CauloBrowser is a systems-biology database for Caulobacter crescentus that organizes the largest repository of experimental data for this model bacterium. The original CauloBrowser provided a web interface for accessing these data, but the growing number of experimental datasets and advances in AI chatbots motivated an updated resource. We developed CauloBrowser 2.0 https://shinyproxy.gateway.harmony.unityhpc.org/app/caulobrowser as an open-source application that broadens access to RNA-seq, microarray, ribosome profiling, Tn-seq, ChIP-seq, and genome annotation data. The CauloChat AI interface allows users to query these datasets in natural language, generate visualizations, and achieve complex biological insights. While the database is hosted freely on the web, users may also download the database and run it locally, reducing dependence on the web-hosted service and allowing laboratories to use compatible unpublished datasets. Altogether, CauloBrowser 2.0 provides a significant update for exploring diverse systems biology data in C. crescentus.
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Using living cells as dynamic biological factories offers immense potential for the continuous, localized production of therapeutic proteins within compromised tissues. This strategy is particularly compelling for treating chronic non-healing wounds, which lack therapeutics that are able to function effectively in the inflammatory microenvironment. Commensal bacteria have been shown to promote healing in these hostile microenvironments, yet applying live microbes directly onto compromised tissue is clinically unfeasible due off-target safety risks and engraftment challenges. Herein, we establish a well-defined commensal Engineered Living Material (ELM) that allows encapsulation, retention, function, and delivery of native beneficial bacteria for localized production and release of a pro-healing secretome over weeks. We specifically encapsulated a pro-healing human wound bacterial isolate, Alcaligenes faecalis, using biocompatible, click-reactive hydrogel building blocks (thiol- or vinyl-sulfone-functionalized multi-arm poly(ethylene glycol)) and systematically varied the density of the polymer network and of the encapsulated bacteria to establish design rules for creating pro-healing cELMs. Physical confinement in the hydrogel network ensured bacterial biocontainment, while the hydrogel mesh size still permitted diffusion of the therapeutic secretome. Notably, controlling encapsulation density revealed an inverse relationship wherein lower cell densities yielded significantly enhanced per-cell metabolic activity and provided sustained production of pro-healing secreted factors. In a murine diabetic wound model, these A. faecalis cELMs shielded the encapsulated bacteria from host clearance, maintained biocontainment, accelerated re-epithelialization, and improved wound bed maturity. Together, these findings establish design principles for cELMs that couple bacterial biocontainment with sustained secretome delivery, providing a promising platform for localized treatment of chronic wounds. 
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Non-oxidative glycolysis (NOG) converts glucose to acetyl-CoA without emitting CO2 during E. coli bioproduction. Although 13C-metabolic flux analysis (13C-MFA) has the potential to evaluate NOG flux distribution in vivo, the precision of these estimates depends on the labeling patterns of glucose tracers. Because E. coli has multiple native glycolysis pathways, selecting the optimal glucose tracer is important for precise estimates. This study used simulations to evaluate the performance of 18 commercially available glucose tracers in precisely predicting NOG flux. We artificially generated flux distributions of central metabolic pathways, including three types of NOG pathways, and obtained 13C labeling patterns of amino acids metabolized from each glucose tracer. Using these data, we then calculated 95% confidence intervals (CIs) for NOG pathway fluxes. Comparing the sum of CI widths from three NOG fluxes showed that estimation with [1,3-13C]-glucose yielded the narrowest CIs. Several tracers, including [2,3-13C]glucose, were capable of determining fluxes with high precision, but [1-13C]glucose was not one of them, despite often being used for estimating glycolytic fluxes. We then experimentally evaluated [2,3-13C]- and [1-13C]glucose as tracers in E. coli strains with and without the expression of phosphoketolase, which is required for NOG pathways to function. The results suggested that [1-13C]glucose could not detect NOG fluxes, whereas [2,3-13C]glucose estimated the fluxes of all three NOG pathways with narrow CIs.
