Biology subjects

Lv, Z.

Publications and source records attributed to Lv, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Mesoscale Soft Robotics for Mechanically Perturbing and Functionally Monitoring Responses in Human Organoids

Human organoids represent promising models for studies of significance to the biology and medical communities. Of the existing approaches for monitoring organoids, none offers the ability to apply programmable mechanical forces while simultaneously recording multi-modal physiological responses, of relevance to phenomena spanning from traumatic brain injuries to cardiac rhythm disorders. Here, we present a mesoscale soft robotic organoid interface that enables programmable delivery of forces to organoids of diverse shapes, and to map electrophysiological activity and mechanical responses. The platform combines actuation elements with integrated sensors for quantitative measurement and control. Studies using this technology reveal that spheroidal cortical organoids exhibit distinct responses to mechanical trauma, including silencing, recovery, and changes in neuronal firing patterns. Additional experiments on cardiac ring tissues demonstrate that sustained stretching induces beat-to-beat rhythm variability and altered conduction. The results establish a framework for investigating force-triggered transitions between physiological and disease-like states in human tissue models.

bioengineering↗

bioRxiv2026

Functional specialization of TAF4b governs early prophase I entry and recombination frequency in Brassica rapa

While the core transcription factor TAF4b is known to regulate meiotic transcription and recombination in model plant Arabidopsis thaliana, its functional specialization and evolutionary dynamics remain poorly understood in paleopolyploid crops. To address this, we investigated how the unique, single-copy TAF4b gene modulates early meiotic progression and reproductive success in Brassica rapa. We characterized multiple independent taf4b TILLING lines for fertility and meiotic behavior, then used this information to select a mutant line for further investigation using a pipeline combining whole-genome background sequencing (DNA-seq), stage-specific anther transcriptomics (RNA-seq), yeast two-hybrid assays, and quantitative cytogenetics. Disruption of the meiosis-enriched TAF4b gene on chromosome A7 universally reduced crossover frequency and depleted HEI10 foci, triggering occasional univalent formation. Global RNA-seq and structural modeling indicate that this phenotype is driven by a precise transcriptional dysregulation and altered molecular interactions restricted to the interphase/leptotene transition. In conclusion, TAF4b acts as a stage-specific upstream gatekeeper of the early prophase I expression program in B. rapa, thereby playing an essential, non-redundant role in its meiotic stability.

molecular biology↗

bioRxiv2026

ForceFlowAb: physics-aware mixture-of-experts flow matching model for antibody CDRs design

Abstract Motivation: Antibodies are a major class of therapeutic molecules, and their recognition of target antigens is largely mediated by complementarity-determining regions (CDRs), making antigen-conditioned CDR design a central problem in antibody engineering. Recent generative methods have enabled antigen-conditioned co-design of CDR sequences and structures, but their limited capacity to capture local interface heterogeneity and lack explicit energy-based guidance during sampling, which may result in unfavorable antibody-antigen interaction energies. Overcoming these limitations requires methods that better represent diverse interface environments via adaptive routing and incorporate physical guidance to steer sampling toward energetically favorable conformations. Results: We present ForceFlowAb, a physics-aware mixture-of-experts flow-matching framework for antigen-conditioned CDR sequence-structure co-design. The framework models heterogeneous interface environments through specialized expert routing and applies differentiable force-field guidance during sampling to guide CDR generation toward energetically favorable conformations. For CDR-H3 design, ForceFlowAb achieved more favorable antibody-antigen interaction energies than FlowDesign and Diffab, with improvement rates (IMP) of 46.5% versus 35.0% and 35.5%, respectively. For simultaneous six-CDR design, ForceFlowAb also outperformed Diffab, with IMP values of 16% versus 9%. These results suggest complementary roles for interface-adaptive modeling and energy-based guidance, with the former capturing binding-mode diversity and the latter leveraging physical constraints to ensure biophysical feasibility. Availability and implementation: The web server is freely available at http://zhanglab-bioinf.com/ForceFlowAb. The source code and implementation are available at https://github.com/iobio-zjut/ForceFlowAb. Contact: zgj@zjut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

A hierarchical activation threshold shapes RSV F antibody repertoires from toddler vulnerability to adult protection

