Biosensors based on transcription factors (TFs) have shown extensive applications in synthetic biology. Due to the complex multi-domain structure of effector-TF-DNA, computational design of TFs remains a challenge. Here, we present the successful structure-guided computational design of the access tunnel, ligand binding, allosteric transition process for an allulose-responsive PsiR. It enables a 20-fold increase in sensitivity, reducing the EC50 of PsiR-allulose biosensors (PABs) from 16 mM to 0.8 mM, and delivers a PAB box possessing the detection range from 10 μM to 100 mM. We further validate its broader applicability in enhancing sensitivity of LacI-IPTG biosensor. Based on the developed PABs, we present the inducer-free allulose-mediated auto-inducible protein expression system, and demonstrate an allulose-triggered CRISPR interference circuit for dynamic metabolic regulation. It facilitates a 68% increase in allulose titer and achieves a high yield of 0.43 g/g glucose. This work provides the versatile TF toolbox for developing allulose-triggered regulation circuits in biotechnology application. Transcription-factor biosensors enable programmable metabolism. Here, the authors integrate structure and computation for mechanism-guided iterative redesign of the allulose biosensor PsiR, increasing sensitivity and dynamic range, enabling allulose-triggered expression system and CRISPRi circuit.

Microbes precisely control their composition and geometry across diverse growth conditions, yet the mechanisms coordinating these processes remain unclear. Here, we integrate quantitative proteomics, microscopy, and biochemical measurements to reveal a biophysical principle linking these properties in Escherichia coli: cytoplasmic and membrane protein densities maintain a tightly conserved ratio across growth conditions, while the periplasmic density varies. Building on this observation, we develop a mathematical model demonstrating that maintaining this density ratio constrains the surface-to-volume ratio as a nonlinear function of proteome composition, specifically the ribosomal proteome fraction and partitioning between cellular compartments. The model holds under guanosine tetraphosphate perturbations that alter ribosome levels, further demonstrating that cellular geometry is not strictly determined by growth rate. These findings provide a biophysical framework for geometry control, underscoring density maintenance as a key physiological constraint that shapes cellular phenotypes. Chure et al. analyse experimental data to show that E. coli bacteria maintain stable protein density ratios between cytoplasm and membranes. In addition, they develop a biophysical model that predicts surface-to-volume ratio from ribosomal content and protein partitioning across cell compartments.

Vaccines are the most effective tool in preventing and managing infectious diseases. One of the critical challenges in vaccine development is the selection of suitable target antigens from the thousands of proteins produced by pathogens. Artificial intelligence is anticipated to play a significant role in addressing this challenge. In this study, we develop a framework termed PLGDL for protective antigen prediction that employs Protein Language and Geometric Deep Learning models. This framework leverages both primary sequence features and three-dimensional structural features of protein antigens, thereby reducing the biases associated with manually curated features. Our integrated model exhibits robustness across both constructed and public datasets and is applicable to viruses, bacteria, and eukaryotic pathogens. Notably, when applied to the ongoing Mpox outbreak, our model not only quickly identifies multiple known antigens but also discovers a protective antigen: G10R. Here, our study provides a high-performance screening tool for protective vaccine antigen prediction by synergistically utilizing the capabilities of protein language and geometric deep learning models, providing substantive insights and methodological advancements for rapid vaccine development. Vaccines are the most effective tool in managing infectious disease and characterizing features of protective epitopes could help in prediction methods. Here the authors use protein language and geometric deep learning frameworks to investigate primary sequence features and structural features to identify and predict potential antigens, showing prediction of a protective mpox epitope using this method.

Heterogeneity within clonal cell populations remains a critical bottleneck within bioprocess engineering, notably by undermining bioproduction yields. Efforts to mitigate its impact have, however, been hampered by technological difficulties quantifying metabolism at the single-cell level. Here, we propose a framework based on single-cell biosensor analysis that enables robust characterization of cell’s metabolic states, leveraging it to detect and isolate isogeneic heterogeneity in response to environmental perturbations and within microbial cell factories. We identify acute and gradual glucose depletion to induce differentiation of metabolically distinct subpopulations and reveal these subpopulations to exhibit differential production capabilities, with lower intracellular pH subpopulations exhibiting enhanced product accumulation within violacein-producing strains but reduced yields within lycopene-producing strains. Lastly, we highlight galactose cultivation as a method to modulate subpopulation dynamics towards higher-producing lycopene phenotypes. Altogether, our research provides insights into subpopulation differentiation and establishes promising avenues for the engineering of more robust and higher-producing strains. Heterogeneity within clonal cell populations affects bioprocess engineering. Here, the authors report a biosensor-based toolkit to investigate phenotypic heterogeneity in engineered yeast, reveal pH-based subpopulations and metabolite production states, and modulate/shift subpopulation dynamics to increase lycopene production.

