was dispensable for cell death. Thus, our results reveal a unique human mechanism of disease and offer therapeutic insight for this inflammasomopathy.

immunology↗

bioRxiv2026

Tirzepatide attenuates atherosclerosis through weight loss-independent anti-inflammatory mechanisms

BackgroundAtherosclerosis is a chronic inflammatory vascular disorder with persistent residual inflammation even after standard lipid-lowering therapy. Mounting evidence from bench to bedside suggests that diabetes and obesity accelerate atherosclerosis development. Tirzepatide (TZP), a dual Glucagon-Like Peptide-1 Receptor/Glucose-Dependent Insulinotropic Polypeptide Receptor (GLP-1R/GIPR) agonist approved for treating diabetes and obesity, has demonstrated proven cardiometabolic efficacy in large cardiovascular outcome trials. However, it remains largely uncertain whether TZP attenuates atherosclerosis independent of its anti-diabetic and anti-obese effects through direct actions on the vasculature. MethodsWe established atherosclerotic mouse models under diabetic, obese, and non-diabetic/non-obese conditions. Analysis of covariance (ANCOVA) and pair-feeding experiments were applied to experimentally decouple weight-dependent metabolic improvement from intrinsic vasculoprotection. Molecular and cell biological assays in human umbilical vein endothelial cells (HUVECs) and human aortic endothelial cells (HAECs) were performed to dissect the underlying signaling mechanisms. ResultsTZP markedly reduced aortic plaque burden and inflammation, restrained necrotic core enlargement, and improved plaque stability across all experimental mouse models. Both ANCOVA and pair-feeding experiments confirmed that these atheroprotective effects were independent of food intake and body weight loss. Furthermore, TZP attenuated systemic and vascular inflammation in Tumor Necrosis Factor- (TNF)-treated C57BL/6J mice, and this protection occurred without changes in body weight or blood glucose levels. Mechanistically, TZP directly targeted endothelial cells and activated the cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA)/endothelial nitric oxide synthase (eNOS) pathway, increased eNOS phosphorylation and nitric oxide bioavailability, consequently downregulating the expression of the pro-inflammatory adhesion molecules Vascular Cell Adhesion Molecule-1 (VCAM-1) and Intercellular Adhesion Molecule-1 (ICAM-1). ConclusionsTZP arrests atherosclerosis progression through weight loss-independent anti-inflammatory mechanisms. These findings implicate TZP as a promising therapeutic drug for mitigating residual vascular inflammation in patients with atherosclerotic cardiovascular disease (ASCVD), irrespective of glycemic status or obesity. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LITirzepatide exerts direct anti-atherosclerotic effects in preclinical mouse models of atherosclerosis under diabetic, obese, and non-obese conditions. C_LIO_LITirzepatide directly targets endothelial GLP-1R/GIPR and downstream cAMP/PKA/eNOS signaling pathway to suppress NF-{kappa}B-driven vascular inflammation, thereby uncovering a previously unrecognized vasculoprotective mechanism underlying its cardiovascular benefits C_LI What Are the Clinical Implications?O_LITirzepatide exerts direct vascular protective effects independent of body weight reduction, suggesting that its cardiovascular benefits may extend beyond glycemic control and obesity management. C_LIO_LITirzepatide may represent a promising therapeutic drug for addressing residual vascular inflammation in ASCVD patients, including those without overt diabetes or obesity C_LI

physiology↗

bioRxiv2026

Genetically informed single-cell and spatial mapping of metabolic programs in human health and disease