Respiratory syncytial virus (RSV) causes severe infant morbidity, yet the mechanisms underlying their suboptimal immunity and the failure of prefusion F-stabilized adult vaccines in infants remain unclear. Here we integrate deep mutational scanning of 761 preF-binding antibodies, repertoire profiling of 102 repeatedly exposed pediatricians and 61 RSV-experienced toddlers, structural analysis, and >40,000 viral genomes to decode RSV F immunity. We resolved 12 immunologically distinct functional antibody subclasses and uncovered a hierarchical activation threshold: apical epitope-targeting antibodies with superior neutralizing potency demand extensive somatic hypermutation and cooperative CDR networks, whereas central-to-basal epitope-directed, marginal or non-neutralizing antibodies engage germline-encoded antibodies through minimal mutations (e.g., S31G). Toddler repertoires are confined to low-threshold, non-productive sites; adult repertoires enrich for apical elite neutralizers whose immune pressure drives contemporary RSV-B evolution. These reframe pediatric RSV vulnerability as a threshold-gated repertoire deficit and prescribe vaccine strategies that actively redirect immunodominance from permissive epitopes toward high-barrier apical targets.

immunology↗

bioRxiv2026

Rational antigen engineering and mucosal delivery design for next-generation RSV vaccines

Respiratory syncytial virus (RSV) prefusion F (preF) vaccines have transformed adult prophylaxis, yet unmet needs in antigen stability, pediatric safety, and mucosal protection persist. Here, we develop an integrated structure-guided RSV vaccine design platform that couples allosteric stabilization, epitope-focused immunogen engineering, and route-specific mRNA delivery for systemic and mucosal immune activations. By mapping prefusion F "breathing" motions and applying a ThermoNet-and Rosetta-guided screening funnel, we identified R296, a stabilized prefusion F immunogen that reinforces the 1-5 hinge and interprotomer interfaces while preserving key neutralizing epitopes. Cryo-EM confirmed that R296 retains a native-like prefusion architecture. And mRNA-LNP vaccination elicited potent, durable, and broadly protective neutralizing responses in mice, rats, and cotton rats, with clearance of detectable infectious virus and no evidence of Th2-skewed enhanced respiratory disease. To address pediatric safety, we designed a stalkless nanoparticle immunogen, Head38-50AB-3, which enriches high-potency apical epitopes while excluding stalk regions associated with low-potency or non-protective responses, conferring protection without VAERD-like pathology. Finally, we engineered an intranasal-delivered LNP that enables intranasal R296 mRNA delivery, inducing systemic neutralization together with robust nasal and bronchoalveolar secretory IgA (sIgA). R296 has now advanced to Phase 1 clinical trials. These results establish a modular framework for next-generation RSV vaccines.

microbiology↗

bioRxiv2026

From Hodgkin-Huxley to Pretrained Neural Inference AI

High-density probes record from thousands of neurons simultaneously, yet resolving single-neuron identity remains an illposed inverse problem. While detailed simulations precisely characterize the biophysical forward process, their utility for interpreting brain signal remains unclear. Here we show that biophysical simulations of population neuronal electrical signals serve as an effective bridge between theory and experiment. By pre-training artificial neural networks exclusively on large-scale synthetic data, we demonstrate robust zero-shot generalization across diverse brain regions, experimental paradigms and species, enabling the accurate inference of single-unit activities and cell-type properties without exposure to real data. Further-more, uncovering a substantial population of functionally competent but weakly active neurons systematically obscured by conventional heuristics, our framework resolves a long-standing discrepancy regarding ocular dominance in mouse primary visual cortex. These findings establish biophysical simulations as a reference standard, bridging the gap between theoretical understanding and experimental observation through data-driven inference.

neuroscience↗

bioRxiv2026

SpaGRD deciphers signaling architectures in spatial transcriptomics using graph reaction-diffusion systems

The rapid emergence of spatial transcriptomics offers unprecedented opportunities to study cell-cell communication (CCC) by capturing gene expression alongside spatial context. However, existing CCC inference methods often rely on static, heuristic models that overlook the inherently spatiotemporal dynamics and mechanistic complexity of intercellular signaling, limiting both accuracy and biological interpretability. Here, we present SpaGRD, a first-principles-based method that explicitly models ligand-receptor interactions through partial differential equations derived from Ficks law of diffusion and the mass action law. Leveraging graph signal processing techniques, SpaGRD solves these equations on spatial graphs, providing a principled and generalizable approach to CCC inference. Through extensive simulations, SpaGRD demonstrates superior accuracy and robustness compared to existing methods. Applications to multiple datasets across diverse tissues and platforms reveal dynamic CCC patterns with spatially resolved signaling heterogeneity, providing biologically meaningful insights into cellular coordination and developmental processes. By bridging physical modeling with spatial transcriptomics, SpaGRD provides an accurate, interpretable, and mechanistically grounded framework for advancing quantitative studies of spatiotemporal cell-cell communication.