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18 glucose tracers: glucose with different 13C positions in which the carbons at the named positions are 13C, and the others are ordinary 12C
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Growing concern over the environmental toxicity and biomedical risks of conventional physical and chemical nanoparticle synthesis has intensified interest in sustainable routes to metal nanoparticle production. Myconanotechnology, which uses fungi as biological nanofactories, has emerged as a versatile green platform: fungi tolerate high metal concentrations and secrete abundant enzymes, proteins, and polysaccharides that act as reducing, stabilizing, and capping agents. Most existing reviews catalog producer species and nanoparticle types; here we instead integrate synthesis mechanisms, the physicochemical determinants of particle properties, and biomedical performance, and we weigh the strength of evidence behind each application. Antimicrobial and anticancer activities are currently the best supported, although the evidence is dominated by in vitro models, whereas vaccine-adjuvant and biosensing applications remain preliminary and mechanistically underdefined. Across silver, gold, copper, zinc, iron, and selenium systems, fungal biomolecules govern not only metal-ion reduction but also surface chemistry, biocompatibility, and immunological behavior, which distinguishes mycogenic NPs from plant-derived and chemically synthesized counterparts. The main barrier to translation is not a shortage of candidate materials but the absence of standardized, mechanistically informed synthesis and characterization protocols, which prevents batch-to-batch reproducibility and meaningful cross-study comparison. Closing this reproducibility gap, through defined culture conditions, quantitative surface characterization, and harmonized safety testing, is the most critical prerequisite for advancing fungi-engineered nanoparticles toward clinical and industrial use.
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Terpenoids are ubiquitous, structurally diverse natural products found across the three domains of life. One of the most commonly observed and well-studied terpenoids is the volatile, petrichoric compound geosmin, responsible for the earthy smell of soil to which humans are receptive down to a few parts per trillion. Bioinformatic analysis of bacterial geosmin synthase (GeoA)-encoding gene clusters reveals that many are colocalized with genes encoding Family 2B encapsulin shell proteins. Here, we focus on the encapsulin-encoding geosmin gene cluster of the model myxobacterium Myxococcus xanthus and show that the two shell proteins form a two-component Family 2B encapsulin with GeoA as the internalized cargo. Structural analysis highlights that the shell contains characteristic, external cyclic adenosine monophosphate (cAMP) binding-fold domains (CBDs), and that mixed shells can adopt a previously unobserved closed two-fold pore conformation. We show that GeoA cargo loading is mediated by repeating, short cargo loading peptides (CLPs), and that GeoA encapsulation provides benefits for enzyme activity and stability. This work expands the short list of Family 2B encapsulins known to be involved in specialized metabolite biosynthesis, and provides insights into a putative mode of cargo protein regulation via pore state modulation.
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Naturally occurring magnetotactic bacteria have an inherent ability to sense and align to earth's geomagnetic field due to the presence of magnetosomes—membranous microstructures containing iron oxide nanoparticles. These functionalities can be exploited for tuning movements and guided spatial patterning in controlled magnetic field. While being arduous to culture in laboratories, these organisms have complex genetic make-up with tight regulation on their magnetosome synthesis. Inspired by the magnetotactic bacteria, we aim to render magnetic behavior to E. coli. We mimic the magnetosomes in magnetotactic bacteria by employing a membraneless proteinaceous compartment from the thermophilic iron storage protein, ferritin, fused with a magnetite-forming peptide, m6A. To enhance iron accumulation, the E. coli iron metabolism is engineered by tailoring the expression of iron influx and efflux genes. Numerical simulations support our observations of a highly responsive magnetic E. coli. This study presents a strategy to engineer magnetic response in E. coli which can be adopted to other bacteria.
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Artificial intelligence is moving from stand-alone models towards systems of interacting agents. These systems do not merely classify, predict or generate: they plan, exchange messages, call tools, divide labour, adapt and coordinate action across tasks and time. This shift has made agentic AI one of the dominant frames for the next phase of the field. Understanding how these agents interact will therefore be as important as improving what each agent can do alone. Here, the authors argue that agentic AI requires a shift from evaluating individual agents alone to analyzing interaction architectures, proposing a framework that links agent interactions, collective configurations and time-dependent outcomes.
