The contemporary shift toward multispecific antibodies, antibody-drug conjugates (ADCs), and bespoke glycoengineered therapeutics have exposed the limitations of standard genomic engineering tools. This paper presents a novel iterative engineering paradigm utilizing the Leap-In Transposase platform. By leveraging a suite of three mutually orthogonal transposase-transposon systems, we demonstrate the sequential modification of the Chinese Hamster Ovary (CHO) genome to achieve three distinct functional outcomes: (i) First, the creation of a glutamine synthetase (GS)-deficient host (CHO-K1-GS) via targeted knockdown, (ii) Second, the integration of multiple copies of a model therapeutic IgG1 for expression, and (iii) Third, the subsequent knockdown of the fucosylation pathway to modulate the glycan profile of the expressed IgG1. Genetic stability (copy number & sequence) of each integration event was confirmed using Targeted Locus Amplification (TLA) and Next-Generation Sequencing (NGS). Functional stability (expression levels, metabolic phenotype, and glycan phenotypes) was confirmed using standard cell culture and analytical techniques. Crucially, the truly orthogonal nature of the transposase-transposon pairs prevents cross-mobilization and ensures the structural and functional integrity of previously integrated cargo. This study establishes a "What You See Is What You Get" (WYSIWYG) methodology that provides a robust, scalable, and predictable framework for developing next-generation complex biopharmaceutical manufacturing cell lines.

Cyanobacteria utilize type IV pili for many behavioral responses, such as phototaxis, aggregation, floating, and DNA uptake. Type IV pilus-dependent functions are regulated by the nucleotide second messengers, c-di-GMP and cAMP. In this study, we investigated the role of a recently identified c-di-GMP receptor (CdgR) in cyanobacteria that harbors a ComFB domain. ComFB-domain proteins are widespread in cyanobacteria and are also present in heterotrophic bacteria. We demonstrated that the CdgR homolog from the cyanobacterium Synechocystis sp. PCC 6803, a model organism for studying type IV pilus-dependent functions, specifically binds to c-di-GMP. Genetic and phenotypic analyses revealed that Synechocystis CdgR is involved in phototactic motility and natural competence. Inactivation of cdgR resulted in altered expression of specific sets of minor pilins, which are essential for motility or natural competence. We identified interactions between CdgR and the CRP-family transcription factors, SyCRP1 and SyCRP2. Disruption of these CdgR-SyCRP1 and CdgR/SyCRP2 complexes is initiated by elevated c-di-GMP levels. Moreover, the assembly and stability of these complexes are influenced by other cyclic nucleotides, such as cAMP and c-di-AMP. These observed interactions imply a complex regulatory mechanism by which CdgR influences gene expression in response to cyclic nucleotide messenger signalling, particularly c-di-GMP. The present findings highlight the importance of CdgR in c-di-GMP signalling and its role in regulating type IV pilus-dependent functions in Synechocystis. The modulation of the expression of specific minor pilin genes by CdgR, through interactions with the transcription factors SyCRP1 and SyCRP2, contributes to the establishment of multiple type IV pilus functions and adaptive behaviours of cyanobacteria.

As computational tools and machine learning models for protein sciences continue to advance and proliferate, bench scientists face increasing technical challenges adopting these tools for specific applications such as drug discovery. Here we present GYDE (Guide Your Design and Engineering), an open-source, versatile, and web-based collaboration platform designed to make computational analyses of proteins and antibodies easily accessible to bench scientists. GYDE enables the exploration of sequence-structure-function relationships through a tightly integrated visual interface, offering researchers a comprehensive exploration of protein functional determinants either via real assay data or computational tools. GYDE's intuitive interface facilitates seamless access to cutting-edge AI models for protein and antibody structure prediction, design, and downstream analyses. The flexible and easy addition of new tools and models is facilitated by the use of the Slivka compute API. The platform supports saved sessions that enable researchers to easily share their findings with other users, fostering a more collaborative scientific community. GYDE is freely available for protein scientists in academia and industry to build drug discovery analytics platforms customized to their needs.

