Developing short, stable, and potent antimicrobial peptides is a promising strategy to combat antibiotic resistance and persistence. We present CAMPER (Constraint-driven AMP Engineering with Ranking), a mechanistic artificial intelligence framework that integrates machine learning with biophysical ranking to prioritize membrane-targeting peptides effective against persister and biofilm forms of methicillin-resistant Staphylococcus aureus. We apply CAMPER to identify WP-CAMPER1 (12mer) that kills S. aureus MW2 at a minimal inhibitory concentration of 4 µg/mL. A 2% topical WP-CAMPER1 formulation reduces S. aureus MW2 burden by 2.5 log10 (p < 0.0002) in a murine prophylactic skin infection model, while its D-enantiomer, WP-CAMPER1-d, achieves 1.37 log10 (p < 0.0001) reduction in an established biofilm infection model. Single-cell analysis using a high-throughput microfluidic system shows that WP-CAMPER1-d reduces exponential-phase persisters of S. aureus USA300, and, in a deep-seated murine thigh infection model, decreases stationary-phase S. aureus MW2 persisters by 1.6 log10 (p < 0.0001). This study introduces CAMPER, a mechanistic artificial intelligence platform for designing antimicrobial peptides targeting MRSA. CAMPER identified a stable peptide that eradicates MRSA biofilms and persister cells and was active in mouse infection models.

Gene regulatory networks (GRNs) explain how the genome controls cellular behavior and tissue morphogenesis, serving to connect molecular mechanism to functional output. Single-cell technologies now provide descriptions of these networks with unprecedented detail, but this advance has also revealed gene regulatory systems that are too complex for our existing conceptual frameworks. GRNs, which should provide mechanistic explanations, are increasingly reduced to statistical correlations — ‘hairballs’ that fail to capture molecular causation. Here, we explore why this dilemma exists and propose a path forward. We argue that methods in ‘representation learning’ can be used to model GRNs, without needing to capture every molecular detail. For this framework, we advocate three linked principles: models must be inherently mechanistic, with structures grounded in cellular and evolutionary biology; molecular principles and constraints must be used to reduce the solution space for learning GRN models; and more sophisticated forms of experimental perturbation and synthetic biological engineering are needed to train models and test predictions. By reimagining GRNs through these principles, we can bridge the gap from data abundance to new conceptual understanding. In this Perspective, Maizels and Briscoe discuss the limitations of current models of gene regulatory networks and outline solutions to harness data abundance without compromising explanatory power.

Accurate determination of initial reaction rates (v0) is essential for characterizing enzyme function, designing inhibitors, and modeling biological systems. Traditional methods rely on linear approximations valid for reaction phases difficult to capture, while substrate excess over the enzyme does not ensure constant rates. To overcome these limitations, we developed ACCU-RATES, a user-friendly web tool that analyzes heuristically product accumulation or substrate depletion curves containing at least two time points. Using a differential form of the Michaelis-Menten equation, ACCU-RATES numerically fits progress curves to interpolate , enabling precise determination of the Michaelis constant (Km) and limiting rate (V). Simulations across diverse scenarios, including data noise and low sampling rates, show that ACCU-RATES delivers reliable, user-independent parameter estimates without relying on linear phases. Compared to existing methods, it offers superior accuracy and robustness against assay interferences, with applications in inhibitor discovery, synthetic biology, and biomarker assays. ACCU-RATES is freely available at https://accu-rates.i3s.up.pt.

Human protein-based biomaterials facilitate the development of preclinical models that integrate predictive value and ethical responsibility. Metatissue uses decellularized extracellular matrix from placenta samples and human blood to create tunable, xeno-free substrates for 3D cell culture, minimizing the reliance on animal models and accelerating translational research.

Nucleic acid degradation is a common strategy for prokaryotic antiphage systems, as exemplified by the CRISPR-Cas system. The PD-(D/E)-XK nucleases constitute a widely distributed family in these defenses. Notably, most members exhibit a single nuclease domain, while variants containing dual nuclease domains within a single polypeptide remain underexplored, and their molecular mechanisms largely obscure. Here, we biochemically and functionally study a single-protein system containing an uncharacterized PD-(D/E)-XK defense protein (Upx). As revealed by single-particle electron cryo-microscopy (cryo-EM) structure, the C-terminal domain (CTD) harboring the conserved PD-(D/E)XK catalytic core is buttressed by the N-terminal domain (NTD) and the middle domain (MD). Functional assays demonstrate that the nucleic acid binding capability of the CTD is enhanced by the MD. The NTD also displays a noncanonical, basal exonuclease activity that is auto-inhibited by MD. IP-MS experiments identify Upx-interacting phage proteins, and substrate profiling defines its physiological preferences, collectively pointing to its potential physiological targets. Notably, the phage protein gp16 was found to relieve MD-mediated inhibition of the NTD, suggesting a virus-triggered mechanism for activating Upx’s dual nuclease activity. Together, these findings establish Upx as a single-protein dual-nuclease anti-phage system, expanding our understanding of bacterial immunity and informing antiviral strategy development. Bacteria employ diverse nucleases to defend against viruses. Here, authors characterize Upx, a single-protein, dual-nuclease system, and reveal that a specific phage protein relieves auto-inhibition to activate its antiviral function.