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Replication origins are essential for both chromosome duplication and plasmid maintenance; however, their annotation remains challenging across different replicon types and across diverse phylogenetic lineages. DoriC 14.0, the latest update of the database of replication origins in prokaryotic genomes, restructures and greatly expands this resource, with particular emphasis on plasmid replication. The latest release contains 530 178 computationally derived origin records, associated with 432 232 distinct sequence accessions, including 434 060 chromosomal and 96 118 plasmid records from bacteria and archaea. For bacterial plasmids, OriV-Finder was applied with RNA II-based refinement, whereas a customized OriV-Finder pipeline was employed for archaeal plasmids, which relies on archaeal replication-associated proteins and origin recognition box motifs. DoriC 14.0 combines literature evidence with computational predictions, maintains traceable record identifiers, and offers versioned data with release history. New features include comparative sequence support, related-origin links, interactive candidate replication initiation protein (RIP) sequence-similarity networks, plasmid-level analyses of origin recurrence, RIP profiles, and RNA II variation. The updated website provides filter-based browsing, record-level views, BLAST searches, and standardized downloads, and is freely accessible at https://tubic.org/doric/ and https://tubic.tju.edu.cn/doric/ without registration.
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Quantitative binding affinities and three-dimensional protein–ligand structures provide complementary information essential for structure-based drug discovery, yet these data remain largely disconnected across existing databases and the biomedical literature. We present BioLiP3, a major extension of BioLiP that expands quantitative affinity coverage 13-fold within its structure-centered framework for biologically relevant interactions. BioLiP3 contains 593 423 affinity records, of which 591 820 are linked to three-dimensional protein–ligand structures: 45 386 experimentally resolved PDB complexes, 528 732 BindingDB-derived records with computationally generated structures, and 17 702 structure-linked records derived from LLM-mined biomedical literature. For records lacking experimental complex structures, we generate structural models using available experimental or predicted receptor structures, binding-site identification, and molecular docking, with experimental and computational structures explicitly distinguished. To complement existing databases, we developed a hybrid literature-mining workflow combining large language models with rule-based validation and independent verification to extract Ki, Kd, IC50, and EC50 measurements from PubMed abstracts while preserving provenance. BioLiP3 further integrates ligand-binding sites and protein, functional, and structural annotations via cross-references to PDB, UniProt, NCBI Taxonomy, PubChem, and original literature. Taken together, these advances make BioLiP3 a unified resource for data-driven protein–ligand interaction studies and structure-based drug design. BioLiP3 is freely accessible at https://zhanggroup.org/BioLiP/.
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Human milk oligosaccharides (HMOs) are a component of breast milk which improve infant health outcomes by modulating immune responses, inhibiting pathogen adhesion, and promoting healthy gut microbiota. Although their effects are well-documented, few HMOs have been clinically or biochemically studied because only a handful of the simplest HMOs out of >200 species are currently possible to chemically synthesize or produce in microbes via metabolic engineering in microbial hosts. In contrast, plants are naturally adept at biosynthesizing a wide range of complex polysaccharides, and they have recently been established as a heterologous system for HMO production. However, much of the structural diversity of HMOs has not yet been reproduced in a plant, particularly fucosylations. Four fucosidic linkages can be found in fucosylated HMOs, but only Fucα1–2Gal has been biosynthesized in planta. Here, we screened bacterial fucosyltransferases using the model plant Nicotiana benthamiana to produce 3-fucosyllactose (Fucα1–3Glc), lacto-N-fucopentaose II (Fucα1–4GlcNAc), and lacto-N-fucopentaose III (Fucα1–3GlcNAc), demonstrating that all four fucosidic linkages can be made in planta. Just twelve glycosidic linkages make up all the HMOs found in breast milk, and our findings increase the number of linkages made in planta from seven to ten, greatly expanding the variety of HMOs accessible in a plant.
