Whole-cell bioelectronic sensors are particularly well-suited for environmental and health monitoring as they can be integrated into compact electronic devices for field deployment over extended periods. However, current...Whole-cell bioelectronic sensors are particularly well-suited for environmental and health monitoring as they can be integrated into compact electronic devices for field deployment over extended periods. However, current engineering strategies lack modularity, are limited to a few microbial chassis and depend on specialized instruments for signal detection. We present the electroactive co-culture sensing system (eCOSENS), a plug-and-play system for whole-cell bioelectronic sensor development. Here a 'sender' bacterium produces electron mediators in response to analytes and a 'receiver' bacterium utilizes the electron mediators to generate electrical signals via extracellular electron transfer. Modularly swapping the sender bacterium and its associated genetic sensing elements achieved bioelectronic sensing of metals, small molecules and peptides in distinct environments, such as urban waterways, milk, saliva and microbial communities. We designed a centimeter-sized bioelectronic device for portable signal readout using a household digital multimeter. The eCOSENS system simplifies the whole-cell bioelectronic sensor design and expands the potential of bioelectronic sensor applications.
Rochette P, Lopez-Rodriguez E, Wen DJ
… +19 more, Régnier L, Fan C, Maikova A, Rostain W, Wang L, Nooraddin I, Maire A, Vittot P, Barrabes N, Cerdas-Mejías KM, Bouvier A, Chrysostomou T, Subrini O, Wolff N, Monasson R, Cocco S, Shipman SL, Laurenceau R, Bikard D
Diversity-generating retroelements (DGRs) are natural systems that accelerate the evolution of diverse bacterial functions through targeted hypermutation. We establish a method using DGRs coupled to recombineering (DGRec...Diversity-generating retroelements (DGRs) are natural systems that accelerate the evolution of diverse bacterial functions through targeted hypermutation. We establish a method using DGRs coupled to recombineering (DGRec), which enables the diversification of any sequence of interest in Escherichia coli. Detailed characterization of reverse transcriptase sequence biases demonstrates how it maximizes the exploration of the sequence space while avoiding nonsense mutations. By leveraging the high error rate of the DGR reverse transcriptase at adenines, DGRec can efficiently diversify user-defined sequence windows of 50-200 bp. Mutations can be focused at specific positions, with rates reaching up to 1.38 × 10 per base per generation, allowing up to 24 mutations to accumulate within a single target sequence after 48 h. We apply DGRec to phage λ host-range engineering, to the evolution of dCas9 variants and to accelerated evolution of specific nanobodies through a bacterial display setup. Lastly, we establish the feasibility of DGR-mediated mutagenesis in yeast by adapting a recombination and selection strategy previously developed for retrons.
El Abiead Y, Seo JI, Charron-Lamoureux V
… +28 more, Strobel M, Gonçalves Nunes WD, Zhao HN, Kvitne KE, Zuffa S, Mannochio-Russo H, Gouda H, Bez C, Patan A, Xing S, Zemlin J, Mohany I, Agongo J, Caraballo Rodriguez AM, Burnett LA, Deleray V, Pakkir Shah AK, Kalinski JC, Petras D, Alygizakis N, Carver J, Yurekten O, Payne T, Fahy E, Subramaniam S, Vizcaíno JA, Wang M, Dorrestein PC
Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric se...Searching and learning from aggregated public metabolomics data spanning thousands of studies remained largely inaccessible. Here we present StructureMASST, a web-based application enabling scalable, structure-centric searches across public metabolomics repositories using molecule names or chemical representations. It queries a precomputed knowledgebase of 2.19 billion spectral matches and 420 million metadata links, supports modification-tolerant and mass-shift searches, and maps chemical structures across taxonomy, biological context and environmental conditions to accelerate discovery.
