Data reveal how the global challenge to reduce deaths and infections from drug-resistant bacteria is not going according to plan.

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We are the Clostridial Cell Biology Group at the University of Sheffield. We study all aspects of cell biology, including the architecture and biogenesis of surface structures on both vegetative cells...

Nature Reviews Microbiology, Published online: 22 October 2025; doi:10.1038/s41579-025-01253-zThis Genome Watch explores how a new antisense oligomer-based approach enables functional genomics of genetically intractable bacteriophages, revealing essential genes and infection mechanisms without requiring genetic modification.

#RNAexcellence Junior Group Leader Positions (f/m/d) Job advertisement no.:   089/2025 Starting date:                  As soon as possible. The con...

Antibacterial compounds are discovered by combining phenotypic screening with deep learning.

PhD Project - Using super-resolution microscopy to investigate the mechanistic principles of bacterial cell wall remodelling at University of Warwick, listed on FindAPhD.com

Abstract. The increasing interest in finding new viruses within (meta)genomic datasets has fueled the development of computational tools for virus detectio

Mutational robustness is the persistence of a phenotype upon mutation. It facilitates molecular evolution and has been characterized in a variety of biological systems, but studies of prokaryotic prom...

Nature Reviews Molecular Cell Biology, Published online: 13 October 2025; doi:10.1038/s41580-025-00896-3Nanopore RNA sequencing is a potent technology for the detection of RNA modifications. Nanopore modification-aware base-calling models have been recently developed, and this Progress article discusses their limitations, including modification cross-reactivities, variability in false positive rates and modification-calling threshold choices.

Transfer RNAs (tRNAs) and their modifications are central to bacterial translation and physiology, yet their roles in stress adaptation remain underexplored. While extensively studied in eukaryotes, a...

Structural RNAs exhibit a vast array of recurrent short three-dimensional (3D) elements found in loop regions involving non-Watson–Crick interactions that help arrange canonical double helices into tertiary structures. Here we present CaCoFold-R3D, a probabilistic grammar that predicts these RNA 3D motifs (also termed modules) jointly with RNA secondary structure over a sequence or alignment. CaCoFold-R3D uses evolutionary information present in an RNA alignment to reliably identify canonical helices (including pseudoknots) by covariation. Here we further introduce the R3D grammars, which also exploit helix covariation that constrains the positioning of the mostly noncovarying RNA 3D motifs. Our method runs predictions over an almost-exhaustive list of over 50 known RNA motifs (‘everything’). Motifs can appear in any nonhelical loop region (including three-way, four-way and higher junctions) (‘everywhere’). All structural motifs as well as the canonical helices are arranged into one single structure predicted by one single joint probabilistic grammar (‘all-at-once’). Our results demonstrate that CaCoFold-R3D is a valid alternative for predicting the all-residue interactions present in a RNA 3D structure. CaCoFold-R3D is fast and easily customizable for novel motif discovery and shows promising value both as a strong input for deep learning approaches to all-atom structure prediction as well as toward guiding RNA design as drug targets for therapeutic small molecules.