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Genome editing technologies hold significant promise for the treatment of genetic disorders, but safe and efficient delivery remains a major barrier to their clinical translation. Cell-derived particles, including extracellular vesicles (EVs) and virus-like particles (VLPs), have emerged as promising non-viral delivery platforms, offering biological compatibility and the capacity to package and deliver genome editors as ribonucleoprotein (RNP) complexes in a transient manner that reduces the risk of off-target editing. The surface composition of these vehicles is shaped by their biogenesis pathway and their cell of origin, which in turn govern their cellular tropism. Through various engineering strategies, including producer cell engineering, viral-glycoprotein pseudotyping, and post-isolation surface modifications, this tropism can be expanded or redirected toward cell types and tissues beyond the liver. Here, we review the biology underlying cell-derived particle surface composition and the targeting strategies employed to engineer cell-type specificity.
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Metabolic traits mediate microbial interactions and underpin mechanistic models of community assembly. With the rapid expansion of genomic data and bioinformatic tools, it is widely assumed that metabolic traits such as substrate utilization can be reliably inferred from genomes alone. Here, we systematically evaluate this assumption by comparing genome-based predictions from four commonly used tools to high-throughput growth phenotypes of 186 marine bacteria. For simple substrates, prediction accuracy was limited and highly variable across taxa and substrates, with many strains predicted at or near chance levels. For three of four tools, most errors were false positives, indicating that apparent pathway completeness frequently fails to translate into growth under laboratory conditions. In contrast, genomic prediction of polysaccharide utilization based on carbohydrate-active enzyme content performed substantially better, suggesting that traits that depend on a few highly specific enzymes are easier to predict. While genome-based approaches can recover broad statistical patterns in metabolic strategies, their limited strain-level reliability constrains ecological inference and modelling. For the foreseeable future, large-scale experimental phenotypic characterization will remain an indispensable component of microbial ecology, both to reveal trait distributions and to ground-truth and improve genomic predictions.
mhryu@live.com's insight:
Focusing first on simple carbon substrates for which metabolic pathways are well-described, we generated predictions for each isolate-substrate pair using four widely used, automated tools: GapMind (Price et al. 2022), CarveMe (Machado et al. 2018), GapSeq (Zimmermann et al. 2021) and KBase (Linsay et al. 2010).
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Bidirectional promoters (BDPs), intergenic regions that coordinate transcription of oppositely oriented genes, pervade eukaryotic genomes, yet sequence signatures remain poorly defined. Existing approaches to BDP identification rely on distance thresholds and cannot infer activity from DNA. Here, we use Evo2, a genomic language model, to predict BDP activity and uncover sequence-encoded regulatory principles across four plant species (Arabidopsis, maize, rice, and wheat). We stratify BDPs by co-expression strength and compare Evo2-derived embeddings against k-mer profiles, transcription factor binding site matrices, and convolutional neural networks trained on sequence. Evo2 embeddings outperform all baselines, demonstrating that genomic language models capture regulatory information underlying BDP activity. Models trained on one or multiple species transfer to held-out species, and in planta validation in Spartina alterniflora confirms predicted high-activity BDPs. Sparse autoencoder analysis links Evo2 embeddings to nucleosome positioning and species-specific transcription factor landscapes, providing insight into sequence determinants underlying BDP activity . This work establishes how genomic language models can extract bidirectional promoter logic from sequence and identify conserved regulatory rules across plants. Bidirectional promoters (BDPs) are intergenic regions that initiate transcription of two oppositely oriented genes. Here, the authors report a framework that employs Evo2 genomic language model embeddings to predict BDP activity directly from raw DNA sequence.
mhryu@live.com's insight:
the intergenic sequence of each candidate between two opposite-facing genes goes through Evo2, which returns 4,096 numbers taken from layer 24. The training label comes from expression: the two flanking genes’ expression is correlated across thousands of public RNA-seq libraries, and the pairs in the top and bottom few percent are labeled 1 (coexpressed) and 0 (uncorrelated). The middle range is dropped. LightGBM, a decision tree model, is the part that learns. It is trained on the table of Evo2 numbers and 0 or 1 labels. To predict, a new sequence goes through Evo2 and then the trained trees, which call it high or low activity. No expression data are needed, so the method can be used on genomes without RNA-seq. It was tested across species. A model trained on maize, rice and wheat was applied to Spartina alterniflora. Of 20 predicted high activity candidates tested in tobacco leaves, 16 showed activity on both strands separately, and 15 of those drove both reporters together.
