Biology subjects

Zhang, J.

Publications and source records attributed to Zhang, J..

At least 451 records · Page 25Linked to original sources

bioRxiv2025

Abnormal shear stress induces ferroptosis in endothelial cells via KLF6 downregulation

Stable laminar flow maintains vascular tone regulation, while abnormal blood flow, such as disturbed flow or extreme shear stress, causes endothelial dysfunction, but the underlying mechanism is yet to be explored. We used a microfluidic device to deform flat microchannel into tunnel-like macrochannel. The cross-sectional area of this macrochannel varies with the flow rate and the thickness of the deformable layer, creating three levels of shear stress: low (0.99 dyn/cm2, LSS), medium (4.78 dyn/cm2, MSS), and high (24 dyn/cm2, HSS). Comparing different shear stress exposure to endothelial cells for 24 h, prominent ferroptosis features emerged under either LSS or HSS compared to MSS. These features included increased C11 BODIPY-labeled lipid peroxidation and 4-hydroxynonenal accumulation, CoQ10 depletion, reduced SLC7A11 protein expression, and diminished cell death with Ferrostatin-1 treatment. RNA-seq analysis (LSS/MSS) showed that LSS significantly downregulated transcription of cholesterol homeostasis and unfolded protein response (UPR). Compared to MSS, Western blot results showed that both LSS and HSS reduced the expression of two key enzymes (MVD and IDI1) in the mevalonate pathway, as well as the expression of two main UPR signaling regulators (PERK and BiP). Based on the binding prediction between transcription factors and gene promoters from differentially expressed genes identified through RNA-seq, we found KLF6 to be a key transcription factor. It regulates the PERK-mediated UPR and the mevalonate pathway, which are associated with SLC7A11 expression and CoQ10 synthesis, respectively. The overexpression of KLF6 restored SLC7A11 and CoQ10 levels under both LSS and HSS, significantly reducing foam cell formation, monocyte adhesion, and lipid peroxidation. Our findings reveal KLF6 as a crucial regulator of atherosclerosis induced by abnormal shear stress.

cell biology↗

bioRxiv2025

Modification of RNAs in natural rG4 (MoRNiNG), A Database of RNA Modification Sites Associated with the Dynamics of RNA Secondary Structures

RNA structures are essential building blocks of functional RNA molecules. Profiling secondary structures in vivo and in real-time remains challenging because RNAs exhibit dynamic structures and complex conformations. Besides the stem-loop canonical secondary structure, the non-canonical structure RNA G-quadruplex (rG4) is of great interest for its potential as a drug target. Early studies have demonstrated that RNAs can form distinct secondary structures. However, it is not well understood how distinct RNA structures, formed from the same RNA sequences, function within the transcriptome. The factors driving and regulating structure transitions remain poorly investigated. Inspired by a segment in the HOXB9 mRNA capable of forming multiple structures, we found that many RNA segments across the transcriptome exhibit multi-faceted structure-forming potential. For the HOXB9 case, we demonstrated that RNA modification influences RNA structure and binding with RNA binding proteins. Therefore, we collected RNA modification sites naturally occurring in the putative G-quadruplex-forming sequences (PQSs) of transcripts and developed MoRNiNG, a freely accessible database at https://www.cityu.edu.hk/bms/morning. The database is designed and organized by reliability tiers determined by the resolution of RNA modification sites and capable of including various large datasets. We experimentally validated the influence of m6A, m5C, and A-to-I editing on rG4-forming sequences and provided evidence to support the modification switch concept. The diversity and transition of secondary structures from the same RNA segment offers valuable insights into the regulation of RNA structure dynamics.

systems biology↗

bioRxiv2025

Decoder-FFPE-seq enables sensitive, genome-wide spatial transcriptomics of archival tissues at single-cell resolution

