The rapid emergence of multidrug-resistant bacteria has created an urgent need for improved antimicrobial discovery and screening platforms. Here, we present ARCADIAMP, a generative and virtual screening platform that couples an iterative-learning discrete denoising diffusion probabilistic model with a two-stage Evolutionary Scale Modeling 2 (ESM2)-based antibacterial activity classifier to generate, classify, and prioritize potent AMPs with high activity, low toxicity, and favorable serum stability. Eight of the ten experimentally screened peptide candidates showed antimicrobial activity (MIC ≤ 32 μg/mL), while one generated candidate, Arcinin, demonstrated strong activity against ESKAPE pathogens (MIC 8–32 μg/mL), low hemolytic activity (LC50 > 512 μg/mL for human red blood cells), and strong serum-retained activity (MIC 32 μg/mL in 50% bovine serum for four ESKAPE species). Electron microscopy, membrane depolarization assays, time-kill kinetics, and molecular dynamics simulations showed that Arcinin acts through sub-microsecond insertion and penetration consistent with the behavior of other well-known AMPs. In a bacteria-infected wound murine model, Arcinin achieved a 4-log reduction in bacterial burden, which facilitated subsequent re-epithelialization and wound recovery. By framing antimicrobial discovery as an AI-assisted iterative optimization problem, ARCADIAMP links activity, toxicity, and efficacy and provides a scalable template for discovering therapeutically promising biologics. In this study, Markakis et al. use a generative AI model to design antimicrobial peptides that are both potent and safe. Their lead candidate, Arcinin, kills drug-resistant bacteria, spares human cells, and clears infection in a mouse wound model.

Transfer RNAs (tRNAs) are essential for protein synthesis and are extensively modified to ensure their structure and function. Direct RNA sequencing with Oxford Nanopore Technologies enables positional modification analysis but is challenged by tRNAs’ short length, redundancy, and dense modifications. We present QutRNA2, a scalable workflow that includes GPU-accelerated local alignment, statistical filtering, pairwise error profile comparison, and customizable visualization. Achieving up to 25-fold speed gains over CPU methods, QutRNA2 identifies enzyme-dependent modifications in nuclear- and mitochondrial-encoded tRNAs, demonstrated in high-volume human and mouse samples. This open-source solution provides a comprehensive, multiplexing-compatible framework for tRNA analysis, addressing a key gap in current tools. QutRNA2 is released under Apache-2.0 license and is available at https://github.com/dieterich-lab/QutRNA2.

One-pot CRISPR diagnostics face a fundamental incompatibility: isothermal nucleic acid amplification enables rapid target accumulation, whereas CRISPR activation irreversibly consumes those substrates, destabilizing reaction kinetics. Here we show that reaction order can be programmed into DNA primers through thermodynamic design. Differences in primer-binding strength create two sequential amplification stages, delaying CRISPR activation until enough amplicons have accumulated without physical separation or external control. The design also introduces the protospacer adjacent motif (PAM), a short sequence required for CRISPR recognition, through the primer rather than relying on its presence in the native target, expanding target accessibility while retaining single-nucleotide discrimination. An ordinary differential equation model captures the threshold behavior and establishes a predictable framework for primer design. Building on this principle, we develop Thermodynamically Encoded Molecular Programming for One-pot diagnostics (TEMPO), which achieves attomolar sensitivity within 30 min and enables sequencing-concordant SNP genotyping and pathogen detection in a single-step microfluidic format. One-pot CRISPR diagnostics are limited by kinetic conflict between isothermal amplification and CRISPR detection. By thermodynamically programming reaction order into DNA primers, the authors create staged amplification enabling rapid, sensitive, single-step nucleic acid testing.