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An organism′s physiology is controlled by its genomic content. Building genomic chimeras would enable melding of the diverse functions and properties of life. However, prior arts in genome synthesis are limited to reconstituting pre-existing functions within a singular genome rather than combining diverse genomic functions across multiple distinct genomes. Existing methods are also prohibitively expensive, labor intensive, and time consuming, and thus fundamentally not scalable for creating large synthetic chimeric genomes. To address all these limitations, we invented Additive Conjugative-CAST Engineering (ACE) combining conjugation with CRISPR-associated transposition (CAST) to deliver and integrate up to half a genome per step from a donor cell into a precisely defined position in the recipient′s genome. We have demonstrated ACE′s capability to integrate at least a 2-megabase donor genomic segment in a single step and at least 3 megabase in two consecutive steps. Importantly, we confirmed ACE′s generality through the creation of multiple genomic chimeras across species, genus, and at least order barriers. With ACE, we further demonstrate such chimeric organisms, or what we call genome expanded organisms (GEOs), can be stably forged from at least three starting bacterial strains, and can be stably maintained to express all their acquired genomic parts in a single chimera. ACE offers a new paradigm of creating artificial lifeforms to combine desired properties and potentially create novel functions beyond the constraints of nature. ACE also offers a unique perspective to probe unexplored plasticity in genome architecture and expression patterns of novel, massively expanded genomes.
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chromosome fusion, 3st, tool, In the donor chromosome, two cassettes are inserted at the boundaries of the segment to be moved, giving the order oriT, RB, segment, LB, oriT. The first cassette (oriT plus RB, with sucrose sensitivity and chloramphenicol resistance markers) is placed at the start, and the second (LB plus oriT, with hygromycin resistance, p-chlorophenylalanine sensitivity and GFP markers) at the end. A helper plasmid with the conjugation machinery is then added to the donor. On arabinose induction, transfer begins at the first oriT and ends at the second, so only the DNA between them enters the recipient as single stranded DNA, with RB and LB travelling just inside the boundaries. In the recipient, which carries a plasmid with the CAST genes and a guide RNA, the DNA is converted to a double stranded circle that retains RB and LB. CAST then uses RB and LB to insert the whole circle at the site chosen by the guide RNA, without double strand breaks or homologous recombination. Selection for hygromycin resistance and against the sucrose sensitivity marker picks recipients that gained the segment and removes donor cells and the unwanted reverse transfer product.
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Which bacterium swims fastest depends on where the race is run. Speeds are usually measured in open liquid, yet many bacteria live in pores, mucus and host passages no wider than a cell. Using a microfluidic device with eight channel widths (0.80-3.00 μm) in one field of view, we tracked unlabeled cells of eight species spanning five motility systems. Chamber height, a polymer additive and channel width each reordered the ranking. Vibrio alginolyticus, the fastest in open liquid, slowed to ~4 μm/s in 1.25-μm channels, whereas Campylobacter jejuni and Bacillus subtilis kept 33-46 μm/s. Open liquid speed did not predict robustness to confinement. In two unrelated lineages, host symbionts that pass through narrow passages during colonization, Caballeronia insecticola and Vibrio fischeri, outran their non-symbiotic relatives in the channels, and crossed them with flagella wrapped around the cell body at nearly the speed of unwrapped cells. Where space is tight, the ability to keep moving, not top speed, sets the ranking.
