Biomolecular condensates have so far been studied in terms of their structural, compositional, and functional properties. However, condensate enzymatic activity - a key aspect of cellular metabolism - remains unexplored due to the complexity of the system. In this study, using a combination of experimental, computational and theoretical techniques, we have discovered that the non-equilibrium activity which originates from catalytic reactions couples with the environment through various feedback mechanisms across five orders of magnitude of length scales. We observe that condensed enzymes catalyze more rapidly in the presence of crowding proteins and show that the increased enzymatic activity within these droplets stems from the emergence of lower-energy protein conformations induced by the highly crowded environment. Despite the crowding in the environment of the droplet, which might suggest an effective increase in its overall viscosity, we find that it becomes more agile, as evidenced by the observation of enhanced diffusion and macroscopic flow, due to the enzymatic activity. These findings shed light on the dynamic interplay between enzymatic activity, composition and crowding in condensates, and their roles on the mobility and accessibility of various functional units in these environments, expanding our understanding of liquid-liquid phase separation in metabolically active conditions. Enzymes operate in highly crowded cellular environments, yet the impact of crowding on their function remains poorly understood. Here, the authors demonstrate that enzyme activity increases molecular mobility in crowded protein droplets, suggesting a feedback mechanism between catalysis and the physical properties of the surrounding environment.

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting. The created IDP/IDR conformational ensembles show good agreement with both NMR and SAXS solution experimental data, and options for biasing with experimental restraints are available if desired. IDPForge with these diverse capabilities facilitates integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use. Intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse structural ensembles. We present IDPForge that exploits a transformer protein language diffusion model to create all-atom IDP and IDR ensembles that maintain folded domains with options to conform to experimental data.

Enhancers are cis-regulatory elements that control gene expression during development, cell differentiation, and disease. Genome-wide studies have mapped large numbers of candidate regulatory elements, but experimentally supported enhancer information remains dispersed across primary studies and experimental systems. Here, we present ENdb 2.0 (http://www.licpathway.net/ENdb/index.php), an updated, manually curated database of experimentally supported enhancers. Compared with ENdb 1.0, the major updates are (i) literature coverage through 2026 and five species: Homo sapiens, Mus musculus, Drosophila melanogaster, Danio rerio, and Rattus norvegicus; (ii) 4831 enhancer evidence records, including 2793 enhancer–TF–target-gene records involving 496 TFs and 1070 target genes; (iii) expanded curation of genomic coordinates, experimental evidence, enhancer type, regulatory state, biological condition, tissues, cell sources, cell types, and diseases, covering 169 tissues, 1255 cell sources, 415 cell types, and 226 diseases; and (iv) biological search, genome browsing, pathway enrichment analysis, agent-assisted query, and structured data download. ENdb 2.0 links experimentally supported enhancer regions with regulatory factors, target genes, biological conditions, and supporting experiments across physiological and disease states.

Homologous genes, especially those sharing close genomic locations and forming gene clusters, often have compensatory roles, therefore pose remarkable challenges for deciphering the functions and relationships of these genes. A comprehensive database of local homologous gene clusters with multimodal information would greatly facilitate such research. Here, we present LoGIC (https://omicslab.genetics.ac.cn/LoGIC/), a Local Gene-cluster Integrative Catalog that includes 20 306 local gene clusters identified from 30 representative eukaryotic species. In addition to the basic genomic features of each gene cluster, LoGIC also allows users to examine the sequence similarity of gene members within a cluster, the functional preference of clustered genes within a species, and the cross-species conservation of gene clusters. Information of tissue-wide expression profile and relationship with chromatin Hi-C structure is also available for human gene clusters. Together, LoGIC can facilitate studies of tandem gene duplication, paralog functional divergence, lineage-specific gene-family expansion, and evolutionary conservation or remodelling of homologous gene clusters across-species.

Antibiotic resistance is a global health threat requiring antimicrobials with alternative modes of action. Growing evidence indicates that bacteria organize key intracellular processes through biomolecular condensates and other forms of spatial organization, yet whether such spatial organization can be exploited as an antimicrobial target remains unclear. Here, we identify intrinsically disordered antimicrobial peptides, generated from the Drosophila sleep-inducing protein Nemuri, that inhibit bacterial growth by targeting ribosomal organization. Mechanistically, these peptides enter bacterial cells without overt membrane disruption and accumulate in ribosome-rich regions, where they bind broadly to RNA and ribosomal surfaces through multivalent interactions. This engagement remodels ribosome-rich regions into condensates with reduced ribosomal mobility, thereby suppressing global protein synthesis and distinguishing these peptides from canonical site-specific ribosome-targeting antibiotics. Furthermore, Nemuri-derived peptides exhibit broad-spectrum antimicrobial activity, including activity against drug-resistant pathogens, with low cytotoxicity toward Drosophila and mammalian cells. Together, our findings identify the organization and material dynamics of bacterial ribosomes as a biophysical vulnerability that can be exploited by antimicrobial peptides, revealing how a naturally evolved metazoan host-defense system turns intracellular organization itself into an antibacterial target. Nishinami et al. show that peptides from the sleep-inducing protein Nemuri, condense bacterial ribosomes and inhibit protein synthesis, revealing a biophysical antimicrobial strategy targeting intracellular organization.

