The biocatalytic conversion of green methanol, a promising feedstock, into high value-added products is seen as an attractive way to establish sustainable biomanufacturing. Although natural and engineered microorganisms can utilize methanol, efficiently converting C1 carbon into valuable chemicals in growing cells remains challenging because non-native pathways often suffer from low carbon utilization and limited catalytic stability. Here, we report the integration of an engineered enzyme cascade into resting cells and its coupling with a screened alcohol oxidase enzyme to develop an efficient and stable enzyme-resting cell cascade system for methanol conversion, achieving customizable production of L-erythrulose (C4 sugar) or L-sorbose (C6 sugar). This system shows a stimulated carbon atom economy for L-erythrulose production from methanol and significantly improved stability compared to free enzyme catalysis. By integrating enzyme cascades with durable resting cells, this strategy provides a versatile platform for converting methanol and other C1 substrates into value-added products. Methanol use in engineered methylotrophs is constrained by toxicity and low efficiency. Here, the authors report the engineering E. coli resting cells coupled with a screened alcohol oxidase to enable biocatalytic production of rare sugars from methanol as the sole substrate.

With this status report, we aim to provide a timely snapshot of the protein engineering field as a broad and rapidly advancing discipline that integrates computational, molecular biology, structure-guided, evolutionary, and synthetic approaches to create new and improved proteins with tailored structures and useful functions. The report is organized into eight thematic areas spanning core methodologies and major application domains, including enzymes, therapeutics, detection, synthetic biology, and materials. Contributions from experts across these areas highlight both the historical foundations and recent advances in their respective fields, with particular emphasis on the growing influence of machine learning and artificial intelligence-based methods. Emerging from this broad overview is a central message: protein engineering appears to be entering a golden age, defined by a rapidly accelerating pace of progress, even as significant challenges in design, screening, and real-world application remain. Looking ahead, the continued integration of computational and experimental strategies is poised to further accelerate the impact of protein engineering across an expanding range of economically and societally important sectors, from therapeutics and molecular imaging to diagnostics, plastic recycling, and industrial chemistry.

Many bacterial species form self-organized macroscale patterns through swarming. Despite its extensive genetic tractability, Escherichia coli remains underexplored for robust, applied control of swarming. Here we develop a set of E. coli strains that generate centimeter-scale swarming patterns to spatially record environmental inputs. Specifically, we modulate the expression of swarming-related genes in response to chemical and optical signals, reshaping baseline swarm patterns in analog or binary-like fashions. To decode bacterial patterns across space and time, we develop scalable computational methods incorporating feature extraction, regression, and deep-learning models. Time-lapse imaging reveals that colonies record inputs dynamically, enabling early-stage classification. This work establishes a strategy for spatial information recording in E. coli and expands the toolkit for programming emergent microbial behaviors at macroscopic scales.

Infectious diseases continue to pose unprecedented challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce, and cause damage to host cells, while antibiotic resistance genes (ARGs) enable pathogens to withstand treatments that would otherwise be effective. The concurrent identification of VFs and ARGs is crucial for efficient pathogen surveillance. However, existing tools for predicting VFs or ARGs typically suffer from high false negative rates and limitations in identifying only high-identity genes against known reference VF or ARG databases. To address these challenges, we developed SEVA, an advanced model that integrates protein language models (pLMs) with structural and evolutionary protein features to predict VFs and ARGs from genome sequencing data. Integrating multiple homologous sequences can identify latent virulence or drug resistance caused by site mutations, reducing false negative rates. Meanwhile, the protein structure remains conserved despite the low sequence identity in some functional domains of VFs or ARGs. The aggregate of protein structure information further improves the identification abilities of VF and ARG. In addition, pLMs enable the model to capture high-dimensional feature representations more effectively. SEVA rigorously collected three datasets with over 20,000 genes and five reference databases. It outperforms state-of-the-art methods, including Diamond, VRprofile, FoldSeek, PreVFs-RG, PLM-ARG, ARG-BERT, and HyperVR, achieving an accuracy of 97.13% and confirming the efficacy of its key components, such as refined feature selection and multiple sequence alignment subsampling. SEVA takes protein sequences as input and derives evolutionary, structural, and statistical representations for prediction, making our model a reliable tool for VF and ARG prediction. This capability is particularly valuable in epidemic prevention and control, where accurate identification of VFs and ARGs is crucial. By providing concurrent and reliable predictions of VFs and ARGs, SEVA enhances our ability to respond to microbial threats effectively. This finding supports robust efforts to mitigate the spread of infectious diseases and safeguard public health, addressing a critical gap in contemporary epidemic response strategies.

