Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

Intrinsically disordered proteins are ubiquitous in biological systems and play essential roles in a wide range of biological processes and diseases. Despite recent advances in high-resolution structural biology techniques and breakthroughs in deep learning-based protein structure prediction, accurately determining structural ensembles of IDPs at atomic resolution remains a major challenge. Here, we introduce bAIes, a Bayesian framework that integrates AlphaFold2 predictions with physico-chemical molecular mechanics force fields to generate accurate atomic-resolution ensembles of IDPs. We show that bAIes produces structural ensembles that match a wide range of high- and low-resolution experimental data across diverse systems, achieving accuracy comparable to atomistic molecular dynamics simulations but at a fraction of their computational cost. Furthermore, bAIes outperforms state-of-the-art IDP models based on coarse-grained potentials as well as deep-learning approaches. Our findings pave the way for integrating structural information from modern deep-learning approaches with molecular simulations, advancing ensemble-based understanding of disordered proteins. Here, the authors present bAIes, which integrates AlphaFold2 information into a molecular dynamics framework to efficiently generate atomic-resolution ensembles of intrinsically disordered proteins.

Plant growth-promoting rhizobacteria (PGPR) are widely recognized as sustainable agricultural inputs because they enhance nutrient availability, reduce reliance on chemical fertilizers, and improve soil and plant health. However, their field performance is often limited by reduced viability and stability under environmental stresses. This study aimed to develop and compare double-layer and multilayer polysaccharide–protein–based encapsulation systems to improve the protection, long-term stability, and controlled release of PGPR. Pseudomonas fluorescens T17-4 and Bacillus velezensis VRU1 were encapsulated using an alginate–whey protein isolate core, with successive outer layers of apricot gum and pectin to form double-layer and multilayer capsules. Mesoporous silica nanoparticles were added to further reinforce the capsule structure. The multilayer encapsulation system outperformed the double-layer formulation, showing higher encapsulation efficiency (> 90%), improved structural integrity, enhanced long-term stability with over 90% bacterial viability after six months, and a more controlled release profile. These findings demonstrate that multilayer polysaccharide–protein encapsulation is an effective strategy for protecting PGPR and ensuring their sustained delivery, highlighting its potential for developing robust biofertilizers in sustainable agriculture.

Archaea, a domain of microorganisms found in diverse environments, including the human microbiome, represent the closest known prokaryotic relatives of eukaryotes. This phylogenetic proximity positions them as a relevant model for investigating the evolutionary origins of nucleic acid secondary structures such as G-quadruplexes (G4s) which play regulatory roles in transcription and replication. Although G4s have been extensively studied in eukaryotes, their presence and function in archaea remain poorly characterized. In this study, a genome-wide analysis of the halophilic archaeon Haloferax volcanii identified over 5800 potential G4-forming sequences. Biophysical validation confirmed that many of these sequences adopt stable G4 conformations in vitro. Using G4-specific detection tools and super-resolution microscopy, G4 structures were visualized in vivo in both DNA and RNA across multiple growth phases. Comparable findings were observed in the thermophilic archaeon Thermococcus barophilus. Functional analysis using helicase-deficient H. volcanii strains further identified candidate enzymes involved in G4 resolution. These results establish H. volcanii as a tractable archaeal model for G4 biology.

Owing to their natural origin and biocompatibility, extracellular vesicles (EVs) are being recognized as next-generation vehicles for targeted drug delivery. Despite their potential as therapeutic carriers, EVs suffer from heterogeneity, low yields, limited cargo loading efficiency and rapid clearance by the mononuclear phagocyte system. Since the first EV-based clinical trial in 2005, more than 100 clinical trials have investigated the use of EVs as therapeutics and drug carriers. Despite this, no EV-based therapies have received regulatory approval to date. This gap between preclinical research activity and clinical translation underscores persistent scientific challenges and regulatory hurdles that continue to impede the advancement of EV-based therapeutics. In this Review, we examine the research articles published in the field between 2012 and 2024 (38,177 articles), highlighting key developments, persistent challenges and evolving assumptions. We review the current EV landscape and clinical trials, focusing on their organotropism and use as carriers for therapeutics. We compare their advantages and limitations in relation to other nanoparticles, such as lipid nanoparticles and liposomes, and examine how labelling strategies and cell sources influence EV biodistribution. Finally, we outline translational considerations for EV-based therapeutics and propose additional reporting standards, complementing the MISEV 2023 guidelines. Extracellular vesicles (EVs) are emerging as biocompatible carriers for targeted drug delivery, yet challenges in yield, cargo loading and biodistribution persist. In this Review, key trends from over a decade of research are analysed, comparing EVs with lipid-based systems and outlining strategies for improving therapeutic translation and reporting standards.

