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Biology subjects

Zheng, P.

Publications and source records attributed to Zheng, P..

At least 19 recordsLinked to original sources

Cell autonomous inflammation in VEXAS is mediated by cGAS-STING

VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) is a severe adult-onset inflammatory disease caused by somatic mutations that reduce cytoplasmic activity of UBA1, the primary initiating enzyme for ubiquitylation. How this hypomorphic state drives cell-intrinsic immune activation in mature myeloid cells is unknown. Using unbiased multi-omic, biochemical, and cell biological analyses of model systems and patient-derived cells, we show that loss of cytoplasmic UBA1 activity convergently disrupts endoplasmic reticulum- associated degradation (ERAD) and mitochondrial homeostasis. ERAD failure arises from preferential under-charging of ERAD E2 enzymes, explaining hallmark VEXAS features, including ER-derived vacuoles and unfolded protein response activation, and promotes accumulation of the ERAD substrate STING. Simultaneously, mitochondrial dysfunction drives cytosolic leakage of mitochondrial DNA, inducing cGAS-dependent STING signaling and inflammatory cytokine production. STING inhibition or reversal of mitochondrial DNA leakage resolves multi-cytokine inflammation in VEXAS models and patient myeloid cells, establishing the cGAS-STING pathway as a therapeutically actionable vulnerability.

genetics↗

Effect of dietary Chitosan supplementation on Intestinal Barrier Function and Growth Performance in weaned piglets challenged by Enterohemorrhagic haemolytic Escherichia coli

Enterohemorrhagic Escherichia coli O157:H7 (EHEC) is an important zoonotic pathogen that disrupts intestinal epithelial barrier integrity and induces excessive inflammatory responses, thereby leading to impaired growth performance and intestinal injury. EHEC is also an important cause Hemolytic Uremic Syndrome (HUS) in children and older adults. In pig production, chitosan is considered a promising alternative to antibiotics due to its bioadhesive and antimicrobial properties, but the effects and underlying mechanisms of chitosan (COS) under pathogenic challenge remain to be elucidated. One hundred and eight pigs were randomly divided into three treatments: an unchallenged control group (CON), an EHEC-challenged control group (ECON), and an EHEC-challenged group supplemented with 100 mg/kg COS (ECOS). Results show that EHEC challenge increased the feed conversion ratio (FCR), increased inflammatory cytokine levels, disrupted intestinal morphology, and downregulated tight junction and nutrient transporter gene expression (P<0.05). Dietary COS supplementation significantly improved average daily gain (ADG) and FCR during day 6-14 (P<0.05). Moreover, COS reduced fecal shedding of total E. coli (P = 0.085) and EHEC, attenuated systemic inflammation by decreasing serum TNF- and IL-6 levels, and enhanced humoral immunity as indicated by increased IgA and IgM concentrations (P<0.05). Importantly, COS alleviated EHEC-induced intestinal injury by restoring villus height and villus-to-crypt ratio, with enhanced mucosal digestive enzyme activities, and upregulated expression of tight junction proteins (ZO-1 and occludin) and nutrient transporters (SGLT-1 and PEPT1) (P<0.05). In conclusion, these findings indicate that dietary COS improves growth performance in EHEC-challenged weaned pigs, with enhanced intestinal barrier integrity and nutrient transport capacity.

microbiology↗

scMOBA: A conversational single-cell Multi-Omics Brain Agent across species

Single-cell and spatial multi-omics are revolutionizing our understanding of the complexity in the developmental, aging, and diseased brain, but integrating this knowledge across modalities and species remains challenging. To bridge this gap, we propose scMOBA, a conversational single-cell Multi-Omics Brain Agent established by a large language model, a gene encoder, and a cross-attention projector. The scMOBA was pre-trained on 130 million single-cell and spatial multi-omics data spanning the entire brain across diverse species, development, aging and diseases. The pre-training utilized a novel multi-omics Feature-Question-Answer (FQA) paradigm, enabling the model to generate biological answers from feature inputs and textual queries. This unique scheme facilitates superior zero-shot inference capabilities without requiring additional fine-tuning. We demonstrate that scMOBA achieves state-of-the-art performance in fine-grained cell type classification across different species and modalities, as well as in batch correction and multi-omics data integration. Furthermore, scMOBA significantly boosts the accuracy of kinds of critical downstream tasks, including cell-type specific aging clock construction and disease status prediction. Overall, scMOBA serves as a powerful scientific discovery engine for multi-omics brain research, advancing the precision prediction and early intervention of neurological aging and diseases.