Formalin-fixed paraffin-embedded (FFPE) tissue-compatible spatial transcriptomics (ST) is transforming biology and histopathology, but achieving high sensitivity, genome-wide coverage, single-cell resolution and researcher-accessibility remains challenging. Here, we present a dendrimeric DNA coordinate-barcoding design for FFPE-adapted spatial RNA sequencing (Decoder-FFPE-seq), by integrating RNA-to-DNA probe conversion with blotting-assisted barcoding. Decoder-FFPE-seq provides single-cell resolution with a 2.54-4.25-fold sensitivity increase in mouse brain transcriptomic profiling over similar assays with an available protocol, successfully mapping rare genes and sparse cell types. It robustly resolves tumor microenvironments (TMEs) in clinical archives preserved 6-20 years. With expanded probe libraries, Decoder-FFPE-seq captures long non-coding and viral RNAs, revealing spatial co-expression networks with mRNAs in oral squamous cell carcinomas (OSCCs). In an OSCC immunotherapy cohort, it delineates TME heterogeneity across pathology response groups and tracks spatial cellular dynamics pre- and post-immuno-chemotherapy. Decoder-FFPE-seq establishes a robust, high-resolution framework for sensitive ST profiling in fundamental and translational studies of archival tissues.

genomics↗

bioRxiv2025

An interpretable molecular framework for predicting cancer driver missense mutations

Missense mutations play a critical role in human disease, contributing to both inherited disorders and cancer. However, accurately predicting their functional impact--particularly for cancer driver mutations--remains a major challenge due to limited validated labels and the complex molecular basis of oncogenesis. Here, we systematically characterized over 120,000 missense variants across pathogenic, benign, driver, passenger, recurrent somatic, and common population classes, using a comprehensive set of mechanistically grounded molecular features. By assessing the statistical burden of variations, we demonstrated that these features effectively discriminate among diverse variant classes and reveal a consistent enrichment of functional sites, structural integrity, and biophysical changes in pathogenic and driver mutations. Building on these insights, we developed MutaPheno, an interpretable framework for predicting the functional consequences of missense mutations. The model integrates 34 molecular-level features, encompassing structural, functional, physicochemical, and contextual descriptors, using a random forest algorithm. Trained exclusively on pathogenic and benign variants, MutaPheno achieved strong accuracy in predicting cancer driver mutations, outperforming both cancer-specific and general pathogenicity tools, while also demonstrating superior robustness when tested on unseen proteins. Our findings highlight the shared mechanisms between pathogenic and driver mutations and emphasize the role of molecular features in improving variant interpretation. MutaPheno provides a transparent and generalizable tool that can facilitate driver discovery and the development of targeted therapies.

bioinformatics↗

bioRxiv2025

The MEK inhibitor trametinib incurs mitochondrial injury and induces innate immune responses in the mouse heart

Trametinib (Trm) is a highly selective MEK inhibitor that potently and persistently abrogates ERK1/2 activation. Trm initially was used to treat BRAF V600E-mutated melanoma but its FDA-approved indications are expanding rapidly. Trm generally is well tolerated but it can cause dose-limiting cardiomyopathy and heart failure. Here we characterize a mouse model of Trm cardiotoxicity using complementary in vitro approaches to show that Trm induces mitochondrial dysfunction in cardiomyocytes and some cancer cell types. In vivo, Trm caused contractile dysfunction within 3 days and heart failure within 2 weeks. High resolution respirometry using isolated cardiac mitochondria revealed that Trm compromises oxidative metabolism, in part through blunted activity of Electron Transport System Complexes. Trm-mediated mitochondrial injury led to the release of mitochondrial Damage-Associated Molecular Patterns including mitochondrial DNA in both mice and humans, triggering activation of canonical innate immune pathways including cGAS-STING. In multiple rodent and human cardiomyocyte platforms, Trm diminished mitochondrial respiratory capacity at nanomolar concentrations but this lesion was reversed by expression of a phosphomimetic STAT3-S727 construct. We also found that Trm induced mitochondrial dysfunction in some but not all cancer cell lines, identifying a previously unrecognized effect that could contribute to Trms anti-cancer efficacy.

physiology↗

bioRxiv2025

Modelling the emergence of spiral colony morphology in the yeast Magnusiomyces magnusii

Yeast species have several adaptations that enable them to survive in harsh environments. These adaptations include biofilm formation, where the secretion of extracellular polymeric substances can protect the cells from a hostile environment, or, under nutrient-limited conditions, pseudohyphal or hyphal growth, where the colony can send out long tendrils to explore the environment and seek nutrients. Recently, we observed a spiral colony morphology emerge in an isolate of the hyphae-forming yeast Magnusiomyces magnusii grown under laboratory conditions. We use an off-lattice agent-based model (ABM) that simulates colony development to investigate the hypothesis that bias in the angle between successive hyphal segments causes the spiral morphology. The model involves biologically-motivated rules of hyphal extension, with key model parameters including the colony size at the onset of hyphal filaments, and the angle between the penultimate and the apical segments. Using one example of an experimentally-grown colony, we use a sequential neural likelihood method to perform likelihood-free Bayesian inference to infer the model parameters. Our results indicate a mean angle between hyphal segments of 2.3{whitebullet} [1.1{whitebullet}, 3.6{whitebullet}] (95% credible interval). To confirm the models applicability to colony growth, we use biologically-feasible parameter values to yield morphologies observed in M. magnusii experiments.