Defining cell-type-specific endogenous metabolic features, the spatial distribution of cell-level metabolic states and cellular responses to exogenous metabolites is very important for understanding disease mechanisms. However, existing transcriptome-based metabolic models primarily infer intracellular reaction or pathway-level activities, and therefore cannot directly assess associations between individual metabolite levels and cellular states, particularly for metabolites that act extracellularly as signalling molecules rather than entering cells as metabolic substrates. To overcome this problem, we introduce the gmMAP (Genetically informed metabolite trait mapping across single-cell and spatial tissues), a framework that integrates metabolite GWAS summary statistics with single-cell and spatial transcriptomes to map metabolic programmes at cellular and spatial resolution. Notably, the gmMAP enables the prediction of endogenous metabolic process activation while also revealing intrinsic associations between exogenous metabolites and diverse cellular functional states. To further capture the connectivity of cellular metabolic networks, we incorporated a constraint-based metabolic flux model to evaluate global metabolic activity. To evaluate the accuracy and generalizability of gmMAP, we applied the framework across representative biological contexts spanning human development, physiological homeostasis, inflammation and cancer. In human kidney development, the gmMAP captured dynamic metabolic programmes, which was validated using paired transcriptomic and metabolomic reference datasets, supporting its reliability in metabolite identification and metabolic-flow inference. At the organ level, the gmMAP reconstructed spatial metabolite distribution patterns across 17 mouse organs under homeostatic and autoimmune inflammatory conditions, and further extension of gmMAP to 24 normal human tissues generated a multi-scale metabolic atlas at both organ and cellular resolutions. In disease settings, gmMAP revealed metabolic reprogramming across 29 pan-cancer cell populations, and identified potential links between exogenous metabolites and inflammation-associated stromal metabolic remodelling in ulcerative colitis. Together, gmMAP can consistently connect genetically informed metabolite traits with cell states, spatial tissue organization and disease pathology.

genomics↗

bioRxiv2026

UTRGen: A unified framework for full-spectrum design of mRNA 5' UTRs

AO_SCPLOWBSTRACTC_SCPLOWThe 5' untranslated region (5' UTR) is a key regulatory element that governs mRNA translation and protein output. However, existing computational methods typically address isolated tasks such as functional prediction or sequence optimization, limiting their ability to support rational design across the full 5' UTR engineering workflow. Here, we present UTRGen, a unified modeling framework for 5' UTRs that integrates sequence generation, multi-property prediction, and constrained function-guided design. UTRGen is pre-trained autoregressively on large-scale 5' UTR datasets from multiple species and subsequently adapted to diverse downstream regulatory tasks. Across systematic evaluations, UTRGen generates novel and diverse 5' UTRs while preserving sequence, structural, and functional characteristics of natural UTRs. After task-specific fine-tuning, UTRGen achieves state-of-the-art performance across 14 benchmark datasets, improving translation efficiency prediction by up to 11.1%, expression level prediction by up to 13.2%, and mean ribosome load prediction by up to 3.0% relative to the strongest baselines. It also achieved the best overall performance for internal ribosome entry site identification. To enable controllable design, we formulate function-guided 5' UTR design as a GRPO-based refinement process over a pre-trained autoregressive sequence prior, using composite rewards to encode functional objectives and biological constraints while regularizing toward the natural 5' UTR distribution. The resulting sequences show consistently improved predicted translation efficiency and expression levels across cellular contexts, and reveal interpretable sequence features associated with high activity, including reduced C content, fewer upstream AUGs, and depletion of inhibitory motifs. Together, our results establish a unified modeling strategy for 5' UTR design and lay a foundation for programmable control of translation.

bioinformatics↗

bioRxiv2026

Acoustic Characteristics of Low-Frequency Vocalizations of Bryde's Whales (Balaenoptera edeni edeni) in the Weizhou Island Waters of the Beibu Gulf, China

Brydes whales (Balaenoptera edeni edeni) are nationally protected in China, and the waters around Weizhou Island in the Beibu Gulf support one of the countrys few regularly observed coastal groups. However, acoustic data for this population remain limited, and the potential effects of local vessel noise are poorly described. We conducted 16 vessel-based surveys around Weizhou Island and adjacent waters in January 2024 using a low-disturbance sailboat platform and passive acoustic recorders, with concurrent visual observations where possible. We identified 734 low-frequency signals classified as putative Brydes whale vocalizations and quantified their temporal and spectral parameters. Call duration was significantly negatively correlated with maximum frequency and center frequency, but not with minimum frequency or bandwidth. Comparisons with published records indicate that the recorded signals are most similar to vocalizations previously reported from juvenile Brydes whales or mother-calf pairs, although individual source attribution could not be confirmed. Speedboat passage significantly increased root-mean-square sound pressure levels, and the dominant noise band overlapped the frequency range of the recorded Brydes whale signals, indicating potential for acoustic masking. These results expand the bioacoustic baseline for Brydes whales in Chinese coastal waters and provide evidence relevant to the management of vessel activity and whale-watching tourism around Weizhou Island.