bioinformatics↗

Scalable integration and prediction of unpaired single-cell and spatial multi-omics via regularized disentanglement

Deciphering cellular states requires methods capable of integrating large-scale heterogeneous single-cell and spatial omics data. However, these data are typically unpaired due to destructive assays and further confounded by modality heterogeneity, technical noise, and immense scale. Here we present scMRDR, a scalable computational framework based on regularized disentangled representation learning for integrating fully unpaired single-cell and spatial multi-omics datasets. Built on a unified and structure-preserving architecture, scMRDR removes the need for pairing supervision while maintaining computational efficiency, enabling scaling to large datasets spanning multiple disparate omics modalities. Across diverse real-world benchmarks, scMRDR demonstrates strong performance in batch correction, modality alignment, and biological signal preservation. The framework further supports cross-modal translation across omics modalities and enables spatial coordinate imputation for non-spatial single-cell datasets using a reference atlas. The resulting spatial mapping allows spatially resolved analyses, including identification of spatially variable genes and characterization of epigenetic regulatory programs in their native tissue context. These capabilities position scMRDR as a scalable and versatile framework for large-scale multi-omics integration.

genomics↗

Transforming a Fragile Protein Helix into an Ultrastable Scaffold via a Hierarchical AI and Chemistry Framework

The rational design of proteins that maintain structural integrity under concurrent thermal, mechanical, and chemical stress remains a challenge in molecular engineering. We present a hierarchical framework that transforms an -helical domain into an ultrastable scaffold by integrating AI-guided design with foundational chemical principles. This approach progresses from global architectural reinforcement, using multiple AI tools to create a stabilized four-helix bundle, to local chemical tuning, where AlphaFold3 guides the installation of salt bridges and metal-coordination motifs. A computational pipeline using physics-based screening such as molecular dynamics (MD) simulations efficiently distilled millions of designs into a minimal candidate set. The resulting -helical proteins exhibit unprecedented multi-axis stability, with mechanical unfolding forces exceeding 200 pN, thermal resilience >100 {degrees}C, and high resistance to chemical denaturants. By systematically dissecting the contributions of hydrophobic packing, electrostatics, and metal coordination, we establish a general blueprint for imparting extreme robustness. This work bridges AI-driven structural generation with chemical precision, advancing the creation of durable proteins for mechanistic studies and synthetic biology.

biophysics↗

DNACSE: Enhancing Genomic LLMs with Contrastive Learning for DNA Barcode Identification

DNA barcoding is a powerful tool for exploring biodiversity, and DNA language models have significantly facilitated its construction and identification. However, since DNA barcodes come from a specific region of mitochondrial DNA and there are structural differences between DNA barcodes and reference genomes used to train existing DNA language models, it is difficult to directly apply the existing DNA language models to the DNA barcoding task. To address this, this paper introduce DNACSE (DNA Contrastive Learning for Sequence Embeddings), an unsupervised noise contrastive learning framework designed to fine-tune the DNA language foundation model while enhancing the distribution of the embedding space. The results demonstrate that DNACSE outperforms direct usage of DNA language models in DNA barcoding-related tasks. Specifically, in fine-tuning and linear probe tasks, it achieves accuracy rates of 99.17% and 98.31%, respectively, surpassing the current state-of-the-art BarcodeBERT by 6.44% and 6.44%. In zero-shot clustering tasks, it raises the Adjusted Mutual Information (AMI) score to 92.25%, an improvement of 8.36%. In addition, zero-shot benchmarking and genomic benchmarking tests are evaluated, indicating that DNACSE enhances the performance of DNA language models in generalized genomic tasks. In summary, DNACSE has demonstrated excellent performance in DNA barcode species classification by making full use of multi-species information and DNA barcode information, providing a feasible way to further explore and protect biodiversity. The code repository is available at https://github.com/Kavicy/DNACSE.