bioinformatics↗

bioRxiv2026

IgG4⁺ plasma cell enrichment and {lambda}-chain-biased BCR remodeling drive low-grade autoimmunity in chronic obstructive pulmonary disease

BackgroundThis study aimed to elucidate B cell subset pathology in COPD, a poorly characterized area, with a focus on its similarities to and differences from classical autoimmune disorders. MethodsThe single-cell RNA-sequencing (scRNA-seq) data from COPD and autoimmune diseases were obtained from Gene Expression Omnibus (GEO) for comparative analyses of B cell subsets and functions via differentially expressed genes (DEGs), KEGG, protein-protein interaction (PPI), and cell-cell communication analyses. Serum IgG4 was measured by ELISA and correlated with clinical parameters. The peripheral blood B cells were sorted by flow cytometry for single-cell B cell receptor (BCR) sequencing. A v-Abl/Bcl2 pro-B cell line was stimulated with cigarette smoke extract (CSE) to assess abnormal development in vitro. ResultsIn lung tissue, IgG4+ plasma cells were enriched and expressed BCR activation/inflammatory genes and TNF/NF-{kappa}B/MAPK pathways. Serum IgG4 concentrations correlated negatively with pre-and post-bronchodilator FEV1/FVC. B cell interacted with monocytes, macrophages, fibroblasts and endothelial cells via IL-1{beta}/IL-6, integrin and chemokine signalling, contributing to chronic inflammation and remodelling. In peripheral blood, transitional T1 B cells were increased, accompanied by {lambda}-chain enrichment and increased IGLV1-47 usage, as well as enrichment of autoimmune pathways. In the bone marrow, the numbers of pre-B I cells were increased while those of small pre-B III cells were reduced, with altered expression of BCR development genes. CSE stimulation of the pro-B cell line reduced {lambda}5 expression in a concentration-dependent manner. ConclusionsThe autoimmune abnormalities in COPD appear more restricted, although IgG4 antibody generation may contribute to immune-mediated lung damage.

immunology↗

bioRxiv2026

A unified population-genetic framework for inferring double reduction and higher-order two-locus linkage disequilibrium in autopolyploids

Polyploid genomes exhibit complex allelic interactions driven by polysomic inheritance that cannot be adequately captured by conventional pairwise linkage disequilibrium (LD) models, leaving higher-order dependency structures largely unresolved. Here, we develop a unified population-genetic framework that jointly infers double reduction and higher-order allelic associations in autopolyploids. By modeling dosage-dependent gametic structure, our approach reformulates LD as a hierarchical system, enabling decomposition of allelic dependencies beyond pairwise interactions. Through simulations, we show that conventional estimators systematically confound higher-order allelic structure with pairwise LD, leading to biased inference and loss of identifiability under dosage uncertainty. In contrast, our joint likelihood framework achieves asymptotically unbiased estimation and reveals that effective double reduction emerges as a composite parameter linking meiotic configuration to population-level allelic reshaping. Applying this model to Arabidopsis arenosa and cultivated potato, we uncover fundamental contrasts in genomic architecture. In natural populations, higher-order LD self-organizes into structured, long-range dependency networks, whereas predominantly clonal populations exhibit collapse of higher-order dependency structure. We further identify a spatial antagonism between double reduction and higher-order LD, demonstrating that elevated double reduction compresses the physical scale of allelic interactions. Our results demonstrate that autopolyploid genomes are organized as higher-order dependency systems rather than collections of pairwise associations, establishing higher-order allelic structure as a fundamental extension of classical LD theory.

genetics↗

bioRxiv2026

Lmna deficiency promotes EPHX2 nuclear translocation to ameliorate cardiac dysfunction in mice