microbiology↗

bioRxiv2025

Guess till correct: Gungnir codec enabling high error-tolerance and low-redundancy DNA storage through substantial computing power

DNA has emerged as a compelling archival storage medium, offering unprecedented information density and millennia-scale durability. Despite its promise, DNA-based data storage faces critical challenges due to error-prone processes during DNA synthesis, storage, and sequencing. In this study, we introduce Gungnir, a codec system using the proof-of-work idea to address substitution, insertion, and deletion errors in a sequence. With a hash signature for each data fragment, Gungnir corrects the errors by testing the educated guesses until the hash signature is matched. For practicality, especially when sequenced with nanopore long-read, Gungnir also considers biochemical constraints including GC-content, homopolymers, and error-prone motifs during encoding. In silico benchmarking demonstrates its outperforming error resilience capacity against the state-of-art methods and achieving complete binary data recovery from a single sequence copy containing 20% erroneous bases. Gungnir requires neither keeping many redundant sequence copies to address storage degradation, nor high-coverage sequencing to address sequencing error, reducing the overall cost of using DNA for storage.

bioinformatics↗

bioRxiv2025

PepCCD: A Contrastive Conditioned Diffusion Framework for Target-Specific Peptide Generation

Peptide-based drug design targeting "undruggable" proteins remains one of the most critical challenges in modern drug discovery. Conventional peptide-discovery pipelines rely on low-throughput experimental screening, which is both time-consuming and prohibitively expensive. Moreover, existing computational approaches for designing peptides against target proteins typically depend on the availability of high-quality structural information. Although recent structure-prediction tools such as AlphaFold3 have achieved break-throughs in protein modeling, their accuracy for functional interfaces remains limited. The acquisition of high-resolution structures is often expensive, time-intensive, and particularly challenging for targets with dynamic conformations, further restricting the efficient development of peptide therapeutics. Additionally, current sequence-based generative methods follow a paradigm that relies on known templates, which limits the exploration of sequence space and results in generated peptides lacking diversity and novelty. To address these limitations, we propose a contrastive conditioned diffusion framework for target-specific peptide generation, referred to as PepCCD. It employs a contrastive learning strategy between proteins and peptides to extract sequence-based conditioning representations of target proteins, which serve as precise conditions to guide a pre-trained diffusion model to generate peptide sequences with the desired target specificity. Extensive experiments on multiple benchmark target proteins demonstrate that the peptides designed by PepCCD exhibit strong binding affinity and outperform state-of-the-art methods in terms of diversity and generation efficiency.

bioinformatics↗

bioRxiv2025

Sodium Selenite Alleviates Hyperglycemia-aggravated Cerebral Ischemic Injury by Mediating Apoptosis through the Fas/FasL Signaling Pathway

ObjectiveTo investigate the intervention mechanism of sodium selenite in alleviating hyperglycemia-induced cerebral ischemia/reperfusion (I/R) injury through the Fas/FasL/NF-{kappa}B/PUMA pathway. MethodsThe SD rat model of hyperglycemia-induced cerebral I/R injury and the HT22 cell model of high-glucose oxygen deprivation/reoxygenation (OD/R) were constructed to systematically evaluate the neuroprotective effect of sodium selenite and its regulatory effect on the Fas/FasL/NF-{kappa}B/PUMA pathway. ResultsThe infarct volume, neurological dysfunction, metabolic level, and apoptosis level in the HG group were significantly higher than those in the NG group. Sodium selenite improved the infarct volume, neurological dysfunction, metabolic level, and apoptosis level in the HG group, and regulated the Fas/FasL/PUMA apoptotic pathway to alleviate injury. In vitro experiments showed that sodium selenite significantly improved morphological damage caused by high-glucose OD/R, increased cell viability, and reduced the expression of apoptotic factors. The application of Fas and FasL inhibitors and knockdown of PUMA expression indicated that sodium selenite alleviated cell apoptosis by inhibiting the activation of the Fas/FasL/PUMA pathway. ConclusionSodium selenite alleviates hyperglycemia-exacerbated cerebral I/R injury by regulating the Fas/FasL/NF-{kappa}B/PUMA signaling pathway.