bioinformatics↗

DNA Sequence Trace Reconstruction Using Deep Learning

Deciphering DNA sequences is fundamental to unlocking the mysteries of life, but the high dimensionality and complexity of biological sequence data significantly hinder knowledge discovery. In particular, the challenges of sequence length, repetitive regions, and structural complexity make it difficult to directly reconstruct complete DNA sequences from raw data. Therefore, this paper proposes a DNA sequence trace reconstruction model, DNARetrace, which performs preprocessing and dataset construction, and then employs a Bidirectional Fourier-Kolmogorov-Arnold Network (Bi-FKGAT), using an extremely unbalanced loss function for link prediction, so as to reconstruct the original DNA sequence. In multi-angle experiments using both simulated and real data, DNARetrace successfully reconstructs DNA sequence traces across large-scale datasets derived from various DNA sequencing methods, overcoming the bias of current approaches toward specific sequencing platforms, and achieves competitive outcomes in DNA storage and genomics downstream tasks. We further validated the expandability of the proposed methods in DNA sequence classification and metagenomic binning tasks. In summary, DNARetrace is compatible with various sequencing scenarios; it reduces the difficulty of discovering novelty knowledge directly from high-complexity raw data, and it provides a reusable tool to accelerate DNA sequence processing and applications.

genomics↗

High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer

Charting the spatiotemporal dynamics of cell fate determination in development and disease is a long-standing objective in biology. Here we present the design, development, and extensive validation of PEtracer, a prime editing-based, evolving lineage tracing technology compatible with both single-cell sequencing and multimodal imaging methodologies to jointly profile cell state and lineage in dissociated cells or while preserving cellular context in tissues with high spatial resolution. Using PEtracer coupled with MERFISH spatial transcriptomic profiling in a syngeneic mouse model of tumor metastasis, we reconstruct the growth of individually-seeded tumors in vivo and uncover distinct modules of cell-intrinsic and cell-extrinsic factors that coordinate tumor growth. More generally, PEtracer enables systematic characterization of cell state and lineage relationships in intact tissues over biologically-relevant temporal and spatial scales.

molecular biology↗

Improved genome assembly of double haploid Prunus persica siblings Lovell 2D and Lovell 5D and the peach NLRome

Prunus persica (peach) has long served as a model fruit tree for studying phenological events. It has a relatively small genome and exhibits tremendous plasticity in climate tolerances due to the high variation of chill requirements, bloom times, and fruit ripening times. The peach variety Lovell 2D was used to generate one of the first high-quality genome assemblies for a tree species, using Sanger sequencing of genetic-map ordered BAC clones. A key to the high quality of this early assembly was the use of a doubled haploid variety, which eliminates the challenges posed by mixed haplotypes. Here, we re-sequenced and assembled the Lovell 2D genome along with a doubled haploid sibling Lovell 5D using 3rd generation technologies. The resulting genomes were significantly more contiguous than the current Lovell 2D reference genome (ver2.0 updated in 2017) and are closer to the estimated total genome size for peach (265Mb). In addition, new gene, transposable element (TE), and Nucleotide-binding domain and Leucine-rich repeat receptor (NLR) annotations were performed to enhance the integrity and utility of the genome. These updated peach doubled-haploid reference assemblies will provide the research community with an improved reference genome for genomics-guided studies and breeding efforts.

genomics↗

scWGBS-GPT: A Foundation Model for Capturing Long-Range CpG Dependencies in Single-Cell Whole-Genome Bisulfite Sequencing to Enhance Epigenetic Analysis

