Biology subjects

Nie, Z.

Publications and source records attributed to Nie, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Listeriolysin O recruits UBA1 to ubiquitinate NLRP3 and suppress inflammasome defence

The NLRP3 inflammasome is a central cytosolic defence pathway against infection, yet intracellular pathogens must restrain this response to preserve their replicative niche. Here we identify a ubiquitin-dependent immune-evasion mechanism in which L. monocytogenes uses listeriolysin O (LLO) to destabilise NLRP3. The N-terminal PEST-like sequence of LLO acted as an adaptor that recruited the host ubiquitin-activating enzyme UBA1 and promoted K48-linked polyubiquitination of NLRP3. This modification drove proteasomal NLRP3 degradation and limited caspase-1 activation, gasdermin D cleavage, IL-1{beta}/IL-18 release, and macrophage pyroptosis. Deleting the PEST region preserved basal haemolytic activity but impaired NLRP3 ubiquitination, thereby enhancing inflammasome activation during infection in macrophages and in vivo. Conversely, UBA1 overexpression suppressed NLRP3-mediated inflammatory responses and supported intracellular bacterial survival. UBA1 depletion restored NLRP3 protein levels, reduced bacterial burden and delayed lethal infection, albeit with increased inflammatory tissue injury. These findings define a ubiquitin-hijacking strategy that operates at the E1 step, whereby a bacterial toxin redirects host ubiquitin activation towards an innate immune sensor.

microbiology↗

bioRxiv2026

ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MDs frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MDs sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

molecular biology↗

bioRxiv2026

PROTOTYPE-BASED CONTINUAL LEARNING FOR SINGLE-CELL ANNOTATION

Large-scale single-cell atlases provide an increasingly comprehensive view of cellular diversity, but their continued expansion across studies poses a fundamental challenge: preserving consistent cell identities while capturing biological variation in cellular states. Most existing annotation frameworks are built around static references, making it difficult to incorporate newly generated datasets into established cellular representations without retraining on historical data. When updated sequentially, these methods are further constrained by catastrophic forgetting and batch-specific biases, limiting scalability and the continuity of knowledge integration. Here we introduce scEvolver, a continual learning framework for single-cell annotation that incrementally accumulates knowledge through memory-guided refinement of cell-type prototypes without revisiting historical data. Across sequencing platforms, tissue contexts and molecular modalities, scEvolver supports robust annotation and external query mapping with substantially fewer labelled reference cells. By preserving consistent cell-type semantics across datasets while capturing biologically meaningful within-class heterogeneity, scEvolver enables the identification of epithelial cell-state transitions in inflammatory gut disease. External mapping to the healthy Human Lung Cell Atlas further reveals shared cell-state deviations across multiple diseases, including an FCGR3A+ inflammatory monocyte programme in sarcoidosis, chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis, highlighting scEvolvers potential to characterize context-specific cellular dynamics in complex disease settings.

bioinformatics↗

bioRxiv2026

BiHiTo: Biomolecular Hierarchy-inspired Tokenization

Three-dimensional atomic arrangements of biomolecules are key to demystifying biological functions. The rapid expansion of accessible structural data, driven by advances in AI for science, highlights the critical challenge of efficiently modeling large-scale biomolecular structures, which are high-dimensional systems shaped by biological assembly principles. To address this, we introduce BiHiTo, a multi-level Biomolecular Hierarchy-inspired Tokenizer that intrinsically mimics natural biological assembly hierarchies. Specifically, we design a multi-codebook quantizer that mirrors the natural hierarchy of biomolecular structure, enabling simultaneous capture of representations spanning atomic motifs to global conformational variations. This hierarchical alignment markedly improves the biological interpretability and reconstruction fidelity of biomolecular structure.Extensive experiments demonstrate that BiHiTo delivers state-of-the-art performance and robust generalization across molecular dynamics trajectories and macromolecular complexes, facilitating advances in structure generation and dynamic conformation exploration. In the reconstruction of the CASP14 and OOD test set FastFolding protein multi-conformation data, our method achieves a 17% and 51% reduction in RMSD compared to Bio2Token, respectively.

molecular biology↗

bioRxiv2025

Caffeic acid promoted deep vein thrombosis resolution in mice by suppressed macrophage M1 polarization through Keap1/Nrf2 pathwayv