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Microbial interactions are shaped by multiple community members, yet the underlying mechanisms remain poorly understood. Here, we investigated a tripartite interaction between the microalga Chlamydomonas reinhardtii and two Pseudomonas bacteria that coexist in the same soil habitat. In pairwise cultures, all three interactions are amensalistic (-like): Pseudomonas protegens Pf-5 antagonizes both C. reinhardtii and Pseudomonas fluorescens HKI0770 without benefit, while P. fluorescens inhibits C. reinhardtii without profiting from the alga. However, in a tripartite culture, P. fluorescens protects C. reinhardtii from P. protegens and promotes algal recovery. This protection is not stopped when the used carbon source, acetate, is supplemented. The P. fluorescens rescue effect is associated with reduced levels of the polyyne toxin protegencin produced by P. protegens. Protegencin levels, detected by Raman microspectroscopy, are decreased by application of spent medium or protein extracts from P. fluorescens, while heat- and protease-treatment abolish this activity. A heat-sensitive, proteinaceous factor, likely an enzyme(s), appears to contribute to protegencin inactivation. Our results show that P. fluorescens rescues C. reinhardtii by inactivating protegencin, despite being inhibited by P. protegens. These findings demonstrate the importance of investigating higher-order effects, since additional community members can transform pairwise antagonistic interactions into conditional protection through toxin inactivation.
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The B cells of the human immune system have evolved somatic hypermutation (SHM) mechanisms that introduce mutations at the immunoglobulin genomic loci at a significantly higher frequency than the rest of the genome thereby allowing them to evolve antibody sequences without compromising fitness due to genome-wide mutations. Inspired by these observations, here we develop a continuous directed evolution platform in human B cell lines (CODE-HB) that repurposes the SHM mechanisms to a stable, non-immunoglobulin genomic locus of the human B cell lines to continuously evolve cytosolic and surface displayed proteins with a broad mutational spectrum comprising of substitutions, deletions and insertions. Using our human B cell surface display platform and CODE-HB, we evolve neutralizing antibodies targeting avian influenza and escape variants of influenza. Given the modularity and simplicity of CODE-HB, we anticipate that this platform can be used for rapidly evolving biotechnologically relevant biomolecules directly in human cells. This study describes the development of a continuous directed evolution platform in human B cell lines (CODE-HB) that repurposes the somatic hypermutation (SHM) mechanisms to evolve cytoplasmic reporter proteins as well as therapeutically relevant antibodies.
mhryu@live.com's insight:
RA 1 is a human B cell line that already hypermutates its immunoglobulin genes constitutively, so it expresses activation induced deaminase and the downstream repair factors. The 213 bp proXIV-1 fragment from the immunoglobulin heavy chain V4-55 region sits upstream of the EF1a promoter. its octamer motifs attract Oct transcription factors, which make the locus a target for hypermutation. Somatic hypermutation is coupled to transcription, so the deaminase acts on the transcribed gene downstream of that element.
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Human breast milk composition is responsive and reflects maternal, mammary gland and infant health, yet remains largely inaccessible to point-of-care analysis. Purpose-built biosensors could transform breastfeeding support, enabling precision lactation care and improved outcomes for mothers and infants.
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Plant factories with artificial lighting (PFALs) are enclosed vertical cultivation systems equipped with customisable LED spectral regulation, whose stacked spatial structure and controlled light environments substantially differ from open fields and conventional greenhouses, bringing unique and specialised challenges to precise plant phenotyping. Driven by high-throughput sensing technologies and artificial intelligence (AI), plant phenotyping in PFALs has achieved considerable advances. This review comprehensively summarises progress in PFALs plant phenotyping, covering sensing techniques including RGB imaging, multispectral imaging, hyperspectral imaging, and 3D point cloud reconstruction, as well as advanced analytical models integrating machine learning, deep learning, large language models, and AI agents for phenotypic data mining. We further highlight the important bottlenecks restricting the industrial application of PFALs phenotyping: commercial phenotyping devices are generally bulky, costly, and incompatible with compact vertical cultivation layouts, while the shortage of PFALs-specific phenotypic datasets and scenario-adaptive optimisation models hinders large-scale industrial translation. To clarify the differentiated technical characteristics of PFALs phenotyping, we systematically compare the discrepancies in phenotyping strategies, technical demands, and application scenarios between PFALs and traditional greenhouses. On this basis, we propose targeted solutions and prospective development directions for PFALs phenotyping, focusing on miniaturised and integrated phenotyping equipment, optimised AI-driven analytical frameworks, and multi-omics intelligent breeding. Distinct from previous general phenotyping reviews, this paper specifically targets the vertical, controlled PFALs cultivation scenario, systematically sorting out its exclusive technical challenges and corresponding coping strategies, and providing actionable insights for the future intelligent upgrading and industrialisation of PFALs / vertical farm plant phenotyping technology.