Single-cell DNA methylomes are challenging to interpret because of sparse CpG coverage and the complexity of genome-wide sequences. We present scDNAm-GPT, a universal foundation model that uses context-aware CpG tokenization, a Mamba backbone, and cross-attention to capture both local and global DNA methylation patterns. Trained on over one million single cells from 35 human and mouse tissues, scDNAm-GPT enables accurate cell clustering, zero-shot prediction of CpG effects on gene expression, improved trajectory inference, and reference-free deconvolution of cell types from cell-free DNA. The model hierarchically learns regulatory features, and its attention maps highlight functionally relevant regions, demonstrating high biological interpretability. These results establish scDNAm-GPT as a scalable and generalizable framework for single-cell epigenomic analysis, offering new opportunities to dissect epigenetic regulation in health and disease. Code is available at GitHub (https://github.com/ChaoqiLiang/scDNAm-GPT).

bioinformatics↗

ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a proteins absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://github.com/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpys capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4,921 samples and 5,766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.

bioinformatics↗

Deep Learning-Powered Colloidal Digital SERS for Precise Monitoring of Cell Culture Media

Maintaining consistent quality in biopharmaceutical manufacturing is essential for producing high-quality complex biologics. Yet, current process analytical technologies (PAT) struggle to achieve rapid and highly accurate monitoring of small molecule critical process parameters and critical quality attributes. While Raman spectroscopy holds great promise as a highly sensitive and specific bioanalytical tool for PAT applications, its conventional implementation, surface-enhanced Raman spectroscopy (SERS), is constrained by considerable temporal and spatial intensity fluctuations, limiting the achievable reproducibility and reliability. Herein, we introduce a deep learning-powered colloidal digital SERS platform to address these limitations. Rather than addressing the intensity fluctuations, the approach leverages their very stochastic nature, arising from highly dynamic analyte-nanoparticle interactions. By converting the temporally fluctuating SERS intensities into digital binary "ON/OFF" signals using a predefined intensity threshold by analyzing the characteristic SERS peak, this approach enables digital visualization of single-molecule events and significantly reduces false positives and background interferences. By further integrating colloidal digital SERS with deep learning, the applicability of this platform is significantly expanded and enables detection of a broad range of analytes, unlimited by the lack of characteristic SERS peaks for certain analytes. We further implement this approach for studying AMBIC 1.1, a chemically-defined, serum-free complete media for mammalian cell culture. The obtained highly accurate and reproducible results demonstrate the unique capabilities of this platform for rapid and precise cell culture media monitoring, paving the way for its widespread adoption and scaling up as a new PAT tool in biopharmaceutical manufacturing and biomedical diagnostics.

biophysics↗

GENETIC ANALYSIS OF ARABIDOPSIS AUTOPHAGY-RELATED 8 FAMILY PROTEINS REVEALS THEIR REDUNDANT AND REGULATORY ROLES IN PLANT AUTOPHAGY

Autophagy, a critical process for the vacuolar degradation of proteins and organelles, is governed by multiple conserved autophagy-related (ATG) proteins. The central component of the ATG machinery is the ubiquitin-like protein ATG8, which is essential for multiple steps of the autophagy process, including phagophore expansion, autophagosome closure, trafficking and fusion with the lysosome/vacuole, and selective cargo recruitment. Currently, our understanding of the roles of ATG8 in plant autophagy and the functional specialization of ATG8 family members is limited due to genetic redundancy. To assess the roles of ATG8 genes in plant autophagy, here we used CRISPR/Cas9 technology to systematically knockout the Arabidopsis ATG8 genes. By analyzing the atg8 mutants, we found that in contrast to mammalian ATG8s, in which the LC3s and GABARAP subfamilies play distinct roles in the autophagic process, Arabidopsis ATG8s perform an overlapping function in controlling autophagic flux. Combinatorial mutations of Clade I and Clade II ATG8s resulted in severely impaired autophagy under nutrient-starved conditions. Furthermore, we found that RABG3 proteins, members of the RAB7/RABG GTPase family, interact with ATG8s through AIM-LDS interfaces, and that such interaction is essential for the association of RABG3 proteins with the autophagosomal membrane and probably for the fusion of autophagosome with the vacuole, but is not required for endosomal trafficking. With the collection of multiple high-order atg8 mutants generated in this study, we now provide a venue to study the roles of ATG8 genes in canonical autophagy and non-canonical autophagy in Arabidopsis.

