Protein-peptide interactions are important mediators of diverse biological processes. While deep learning has revolutionized protein structure prediction, comparative evaluation of these methods, specifically for protein-peptide complexes, remains an area of active investigation. Here, we present a systematic benchmarking of AlphaFold2 (AF2) and OpenFold3 (OF3) on a curated, non-redundant dataset of 271 protein-peptide complexes evaluated under CAPRI peptide criteria, partitioned into disordered (IDR) and structured (Non-IDR) peptide subsets. Results show that AF2 consistently outperformed OF3 across both subsets in overall success rate and proportion of high-quality models, while both methods exhibited comparable global fold prediction accuracy. We further demonstrate that AF2 exhibited memorization on a large set of protein-peptide complexes that were in its training data. Analysis of built-in and post-hoc confidence scores demonstrated that PAE-derived metrics, particularly pDockQ2, LIS, and ipSAE, provided the most reliable proxies for structural accuracy in AF2 predictions, whereas OF3's PAE distributions substantially diminished the discriminative power of its derived scores. Furthermore, we find that canonical DockQ threshold cutoffs for protein-protein complexes are not directly transferable to protein-peptide complexes, underscoring the need for method- and dataset-specific calibration. Peptide sequence composition and length were identified as potential modulators of prediction success, with glycine-rich short peptides and long receptors posing challenges to both methods. Collectively, these findings establish a peptide-specific evaluation framework and highlight the need for dataset/method-calibrated metrics to support the continued development of structure prediction tools for protein-peptide interactions.

Codon usage bias is highly species-specific, posing a major challenge for heterologous protein expression. Existing deep learning approaches to codon optimization rely primarily on DNA or protein sequence information and largely neglect constraints imposed by protein structure and folding. Here, we present Protein structure-Informed Species-specific Codon Optimization (PISCO), a Geometric Vector Perceptron (GVP)-based model that integrates protein sequence, three-dimensional protein structure, and host codon usage statistics to generate optimal, host-specific codon sequences. Compared with protein-structure-agnostic models, PISCO improves codon recovery by 6% and substantially increases similarity to natural coding sequences, reducing divergence by at least 42% in Codon Similarity Index (CSI), 50% in Codon Frequency Distribution (CFD), and 14% in Dynamic Time Warping (DTW) metrics. Ablation analyses demonstrate that incorporating protein folding kinetics and host-specific information is critical to these gains. Moreover, by leveraging host codon usage statistics, PISCO generalizes to optimize codon sequences for species absent from the training data. An autoregressive variant of PISCO further enhances concordance with natural codon usage patterns, at the cost of a modest reduction in codon recovery rate. Wet-lab validation confirms that PISCO-optimized sequences significantly enhance protein solubility and functional expression. Together, these results establish protein structure as a key determinant of species-specific codon optimization and provide a transferable framework for structure-aware gene design.

Tryptophan (Trp) metabolism follows three main branches: the kynurenine (KP), serotonin, and indole (IP) pathways. These pathways generate bioactive metabolites that regulate immune responses, redox balance, neurotransmission, metabolic homeostasis, inflammation, and circadian rhythms. A common theme across these pathways is the activation of the aryl hydrocarbon receptor (AhR). Several metabolites from KP, IP, and serotonin act as endogenous AhR ligands or interact indirectly with AhR, but the downstream consequences of this interaction depend on the cellular environment and inflammatory context. In addition, Trp metabolites also impact other signaling pathways, including GPR35, NMDA receptors, serotonergic receptors, and NAD+ biosynthesis. Notably, our group recently discovered that the upregulation of the KP results in the formation of the novel redox-active mediator kynurenine-carboxyketoalkene. This finding expands the signaling repertoire of Trp metabolism to include the modulation of cysteine-dependent pathways, with important implications for the maintenance of cellular homeostasis and immune control. Overall, flux through the oxidative arm of the KP links inflammation to cellular energy metabolism, while microbial indole derivatives influence host–mucosal immunity and host–microbe communication. The serotonin pathway connects neuroendocrine signaling with the peripheral nervous system’s regulation of metabolism. Shifts in Trp homeostasis caused by inflammation, alterations in microbial composition, or metabolic demand modify downstream signaling outputs under physiological and pathological conditions. In this regard, dysregulation of Trp metabolism is implicated in neurodegeneration, cancer, metabolic disease, cardiovascular dysfunction, and chronic inflammation. This review presents Trp catabolism as a distributed signaling network and offers new insights into its physiological functions.