Anthropogenic nitrogen (N) enrichment profoundly influences the growth of N-fixing plants, but the drivers underlying their highly variable biomass responses remain poorly quantified. Through a meta-analysis of 2,110 paired observations across 76 N-fixing plant species from 121 experiments, we demonstrate that N enrichment increases N-fixing plant biomass by 14.2% on average, with responses ranging from −99.4% to 1301.3%. Intraspecific variability (26.9%; i.e., variation among individuals within species) and plant phylogeny (21.1%) represent substantial yet frequently overlooked sources of uncertainty in biomass responses of N-fixing plants to N enrichment, even though environmental conditions (climatic and edaphic factors) and experimental design (N enrichment rate and duration) account for a significant portion of biomass response variation. Intraspecific variability, phylogeny, and experimental design collectively explain a larger proportion of variance in root than in shoot biomass responses, whereas environmental conditions show the opposite trend. These results provide quantitative evidence that phylogeny and intraspecific variability are indispensable for accurately predicting the response of N-fixing plants to global change. Nitrogen enrichment alters the growth of nitrogen-fixing plants, but responses vary widely. This meta-analysis shows that phylogeny and intraspecific variation are major contributors to that variability.

Protospacer adjacent motif (PAM) requirements constrain the targeting scope of CRISPR-Cas9 systems and limit genome editing applications that require precise nuclease positioning. Although broad-PAM and near-PAMless Cas9 variants have expanded the editable genome, robust targeting across certain PAM families remains difficult, particularly at NNAN PAMs. Here, we apply Sequence Display to generate multiplexed, large-scale sequence–activity datasets for Staphylococcus lugdunensis Cas9 (SlugCas9) across the NNAN PAM family, including NNAA, NNAT, NNAC and NNAG. These datasets quantitatively resolve how individual and combinatorial mutations reshape nuclease activity across distinct PAM contexts. By integrating Sequence Display-derived datasets with protein language models, we construct fine-grained activity landscapes and identify SlugCas9 variants with enhanced and broadened activity across NNAN PAMs. The resulting variants reprogram PAM recognition beyond the native G-compatible landscape of SlugCas9 and enable improved base editing at selected endogenous NNAN-constrained sites in cultured mammalian cells, including disease-relevant sites in APOC3 and PCSK9. Together, our results establish a strategy for reprogramming PAM preference in compact Cas9 nucleases and highlight Sequence Display as a general platform for evolving genome editors toward specific and difficult-to-access PAM spaces. Compact CRISPR-Cas9 enzymes are easier to deliver but recognize few targets. Here, authors use Sequence Display to generate large-scale SlugCas9 sequence–activity datasets and apply protein language models to engineer variants that recognize difficult NNAN PAMs and edit selected human genomic sites.

Despite its prominent industrial use for L-proline production, microbial fermentation by Corynebacterium glutamicum achieves a suboptimal yield of glucose conversion, far below the theoretical maximum of 0.63 g/g. This study aimed to develop an efficient engineered E. coli chassis to produce L-proline from glucose by addressing the rate-limiting enzymatic step and high glucose conversion. The γ-glutamyl kinase encoding gene (proB) was screened by directed evolution, and the catalytic efficiency of the ScProB** variant toward L-glutamate increased 2.75-fold. After channeling 2-oxoglutarate flux and supplying sufficient NADPH, L-proline production reached 86.27 ± 2.58 g/L, with a 0.33 ± 0.01 g/g glucose yield in 10-L fed-batch fermentation. Further increasing the flux through central carbon metabolism by implementing a non-PTS glucose uptake system, amplifying the anaplerotic reaction, and redirecting the flux away from the glyoxylate cycle, the resulting strain BP23 achieved 119.89 ± 5.66 g/L L-proline, with a productivity of 1.93 ± 0.03 g/L/h. The glucose conversion yield improved to 0.49 ± 0.01 g/g after 62 h of fed-batch fermentation, indicating that the E. coli strain is a promising workhorse for producing L-proline with high glucose conversion.