plant biology↗

CD24-Fc resolves inflammation and rescues CD8 T cells with polyfunctionality during HIV-1 infection under cART

The persistence of HIV-1 reservoirs during combination anti-retroviral therapy (cART) leads to chronic immune activation and systemic inflammation in people with HIV (PWH), associating with a suboptimal immune reconstitution as well as an increased risk of non-AIDS events. This highlights the needs to develop novel therapy for HIV-1 related diseases in PWH. In this study, we assessed the therapeutic effect of CD24-Fc, a fusion protein with anti-inflammatory properties that interacts with danger-associated molecular patterns (DAMPs) and siglec-10, in chronic HIV-1 infection model using humanized mice undergoing suppressive cART. Our findings show that CD24-Fc treatment significantly reduced inflammation and immune hyperactivation in vivo when combined with cART. CD24-Fc mediated resolution of inflammation was associated with improved recovery of CD4 T cells, reduced immune activation, restored central memory T cells and reversal of immune cell exhaustion phenotype. Notably, CD24-Fc treatment rescued CXCR5+ CD8 central memory T cell (TCM) which correlated with increased polyfunctionality in HIV-specific T cells in humanized mice and in cultured peripheral blood mononuclear cells (PBMCs) from PWH. This restoration of CXCR5+ memory CD8 T cells was associated with HIV replication inhibition, delayed viral rebound and reduced HIV-1 pathogenesis upon cART cessation. This study suggests that CD24-Fc treatment could represent a promising new therapeutic strategy for managing chronic systemic inflammation and associated diseases in PWH. Author summaryCombination antiretroviral therapy (cART) cannot block viral gene expression from activated HIV proviral DNA in reservoir cells, contributing to chronic immune activation and inflammation associated diseases in people with HIV (PWH). The therapeutic treatment of anti-inflammatory fusion protein CD24-Fc in humanized mice during suppressive cART (i) resolves inflammation and chronic HIV-1 immune pathogenesis during suppressive cART, (ii) rescues CXCR5-expressing CD8 memory T cells and enhances antiviral response in humanized mice and PWH PBMCs, (iii) delays virus rebound and reduces viral pathogenesis after cART cessation. Thus, CD24-Fc could provide a novel therapeutic strategy for treating chronic systemic inflammation and associated diseases in PWH.

immunology↗

Innovations in Alginate Catabolism Leading to Heterotrophy and Adaptive Evolution of Diatoms

A major goal of evolutionary biology is to identify the genetic basis for the emergence of adaptive traits. Diatoms are ancestrally photosynthetic microalgae. However, in the genus Nitzschia, loss of photosynthesis led to a group of free-living secondary heterotrophs whose manner of energy acquisition is unclear. Here, we sequence the genome of the non-photosynthetic diatom Nitzschia sing1 and identify the genetic basis for its catabolism of the brown seaweed cell wall polysaccharide alginate. N. sing1 obtained an endolytic alginate lyase enzyme by horizontal gene transfer (HGT) from a marine bacterium. Subsequent gene duplication and transposition led to 91 genes in three distinct gene families. One family retains the ancestral endolytic enzyme function. By contrast, the two others underwent domain duplication, gain, loss, rearrangement, and mutation to encode novel functions that can account for oligosaccharide import through the endomembrane system and the exolytic production of alginate monosaccharides. Together, our results show how a single HGT event followed by substantial gene duplication and neofunctionalization led to alginate catabolism and access to a new ecological niche. HighlightsO_LIN. sing1 acquired an alginate lyase (ALY) gene by horizontal gene transfer from a marine bacterium C_LIO_LIThis founding gene expanded and diversified to comprise 3 major families across 30 loci C_LIO_LIDerived functions account for alginate import and processing into monomers C_LIO_LIDomain duplication, gain, loss, mutation, and de novo sequence evolution underlie ALY gene neofunctionalization C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/610029v1_ufig1.gif" ALT="Figure 1000"> View larger version (44K): org.highwire.dtl.DTLVardef@7a47c7org.highwire.dtl.DTLVardef@1fe958forg.highwire.dtl.DTLVardef@10ce362org.highwire.dtl.DTLVardef@12465a1_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