pathology↗

bioRxiv2025

MBOAT7 and CDS2 coordinate lipid droplet dynamics and cholesterol metabolism in liver

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent disorder worldwide characterized by the accumulation of hepatic lipid droplets (LDs). However, the mechanisms by which hepatocytes adapt to the dynamic changes in LD morphology to maintain metabolic homeostasis remains incompletely understood. Here, we show that MBOAT7, a known MASLD-associated protein, cooperates with CDS2 to regulate lipid droplet dynamics and lipid metabolism. Under physiological conditions, their interaction in the endoplasmic reticulum (ER) maintains metabolic homeostasis. Disruption of this interaction, such as through CDS2 knockdown or loss of function, triggers an adaptive response wherein MBOAT7 translocates to ER-LD contact sites in a RAB1-dependent manner. This redistribution inhibits DGAT2-mediated LD growth and promotes lipolysis. Our findings highlight the redistribution of MBOAT7 to ER-LD contacts serves as a critical mechanism for adaptive control LD size and lipid metabolic homeostasis.

cell biology↗

bioRxiv2025

Acoustic Transparency Enabling Functional Ultrasound Imaging Through Mouse and Human Skulls

Ultrasound is one of the most widely used non-invasive imaging modalities for internal organs, yet its application to the brain has been hindered for decades by the skulls acoustic barrier. This same barrier has kept functional ultrasound imaging (fUSI)--an emerging technology capable of capturing brain-wide neural activity in real time at sub-100 m resolution--from reaching its transformative potential in neuroscience and clinical medicine. Here, we present an acoustic transparency strategy by modulating skull acoustic properties, to render the brain visible to ultrasound. A brief topical application of an FDA-approved chelating agent matches the skulls acoustic impedance and sound speed to those of soft tissue, enabling nearly complete ultrasound transmission (94.0 {+/-} 4.4%) with minimal energy loss or distortion. Leveraging this discovery, we developed an acoustic-transparent fUSI platform that maps brain activation across the full brain depth at [~]20 m spatial resolution without skull removal. This method enables safe, longitudinal brain imaging, is fully reversible, and is demonstrated to be applicable to the ex vivo human skull. This conceptually distinct paradigm--controlling acoustic wave propagation via acoustic property modulation--offers a practical and generalizable solution to one of the most persistent obstacles in transcranial ultrasound imaging, opening the door to broader clinical and research application of ultrasound neuroimaging.

bioengineering↗

bioRxiv2025

End-to-end proteogenomics for discovery of cryptic and non-canonical cancer proteoforms using long-read transcriptomics and multi-dimensional proteomics

Modern mass spectrometry now permits genome-scale quantitative measurements of biological proteomes. However, current approaches remain hindered by incomplete representation of biological variability of protein sequences in canonical reference proteomes, limiting studies to already annotated proteoforms. This, combined with reliance on tryptic peptides and limited sample complexity reduction techniques render detection of variant proteoforms missing from current reference annotations virtually impossible. Here, we report a method for end-to-end proteogenomics which integrates (i) sample-specific transcriptome assembly using long-read sequencing with (ii) multi-protease, multi-dimensional (MPMD) trapped ion mobility spectrometry (TIMS) parallel accumulation-serial fragmentation (PASEF) mass spectrometry. Termed ProteomeGenerator3, we benchmark its sensitivity and specificity for unbiased human cancer proteome discovery. Using long-read transcriptome sequencing of human Ewing sarcoma cancer cells, we detected over 40,000 full-length transcript isoforms, including aberrantly expressed neogenes, splice isoforms, and gene fusions. Combined with MPMD-TIMS-PASEF mass spectrometry, our proteogenomics workflow enabled the detection of 12,189 unique proteoforms with an average sequence coverage exceeding 50%, including 3,216 putative, non-canonical proteoforms, constituting one of the most complete human proteomes to date. Likewise, we report the proteogenomic performance metrics for current state-of-the-art implementations of DIA-NN, Spectronaut and diaTracer-MSFragger algorithms for data-independent acquisition (DIA) and PASEF mass spectral data, including optimal Double Rainbow-diaPASEF parameters for non-tryptic proteomes. In all, end-to-end integrative ProteomeGenerator3 provides a versatile platform for deep sample-specific proteogenomic discovery, suitable for the identification of disease biomarkers and therapeutic targets, and is available open-source from https://github.com/kentsislab/proteomegenerator3.