BackgroundCardiovascular diseases are often associated with altered protein subcellular localization. As a major cause of inherited cardiomyopathy, LMNA deficiency could trigger nuclear envelope rupture and broadly impair the localization of nuclear and cytoplasmic proteins. Systemic approaches to identify, dissect and manipulate the localization of endogenous proteins are important for mechanistic and therapeutic investigation. MethodProximity proteomics of the nuclear lamina was performed specifically in cardiomyocytes in Lmna-deficient murine models. AAV-mediated Cas9-based gene silencing and subcellular gene upregulation were conducted via the nuclear localization signal (NLS) and nuclear export signal (NES). Cas9-based somatic mutagenesis was supplemented with the single-strand DNA templates of AAV to achieve robust homology-directed repair (HDR) and targeted NLS knock-in, which translocated cytoplasmic proteins into nuclei. ResultIn vivo proximity proteomics detected increased epoxide hydrolase 2 (EPHX2) in cardiomyocyte nuclei in mice carrying germline or cardiac-specific Lmna truncating variants. This phenotype was associated with ruptured nuclear envelope. Cas9-mediated Ephx2 knockout in cardiomyocytes ameliorated cardiac dysfunction in Lmna-deficient mice. Strikingly, overexpression of NLS-EPHX2, but not NES-EPHX2, also mitigated cardiac dysfunction. The cardiac protective EPHX2 substrates, epoxyeicosatrienoic acids (EETs), did not alter upon NLS-EPHX2 overexpression. By contrast, the Lmna-related DNA damage marker {gamma}-H2AX was reduced. The EPHX-D333A mutant lacking hydrolase activity recapitulated the effects of wildtype EPHX2 in nuclei. AAV-Cas9-based HDR achieved efficient NLS knock-in and EPHX2 nuclear translocation in more than 60% cardiomyocytes, which improved cardiac function. ConclusionLmna deficiency leads to the nuclear translocation of EPHX2, which ameliorated cardiac dysfunction in a hydrolase-independent manner. AAV-HDR-mediated somatic gene editing provides an efficient approach to manipulate the subcellular localization of endogenous proteins in cardiomyocytes in vivo. What is Known?O_LICardiovascular diseases are regulated by the changes in protein subcellular localization. In particular, LMNA-related cardiomyopathy is associated with nuclear rupture and impaired separation between nuclear and cytoplasmic proteins. C_LIO_LIEpoxide hydrolase 2 (EPHX2) is a cytoplasmic hydrolase that catalyzes the hydrolysis of cardioprotective epoxyeicosatrienoic acids (EETs) and aggravates an array of heart diseases including myocardial infarction and heart failure. C_LIO_LICRISPR/Cas9-mediated homology-directed repair (HDR) exhibits uniquely high gene editing efficiency in cardiomyocytes with an AAV-based DNA donor, which is suitable for somatic genetic knock-in of small DNA fragments in vivo. C_LI What New Information Does This Article Contribute?O_LILamin-targeted proximity proteomics specifically in cardiomyocytes uncovers novel proteins undergoing subcellular localization changes upon Lmna deficiency. C_LIO_LILmna deficiency leads to EPHX2 nuclear translocation that ameliorates cardiac dysfunction by both mechanisms of cytoplasmic reduction and nuclear induction. C_LIO_LIAAV-HDR-mediated knock-in provides a robust platform to manipulate subcellular localization of endogenous proteins specifically in cardiomyocytes in vivo. C_LI Cardiovascular diseases are often associated with altered protein subcellular localization, but systemic approaches to identify, study and manipulate subcellular localization remain incomplete. This study established an in vivo proximity proteomics approach to identify novel proteins undergoing localization changes relative to the nuclear lamina. In murine models of LMNA-related cardiomyopathy, this approach uncovered EPHX2, a classic cytoplasmic hydrolase that aggravates cardiovascular diseases, as a new protein that translocated into cell nucleus and exerted a cardiac protective effect. CRISPR/Cas9-based cardiomyocyte gene editing with an AAV-based DNA donor efficiently achieved NLS knock-in into the Ephx2 gene, promoted EPHX2 protein nuclear translocation and mitigated cardiac dysfunction with Lmna deficiency. These findings indicated a novel avenue to identify and manipulate the subcellular localization changes of endogenous proteins for basic and translational cardiology.

pathology↗

bioRxiv2025

3' to 5' Translation of Circular RNAs?

For decades, the 5[->]3 direction of translation has been a central dogma of molecular biology(1). Here we present evidence that eukaryotic circular RNAs (circRNAs) can serve as templates for 3[->]5 backward translation (BT), yielding polypeptides with distinct sequence and structural features not found in canonical proteomes. Mining of more than 6,000 multi-omics datasets identified [~]1 million candidate BT proteins across eukaryotes, including 59,000 high-confidence human BT proteins supported by mass spectrometry. Genetic combinatorial experiments (KO and KI) and cell-free translation of synthetic circRNAs establish BT as a conserved mechanism. Loss-of-function studies of hs.circCAPN15 in human and dm.circROLS in Drosophila underscore the functional importance of BT-derived proteins. Our work challenges the long-standing unidirectional translation paradigm, expands the functional landscape of the genetic code, and reveals a hitherto hidden layer of proteomic complexity with broad implications for biology and therapeutics.