BackgroundDeep vein thrombosis (DVT) carries significant health risks, with macrophages playing a key role in thrombus resolution. Caffeic acid (CA) has been shown to inhibit thrombosis, but its role in DVT resolution remains unclear. PurposeThis study aimed to investigates the effects and mechanisms of CA in accelerating DVT resolution. MethodsA stasis-type DVT model was established in mice via inferior vena cava ligation. The effects of CA were analyzed. Thrombus resolution was assessed using histological staining, gelatin zymography, immunoblotting, and immunofluorescence. Macrophage depletion was conducted with chlorophosphate liposomes, and bone marrow-derived macrophages (BMDMs) were used for polarization studies. RNA sequencing identified potential molecular pathways. Nrf2 gene-deficient mice were used to verify the role of Nrf2 in deep vein thrombus resolution. ResultsCaffeic acid reduced thrombus size, enhanced collagenolysis via increased MMP-2 activity, promoted neovascularization, increased macrophage infiltration, and suppressed M1 polarization and inflammation. CA activated the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway by inhibiting Keap1, leading to enhanced antioxidant responses in both BMDMs and thrombus tissue. Macrophage depletion negated CAs benefits, confirming the central role macrophages. ConclusionCA promoted early thrombus resolution via macrophage recruitment, M1 polarization suppression, and Nrf2 activation, highlighting its potential as a preventive strategy for DVT.

pharmacology and toxicology↗

bioRxiv2025

An efficient beet severe curly top virus-based VIGS vector in Beta vulgaris

Sugar beet (Beta vulgaris L.) is one of the most important sugar crops worldwide. However, studies on sugar beet gene functions are still lagging compared to other crops due to the lack of an effective genetic manipulation. In this study, we generated an infectious clone of beet sever curly top virus (BSCTV) and engineered it into a VIGS vector. BSCTV-BvPDS can induce strong and persistent bleached PDS-silencing phenotype in 8 tested sugar beet varieties. We further efficiently silenced B. vulgaris MYB1 gene that regulating the betalain pathway in sugar beet by BSCTV-based VIGS system. Finally, we used this system to silence the effector CbNip1 of Cercospora beticola through cross-kingdom RNAi, resulting in reduced disease symptoms in sugar beet plants. Therefore, the BSCTV VIGS system can be used to study sugar beet-pathogen interactions. In summary, we have established an easy and efficiency BSCTV-based VIGS vector in sugar beet. This system will be an attractive tool for functional genomic studies for sugar beet.

plant biology↗

bioRxiv2025

MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing

Breakthroughs in artificial intelligence (AI) for natural language processing and computer vision have been largely driven by high-quality, large-scale datasets such as OpenWebText and ImageNet. Inspired by this, we present MassNet, a foundational resource for proteomics designed to accelerate deep learning applications. MassNet is the largest known corpus of data-dependent acquisition (DDA) mass spectrometry (MS) data, derived from ~30 TB of raw files and comprising 1.54 billion MS/MS spectra, resulting in 558 million peptide-spectrum matches (PSMs) across 35 species, including animals, plants, and microbes. Within the human subset, MassNet includes more than 1.7 million precursors and 19,966 proteins, covering 98% of annotated human proteins. To enable efficient AI training, we developed the Mass Spectrometry Data Tensor (MSDT), a structured format based on Parquet that enables standardized, high-performance batch access and seamless integration with GPU and TPU platforms for distributed training. We further extended MassNet to support de novo peptide sequencing, which infers peptide sequences directly from MS/MS spectra without reference databases, and is critical for discovering novel proteins, characterizing non-model organisms, and identifying post-translational modifications (PTMs). We introduce XuanjiNovo, a non-autoregressive Transformer model that leverages a curriculum learning strategy to enhance training stability. By dynamically adjusting learning difficulty based on model performance, XuanjiNovo achieves smooth convergence on complex, multi-distributional data without manual hyperparameter tuning. Trained on 100 million PSMs from the MassNet, it consistently outperforms state-of-the-art methods across diverse benchmarking tasks. Peptide recall exceeds 0.8 on the Bacteroides thetaiotaomicron and Zea mays datasets. On human data acquired using the Orbitrap Astral platform, XuanjiNovo achieves achieves 38.8% to 144.3% improvement over existing models. MassNet represents the first large-scale, standardized foundational dataset in proteomics, marking a critical milestone in the integration of artificial intelligence into proteomics research.