evolutionary biology↗

Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors

During development, stem and progenitor cells divide and transition through germ layer- and lineage-specific multipotent states to generate the diverse cell types that compose an animal. Defined changes in biomolecular composition underlie the progressive loss of potency and acquisition of lineage-specific characteristics. For example, multipotent cardiopharyngeal progenitors display multilineage transcriptional priming, whereby both the cardiac and pharyngeal muscle programs are partially active and coexist in the same progenitor cells, while their daughter cells engage in a cardiac or pharyngeal muscle differentiation path only after cell division. Here, using the tunicate Ciona, we studied the acquisition of multilineage competence and the coupling between fate decisions and cell cycle progression. We showed that multipotent cardiopharyngeal progenitors acquire the competence to produce distinct Tbx1/10 (+) and (-) daughter cells shortly before mitosis, which is necessary for Tbx1/10 activation. By combining transgene-based sample barcoding with single cell RNA-seq (scRNA-seq), we uncovered transcriptome-wide dynamics in migrating cardiopharyngeal progenitors as cells progress through G1, S and G2 phases. We termed this process "transcriptome maturation", and identified candidate "mature genes", including the Rho GAP-coding gene Depdc1, which peak in late G2. Functional assays indicated that transcriptome maturation fosters cardiopharyngeal competence, in part through multilineage priming and proper oriented and asymmetric division that influences subsequent fate decisions, illustrating the concept of "behavioral competence". Both classic feedforward circuits and coupling with cell cycle progression drive transcriptome maturation, uncovering distinct levels of coupling between cell cycle progression and fateful molecular transitions. We propose that coupling competence and fate decision with the G2 and G1 phases, respectively, ensures the timely deployment of lineage-specific programs.

developmental biology↗

Genomics-FM: Universal Foundation Model for Versatile andData-Efficient Functional Genomic Analysis

Artificial intelligence (AI) plays a crucial role in functional genomic analysis, offering great potential for comprehending biological phenomena such as heredity, development, diseases, and evolution. However, the development of AI models needs substantial labeled data, and these models are typically task-specific with limited generalizability to various applications. Here, we develop Genomics-FM, a genomic vocabulary driven foundation model that enables versatile and label-efficient functional genomic analysis. Specifically, Genomics-FM is first pretrained with ensemble genomic vocabulary on vast unlabelled data to learn comprehensive and generalizable representations and then finetuned with specific genomic vocabulary on limited labeled data to selectively activate and adapt the pretraining knowledge for specific tasks. We show that Genomics-FM significantly reduces the dependence on labeled data, and demonstrates the capability to outperform existing models across a comprehensive suite of tasks including genome annotation, epigenomic and expression profile prediction, and variant effect assessment. Remarkably, Genomics-FM even shows impressive zero-shot predictive capabilities across diverse species and tissues and exhibits noticeable adaptability to RNA-related tasks. With feasibility in data scarcity and even cross-domain biological scenarios, Genomics-FM will promote the broad application of AI and empower researchers to tackle previously insurmountable challenges, paving the way for groundbreaking research and discoveries.

bioinformatics↗

Discovery of a Heparan-Sulfate Binding Domain in Monkeypox Virus H3 Protein as a New Anti-poxviral Drug Target

Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged -helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.

biophysics↗