Phage therapy is a re-emerging approach for antimicrobial-resistant bacterial infections. However, the narrow host range of most phages remains a major barrier to the success and wider adoption of phage therapy. Although receptor incompatibility is often assumed to define phage-host specificity, we demonstrate that anti-phage defense systems are major determinants of host range in Staphylococcus aureus. Using a methicillin-resistant S. aureus (MRSA) clinical isolate as a model, we characterized the targeting profiles of its 15 defense systems and, for the first time, generated therapeutic phages that evade the full defense repertoire of a multi-phage-resistant strain. In particular, we show that defense-guided phage recombination is a powerful tool that leverages the modular design of phage genomes to replace targeted with untargeted components. Our holistic approach unveils defense synergies that constrain phage evasion and redundancies that allow the simultaneous evasion of multiple defenses. Finally, we show that an engineered phage cocktail prevents the emergence of phage resistance in the model and a second clinical strain with similar defenses. Our work provides a blueprint for translating our expanding knowledge of defense system identity and mechanism into the rational design of effective, next-generation phage therapeutics.

Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful results is far from standardized: workflows are often complex, fragmented, and implemented through highly customized, case-specific code. This challenge is compounded by the lack of unified open-source pipelines and the diversity of acquisition systems, each introducing its own file formats, calibration schemes, and correction requirements. Consequently, researchers must frequently rely on manual, ad hoc reconciliation of processing steps. To address this gap, we introduce TRaP (Toolbox for Reproducible Raman Processing), an open-source, GUI-based Python toolkit designed to bring reproducibility, transparency, and portability to Raman spectral analysis. TRaP unifies the entire preprocessing-to-analysis pipeline within a single, coherent framework that operates consistently across heterogeneous instrument platforms (e.g., Cart, Portable, Renishaw, and MANTIS). Central to its design is the concept of fully shareable, declarative workflows: users can encode complete processing pipelines into a single configuration file (e.g., JSON), enabling others to reproduce results instantly without reimplementing code or reverse-engineering undocumented steps. Beyond convenience, TRaP integrates configuration management, X-axis calibration, spectral response correction, interactive processing, and batch execution into a workflow-driven architecture that enforces deterministic, repeatable operations. Every transformation is explicitly recorded, making the full processing history transparent, inspectable, and reproducible. This eliminates ambiguity in how results are generated and ensures that identical protocols can be applied consistently across datasets and experimental contexts. Through representative use cases, we show that TRaP enables seamless, reproducible preprocessing of Raman spectra acquired from diverse platforms within a unified environment. We hope TRaP can empower Raman data processing as a reproducible, shareable, and systematized scientific practice, aligning it with modern standards for computational research. TRaP is released as an open-source software at https://github.com/hrlblab/TRaP

Bacteria are adaptable microorganisms capable of surviving in a wide variety of environments. One common strategy for persistence and attachment to surfaces is the formation of microcolonies, often controlled by cell surfaces structures that mediate adhesion such as fimbriae. In this study, we show that type 1 fimbriae, and other adhesins structures such as autotransporter adhesins, trigger microcolony formation which confer resistance against a broad range of short-range contact-dependent weapons (e.g. T6SS, T4SS, CDI) but not against long-range diffusible weapons (e.g. diffusible toxins like colicins). Interestingly, we identify sheltered bacteria within microcolonies that benefit from collective protection without expressing the adhesive structure. Our findings demonstrate that adhesive structures not only improve survival in hostile environments by promoting microcolony formation but also maintain phenotypic heterogeneity within the microcolony, highlighting the importance of social behavior in bacterial adaptation to changing environments.

