The gut microbiome plays an important role in maintaining human health. Despite the rapid growth of literature and publicly accessible data on the gut microbiome, a large-scale, well-curated database integrating a unified matrix with multiple analytical tools is still lacking. Here, we introduced GutBiomeDB, a comprehensive database and analysis platform for the human gut microbiome. GutBiomeDB contains 168 464 samples from 482 BioProjects, spanning 72 countries, 224 disease conditions, and seven life stages. To improve the reusability and accessibility of data, the uniformly processed taxonomic profiles generated by Abdill et al. were reused without reprocessing to construct a matrix of 4680 taxa and 3142 genera, and disease, life-stage, sex, and geographic metadata were manually curated by our team. Additionally, GutBiomeDB offers four tools for in-depth analysis: “Differential Analysis” for comparing diversity and abundance of user-selected cohorts and performing random-effects meta-analysis across projects; “Interaction Network” for comparing disease and healthy-control co-occurrence networks; “Lifecycle Atlas” for visualizing genus trajectories across seven life stages; and “Health Index” for searching similar samples and scoring uploaded genus profiles. GutBiomeDB serves as a versatile and valuable resource for advancing a broad spectrum of gut microbiome research and clinical relevance. GutBiomeDB is freely accessed at https://gutbiomedb.online.

GlyTouCan (https://glytoucan.org) is the international, species-neutral glycan structure repository that assigns stable, globally unique accession numbers to glycan structures independently of the experimental method used to determine them. Here, we describe GlyTouCan version 4, released on June 22, 2026, which introduces a completely redesigned user interface with substantially faster browsing and searching, including a new “archetype” view that groups registrations that differ only in the anomeric or ring form of the reducing-end residue together for clearer filtering of the >256,000 glycans now registered. Version 4 provides an improved graphical structure input, via the updated browser-embedded GlycanBuilder application, for both searching and registering structures. Users may now sign in with an ORCID ID in addition to a Google account, and all previously released v3 REST API endpoints remain fully backward compatible. Version 4 also introduces a framework for industry affiliates, providing a structured pathway for commercial organizations that work with or synthesize glycans, while all existing academic database-partner relationships are maintained. GlyTouCan remains freely available to all users without charge, login, or registration for browsing and searching. We also summarize the repository’s data content, which spans >600 monosaccharide types and, judging from these, includes many glycans of probable bacterial or plant origin.

Quantifying enzyme kinetic parameter changes upon mutations is essential for accelerating enzyme evolution. Current methods focus on predicting absolute values of kinetic parameters (turnover numbers kcat and Michaelis constants Km), yet they fail to accurately capture mutation effects. To our knowledge, no method directly models mutation effects (Δkcat and ΔKm) due to the scarcity of such training data. Moreover, these methods haven’t fully exploited protein structural geometry. Here, we establish a high-quality enzyme mutation effect dataset by pairing wild-type and mutant kinetic parameters measured under identical experimental conditions via a semi-automatic procedure combining language model-assisted parsing and manual verification. Leveraging this dataset, we develop DeltaCata, a Siamese geometric graph neural network to directly predict the changes in kinetic parameters induced by residue substitutions. DeltaCata demonstrates substantial performance improvements over existing methods, especially for enzymes unseen in the training sets. Its utility is further validated through in silico beneficial mutation screening experiments using deep mutational scanning datasets. Finally, we apply DeltaCata to engineer the methyltransferase sGmOMT2, achieving up to a 7.0-fold improvement in catalytic efficiency for in vitro icaritin synthesis. These results suggest the high potential of DeltaCata for industrial enzyme engineering. Predicting how mutations affect enzyme catalytic efficiency remains challenging. Here the authors build a benchmark dataset and develop DeltaCata, a geometric deep learning model that predicts changes in enzyme kinetics and guides enzyme engineering.

The intracellular delivery of therapeutic macromolecules remains a major challenge in biomedicine. Capsid-forming proteins are one promising vehicle, but to date, these have required cellular assembly or rely on fusogens for cell entry, limiting their scalability and utility. Here we reconstitute the Drosophila melanogaster Arc1 (dArc1) retroelement-derived capsid entirely from purified recombinant protein components and in vitro transcribed RNA, creating a fully defined, cell-free assembled protein nanoparticle system. We engineered the dArc1 RNA-binding domain to efficiently encapsulate both mRNA payloads and Cas9 ribonucleoproteins. We show that dArc1 capsids bind mammalian cells through a direct interaction with the surface receptor SORCS2. Leveraging this interaction, we demonstrate that intramuscular injection of dArc1 capsids carrying Cas9 gene editors achieves up to 18% exon skipping and restores dystrophin expression in muscle fibers of mdx mice, a model of Duchenne muscular dystrophy. This work demonstrates the potential of in vitro-assembled protein nanoparticles for delivery of molecular cargoes. Capsid-forming proteins are promising vehicles for macromolecule delivery, but have required cellular assembly or fusogens for entry. Here, authors assemble Drosophila Arc1 capsids cell-free, show entry via the receptor SORCS2, and deliver Cas9 editors that restore dystrophin in mdx mice.