We present FLOWR.ROOT, an SE(3)-equivariant flow-matching foundation model that unifies pocket-aware 3D ligand generation with multi-endpoint binding affinity prediction (pIC50, pKi, pKd, pEC50) and pLDDT-based confidence estimation in a single backbone. One trained model supports de novo pocket-conditional generation, interaction- and pharmacophore-conditional sampling, scaffold hopping and elaboration, and fragment growing or replacement, enabled by a mixed isotropic–anisotropic prior placement strategy. Training proceeds in three stages: large-scale pre-training on billions of ligand conformations and millions of mixed-fidelity protein–ligand complexes, refinement on curated co-crystal data, and project-specific adaptation via parameter-efficient LoRA finetuning. Joint structure–affinity modelling enables inference-time importance-sampling guidance for single- and multi-objective design without external scoring functions. Case studies on kinase selectivity (CK2α/CLK3) and scaffold elaboration on TYK2, ERα, and BACE1 illustrate utility from hit identification through lead optimization. Structure-based generative modeling is rapidly reshaping drug discovery by enabling pocket-aware ligand design alongside predictive evaluation of binding properties. This manuscript introduces FLOWR.root, an SE(3)-equivariant flow-matching framework that jointly generates high-quality 3D ligands and predicts multi-endpoint binding affinities, demonstrating state-of-the-art performance, efficient domain adaptation, and practical impact across de novo design, scaffold elaboration, and lead optimization workflows.

Autonomous self-reproduction is a major goal of bottom-up synthetic biology aimed at building artificial cells. This requires that the genome be replicated by its self-encoded replication machinery. While the reconstituted E. coli chromosomal replication system, termed the Replication-Cycle Reaction (RCR) system, offers a promising platform for genome-scale replication, its generation from genetic information has not yet been achieved. Here we show that a 53 kb circular DNA, termed RCR module-genome, encoding all 26 RCR proteins, can self-replicate in a one-pot reaction when expressed using the protein synthesis using recombinant elements (PURE) system. We first built a prototype of the RCR module-genome and then optimized reaction conditions and solved expression bottlenecks to achieve robust self-replication. This artificial module-genome supports more than 28 doublings of recursive self-replication. This system, termed PRIMES (PURE-driven RCR for In-vitro Module-gEnome Self-replication), represents a milestone toward constructing self-reproducing artificial cells.

Bacteria residing in biofilms are embedded in an extracellular matrix. Whereas biofilm formation is well studied, less is known about biofilm dispersion, although enzymatic extracellular matrix degradation is suspected to play a key role. Here we show that Bacillus subtilis biofilms can alternatively eject a specific cell type, locally and anisotropically, using mechanical forces arising from a self-generated hydrogel. Single-cell resolution imaging combined with mathematical modelling, and chemical and genetic perturbations, show that the production of the extracellular poly-γ-glutamic acid (γ-PGA) polymer is necessary to drive this cell ejection. Specifically, osmotic pressure from the γ-PGA hydrogel propels interior cells through the outer layers to break free from the biofilm. We demonstrate control over this process through γ-PGA modulation such that biofilm dispersion can be either inhibited or promoted. Forceful ejection driven by γ-PGA has so far only been described in marine organisms such as jellyfish. Our discovery of biofilm cell ejection via γ-PGA thus reveals not only a previously uncharacterized biofilm dispersion mechanism but also an unexpected mechanistic parallel to evolutionarily distant Cnidaria. Bacillus subtilis biofilms locally disperse by ejecting motile cells through osmotic pressure generated by their poly-γ-glutamic acid hydrogel, a mechanism resembling that through which Cnidaria eject nematocysts.

The functional diversification of O2-tolerant [NiFe]-hydrogenases using orthogonal translation systems (OTSs) offers a promising strategy for developing advanced biocatalysts and biohybrid energy platforms. However, plasmid-based OTSs frequently impose metabolic burdens and suffer from plasmid instability during fermentation, particularly when co-produced with complex metalloenzymes. To overcome these bioprocess limitations, we employed CRISPR/Cas9-mediated genome editing to integrate a psychrophilic pyrrolysyl-tRNA synthetase/tRNA pair into the E. coli BL21 genome. The resulting strain provided a plasmid-free orthogonal translation background that supported amber suppression-mediated expression of the regulatory [NiFe]-hydrogenase (RH) of Cupriavidus necator. Using this genomically integrated OTS, we achieved the production of a full-length, catalytically active RH variant. Our results demonstrate that chromosomal OTS is compatible with the efficient production and maturation of complex metalloenzymes. The present work lays the groundwork for the bio-orthogonal engineering of hydrogenases and related hybrid biocatalysts.