Richard Lenski traces the legacy of Escherichia coli and how science is evolving to use this model organism in new ways.

Point-of-use diagnostics based on allosteric transcription factors (aTFs) are promising tools for environmental monitoring and human health. However, biosensors relying on natural aTFs rarely exhibit the sensitivity and selectivity needed for real-world applications, and traditional directed evolution struggles to optimize multiple biosensor properties at once. To overcome these challenges, we develop a multi-objective, machine learning (ML)-guided cell-free gene expression workflow for engineering aTF-based biosensors. Our approach rapidly generates high-quality sequence-to-function data, which we transform into an augmented paired dataset to train an ML model using directional labels that capture how aTF mutations alter performance. We apply our workflow to engineer the aTF PbrR as a point-of-use diagnostic for lead contamination in water. We tune the sensitivity of PbrR to sense at the U.S. Environmental Protection Agency (EPA) action level for lead and modify the selectivity away from zinc, a common metal found in water supplies. Finally, we show that the engineered PbrR functions in freeze-dried cell-free reactions, enabling a diagnostic capable of detecting lead in drinking water down to ~5.7 ppb. Our ML-driven, multi-objective framework powered by directional tokens can generalize to other biosensors and proteins, accelerating the development of synthetic biology tools for biotechnology applications. Allosteric transcription factors (aTFs) are promising tools for environmental and human health monitoring. Here the authors develop a multi-objective, machine learning-guided method to engineer an aTF-based portable diagnostic for environment sensing of lead in drinking water at the legal limit.

The production of recombinant proteins in E. coli is often hampered by the formation of inclusion bodies. While fusion tags can enhance solubility, existing systems are hampered by a lack of standardization, with tags scattered across disparate plasmid backbones and inconsistent cloning sites, complicating parallel screening. To address this, we constructed a standardized series of expression vectors, termed pNX, by incorporating nine small fusion tags (SUMO, LD, ACP, BCCP, GB1, Fh8, SmbP, TolA, and TrxA) into a uniform pET-28b backbone. Each pNX vector features an identical configuration: a T7 promoter, an N-terminal fusion tag, a synthetic linker, a TEV protease cleavage site, and a multiple cloning site (MCS) flanked by dual 6×His tags. We evaluated this system using four model proteins (EcFabG, eGFP, XccXanA2, and XccXanL). Our results showed that specific tags significantly improved both the expression level and solubility of the target proteins without compromising their biological activity. Notably, the lipoyl domain (LD) was identified, to our knowledge for the first time, as an effective solubility enhancer. The standardized MCS enabled rapid, parallel cloning, facilitating the efficient screening of optimal fusion partners. The pNX vector series provides a versatile and efficient platform for enhancing the soluble expression of challenging recombinant proteins in E. coli, streamlining the empirical identification of ideal fusion tags.

The discovery of the antiviral-responsive RNA editing system is a significant advance in eukaryotic biology. It reveals new dimensions of RNA editing diversity and fungal antiviral strategies, and provides compelling evidence for the adaptive significance of RNA editing in host-virus coevolution. This discovery establishes a novel model for investigating both the molecular mechanisms of RNA editing and the evolutionary dynamics of altruistic defense.

Microplastics have emerged as major environmental hazards that require efficient, cost-effective, and sustainable remediation technologies. This study introduces an integrative platform for the remediation and upcycling of microplastics by algae, while synergizing with plastic upcycling, wastewater treatment, and algal production. The strategy employs a mechanism that enhances hydrophobic interactions between the cell surface and microplastics, enabling rapid aggregation and removal. The platform achieves a superior microplastic removal efficiency of 91.4% within 1 hour, with a capacity of 0.1-gram microplastic per gram of biomass. Furthermore, the study demonstrates an upcycling strategy that converts microplastics-enriched cyanobacteria into plastic composites with unique performance. This work also integrates microplastic removal with cyanobacterial bioproduction and wastewater treatment, offering an approach that synergizes remediation with these value-added processes. Ultimately, this platform provides a viable and sustainable pathway to address microplastic pollution by creating value through plastic upcycling, wastewater nutrient removal, and CO2-based bioproduction. Microplastics (MPs) represent an environmental hazard which must be resolved by efficient, cheap, and sustainable remediation technology. Here the authors use an engineered algae to capture MPs and treat wastewater, the captured algae-plastic mix is upcycled into a tougher bioplastic composite.