Ren D, Wang S, Yamada K
… +17 more, Liu Y, Hapke R, Alpsoy A, Ho Y, Zhang C, Lan Y, Zhang S, Milazzo JP, Lohia R, Berríos KN, Li Y, Weber EW, Li Q, Vakoc CR, Minn AJ, Kohli RM, Shi J
Cancer functional genomics using CRISPR base editors (BEs) holds great promise for molecular characterization and new target discovery. However, traditional BEs, using intact DNA deaminases as mutators, are often constra...Cancer functional genomics using CRISPR base editors (BEs) holds great promise for molecular characterization and new target discovery. However, traditional BEs, using intact DNA deaminases as mutators, are often constrained by limited control and nonspecific toxicities. Here we developed a small-molecule-controllable system using split-engineered BEs (seBEs). By placing deaminase activity under small-molecule control, seBEs significantly reduced cellular toxicity and enabled robust and inducible in vivo functional genomics screens. High-density seBE genetic screens using ~11,000 single guide RNAs in vitro and ~3,700 single guide RNAs in vivo reveal known and previously unknown loss-of-function and dominant-negative mutations in cancer therapeutic targets. A deeper tiling seBE screen against Adar1, a key mediator in cancer immunotherapy, reveals critical residues within functional domains that show no phenotype in vitro but distinctively elicit non-cell-autonomous cancer dependencies in vivo. Overall, our seBE system offers a generalizable, controllable and highly efficient method to systematically identify key residues in cancer functional genomics.
Guo Z, Smutok O, Lee GR
… +17 more, Cui Z, Qianzhu H, Kish M, Ergun Yva C, Wu K, Mutschler R, Jackson CJ, Fiorito MM, Warden AC, Smith OB, Quijano-Rubio A, Huber T, Phillips JJ, Otting G, Katz E, Baker D, Alexandrov K
Protein allostery underlies most information and energy processing in biology and the development of artificial allosteric proteins is a key objective of synthetic biology and biotechnology. We show that machine-learning...Protein allostery underlies most information and energy processing in biology and the development of artificial allosteric proteins is a key objective of synthetic biology and biotechnology. We show that machine-learning-engineered minimal ligand-binding domains act as efficient receptors in single-component allosteric switches, despite lacking global conformational change. Such colorimetric, luminescent and electrochemical biosensors of small molecules, peptides and proteins can be compiled into intramolecular YES and AND logic gates. Furthermore, we report fully synthetic allosteric switches composed of artificial receptor and reporter domains. Hydrogen/deuterium exchange mass spectrometry and F nuclear magnetic resonance analyses suggest that ligand binding reduces the conformation entropy of the system, increasing the catalytic activity of the reporter domain. The potential practical utility of this approach is demonstrated by engineering Escherichia coli cells with steroid-dependent antibiotic resistance and by developing bioelectronic devices capable of quantifying steroid hormones.
We introduce FAMSA2, an algorithm that produces high-accuracy multiple-protein-sequence alignments at high speed. Across structural, phylogenetic and functional benchmarks, FAMSA2 matches or exceeds the accuracy of state...We introduce FAMSA2, an algorithm that produces high-accuracy multiple-protein-sequence alignments at high speed. Across structural, phylogenetic and functional benchmarks, FAMSA2 matches or exceeds the accuracy of state-of-the-art tools while running 400 times faster on average than existing aligners. By combining progressive alignment with medoid clustering-based guide tree construction and a dissimilarity measure derived from the longest common subsequence, it enables scalable alignment of massive protein families.
Cheng L, Zheng X, Jiang SJ
… +17 more, Hu Y, Liu Y, Yang K, Rui J, Ding H, Zhang M, Yuan T, Lu Q, Ye H, Li CL, Guo Y, Tian Z, Qin A, Zhou B, Yang KK, Huang X, Xiao H
Nat Biotechnol
· 2026 Apr · PMID 41951911
·
Full text
Engineering proteins with desired functions remains challenging and usually requires multiple rounds of screening and selection. Here, we present Sequence Display, a platform that generates large-scale protein sequence-a...Engineering proteins with desired functions remains challenging and usually requires multiple rounds of screening and selection. Here, we present Sequence Display, a platform that generates large-scale protein sequence-activity datasets in a single round. Sequence Display enables multiplexed assessment of individual variant activity within a single experiment, offering a robust approach to mapping detailed sequence-function relationships. We demonstrate the platform's broad applicability by generating datasets for cytosine deaminase, uracil glycosylase inhibitor, aminoacyl-tRNA synthetase and a compact Cas9 nuclease. Integrating these datasets obtained from Sequence Display with pretrained protein language models, fine-grained, variant-specific activity landscapes can be constructed. We discovered several Cas9 variants with expanded protospacer-adjacent motif recognition and evolved aminoacyl-tRNA synthetase variants capable of recognizing different noncanonical amino acids. Together, this study establishes Sequence Display as a powerful tool for mapping protein activity landscapes and accelerating the discovery of optimized proteins for biological and medical applications.