Molecular docking aims to predict the binding conformation of a small molecule to its protein target. Recent work has proposed diffusion models for this task, from rigid-body docking that diffuses over ligand degrees of freedom to co-folding approaches that jointly generate protein structure and ligand pose. However, diffusion-based docking models have been shown to frequently produce physically implausible poses and fail to consistently recover key protein-ligand interactions. To address this, we introduce a reinforcement learning framework for training diffusion-based docking models directly on non-differentiable objectives. Fine-tuning DiffDock-Pocket for physical validity with our approach substantially increases the number of generated poses that are physically valid and interaction-preserving, with no increase in inference-time compute. Importantly, this comes without sacrificing structural accuracy; in fact, our approach increases the proportion of structures with near-native poses. These effects are most pronounced for protein targets that are dissimilar to the training data. Our fine-tuned DiffDock-Pocket model outperforms both classical docking algorithms and machine learning-based approaches on the PoseBusters set. Our results demonstrate that reinforcement learning can teach diffusion-based docking models to better respect physical constraints and recover key interactions, without the requirement to rely on inference-time corrections.

Tailocins are phage tail-like bacteriocins (PTLBs) thought to be remnants of prophages that have lost the ability to package viral genomes while retaining the ability to kill closely-related bacterial strains, thereby mediating bacterial competition. Tailocins produced by Pseudomonas aeruginosa are referred to as pyocins. Apart from their contribution to ecological fitness, they also have the potential to be harnessed as highly-specific antimicrobials to treat antibiotic resistant bacterial infections. Although pyocins lack the genetic components to package viral genomes, pyocin-encoding gene clusters share a high degree of genetic homology to phage tail genes, attributed to their shared ancestry. This poses a significant annotation-based challenge, as current prophage prediction tools, which rely on phage homology for prediction, can misclassify pyocins or tailocins as prophages. Pyocins unknowingly being misannotated as prophages is not only a bioinformatic issue, but can certainly confound experiments examining bacterial competition and prophage induction, if the experimental setup is based on this unintentional misannotation. In this study, we present "TattleTail", the first version of a bioinformatic tool designed to accurately identify tailocins in genome sequences, with a focus on identifying phage tail-derived pyocin-encoding gene clusters in P. aeruginosa in its first iteration. The tool leverages conserved pyocin gene cluster markers and accounts for the absence of canonical phage features, such as capsid, terminase and integrase genes, thereby distinguishing pyocins from intact and cryptic prophages. Validation in P. aeruginosa and non-P. aeruginosa genomes confirmed the presence of pyocin regions in all P. aeruginosa genomes, while none were detected in any non-P. aeruginosa genomes. Notably, TattleTail enabled the identification of representative pyocin-encoding gene clusters in clinical P. aeruginosa isolates. The identified pyocins in the clinical isolates were induced using mitomycin C, visualized via transmission electron microscopy, processed via tangential flow filtration and demonstrated bactericidal activity, thereby confirming TattleTail predictions. TattleTail aims to complement existing prophage prediction tools during genomic analyses involving phage-derived elements in bacterial genomes, allowing more accurate identification of these elements facilitated by robust discrimination between prophages and tailocins.

Secreted mucins are the major component of the mucus layer that protects intestinal epithelial surfaces by blocking excessive interactions with the microbiota. Mucins are complex glycoproteins decorated with over 100 different O-glycans. Some bacteria can utilize mucins and excessive degradation has been associated with disruption of the mucus barrier and inflammation. Despite the importance of mucins, a detailed enzymatic pathway by which gut bacteria degrade colonic mucin O-glycans and the impact of this process on gut colonization are unknown. Here, we identified >100 genes that are expressed by the symbiont Bacteroides thetaiotaomicron during growth on different O-glycan substrates, revealing effects of glycan structure on gene expression. The characterization of 33 glycoside hydrolase enzymes revealed the pathway for colonic O-glycan degradation by this bacterium. In vivo competition experiments show that multiple exo-acting enzymes targeting mucin capping structures are central to gut colonization and may provide targets to inhibit bacterial mucin degradation.