A vast portion of genes in microbial genomes, termed the “unknome,” remains functionally uncharacterised. This genetic “dark matter” represents a significant bottleneck in microbiology, as it is often excluded from genomic studies. We argue that a substantial part of the unknome encodes functions critical for biotic interactions, the complex dialogues among microbes or between microbes and their hosts. These functions are rarely observed under standard laboratory conditions, which rely on simplified pure cultures. Unlocking the unknome therefore calls for a stronger emphasis on ecologically relevant experimental systems. By embracing complexity through co-culture and in situ analyses, we can begin to decipher this hidden genetic repertoire, deepening our understanding of microbial communication, adaptation, and evolution. Crucially, the conceptual and methodological challenges raised by the microbial unknome resonate well beyond microbiology: parallel “dark” fractions of uncharacterized genes and proteins pervade eukaryotic genomes, from lineage-specific (orphan) genes underpinning novelties in plants and animals, including humans. Embracing ecological and systems-level approaches to dissect the unknome therefore has the potential to reframe how we link genotype to phenotype in context-dependent, interaction-driven biological systems.

Bacterial nanocellulose (BC), produced by Komagataeibacter species, is an ideal scaffold for biological Engineered Living Materials (ELM) research. Current BC functionalization strategies often rely on secondary microbial hosts or post-production enzyme immobilization, limiting the scalability and modularity required for programmable bioELMs. Establishing a single-chassis system capable of simultaneous biopolymer synthesis and in situ functionalization remains a primary objective in bioELM research. This study addresses the need by benchmarking signal peptide-mediated protein translocation in K. rhaeticus iGEM, a model bacterium for BC-based bioELMs, enabling a synthetic biology framework for single-chassis based biomaterial functionalization. Genome-wide analysis confirmed the presence of a complete Sec translocation machinery in K. rhaeticus. Through liquid chromatography-tandem mass spectrometry and SignalP 5.0 prediction, native signal peptides were identified and evaluated alongside previously characterized heterologous signal peptides using β-lactamase and mScarlet as cargo proteins. Protein translocation was found to depend on signal peptide identity, cargo type, and expression mode. Fluorescence imaging revealed cytoplasmic, polar, and peripheral localization patterns, confirming functional engagement with the native translocation machinery. A key limitation identified was the retention of recombinant proteins within the periplasm, restricting extracellular availability. Despite this, signal peptide-mediated translocation enabled the incorporation of enzymatic activity into BC during biosynthesis. A post-growth osmotic shock-release strategy increased measurable enzymatic activity by 30%, demonstrating a practical route to overcome this physiological bottleneck while maintaining the biomaterial production capacity. This study benchmarks signal peptide-dependent protein translocation in K. rhaeticus and identifies periplasmic retention as a key constraint for extracellular protein release. By linking protein translocation to in situ BC functionalization, this work establishes a synthetic biology framework that supports the development of K. rhaeticus as a single-chassis platform towards the production of functionalized bioELMs.

Microbial biosynthetic gene clusters (BGCs) encode diverse bioactive molecules but remain mostly silent under lab conditions, reflecting a balance between regulatory control and the metabolic cost of secondary metabolite production. While activation strategies exist, the yields stay low because switching on a cluster doesn’t guarantee the host can sustain biosynthesis. This review covers the regulatory architecture and metabolic constraints, including precursor availability, energy status, and nutrient sensing, that shape BGC output. We argue that coupling regulatory network models with genome-scale metabolic models offers a powerful framework for unlocking silent BGCs and realising the full biosynthetic potential within microbial genomes. This review summarises multilayered regulatory and metabolic systems governing silent biosynthetic gene clusters in microbes, and how coupling regulatory network models with genome-scale metabolic models can unlock their biosynthetic potential