Rapid and reliable quantification of bacterial dynamics at the cellular level is critical for pathogen sensing, live-dead bacterial assays, and monitoring of bacteria fitness and viability. Here, we demonstrate bacterial fitness quantification by capturing individual cells on topological defects of micro-scale liquid crystal emulsion droplets. The emulsion droplets are composed of phase-separated nematic liquid crystal and fluorocarbon components and exhibit an asymmetric mass distribution. A topological singularity in the director field of the liquid crystal phase localizes tailormade surfactants that tether a single bacterium per droplet. Active motion of the bacterium induces a tilt and azimuthal rotation of the droplet trap, which is counteracted by gravity acting on the droplet center of mass. By comparing the observed dynamics of a tethered bacterium’s stochastic movement to a computational model of bacterial motion on spherical surfaces that is based on the classical Ornstein-Uhlenbeck process, we quantify the fitness of bacteria subjected to starvation over several days. This pathogen fitness sensing concept, which relies on the scalable chemical design of single bacterial cell traps, a robust optical readout, and a theoretical understanding of bacterial dynamics on spherical surfaces, offers opportunities for rapid pathogen activity assessment, micro-biological sensing, and biologically powered micro-actuator systems. The ability to rapidly and accurately assess bacterial fitness is crucial for effective pathogen detection and monitoring. Here, authors develop a method using microscale liquid crystal emulsion droplets to capture individual bacteria, enabling real time quantification of their fitness through the analysis of droplet dynamics influenced by bacterial motion.

Lead (Pb) and cadmium (Cd) remain among the most persistent and hazardous heavy-metal contaminants in industrial effluents, posing severe risks to ecosystems and human health due to their non-biodegradable nature and high toxicity. In response to the limitations of conventional chemical remediation technologies, this study evaluates the potential of E. coli K-12 MG1655 to function as a microbially driven system for the detoxification, sequestration and recovery of Pb and Cd. Emphasis is placed on oxalic acid production as a mechanistic basis for metal tolerance. HPLC confirmed that E. coli K-12 MG1655 synthesizes oxalic acid under metal stress, with Pb exposure eliciting the better oxalate output, providing evidence consistent with metal–oxalate-associated detoxification during metal stress. Bioaccumulation studies using inductively coupled plasma–optical emission spectrometry revealed exceptional metal removal efficiencies, reaching 99.94% for Pb and 97.77% for Cd at 1,000 p.p.m., while Pb+Cd mixed-metal systems maintained high overall uptake (98.19%). These results demonstrate that E. coli can sequester metals across a wide concentration range with minimal inhibition from competitive ion interactions. Metal recovery from loaded biomass was evaluated through acid desorption and ohmic heating. Nitric acid (0.1 M HNO3) achieved the highest recovery efficiencies (Pb, 98.5%; Cd, 91.5%), whereas ohmic heating yielded moderate (Pb, 45.38%; Cd, 45.83%) but environmentally favourable recovery without chemical additives. The integrated findings illustrate a complete microbial bioremediation–recovery cycle encompassing detoxification via oxalic acid, high-efficiency metal sequestration and effective downstream recovery. This integrative study establishes E. coli K-12 MG1655 as a promising candidate for closed-loop bioremediation systems linking detoxification, sequestration and recovery of heavy metals.

Mutagenesis is a fundamental, yet poorly understood, source of genetic variation that underpins microbial evolution and adaptation. When the Bacillus subtilis replicative DNA polymerase (DNAP) PolC encounters DNA lesions induced by endogenous or exogenous insults, it stalls and disassembles. If error-free DNA damage tolerance (DDT) sub-pathways fail to circumvent the lesion, bipartite translesion synthesis (TLS) DNAPs (PolY1 or PolY2 together with PolA) may bypass the damage to resume DNA synthesis. Mismatch repair subsequently removes misincorporated nucleotides. Here, we investigate which proteins loaded at stalled replication forks influence mutation dynamics mediated by TLS DNAPs. We demonstrate that ΔrecA, ΔpolA, or ΔpolY1 ΔpolY2 mutations strongly reduce cell survival following DNA damage and mutagenesis. The accessory proteins DisA, RarA, RecD2, DinG, and Mfd, which physically interact with PolA and/or RecA, differentially affect cell survival after DNA damage in the absence of TLS DNAPs and differentially modulate mutagenesis by regulating the activity of distinct TLS DNAPs, highlighting their roles in error-prone DDT. We also reveal that SOS-independent mutagenesis operates in the ΔpolA ΔrecA background. Elucidating the regulatory network underlying TLS provides a framework to understand bacterial speciation and may uncover new avenues to limit antibiotic resistance emergence.