bioinformatics↗

bioRxiv2025

A Reductive Uric Acid Degradation Pathway in Anaerobic Bacteria

Uric acid is a key intermediate in purine degradation across diverse organisms, while its accumulation in humans leads to inflammation and gout disease. Aerobic organisms degrade uric acid via a well-known "oxidative pathway" involving dearomatization of the purine core catalyzed by uric acid oxidases or dehydrogenases. The ability to degrade uric acid is also widespread in anaerobic bacteria, including gut bacteria, although the mechanisms are incompletely understood. Here, we report the biochemical characterization of a recently identified uric acid degradation gene cluster from Escherichia coli, and show that it encodes a "reductive pathway" for uric acid degradation. In this pathway, uric acid is first reduced to isoxanthine (2,8-dioxopurine) by a xanthine dehydrogenase homolog (XdhD), followed by dearomatization of the purine core catalyzed by a flavin-dependent reductase (YgfK). Stepwise cleavage of the pyrimidine and imidazole rings forms 2,3-diureidopropionate, and stepwise cleavage of the 2- and 3-ureido groups then forms 2,3-diaminopropionate, which is cleaved by a pyridoxal 5-phosphate-dependent lyase (YgeX) to pyruvate and ammonia. Detection of isoxanthine in clinical serum samples suggests it is a physiologically relevant circulating metabolite. A probiotic E coli Nissle strain was engineered for constitutive overexpression of the gene cluster, and oral administration in a uricase-knockout hyperuricemic mouse model significantly reduced the serum uric acid level and alleviated associated kidney injury, suggesting a potential route towards uricolytic probiotics.

biochemistry↗

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

Spatial distribution of the proteome in human body and cancers

A comprehensive spatial distribution of the proteome in human body and cancers is fundamental for understanding human biology and diseases including cancers. Here, we present an anatomically resolved human proteome derived from 1781 benign and malignant samples from 58 major tissue types encompassing 251 specific tissues and 25 carcinomas. Based on a spectral library covering over 75% of the human protein-coding genes with 208 understudied and 82 missing proteins characterized, we quantified over 13,000 proteins in these samples using data-independent acquisition proteomics. This data resource presents the so far most comprehensive quantitative proteomic landscape of human tissues and common carcinomas. It allows systematic evaluation of tissue-specific drug responses, identification of drug candidates that may be repurposed as antineoplastics, and discovery of novel targets for anticancer therapy. This resource, available as an online knowledgebase, refines our knowledge of spatial distribution of the human proteome and tumor-specific protein modulation.

systems biology↗

bioRxiv2025

Collective biosynthesis of plant spirooxindole alkaloids through enzyme discovery and engineering

Collective synthesis is a well-established strategy to produce natural product collections from common intermediates. Extending this concept to enzymatic systems, collective biosynthesis offers a sustainable approach to diverse molecular collections but faces distinct challenges, including incomplete pathway knowledge and narrow substrate specificity of biosynthetic enzymes. Here, we overcome these limitations through enzyme discovery and protein engineering to achieve collective biosynthesis of spirooxindole alkaloids, an important class of natural products with diverse biological activities. We identified two key enzymes that catalyze enzymatic epimerization, working sequentially with a previously discovered cytochrome P450 enzyme to fully elucidate the biosynthetic pathway of pentacyclic spirooxindole alkaloids. Structure-guided engineering expanded substrate scope, enabling collective biosynthesis of 12 tetracyclic and pentacyclic spirooxindole natural products. We also generated new-to-nature fluorinated and deuterated derivatives through precursor-directed biosynthesis. This work provides crucial insights into plant spirooxindole alkaloid biosynthesis while establishing a powerful approach for sustainable production of complex natural products.

biochemistry↗

bioRxiv2024

Deep-learning-enabled generative prevalent mutation prediction through host-to-herd in silico virus evolution

Predicting the mutation prevalence trends of emerging viruses in the real world is an efficient means to update vaccines or drugs in advance. It is crucial to develop a computational method for the prediction of real-world prevalent SARS-CoV-2 mutations considering the impact of multiple selective pressures within and between hosts. Here, a deep-learning generative framework for real-world prevalent SARS-CoV-2 mutation prediction, named ViralForesight, is developed on top of protein language models and in silico virus evolution. Through the paradigm of host-to-herd in silico virus evolution, ViralForesight reproduced previous real-world prevalent SARS-CoV-2 mutations for multiple lineages with superior performance. More importantly, ViralForesight correctly predicted the future prevalent mutations that dominate the COVID-19 pandemic in the real world more than half a year in advance with in vitro experimental validation. Overall, ViralForesight demonstrates a proactive approach to the prevention of emerging viral infections, accelerating the process of discovering future prevalent mutations with the power of generative deep learning.

evolutionary biology↗

bioRxiv2024

DDA-BERT: leveraging transformer architecture pre-training for data-dependent acquisition mass spectrometry-based proteomics