Bacteria in fluctuating environments must balance the high metabolic costs of motility against risks of bacteriophage predation and immune clearance. While flagellar trade-off mechanisms are well-documented, regulation of type IV pilus (T4P) activity during environmental transitions remains unclear. We show that Pseudomonas aeruginosa uses an energy-dependent idling strategy to synchronize T4P-mediated surface motility with nutrient availability. In nutrient-depleted stationary phase, T4P transcription and protein levels remain constant, pre-assembled machines persist at the cell pole, yet cells produce only sparse, truncated pili that extend and retract slowly. Using a single-cell ATP biosensor, we show that T4P dynamics respond directly to cellular adenylate energy charge. Carbon source addition rapidly elevates intracellular ATP, reactivating pre-assembled T4P within minutes. This bypasses de novo protein synthesis, restoring pilus number, length, and extension/retraction rates. This rapid response drives opportunistic biofilm dispersal but, at the same time, creates an immediate tradeoff: reactivated T4P restore susceptibility to pilus-specific phages upon nutrient upshift. Thus, energetic gating of T4P enables P. aeruginosa to minimize exposure to phages during starvation while remaining poised for rapid reactivation. Importantly, T4P promote resistance to opsonization and phagocytosis by macrophages and neutrophils. Upon nutrient upshift, full T4P activity therefore supports dispersal and host colonization while conferring immune protection, revealing a fundamental dispersal-infection tradeoff at the host-microbe interface in fluctuating environments such as the lung and gut.

The inference and removal of horizontally acquired genomic regions is a crucial step in phylogenomics analyses for evolutionary studies. Existing tools perform well on clonal lineage-focused datasets on the scale of hundreds of genomes, but are limited in their ability to analyse larger or more diverse datasets. Here we present Verticall, a tool to identify recombinant regions in bacterial assemblies and generate recombination-free phylogenies, which scales to thousands of genomes from clonal to genus-level diversity. Verticall uses a non-parametric approach to assign genomic regions as horizontally or vertically related based on the distribution of pairwise genetic distances between genomes. Recombination-free phylogenetic trees may be inferred by either calculating a pairwise genetic distance matrix from vertical-only regions (distance-tree approach) or by pairwise comparisons of all genomes to a reference and then masking horizontally acquired regions in a pseudo-alignment to the reference (alignment-tree approach). We demonstrate Verticall's performance using four publicly available whole-genome sequence datasets of varying sample sizes (range: 154 - 4,857 genomes) and evolutionary scales (ranging from within-lineage to genus-wide diversity). Across all four datasets, Verticall showed comparable or superior performance to the established tools Gubbins and ClonalFrameML in terms of computational efficiency, plausibility of inferred phylogenetic trees, and recovery of temporal signal for molecular dating. Our results show that Verticall is a useful tool to more efficiently and accurately detect recombination, particularly applied to datasets for which existing tools are limited, including large datasets with hundreds to thousands of genomes and those that span entire species or genera. Verticall is available free and open source at https://github.com/rrwick/Verticall.

E. coli is a well-established model organism in molecular biology and biotechnology. Despite its long history as a laboratory workhorse, the efficient single-step chromosomal integration of large DNA fragments remains a challenge. Currently known methods are either simple but have limitations on insert size, or flexible but laborious requiring plasmid construction or multi-step procedures. Here, we present PhAGE (Phage-Assisted Genome Engineering), which enables the integration of ~20 kb DNA fragments into E. coli genome within a single day. PhAGE method uses in vitro packaging of recombinant DNA into bacteriophage capsids, followed by general transduction to introduce pre-assembled DNA with flanking homology arms into recipient cells. This approach allows efficient and landing pad-free integration of large constructs into the target loci. We demonstrate its usefulness through rapid integration of multi-gene operons. PhAGE resolves the long-standing trade-off between simplicity and insert size in E. coli genome engineering, accelerating strain construction across a wide range of applications, from biosynthetic pathway engineering to genome-scale design.

We previously introduced a genetically encoded, metal-responsive system for reversible control of protein function based on metal chelation by bipyridylalanine (BpyAla) residues. The efficacy of this linking group approach was demonstrated in two structurally and functionally distinct enzymes, Pyrococcus furiosus prolyl oligopeptidase (Pfu POP) and Photinus pyralis luciferase (Pluc). Here, we investigate the mechanistic basis of this switching in Pfu POP. Fluorescence-based metal competition assays and molecular dynamics (MD) simulations were conducted to quantify Ni(II) binding affinity and evaluate the structural response to Bpy2Ni(II) complex formation. 19F NMR spectroscopy and MD simulations further indicate that linking group-controlled conformational changes near the catalytic triad, particularly within the loop containing H592, drive the observed activity modulation upon metal binding. These findings establish that genetically encoded metal-binding motifs can regulate enzyme function through subtle, localized conformational changes, providing a versatile platform for engineering responsive protein systems in synthetic biology, biosensing, and programmable catalysis.