bioinformatics↗

bioRxiv2025

Seq2Karyotype (S2K): A Method for in-silico Karyotyping Using Single-Sample Whole-Genome Sequencing Data

DNA abnormalities characterized by cytogenetic imaging at the single cell resolution, i.e. karyotyping, have long served as cancer diagnostic and prognostic biomarkers. To enable in-silico karyotyping using unpaired whole-genome sequencing data, we developed Seq2Karyotype (S2K), a tool that fits karyotype models with clonality estimation based on read-depth and allelic imbalance in a bulk sample and supports visualization-guided refinement. Analysis on 19 adult and pediatric cancer cell lines revealed unexpected intratumoral heterogeneity involving multiple copy number variation (CNV) states including whole-genome duplication, which were validated by imaging and single-cell omics profiling. Analyses on patient samples showed high concordance with clinical cytogenetic reports for acute myeloid leukemia, and revealed evolutionary trajectories from multi-region metastatic neuroblastomas implicating reversion. These findings highlight extensive and dynamic intratumoral heterogeneity contributed by CNV in both cell line models and patient samples, which may inform future research on tumor evolution under selective pressure such as drug exposure.

bioinformatics↗

bioRxiv2025

GASPS: A Multi-Omics Framework for Defining Genomic Aberration-Driven Signatures and Predicting Patient Outcomes in Lung Cancer

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

bioinformatics↗

bioRxiv2025

CanID: a robust and accurate RNAseq Expression-based diagnostic classification scheme for pediatric malignancies

Cancer subtype classification is critical for precision therapy and there is a growing trend of augmenting histopathology testing procedures with omics-based machine learning classifiers. However, analytical challenges remain for pediatric cancer on the scope and precision of the current classifiers as well as the evolving subtype standardization. To address these challenges, we built Cancer Identification or CanID, a stacked ensemble machine learning classification scheme, using the transcriptomic features derived from gene-level RNA sequencing count data as the sole input. CanID was developed primarily from 3203 pediatric cancer samples of 13 solid tumor subtypes and 38 hematologic malignancy subtypes with subtype labels curated without the use of RNA-seq data. The accuracies of independent testing in three independent or external data sets for Solid Tumor and Hematologic Malignancy are 99% and 92-93%, respectively. Notably, CanID was able to classify subtypes challenging for clinical histology evaluation and was robust to both biological and technical challenges, including differences in data collection protocols, class imbalance, potential mislabeled training samples and classes unobserved in training. The high accuracy, robustness, biological interpretability of this transcriptome-based classification scheme represents a valuable approach to advance tumor diagnosis and clinically meaningful stratification of tumor types. CanID can be accessed on GitHub at https://github.com/chenlab-sj/CanID.

bioinformatics↗

bioRxiv2025

Moderate-Intensity Intermittent Training Inhibits miR-34a-5p to Reduce Prefrontal Cortex Apoptosis and Improve Cognitive Ability in Aging Rats Reduced by D-galactose

Changes in microRNA (miRNA) play a role in brain aging. They are considered potential therapeutic targets. Regular long-term exercise benefits brain health. However, its exact mechanism is not fully understood. This study explored how moderate-intensity intermittent training (MIIT) improves cognitive function and reduces apoptosis in the aging brain. This study induced aging in rats by giving them D-gal injections (150 mg/kg/day) for 6 weeks. After confirming the model, the rats underwent MIIT. They exercised 45 minutes a day, 5 days a week, for 8 weeks. The results from behavioral, morphological, and molecular tests showed that 8 weeks of MIIT significantly slowed the decline in spatial learning and memory in D-gal aging rats. The exercise also improved the structure of the prefrontal cortex (PFC) and reduced apoptosis. Aging caused an overexpression of miR-34a-5p in the prefrontal cortex. MIIT reduced this overexpression. It also up-regulated Notch1 and inhibited excessive apoptosis by regulating Bcl-2 and Bax expression. In conclusion, MIIT may improve brain health by targeting miR-34a-5p and regulating apoptosis-related pathways.