Demand for recombinant proteins is rapidly growing, driven by their use as biotherapeutics, vaccine components, industrial enzymes, and food ingredients. The growing market requires novel strategies for increasing protein production in cellular hosts. Systems-level frameworks have been used to improve production, but have had difficulty relating complex cellular pathways with protein expression. Here, we demonstrate a method for mapping relationships between gene expression signatures and carbon source-related phenotypes related to recombinant protein production.  Our approach induces systematic perturbations in cultures of K. phaffii using varied co-feeds of carbon sources. The different carbon sources significantly impacted cell growth, specific productivity, and transcriptional states. With these data, we identified metagenes for both immunoglobulin G1 monoclonal antibody (IgG1) and Variable domain on a heavy chain (VHH) antibody that explained significant transcriptomic variance. These metagenes strongly associated with two phenotypes: production of recombinant protein-to-biomass ratio, and response to methanol induction. We used these results to identify and knockout 31 novel gene targets for which expression inversely correlated with productivity. Nine of these genes improved productivity of IgG1 by up to 3x and ten genes increased productivity of VHH by up to 1.7x. Many of these genes are involved in the modulation and progression of the cell cycle but interestingly, disruption had little to no impact on cell growth.  This study establishes a framework for relating gene signatures to complex cellular phenotypes, providing a robust methodology for assessing production processes and identifying new targets for cellular engineering. While the identified specific metagenes depend on the complexity and structure of the recombinant protein produced, this framework is extensible across diverse proteins and potentially other host organisms. These signatures may serve as scale-independent, cellular-level metrics for traits like efficiency of production of recombinant proteins, facilitating the translation of findings across different scales and cultivation modes. Furthermore, this framework enables the identification of novel targets for genomic modifications that can improve strain performance, offering a predictive tool for the rational design of high-performing microbial cell factories. 

Genetic interaction and protein-protein interaction networks have proved among the most important tools for inference of gene function and genotype-phenotype relationship. It remains unclear, however, how these two networks are related to each other. Here we demonstrate that the strengths of epistatic genetic interactions between two genes strongly correlate with the binding free energies of interactions between their cognate proteins, for both yeast and human genomes. Consequently, we show that genetic interaction and protein-protein interaction networks can reciprocally predict each other. Further, we observe that functional divergence and redundancy in duplicated genes (paralogs) are reflected in the binding affinity of their interactions with other proteins and in their genetic interaction strengths. Finally, we demonstrate that the overall topologies of genetic interaction and protein-protein interaction networks significantly overlap in two topological features: modules, in which genes/proteins are organized by common function, and connectors, which link modules to each other. This opens avenues for integrated network approaches in understanding cellular processes and flow of genomic information. Genetic and protein-protein interaction networks can be used to infer gene function and genotype-phenotype relationships. Here, the authors show that genetic interaction patterns reflect protein binding affinities.

Sparked by innovations in generative artificial intelligence (AI), the field of protein design has undergone a paradigm shift with an explosion of new models for optimizing existing enzymes or creating them from scratch. After more than one decade of low success rates for computationally designed enzymes, generative AI models are now frequently used for designing proficient enzymes. Here, we provide a comprehensive review and classification of generative AI models for enzyme design, highlighting models with experimental validation relevant to real-world settings and outlining their respective limitations. We argue that generative AI models now have the maturity to create and optimize enzymes for industrial applications. Wider adoption of generative AI models with experimental feedback loops can speed up the development of biocatalysts and serve as a community assessment to inform the next generation of models.