molecular biology↗

bioRxiv2025

High-accuracy structure modeling for antibody-antigen complexes

Accurate modeling of antibody-antigen complexes is crucial for advancing therapeutics and diagnostics, yet predicting their binding interface remains a formidable challenge. To address this, we introduce DeepAAAssembly, a protocol that enhances static structural information from AlphaFold3 by integrating dynamic interaction patterns to guide complex assembly. Our approach leverages predicted inter-chain residue distances to construct a flexibility-aware energy function, which drives a two-stage conformational sampling process for global exploration and local exploitation. On a benchmark set of 67 representative antibody-antigen complexes, by incorporating a built-in confidence selection mechanism, DeepAAAssembly outperforms AlphaFold3, achieving not only a 12.9% higher average DockQ score and more medium- and high-quality models, but also reliably elevating the most challenging cases from incorrect to acceptable accuracy. These results demonstrate that DeepAAAssembly effectively captures conserved interaction motifs and conformational flexibility, offering a robust framework for high-accuracy antibody-antigen modeling.

bioinformatics↗

bioRxiv2025

ProSiteHunter: A unified framework for sequence-based prediction of protein-nucleic acid and protein-protein binding sites

Accurate prediction of protein binding sites is essential for elucidating protein function, understanding molecular interaction mechanisms, and facilitating drug design. However, existing sequence-based approaches are often designed for specific binding-site types and therefore lack generality, whereas structure-based methods typically rely on high-quality structural models, limiting their applicability. Here, we introduce ProSiteHunter, a unified sequence-based framework for protein binding-site prediction, which integrates a fine-tuned protein language model (SiteT5) with a multi-source feature-fusion network that incorporates evolutionary, geometric, and statistical features, while employing bidirectional semantics, local associations, and global dependencies for comprehensive binding-site characterization. The method was systematically evaluated on diverse binding sites prediction tasks, where ProSiteHunter achieved a 39.1% average improvement in PRAUC for protein-DNA/RNA/protein tasks and a 7.4% PRAUC enhancement on the particularly challenging antibody-antigen task over state-of-the-art methods. Moreover, ProSiteHunter is capable of identifying local flexible sites that complement AlphaFold3 predictions and improving the accuracy of antibody-antigen interaction prediction. These results highlight ProSiteHunter as an efficient and unified approach for accurate and robust prediction of diverse protein binding sites.

bioinformatics↗

bioRxiv2025

Dorsal Attention Network as a Convergent Hub of Diverse Non-pharmacological Interventions Preserving Functional Gradients and Cognition in Aging

Non-pharmacological interventions (NPIs) in aging neuroscience have largely focused on intervention-specific regional effects, with limited understanding of generalizable network-level mechanisms. Here, adopting a previously unexplored gradient-based perspective of functional brain organization, we analyzed an NPI dataset involving four interventions in older adults (training/control group: n = 112/59). NPIs led to strengthened intra-network functional integration and maintained macroscale gradient architecture. Virtual lesion analyses identified the dorsal attention network (DAN) as a key contributor to gradient maintenance. Critically, enhanced post-intervention DAN connectivity was associated with maintained gradient structure and improved global cognition. These findings establish a unifying framework in which the DAN acts as a convergent hub through which diverse NPIs preserve functional brain organization and attenuate cognitive aging.

neuroscience↗

bioRxiv2025

Tetraploid turnips (Brassica rapa ssp. rapa) are highly tolerant of tetravalent formation and aneuploidy

Prior to 1980, experimental induction of polyploidy (chromosome doubling) led to the release of several tetraploid Brassica rapa ssp. rapa as fodder turnip cultivars. Most experimentally induced polyploids are meiotically unstable and show reduced fertility. However, we hypothesized that based on the requirement to produce large amounts of commercial seed to sell to farmers, these turnip lines should have managed to restore fertility and stabilize meiosis. We collected and tested all B. rapa listed as or referred to as tetraploid from the IPK Gatersleben, CGN Wageningen and Nordgen germplasm banks, and subsequently investigated chromosome karyotypes, meiotic chromosome behaviour and fertility in confirmed tetraploid turnip accessions, using a combination of resequencing and fluorescent in situ hybridisation. Contrary to our expectations, all accessions showed unstable meiosis: the average tetravalent frequency per meiosis per plant ranged from 4.8 to 6.4 per line, and these tetravalent associations also persisted from diakinesis to metaphase I. Using chromosome-specific fluorescence in situ hybridisation probes, we found that most chromosomes showed similar frequencies of tetravalent formation except for chromosomes A03 and A06, which predominantly formed tetravalents (>90%). Of the 21 individuals sequenced (one per accession), approximately half (9/21) were aneuploid (loss or gain of a whole chromosome), and two displayed additional chromosomal rearrangements. We nevertheless observed no significant phenotypic abnormalities or reductions in fertility (although all accessions were self-incompatible). Our findings indicate that stabilizing meiosis may not always be necessary to produce relatively fertile and homogeneous outcrossing polyploid populations.