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Uridine diphosphate-dependent glycosyltransferases (UGTs) coupled with sucrose synthase (SuSy) offer a selective and highly efficient route to natural product glycosylation. However, harnessing the full potential of these biocatalysts at industrial scale is challenged by their low operational stability. Enzyme mining and engineering offer a powerful strategy to address this challenge. A vast array of in silico tools supports this effort, which use sequence, structure, and thermodynamic knowledge to discover, predict and screen thermostabilized variants. In this review, we discuss the recent applications of those tools to improve the thermostability of UGTs and SuSy jointly, and their resulting impact on UGT-SuSy glycosylation cascades. We highlight integrated methodological approaches that predict and filter candidate mutations, allowing the reduction of false positives without sacrificing activity. These efforts have substantially advanced UGT thermostability, with melting temperatures reaching up to 90 °C and half-lives extending to several days at 60 °C. Comparatively fewer studies, however, have applied similar strategies to SuSy, and none have yet achieved the magnitude of improvement reported for UGTs. This asymmetry positions SuSy as a comparatively underexplored target, emphasizing the need for further mining and engineering to obtain stable variants that perform alongside UGTs under industrial conditions.
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thermo
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The Evolutionary Classification of Domains (ECOD) organizes protein domains into a hierarchy of evolutionary relationships, encompassing both experimentally determined and computationally predicted structures. ECOD now classifies 2.9M domains, of which ∼1.37M derived from the Protein Data Bank and 1.51M from predicted models in the AlphaFold Protein Structure Database. The principal development we report here is a complete rebuild of the ECOD web resource. Search has been reconstructed around three modalities: (i) sequence, (ii) structure, and (iii) keyword. Foldseek now is the primary search method for the ECOD web page, searching a ∼457k representative domain database that is also distributed for local use. Results are presented through a Mol* interactive viewer, and a new API has been developed for programmatic access to the database. Alongside the interface, the pipeline for classifying new PDB entries has been rebuilt and returned to a regular cadence, family groups have been synchronized with Pfam v38.2, the bulk distributables have been reorganized and now include the search databases, the ligands in contact with each domain are recorded for experimental structures and annotated with drug cross-references from DrugDomain, and a registry has been introduced through which ECOD assigns stable identifiers to gene-called proteins and records the structures it predicts. ECOD is freely available at http://prodata.swmed.edu/ecod.
mhryu@live.com's insight:
domain search: give a sequence, a structure, or a keyword, and it returns the matching domains and shows where they sit in the hierarchy.
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Gluten, the main storage protein in wheat, barley, and rye, contains sequences rich in proline and glutamine that resist complete gastrointestinal degradation resulting in generation of immunogenic peptides. The peptides contain immunogenic epitopes that, following deamidation by tissue transglutaminase 2 (TG2), can be presented by HLA-DQ2/DQ8 molecules to gluten-specific CD4 + T cells, resulting in intestinal inflammation. This gluten-induced intestinal inflammation is known as celiac disease (CD) in susceptible individuals. A gluten-free diet (GFD) has been recognised as gold standard treatment to date, which requires lifelong strict compliance, therefore demanding alternative novel therapies. Fermented foods have emerged as promising reservoirs for selection of gluten-degrading probiotic strains as adjunctive therapeutic candidates. These strains may hydrolyse gluten and its immunogenic gluten peptides through specialised array of proteolytic enzymatic systems comprising endopeptidases, prolyl endopeptidases, aminopeptidases and dipeptidyl peptidases. This review critically examines the significance of gluten-degrading microorganisms with probiotic potential from fermented foods and their proteolytic enzyme system. Owing to their origin from fermented foods, the effect of fermentation parameters such as pH, strain selection, and substrate composition is also highlighted. The assessment of studies indicates that the fermentation mediated by selected gluten-degrading strains reduces the gluten content; however, these did not detoxify the immunogenic peptides completely. The potential strains support intestinal health through modulation of gut microbiota and attenuation of inflammatory responses which has been validated through in vitro and clinical studies, although their efficacy in managing CD remains unestablished. However, strain-specific differences, incomplete degradation of immunogenic peptides, and the lack of standardised protocols for evaluating gluten detoxification remain significant barriers to their application.