physiology↗

bioRxiv2025

Epilepsy-Associated SCN2A-L1342P Mutation Drives Network Hyperexcitability and Widespread Transcriptomic Changes in Human Cortical Organoids

ObjectiveSCN2A pathogenic mutations, such as the recurrent heterozygous Nav1.2-L1342P, are monogenic causes of epilepsy. In this human-induced pluripotent stem cell model system, we aim to investigate the molecular and cellular mechanisms underlying the SCN2A-L1342P-associated pathology. MethodsUsing a human male iPSC reference line (KOLF) carrying the Nav1.2-L1342P mutation, we generated 3D cortical organoids for functional studies. Patch clamp and multi-electrode array (MEA) recordings, immunocytochemistry, and RNA sequencing were used to characterize the disease phenotypes. ResultsNav1.2-L1342P organoid neurons displayed increased intrinsic excitability, and amplified excitatory post-synaptic currents, which are consistent with an increase in excitatory synapse formation revealed by PSD95/SYN1 immunostaining. Moreover, elevated network firing activity, as demonstrated by MEA, indicates a pronounced network hyperexcitability. Transcriptomic profiling of organoids carrying the Nav1.2-L1342P mutation further revealed significant alterations in synaptic, glutamatergic, and developmental pathways. SignificanceOur findings demonstrate that the Nav1.2-L1342P mutation drives a multifaceted disease phenotype, including network hyperexcitability and disruption of pathways related to neuronal and synaptic functions. These results advance our understanding of SCN2A-related Developmental and Epileptic Encephalopathy (DEE), laying a foundation for personalized interventions. Key PointsO_LIKey Point 1: Human neurons carrying epilepsy-causing SCN2A-L1342P display intrinsic hyperexcitability in cortical organoids. C_LIO_LIKey Point 2: Synaptic transmission is enhanced in organoids carrying the SCN2A-L1324P mutation, consistent with the presence of elevated excitatory synapses, which leads to increased network hyperexcitability. C_LIO_LIKey Point 3: Transcriptomics analysis reveals that the SCN2A-L1342P mutation causes significant differential changes in forebrain developmental genes, synaptic and glutamatergic signaling pathways, and enhances cellular senescence and apoptosis. C_LI

neuroscience↗

bioRxiv2025

ECOD: Classification of domains in AFDB Swiss-Prot structure predictions

The development of highly accurate protein structure prediction algorithms has led to an explosion of structural data, transforming our understanding of protein structure-function relationships across diverse organisms. Domain classifications such as the Evolutionary Classificatioxn of Protein Domains (ECOD) have incorporated these computational predictions alongside experimental structures to create comprehensive resources for the research community. The AlphaFold Database (AFDB) plays a unique role, providing millions of predicted structures that ECOD has systematically classified for human proteins, small pathogens, and reference proteomes. Here, we extend this classification framework to the Swiss-Prot/UniProtKB dataset, applying the Domain Parser for AlphaFold Models (DPAM) pipeline to classify domains from over 542,000 Swiss-Prot protein structure predictions, resulting in more than 1,032,000 classified domains. These domains span 3,493 ECOD topologies and display high assignment confidence (mean DPAM probability: 0.992), with extensive taxonomic and functional diversity. Notably, over 100,000 domains lack existing Pfam mappings, indicating novel evolutionary groups. These results significantly expand ECODs coverage into a functionally and taxonomically diverse protein space, anchoring high-confidence structure predictions in an evolutionary framework. By integrating Swiss-Prot predictions, we enhance the utility and interpretability of AlphaFold models and establish a foundation for future large-scale, functionally informed domain classifications. Author SummaryLarge-scale protein structure prediction using deep learning has revolutionized our ability to study protein families and infer biological function. However, connecting these predicted structures to well-understood evolutionary classifications remains challenging. In this work, we apply a domain parsing pipeline to classify over half a million AlphaFold-predicted Swiss-Prot proteins into evolutionary groups using ECOD, a structure-based domain classification system. This enables the systematic integration of structural predictions with functional annotation across a broad range of species. Our analysis reveals taxonomic and functional diversity, highlights domain clusters with no prior annotation, and expands ECOD coverage with high-confidence, evolutionarily meaningful predictions.

bioinformatics↗