Many antibiotics are ineffective against the Gram-negative pathogen Pseudomonas aeruginosa because of intrinsic defence mechanisms, such as the impermeable bacterial outer membrane. Here, we show that protein antibiotics called L-type pyocins kill P. aeruginosa by inhibiting the β-barrel assembly machinery (BAM) complex at the cell surface, halting outer-membrane protein assembly. Using single-particle cryo-electron microscopy, we show that L-type pyocins bind a surface-exposed region of BamA and deploy a C-terminal peptide that competitively inhibits the BAM complex, demonstrating that cell entry is not required for antibiotic activity. We combine genetics, multi-omics and cryo-electron tomography to show that BAM complex inhibition by L-type pyocins or the cyclic-peptide antibiotic, darobactin, triggers a multifaceted transcriptomic, proteomic, and morphological response. BAM inhibition ultimately leads to a catastrophic loss of membrane integrity and cell death. These results validate BAM as a target for antibiotics that do not enter the cell and define an engineerable system for their development. L-type pyocins are a class of bacteriocin-like proteins with antibacterial activity. Here, the authors show that these proteins kill the pathogen Pseudomonas aeruginosa by inhibiting the BAM complex at the bacterial cell surface, halting outer-membrane protein assembly.

Schlafen nucleases restrict viral infection in mammals by cleaving self RNAs, however, their function and mechanism in prokaryotic immunity is unknown. Here, we uncover CRISPR-associated Schlafen (Cash) proteins containing a Schlafen domain fused to Csx15, an uncharacterized member of Rossmann-like nucleotide-binding sensors. Cash is activated by cyclic tetra-adenylate (cA₄) produced during type III CRISPR interference and induces cell toxicity by cleaving tRNAs, primarily in the T-loop. Cryo-electron microscopy structures of Chloroflexi bacterium Cash reveal an inactive dodecamer, the formation of a filament upon cA₄ binding to align catalytic interfaces, and the molecular basis of substrate recognition and cleavage in a tRNA–bound complex. We identify numerous families of prokaryotic Schlafen proteins associated with diverse antiviral defense systems and characterized by unique sensor domains. This work highlights tRNA depletion by Schlafen nucleases as an evolutionary recurring antiviral strategy and reveals mechanistic differences between Cash and human Schlafen members. Schlafen nucleases restrict viral infection in mammals by cleaving self RNAs. Here, the authors identify bacterial Schlafen nucleases as components of anti-phage type III CRISPR systems that restrict viral infection through tRNA depletion.

To advance the computational simulation of cellular life, we propose a virtual yeast, an artificial intelligence (AI)-driven agent that models eukaryotic cellular behaviors by integrating multimodal biological data, mechanistic reasoning and active experimentation using Saccharomyces cerevisiae as a genetically tractable and data-rich model system. Cellular complexity is decomposed into eight function-centred modules, spanning genetic, metabolic and structural systems, each realized as a domain-specific AI tool coordinated through a large language model-based orchestration layer. Built on three data pillars, namely, mechanistic knowledge, subcellular architecture and dynamic states, the system integrates representation learning and generative modelling within a closed-loop learning pipeline that autonomously designs and executes experiments. The virtual yeast serves as both a conceptual and an operational platform to optimize biosynthetic pathways, support the generation and prioritization of hypotheses across diverse cellular processes, and accelerate target discovery. By coupling biological realism with autonomous AI reasoning, the virtual yeast establishes a generalizable blueprint for constructing virtual eukaryotic cells and advancing synthetic biology. A virtual yeast, an artificial intelligence-driven agent for modelling eukaryotic cellular behaviours, is described.

The recent revolution in genome sequencing and protein structure prediction has opened new frontiers in understanding, predicting and designing enzyme function. Central to these efforts is the discovery and functional annotation of novel enzymes, which is essential for elucidating the connection between genotype and phenotype and for developing biocatalysts for industrial applications. However, accurately predicting enzymatic function remains a major challenge, and the discovery of new enzymes often relies on serendipity. Here we present a metal-coordination-guided strategy that uses atomic-level mechanistic principles to mine protein structure databases for the targeted discovery of metalloenzymes. We apply this framework to the AlphaFold2 Protein Structure Database to identify new members of the FeII/α-ketoglutarate-dependent halogenase family, which selectively functionalize unactivated C(sp3)-H-bonds, a crucial transformation in the production of pharmaceuticals and other high-value compounds. These radical halogenases constitute a low-abundance class within the large and diverse cupin superfamily. Owing to low sequence conservation, they have been especially challenging to find against the complex background of related family members, such as hydroxylases, desaturases and epimerases. Our metal-coordination mining methodology reveals several previously unrecognized radical halogenase families spanning diverse phylogenetic space, at minimal computational cost. Our predictions are validated by the experimental characterization of two new radical halogenases, AspX and BtnX. Notably, BtnX shows a substrate promiscuity that is unprecedented in radical halogenases, opening the way for a broad range of biocatalytic applications. A methodology for mining protein structure databases on the basis of the distinct intrinsic structures of metal-binding active sites in enzymes enables the discovery of new families of radical halogenases.

Synthetic mRNA therapeutics offer a versatile platform for treating diverse conditions, including cancer and infectious diseases. For delivery into cells, these mRNAs are encapsulated in lipid nanoparticles and commonly incorporate modified ribonucleotides to improve stability, enhance translation and mitigate immune recognition. N1-Methylpseudouridine (m1Ψ) has become the industry standard for synthetic mRNAs owing to its effectiveness in promoting translation and reducing immunogenicity. However, recent studies have shown that m1Ψ can compromise translational fidelity, leading to errors such as premature termination and ribosomal frameshifting. Here we reveal N4-acetylcytidine (ac4C) as a functionally distinct alternative to m1Ψ. Across cultured cell lines, primary human monocyte-derived dendritic cells and mouse liver, ac4C suppressed inflammatory responses as effectively as m1Ψ while driving higher protein yields. Single-molecule imaging of translation revealed broadly similar ribosome densities per mRNA for ac4C-modified and m1Ψ-modified transcripts. However, translation elongation with m1Ψ-modified mRNA was nearly twofold slower than with ac4C, which resulted in reduced protein output and increased ribosome collisions that further limited protein production through the engagement of quality-control pathways and +1 frameshifting. These findings underscore the importance of context in designing therapeutic mRNAs and position the translation elongation rate as a key determinant of the efficacy of modified ribonucleotides. Different RNA modifications elicit different translation elongation rates for in vitro transcribed mRNAs that result in disparate translation outputs and fidelity, as shown here for N4-acetylcytidine versus the industry standard for synthetic mRNAs, N1-methylpseudouridine.