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Bacterial cyclic-oligonucleotide-based antiphage signaling systems (CBASS) are evolutionarily related to the mammalian cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway. The bacterial CdnB-Cap14 CBASS system is notable for producing 2′,3′-cyclic GMP-AMP (2′,3′-cGAMP), the same second messenger used by the mammalian cGAS-STING pathway; however, its functional mechanism has remained unclear. Here, we show that CdnB-Cap14 protects against phage infection through abortive infection (Abi) by triggering cell death. Structures of apo CdnBΔC and its complexes with ATP and guanosine triphosphate (GTP), together with inactive and activated 2′,3′-cGAMP-bound Cap14, reveal that CdnB is autoinhibited by its C-terminal AGS-C domain and becomes activated by DNA together with intracellular ATP/GTP depletion to synthesize 2′,3′-cGAMP in vitro. This signal drives Cap14 assembly from dimers into zipper-like two-layered filaments that partition the membrane and disrupt its integrity. We further show that phage-encoded Acb2 sequesters 2′,3′-cGAMP to block signaling. These findings reveal mechanistic parallels between bacterial CBASS and eukaryotic cGAS-STING immunity and provide a molecular basis for therapeutic engineering.
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Watson–Crick base-pairing defines the classical paradigm of nucleic-acid interactions, but growing research reveals noncanonical modes beyond this framework. Here we uncover a mode of interactions by which two DNA strands associate through a ligand-induced conformational rearrangement. Upon binding a small molecule, a structure-switching primary aptamer folds into a conformation that exposes a previously inaccessible docking interface. An independently evolved secondary aptamer recognizes this interface primarily through noncanonical interactions rather than sequence complementarity, reporting the ligand-bound state of the primary aptamer. This interaction is conformation-sensitive: single base-pair changes in the primary aptamer modulate secondary aptamer recognition. Across four chemically distinct small-molecule targets—serotonin, dopamine, testosterone and deoxycorticosterone 21-glucoside—secondary aptamers were identified for three, and the serotonin system was translated into surface-based sandwich assays. These results demonstrate that a ligand-induced conformational state, rather than sequence complementarity or the chemical identity of the ligand, can serve as the epitope for nucleic-acid recognition, expanding the molecular features that DNA can be evolved to detect. Aptamers have generally been understood as nucleic acids that recognize a specific ligand. Now it has been shown that secondary aptamers can recognize the ligand-bound conformation of a primary aptamer, enabling recognition of molecules that lack multiple binding sites for sandwich assays. This mechanism can be integrated into lateral flow assays for targets, including serotonin.
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The high redundancy of many bacterial proteomes can be used to evaluate proteome quality and identify sequence errors. We have used MMseqs2 clustering with subsequent filtering to identify clusters that contain sequences from at least 50% of the clustered proteomes to build sets of core proteins that include proteins from 95% of the clustered bacteria. These clusters typically capture >80% of proteins in the bacteria. Because these clusters have highly uniform length (the median cluster has >99% of its proteins at the mode length), short (<75% of mode length) or long (>133%) proteins are likely artifacts. Most “outlier” proteins are found in fewer than 10% of clusters, and “high-outlier” clusters are over-represented in a small fraction of proteomes, which often have poor proteome BUSCO fragment scores. Short-outlier proteins are artifacts; at least 80% of short-outlier genomes contain mode-length copies of the protein, which were missed because of frameshifts, termination codons, or initiation codon choice. MMseqs2 clustering with 50% participation provides robust sets of core bacterial proteins and can be used to identify lower-quality proteomes and proteins.
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