Microplastic biofilms, known as the “plastisphere”, harbor diverse microbial communities and serve as reservoirs for antibiotic resistance genes (ARGs). This review discussed the mechanisms driving bacterial community alteration on microplastics and delineated the pathways through which ARGs transfer within microplastic biofilms. We expected to provide a comprehensive understanding of the ecological and human health impacts associated with microplastic biofilms and ARGs, thereby informing strategies to mitigate plastic pollution and its risks.

Cell-to-cell variability often limits the efficiency of microbial bioproduction, yet how individual cells fluctuate over time and how these fluctuations shape population-level output remain unclear. To address this issue, we tracked a heterologous betaxanthin pathway in Escherichia coli using microfluidics-assisted time-lapse microscopy, allowing simultaneous measurement of fluctuations in betaxanthin, its biosynthetic enzyme DOD and growth across generations. Here we show that over 50% of high betaxanthin producers become medium or low producers after two divisions. Betaxanthin variation primarily originates from DOD noise, with a smaller contribution from growth rate fluctuations. We further develop a stochastic model to explore various control circuits and find that pathway enzyme or metabolite-based growth selection strategies are most effective in enhancing production. We experimentally validate the model by coupling enzyme expression to nutrient availability, which enriches high producers and boosts titer by 4.4-fold. Our results highlight key sources of metabolic heterogeneity and provide a framework for designing robust microbial processes. Cell-to-cell variability limits efficient microbial production. Here, the authors track single cells to reveal enzyme noise as the main source of bioproduction variation, and by coupling growth to pathway performance, they selectively enrich high producers and substantially boost overall titres.

Light-sensitive proteins allow organisms to perceive and respond to their environment, and have diversified over billions of years. Among these, Light-Oxygen-Voltage (LOV) domains are widespread photosensors that control diverse physiological processes and are increasingly used in optogenetics. Yet, the evolutionary constraints that shaped their protein dynamics and thereby their functional diversity remain poorly resolved. Here we systematically characterize the dynamics of 21 natural LOV core domains, significantly extending the spectroscopically resolved catalog through the addition of 18 previously unstudied variants. Using time-resolved spectroscopy, we uncover an exceptional kinetic diversity spanning from picoseconds to days and identify distinct functional clusters within the LOV family. These clusters reflect evolutionary branching, including a divergence of  1.0 billion years between investigated LOV variants from plants and 0.4 billion years of separation within one of these functional clusters. Individual variants with extreme photocycles emerge as promising anchor points for optogenetic applications, ranging from highly efficient adduct formation to ultrafast recovery. Beyond natural diversity, we introduce a LOV domain generated by artificial intelligence-guided protein design. Despite being sequentially remote from its maternal template, this variant retains core photocycle function while exhibiting unique biophysical properties, thereby occupying a new region on the biophysical landscape. Our work emphasizes how billions of years of evolution defined LOV protein dynamics, and how protein design can expand this repertoire, engineering next-generation optogenetic tools.

Although virus ecogenomics has expanded access to and understanding of the virosphere, existing classification tools lack taxonomic resolution and are unable to scale to modern discovery-based datasets or classify previously unknown sequence space. Here we develop vConTACT3—a machine learning-based tool that improves scalability and accuracy of virus taxonomy. By optimizing gene-sharing thresholds and leveraging adaptive, realm-specific cut-offs, vConTACT3 expands classification to both eukaryote and prokaryote viruses for four of the six officially recognized realms, and establishes accurate hierarchical taxonomy from genus to order. Specifically, vConTACT3 achieves >95% agreement with official taxonomy for 35,545 and 13,524 public prokaryotic and eukaryotic virus genomes, respectively, to surpass vConTACT2 across most realms, while still uniquely classifying previously uncharacterized taxa, and doing so even faster. vConTACT3 application provides taxonomy assignments for tens of thousands of unclassified taxa rapidly, automatically and systematically; evaluates virus sequence space to reveal support for fewer taxonomic ranks than currently available and identifies taxonomically challenging areas across the virosphere. vConTACT3 enables multirank, large-scale classification of eukaryotic and prokaryotic viruses.

Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models–a protein language model and an inverse folding model–and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stability predictions and generalizes to unseen proteins and mutations. Beyond stability prediction, SPURS enables broad applications in protein informatics, including zero-shot identification of functional residues, improved low-N protein fitness prediction, and systematic dissection of stability-pathogenicity for human diseases. Together, these capabilities establish SPURS as a versatile tool for advancing protein stability prediction and protein engineering at scale. Understanding how mutations alter protein stability is essential for biology and disease research. Here, the authors develop SPURS, a model that rewires pre-trained protein generative models to accurately predict stability changes.