In data-dependent acquisition mass spectrometry (DDA-MS)-based proteomics, machine learning based rescoring methods are often employed to integrate multiple scores measuring quality of peptide-spectrum matches (PSMs) from different aspects. Existing rescoring tools face limitations incurred by manual feature extraction, shallow machine learning models, and limited training data. Here, we introduce DDA-BERT, a transformer-based end-to-end deep learning model trained with 95 million human spectra for PSM rescoring. DDA-BERT demonstrates superior performance across various sample types, mass spectrometer platforms, trace sample proteomics, and multiple species proteome data. It consistently outperforms state-of-the-art methods of its kind for DDA-MS spectra analysis with up to 103.5% increased protein group identifications. For single cell proteomics, DDA-BERT identifies up to 85.6% more protein groups compared to existing tools. In addition, DDA-BERT can be effectively extended to proteome of non-human species. DDA-BERT offers a robust and scalable solution for enhancing PSM rescoring in proteomics.

bioinformatics↗

bioRxiv2024

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) plays an increasingly important role in quantitative proteomics. Here, we introduce DIA-BERT, a software tool that leverages a transformer-based pre-trained artificial intelligence (AI) model for the analysis of DIA proteomics data. Over 276 million of high-quality peptide precursors extracted from existing DIA-MS files were used for training the identification model, while 34 million peptide precursors from synthetic DIA-MS files were used for training the quantification model. Compared to DIA-NN, DIA-BERT led to on average 54% more protein identifications and 37% more peptide precursors in five different human cancer (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma) sample sets with a high degree of quantitative accuracy. This study highlights the potential of utilizing pre-trained models and synthetic datasets to advance DIA proteomics analysis.

systems biology↗

bioRxiv2024

DNA-SaM, a robust system for large-scale data storage

DNA data storage offers a viable strategy to address the impending data explosion. Early attempts to harness DNA as a storage medium have encountered scalability limitations, largely due to the complexity of codec algorithms, the generation of biochemically harmful sequences and lack of a robust architecture. We present "DNA-SaM", a novel system designed for DNA data storage, which achieves linear computational complexity and strict bio-constraint adherence, ensuring high coding efficiency and fidelity. It encoded data at speeds surpassing classic systems by over 2 orders of magnitude, with this superiority changes across various encoding algorithms. Importantly, DNA-SaM effectively eliminates any sequence that could be deleterious to in vitro and in vivo biochemical processes, including homopolymer runs, tandem repeat motifs, and potential promoter sequences, etc. It also involves an advanced DNA data storage architecture that incorporates a two-tiered indexing system and a novel "storage unit" distribution paradigm for large-scale data storage. It is further validated by practical data storage both in vitro and in vivo with a 100% success rate. Our system is capable of storing data over 1039 PB, which marks a critical advancement in the scalability of DNA-based data storage.

synthetic biology↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗

bioRxiv2024

MOL-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective

3D molecular representation learning has gained tremendous interest and achieved promising performance in various downstream tasks. A series of recent approaches follow a prevalent framework: an encoder-only model coupled with a coordinate denoising objective. However, through a series of analytical experiments, we prove that the encoderonly model with coordinate denoising objective exhibits inconsistency between pre-training and downstream objectives, as well as issues with disrupted atomic identifiers. To address these two issues, we propose MO_SCPLOWOLC_SCPLOW-AE for molecular representation learning, an auto-encoder model using positional encoding as atomic identifiers. We also propose a new training objective named 3D Cloze Test to make the model learn better atom spatial relationships from real molecular substructures. Empirical results demonstrate that MO_SCPLOWOLC_SCPLOW-AE achieves a large margin performance gain compared to the current state-of-the-art 3D molecular modeling approach. The source codes of MO_SCPLOWOLC_SCPLOW-AE are publicly available at https://github.com/yjwtheonly/MolAE.

bioinformatics↗

bioRxiv2024

Interpretable antibody-antigen interaction prediction by bridging structure to sequence

With the application of personalized and precision medicine, more precise and efficient antibody drug development technology is urgently needed. Identification of antibody-antigen interactions is crucial to antibody engineering. The time-consuming and expensive nature of wet-lab experiments calls for efficient computational methods. Taking into account the non-overlapping advantage of current structure-dependent and sequence-only computational methods, we propose an interpretable antibody-antigen interaction prediction method, S3AI. The introduction of structural knowledge, combined with explicit modeling of chemical rules, establishes a sequence-to-function route in S3AI, thereby facilitating its perception of intricate molecular interactions through providing route and priors guidance. S3AI significantly and comprehensively outperforms the state-of-the-art models and exhibits excellent generalization when predicting unknown antibody-antigen pairs, surpassing specialized prediction methods designed for out-of-distribution generalization in fair comparisons. More importantly, S3AI captures the universal pattern of antibody-antigen interactions, which not only identifies the CDRs responsible for specific binding to the antigen but also unearths the importance of CDR-H3 for the interaction. Structure-free design and superior performance make S3AI ideal for large-scale, parallelized antibody optimization and screening, enabling the rapid and precise identification of promising candidates within the extensive antibody space.

bioinformatics↗