animal behavior and cognition↗

bioRxiv2026

ConformFlow: scalable normalizing flow for protein conformational ensemble generation

AO_SCPLOWBSTRACTC_SCPLOWMolecular dynamics (MD) simulations remain the standard tool for characterizing protein conformational landscapes, but their high computational cost limits large-scale and long-timescale applications. Recent generative models, especially diffusion-based approaches, provide promising alternatives by learning equilibrium conformational distributions across diverse protein systems. We present ConformFlow, the first scalable normalizing-flow framework for sequence-conditioned protein conformational ensemble generation. ConformFlow combines a continuous backbone latent representation with a RealNVP-style flow parameterized by sequence-aware Transformer coupling networks, enabling exact likelihood training, single-step sampling, and plug-and-play conditioning on flexible geometric constraints. Across diverse protein systems, ConformFlow generates ensembles that agree well with reference MD simulations, generalizes to proteins beyond its training data, and achieves substantially faster sampling than diffusion-based baselines. These results establish ConformFlow as an efficient and controllable alternative for protein conformational ensemble generation. Codehttps://github.com/Harrydirk41/ConformFlow.git

biochemistry↗

bioRxiv2026

Improving All-Atom Molecular Dynamics Models for Quantitative Prediction of Nanopore Blockade Current

All-atom molecular dynamics has become an indispensable tool in development of nanopore sensors of biological information. In a typical nanopore experiment, measurements of ionic current flowing through a nanopore report on the chemical structure of biomolecules that pass through the nanopore. Such experiments alone are often insufficient to relate the structure of the biomolecules to the ionic current modulations. The molecular dynamics method can establish such a relationship directly through a brute force simulation under applied electric field. Here, we examine the ability of molecular dynamics force fields to reproduce experimentally measured nanopore blockade currents produced by single-stranded DNA. Our simulations show that none of the "off the shelf" force fields (CHARMM36, AMBER Parmbsc1 and DES-AMBER) is capable of reproducing experimental data with the desired level of accuracy. To improve the accuracy, we examined and refined interactions between ions, protein nanopores and DNA, guided by experiments designed specifically for this purpose. Ultimately, the introduction of surgical corrections to non-bonded interactions within the CHARMM36 force field produced a favorable agreement between simulation and experiment. This refined parameterization, initially developed for nanopore sensing simulations, may have broader applications in computational studies of DNA-protein systems.

biophysics↗

bioRxiv2026

Systematic Profiling and Functional Characterization of Alternative Splicing in C. elegans Olfactory Learning

Alternative splicing (AS) is prevalent in neuronal gene expression. However, its function in learning has not been systematically characterized. Here, by profiling pan-neuronal translatome changes during a C. elegans learning paradigm, we show that AS remodels expression of neuronal genes with critical function in learning at the genome-wide scale. Intriguingly, AS operates on a functionally distinct gene set from those showing transcript abundance changes, serving as a separate regulatory layer in response to experience. Specifically, a neuronally enriched worm ortholog of a mitochondrial DNA helicase twnk-1 displays significant learning-associated AS changes, with both isoform types acting in a pair of sensory neurons to regulate learning with distinct functions. AS of twnk-1 modulates a cell-nonautonomous signal from neuronal mitochondria to peripheral tissues to regulate physiological states critical for learning. These results establish AS as a systematic regulator of learning and mechanistically reveal how AS shapes physiological states to facilitate learning.

neuroscience↗