R-loops are non-canonical nucleic acid structures implicated in diverse cellular processes and diseases. We previously developed R-loopBase to address the pressing challenges of inaccurate R-loop maps and the limited understanding of the R-loop regulatory network. However, R-loopBase provided only human R-loop maps with categorical confidence and did not account for cell type specificity. These limitations, coupled with the emerging R-loop profiling technologies and advances in R-loop regulatory mechanisms, have motivated the development of R-loopBase v2.0 (https://rloopbase.nju.edu.cn). After uniformly re-analysing 358 R-loop mapping datasets generated by 16 technologies, we introduce a quantitative R-loop scoring framework that assesses genome-wide R-loop confidence in human, demonstrating strong concordance with known R-loop biology. We further extend the framework to specific cell types and mouse, fruit fly, and yeast. In parallel, we substantially expand the collection of R-loop regulators across species, enabling the inference of changes in R-loop homeostasis. Building on these expanded datasets, we implement several new interactive features and analytical tools, including an expanded multi-species genome browser, deep learning-based R-loop prediction, R-loop homeostasis analysis, and a streamlined data download portal. We anticipate that R-loopBase v2.0 will serve as an enhanced, up-to-date one-stop resource for the R-loop research community.

RNA chemical probing generates nucleotide-resolved profiles in vitro and in cells, while X-ray crystallographic or cryo-electron microscopy models provide high-resolution structural models but remain comparatively low throughput. Aligning these experiments from different modalities may provide insights into RNA structure formation and molecular functions. Here, we present Ribocentre-FoldBridge (https://foldbridge.ribocentre.org/), an AI-assisted, curator-reviewed database linking curated chemical-probing profiles with experimentally determined 3D structures. This database catalogues 12 types of RNAs, linking 256 929 probing profiles across five experimental categories to 5321 structure-linked cases. It interactively visualizes the probing profiles that align with RNA secondary and tertiary structures at nucleotide resolution. We demonstrate probe-specific reactivity patterns across paired, unpaired, and solvent-accessible structural contexts. The database aims to provide a reproducible evidence layer for examining RNA structure–function relationships across heterogeneous experiments, and thus facilitate the development of new RNA structure or function prediction programs.

The BioImage Archive (https://www.ebi.ac.uk/bioimage-archive) is EMBL-EBI’s flagship resource for life sciences image data. It serves as a deposition database for biological images across scales from near-atomic through to whole organisms, and across imaging modalities. The Archive’s mission is to support the reproducibility of quantitative results based on imaging, support methods development including modern deep-learning-based approaches, and allow large-scale reanalysis of image data. Across the Archive’s ecosystem, we provide access to over 11 PB of image data. Recent developments in underpinning infrastructure now allow interactive exploration, visualization, and streaming access to a subset of the Archive’s data using the new OME-Zarr file format, and will ultimately provide access to all of EMBL-EBI’s archived image data, including data from the Electron Microscopy Public Image Archive. Migration of all holdings and a unified submission system are in progress.

GlycoPOST (https://glycopost.glycosmos.org) is a public mass spectrometry (MS) data repository for submitting and sharing glycan-related raw MS data along with structured experimental metadata, in accordance with the Minimum Information Required for A Glycomics Experiment (MIRAGE) guidelines. As of 1 September 2026, GlycoPOST hosts 602 projects containing 7.6 TiB of data, of which 445 are publicly available. In this update, we implemented major updates to GlycoPOST, expanding its scope from glycomics to glycoproteomics by incorporating the recent revisions published on the MIRAGE MS guidelines, in addition to enabling users to filter “presets,” which are units for metadata in GlycoPOST, by keywords and last update date. The newly introduced concept of “project type” enables each GlycoPOST submission, referred to as a “project,” to be classified according to whether it contains glycomics or glycoproteomics data. This makes it easier to select reusable metadata profiles (presets) by displaying only categories relevant to the experimental field. Furthermore, with the introduction of a project type specifically designed for glycomics-assisted glycoproteomics experiments, it is now possible to register data and metadata from both glycomics and glycoproteomics within a single project. These updates will promote standardized data sharing and reanalysis across glycomics and glycoproteomics using GlycoPOST. glycosylation