High-quality datasets that span broad sequence diversity are essential for understanding protein sequence-function relationships beyond local mutational landscapes. Here, we applied Growth-based Quantitative Sequencing (GROQ-seq) to measure function across an 11,722 member protease library, comprised of natural homologs and AI-shrunken variants. This library spans vast sequence diversity, with Levenshtein distances of up to 245 and a mean pairwise sequence identity of 41% to TEV protease S219V. We identified sequence-divergent TEV protease homologs that preserve function against the native TEV protease substrate. These findings reveal the robustness of protease activity across highly diverse sequences. Here, we demonstrate the aptitude of the GROQ-seq assay for screening large, diverse protein libraries for function, enabling efficient data generation at scale for training machine learning models across broad sequence landscapes.

Scalable genetic circuits are essential for implementing complex functions in living cells. Toward this goal, RNA regulators can provide a much-needed parts library with added benefits of low metabolic load, design flexibility, and logic capacity. However, despite the great potential of synthetic RNA circuits, constructing such circuits with wide dynamic ranges and multiplexed regulatory cascades remains a challenge. To address this, we introduce RATEX (Ribosome-Assisted Transcriptional EXpression controller) by integrating a translation-to-transcription converter with synthetic RNA regulators, enabling a compact and scalable RNA-programmed circuit architecture. The RATEX platform repurposes a large library of well-characterized translation regulators with up to 1,492-fold gene regulation, while leveraging natural ribosome-mediated sensing of diverse environmental inputs, such as metabolites. We demonstrated multi-input logic processing with up to a 6-input OR logic gate for RNA inputs and hybrid 3-input logic gates to sense diverse metabolite and small-molecule inputs alongside RNA signals. Signal amplification with multiplexed combinatorial control of RNA outputs was achieved through multiplexed signaling cascades. Finally, the RNA- and metabolite-sensing 3-input AND gates were used to control cellular morphology and intracellular spatial organization. Together, the RATEX platform, with its scalable and modular architecture, offers a broad potential design space for synthetic biology and biotechnology.

Agriculture is a major source of anthropogenic greenhouse-gas emissions, being the largest source of nitrous oxide (N2O), an extremely potent greenhouse gas and ozone-depleting agent. Soil N2O emissions are largely driven by microbial nitrification, in which ammonia-oxidizing microorganisms catalyze the rate-limiting oxidation of ammonia to nitrite. Nitrification not only mediates N2O fluxes but also reduces fertilization efficiency and contributes to eutrophication through nitrate leaching. Bacteriophage-based control of microbial communities is rapidly garnering interest in a number of fields; however, phages infecting ammonia-oxidizers are largely uncharacterized, with only one lytic phage having been described, limiting the potential for phage-mediated nitrification inhibition. Here, we show the largest set of phages infecting ammonia-oxidizing bacteria (AOB) to date: 45 dsDNA phages identified from urban wastewater, infecting four AOB species, with 16 demonstrating cross-genus host ranges and capable of eliminating nitrification activity in liquid cultures. Phylogenetic and taxonomic analyses revealed six proposed families of Caudoviricetes and numerous monophyletic clades, likely representing higher-level lineages. Structure-guided genome annotation revealed these phages to carry diverse and seldom-seen auxiliary metabolic genes, ranging from a complete ABC transporter cassette to a large antimicrobial resistance gene cluster. These results unveil the previously unrecognized diversity of AOB phages and their potential to alter host physiology. Our data demonstrates a broad taxonomic and functional repertoire of cultured AOB phages, greatly expanding the panel of known AOB phages, suggesting that viruses play a more significant and complex role in nitrification than previously understood. Moreover, we outline an effective methodological framework for isolating AOB phages from environmental samples. These results will help reframe our understanding of environmental nitrification and enable intensified selection and use of phages for its control.

Protein language models (pLMs) capture evolutionary sequence constraints but are limited in modeling underrepresented functional classes due to training data imbalance. Metalloproteins constitute a fundamental but sparsely represented class in sequence databases. We therefore assess whether structure-conditioned synthetic sequences can be used to specialize pLMs toward metal-binding functionality. We fine-tuned the generalist model ProtGPT2 on synthetic sequences generated by the inverse-folding model ProteinMPNN, constructing training sets with controlled variation in size and diversity. Fine-tuning increased recovery of canonical metal-binding motifs from 43% in the baseline model to 91% in the fine-tuned models. Generated sequences retained high predicted structural confidence and structural similarity to known folds, despite low sequence identity. Analysis of latent representations from ProtGPT2 indicated that fine-tuned models occupy distinct regions of embedding space relative to both the baseline model and structure-conditioned sequences, consistent with partial incorporation of structural constraints while preserving sequence diversity. A multi-step filtering pipeline applied to sequences lacking canonical motifs identified candidate metal-binding sites in four-helical bundle topologies not detected in a non-redundant subset of Protein Data Bank structures or in AlphaFold-predicted proteomes.  https://doi.org/10.5281/zenodo.18672158  https://huggingface.co/gsgueglia 

Microbial whole-genome sequence data is now generated at scale, including to support antimicrobial resistance (AMR) surveillance and understand resistance mechanisms, yet analytical infrastructure for systematically linking AMR genotypes to measured phenotypes remains fragmented. Here we present AMRgen, an R package to support systematic AMR genotype-phenotype analysis. AMRgen imports and harmonises genotypic data from common bioinformatics tools, alongside phenotypic data from automated antimicrobial susceptibility testing instruments and public repositories. It supports common analyses linking data to reference distributions, modelling associations, quantifying concordance, and producing publication-ready visualizations including UpSet plots that jointly display genotypic marker combination frequencies and associated phenotypic distributions. We demonstrate AMRgen's utility using publicly available surveillance data for World Health Organization priority AMR pathogens, Neisseria gonorrhoeae, Klebsiella pneumoniae, Escherichia coli and Salmonella enterica. AMRgen, available free and open-source at https://AMRgen.org provides a reproducible end-to-end foundation for genotype-phenotype research in AMR genomics, clinical microbiology, and public health surveillance.