genetics↗

bioRxiv2025

MultiSAAI: Sequence-informed Antibody-Antigen Interactions Prediction using Multi-scale Deep Learning

MotivationAntibodies possess high specificity toward target antigens, making them critical for therapeutic applications. However, experimental screening of antibody-antigen interactions is labor-intensive and costly. Existing structure-based computational methods often face significant challenges in modeling the dynamic conformational flexibility that governs antibody-antigen interactions, which limits their ability to accurately identify binding. Therefore, there is an urgent need for accurate and efficient sequence-based prediction method to accelerate antibody discovery and reduce experimental burden. ResultsWe propose MultiSAAI, a sequence-informed framework that models antibody-antigen interactions by explicitly accounting for the distinct roles of antibody heavy and light chains in antigen binding. MultiSAAI integrates language model embeddings, physicochemical properties, geometric constraints, and residue substitutability to characterize antibody-antigen interactions across multiple scales, employing a multi-scale network architecture that simultaneously evaluates global residue-pair compatibility and local amino acid fitness at the binding interface. Furthermore, the incorporation of site-specific information and biologically grounded binding principles allows the model to more closely reflect the actual mechanisms of interactions. Benchmark results demonstrate that MultiSAAI achieves AUROC scores of 0.757 on the generic antibody-antigen interactions dataset and 0.946 on the SARS-CoV-2 dataset, outperforming existing methods such as A2binder and AbAgIntPre. Finally, large-scale preliminary antibody screening further validates the potential of MultiSAAI for high-throughput therapeutic antibody discovery.

bioinformatics↗

bioRxiv2025

FASN Inactivation-induced Progranulin (GRN) Expression Promotes Lysosome-dependent Cell Death to Suppress Leukemogenesis

Cancer cells rely on lipogenesis in addition to exogenous lipid uptake, and fatty acid synthase (FASN) is aberrantly overexpressed in myeloid leukemia. However, the precise role of FASN in leukemogenesis in vivo remains elusive. Here, we demonstrated that FASN is essential for leukemogenesis. Ablation of FASN impeded leukemic cell growth, survival, clonogenicity in vitro, and the leukemic burden in vivo. Conditional knockout of FASN barely affected hematopoiesis but significantly attenuated the leukemic progression of MLL-AF9 transplants. By screening a library of platensimycin derivatives, we identified compound MS-C19 as a potent FASN inhibitor. Pharmacological targeting of FASN by MS-C19 suppressed leukemic cell growth and clonogenicity in clinical AML blasts. Mechanistically, both MS-C19 treatment and FASN deficiency triggered the activation of lysosomal and inflammatory gene expression. Loss of FASN led to lysosomal membrane permeabilization and subsequent lysosome-associated cell death, but not lysosome biogenesis. We further identified that GRN, a lysosomal and neuroinflammatory gene, was potently transcribed by TFEB upon FASN loss. Depletion of GRN significantly reversed the inhibitory effects caused by FASN knockdown. Our work demonstrates that FASN is a therapeutic target for myeloid leukemia, and inactivation of FASN by the lead compound MS-C19 provides an alternative approach for leukemic intervention. Key PointsO_LIGenetic ablation of FASN induces apoptosis, impedes leukemia cell growth, and improves the survival of leukemic xenografts. C_LIO_LITargeting FASN by the compound MS-C19, a derivative of the natural microbe antibiotic platensimycin, reduces the cell survival of leukemic cell lines, the clinical leukemic blasts, and MLL-AF9 transformed murine leukemic cells. C_LIO_LIFASN inhibition induces lysosome-dependent cell death that relies on Progranulin (GRN) expression. C_LI

cancer biology↗

bioRxiv2025

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 has made a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals.

bioinformatics↗