Rational control over diverse, ligand-responsive output networks is a foundational challenge in synthetic biology, particularly for systems based on post-translational signaling. Here, we present the design and engineering of minimal, modular protein architectures for chemically responsive molecular inverters and digital switches. Our inverter design modifies the plant-derived PYR1-HAB1 chemically inducible dimerization module by incorporating a constitutive activator, which is then competitively displaced by the ligand-bound PYR1 receptor, converting the native 'dimerization-on' mechanism into a 'signal-off' inverter. We establish key design features and demonstrate predictable tuning of the inverter's transfer function, including its maximum output, half-maximal inhibitory concentration, and minimum output, solely by adjusting protein stoichiometry. The architecture is modular, enabling plug-and-play response to diverse, user-defined drug-like small molecules. We show that an inverter biosensor for an environmental contaminant functions in engineered living cells with a low nanomolar sensitivity. Additionally, we convert the PYR1-HAB1 sensor into a digital switch by adding an engineered molecular titrant to the system. Overall, this work provides a generalizable, minimal, and tunable protein scaffold for programming complex, post-translational signaling logic, significantly expanding the toolkit for sophisticated biological circuit design.

Sporosarcina pasteurii is the most widely studied bacterium for microbially-induced calcium carbonate precipitation (MICP), a process of intense interest for materials and construction applications. Despite two decades of investigation, S. pasteurii has remained genetically intractable, limiting our mechanistic understanding of biomineralization pathways and constraining efforts to engineer scalable solutions. Here, we present the first genetic toolkit for S. pasteurii, including a stable replicating plasmid, a conjugation-based DNA delivery protocol, engineered inducible promoters, and methods for genome modification. Using homologous recombination, we precisely deleted 5.7 kb of the genome spanning two operons encoding urease activity and demonstrated complete loss of biocementation. We also screened a library of engineered transposon constructs for activity in S. pasteurii and generated a genome-wide mutant library with >15,000 unique insertion sites. Using this library, we identified putative genes affecting ureolytic growth, revealing previously inaccessible aspects of S. pasteurii genetics. This work establishes S. pasteurii as a genetically tractable platform for rational engineering of MICP and constitutes the first genetic modification capability within the Sporosarcina genus.

Fluorescent proteins (FPs) are widely used reporters for visualizing cellular structures and processes. Traditional wet-lab strategies for FP engineering (rational design and directed evolution) have enabled substantial improvements in photophysical performance but are limited by their requirement for deep expert knowledge or labor-intensive screening. AI-driven approaches have recently gained traction for engineering variants of green FPs, yet applications to red fluorescent proteins (RFPs) remain scarce. Here, we applied machine learning models to an RFP sequence-function dataset and trained these models to predict functional single-mutation variants of the state-of-the-art RFP mScarlet-I3. Guided by model predictions, we identified variants exhibiting red-shifted emission peaks, large Stokes shifts, or brightness comparable to the parental protein. Our findings show that even lightweight, data-efficient models can extract actionable design principles for improving RFPs. This work demonstrates the feasibility of AI-guided design for RFPs and provides a reliable benchmark for future development of more powerful AI-driven strategies for FP engineering.

The methylotrophic yeast Pichia pastoris (also known as Komagataella phaffii) is a prominent platform for recombinant protein production, offering benefits such as thermo- and osmotolerance, high-density growth, and efficient protein secretion. Its ability to metabolize methanol, an increasingly available carbon source, enhances its cost-effectiveness and sustainability for industrial use. As a eukaryotic host, P. pastoris ensures proper protein folding and post-translational modifications (PTMs), including glycosylation, which is essential for correct folding and endoplasmic reticulum (ER) quality control. While ER-transferred glycans are critical for maturation, additional modification in the Golgi apparatus can yield larger glycans whose impact on stability, solubility, and bioactivity may be either beneficial or undesirable, depending on the application of the heterologous protein. The impact of induction conditions on glycosylation of proteins secreted by P. pastoris SuperMan5 was examined, using the DS-1 (G2P[4]) and WA (G1P[8]) VP8* rotavirus capsid proteins as a model. An ELISA-based screening system was employed for clone selection and media optimization, with results showing easy integration into automated workflows. Methanol concentration was found to impact both N- and O-linked glycosylation complexity, shaping the glycosylation profile of the target protein as well as the P. pastoris secretome. This study underscores the importance of optimizing cultivation conditions to enhance protein yield, refine glycosylation, and minimise impurities, all of which are crucial for large-scale production and efficient downstream processing. It also suggests a method for easy modulation of glycosylation depending on the target application and the desired level of glycosylation.

Adaptation occurs through the selection of beneficial mutations enhancing fitness in a specific environment. However, since environments vary across time and space, mutations that are positively selected in one environment may be less beneficial or detrimental in others. Here, we investigate the evolution of microbial robustness (i.e. a consistent fitness across many diverse environments) through the adaptive evolution of two genetically distinct Saccharomyces cerevisiae populations in fluctuating conditions, followed by fitness assays and whole-genome sequencing. Our results indicate that the haploid laboratory strain S288C achieved higher average fitness than its parental strain, particularly when evolved in fluctuating environments compared to constant environments, but did not show increased robustness. In contrast, populations of the industrial diploid strain Ethanol Red failed to achieve significant fitness improvement under both fluctuating and constant evolution regimes but became more robust. Populations that adapted to fluctuating conditions acquired mutations in genes involved with cell morphology and protein degradation. Overall, our results emphasise the importance of parental traits in shaping fitness and robustness during adaptive laboratory evolution.

Loop engineering of enzymes remains challenging due to high flexibility and conformational complexity, posing a bottleneck for deep-learning-based design. Here, we constructed mutant libraries for three loops of TEV protease to assess combining directed evolution with deep learning. Using an M13 phagemid-based selection system, the three libraries were screened, resulting in a Loop 1 variant (HyperTEV60/L1) that significantly enhanced the Michaelis constant (Km) of the HyperTEV60 scaffold, a highly active mutant identified by ProteinMPNN. Structural modeling suggested that a single-residue deletion and substitution in Loop 1 expands the substrate binding pocket, accounting for the improved Km. Although the catalytic efficiency kcat/Km of HyperTEV60/L1 was only marginally higher than HyperTEV60, due to a kcat decrease, our results reveal that the phagemid-based selection system tended to find variants optimizing Km. This study demonstrates that combining deep-learning-based global optimization with localized directed evolution maximizes the probability of discovering distinct, high-performance enzyme variants.

Restriction-modification (R-M) systems are one of the most widespread and, due to their often plasmid-based nature, transmittable antiphage systems bacteria have. The CfrBI R-M system studied here consists of a methyltransferase (MT) and a restriction endonuclease (RE) that are divergently expressed and share a promoter region that harbors a single CfrBI recognition site. Previously, the methylation of this site has been shown to regulate the expression of the R-M system. Here, we show that the expression dynamics of the CfrBI R-M system and its protective properties depend on the copy number of the plasmid harboring it. A higher copy number results in higher expression, but the expression on a medium-copy number plasmid interestingly conferred the highest phage resistance. After transformation of naïve cells however, expression of the RE was fastest in the high-copy plasmid background. In vivo we show that the expression strength of the MT inhibits its own expression, while enhancing RE expression. To conclude, the results indicate that for phage resistance the overall expression strength might not be the predominant factor in case of the CfrBI R-M system, while for the establishment of the system the initial expression rate of the MT seems to be the determining factor.