91̽»¨

Abstract:

Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is a complex exercise, which has recently been the focus of approaches relying on deep learning. Here, we survey such artificial-intelligence-based approaches and compare them with classical methods for smFRET analysis. The use of deep learning has shown potential to enhance precision, accuracy, and speed in analysing massive smFRET datasets.

Abstract:

Single-molecule imaging was developed to resolve behaviours obscured by ensemble averaging, but early tracking experiments typically captured only brief temporal windows, restricting analysis to individual states rather than the progression between them. Observation times now extend to minutes, revealing complete multi-stage biological processes that require new analytical approaches to capture sequences of events. Here we present DeepTRACE, a flexible tool for analysing single-molecule tracks in living cells that learns sequences of molecular events using past and future context from subcellular location, mobility, and photometric properties. It learns any molecular behaviour that can be annotated with natural-language labels, enabling users to tailor models themselves to specific biological questions without ML expertise. DeepTRACE generalises rapidly from very small datasets, training in minutes on a few hundred tracks, and 91̽»¨s extensive downstream analysis, including discovery of relationships absent from the training data. As DeepTRACE natively handles any numerical feature outside of its standard feature set, it incorporates photometric readouts, including measurements of internal conformation that reflect molecular action, alongside motion, temporal context, and subcellular location. We anticipate that researchers will use DeepTRACE to define biological states by molecular behaviour rather than mobility alone in complex multi-stage processes.

Abstract:

Biological function is fundamentally determined by nucleic acid and protein sequence. Beyond encoding genetic information, nucleic acids also display complex physicochemical parameters that shape structure, dynamics, and interactions. Understanding how sequence variation sculpts the energetic landscapes underlying these properties requires methods that capture both molecular diversity and dynamic behavior. Single-molecule techniques are ideally suited to this task, but conventional formats remain time and cost intensive. Recent breakthroughs have enabled highly multiplexed approaches for observing molecular dynamics across millions of individual molecules representing thousands of sequences or barcoded entities. Though still in development, these methods have begun to bridge sequence, structure, dynamics, and function at scale, opening new opportunities in drug discovery, molecular diagnostics, and functional genomics.

Abstract:

Conjugal transfer of plasmids between bacteria is a major route for the spread of antimicrobial resistance. Many conjugative plasmids encode exclusion systems that inhibit redundant conjugation. In incompatibility group F (IncF) plasmids surface exclusion is mediated by the outer membrane protein TraT. Here we report the cryoEM structure of the TraT exclusion protein complex from the canonical F plasmid of Escherichia coli. TraT is a hollow homodecamer shaped like a chef’s hat. In contrast to most outer membrane proteins, TraT spans the outer membrane using transmembrane a-helices. We develop a microscopy-based conjugation assay to  probe the effects of directed mutagenesis on TraT. Our analysis provides no 91̽»¨ for the idea that TraT has specific interactions with partner proteins. Instead, we infer that TraT is most likely to function by physical interference with conjugation. This work provides structural insight into a natural inhibitor of microbial gene transfer.

Abstract:

Influenza A, a negative-sense RNA virus, has a genome that consists of eight single-stranded RNA segments. Influenza co-infections can result in reassortant viruses that contain gene segments from multiple strains, causing pandemic outbreaks with severe consequences for human health. The outcome of reassortment is likely influenced by a selective sequence-specific genome packaging mechanism. To uncover the contributions of individual segment pairings to selective packaging, we set out to statistically analyse packaging defects and inter-segment distances in individual A/Puerto Rico/8/34 (H1N1) (PR8) virus particles. To enable such analysis, we developed a multiplexed DNA-PAINT approach capable of assessing the segment stoichiometry of >10 000 individual virus particles in one experiment; our approach can also spatially resolve the individual segments inside complete virus particles with a localization precision of ∼10 nm. Our results show the influenza genome can be assembled through multiple pathways in a redundant and cooperative process guided by preferentially interacting segment pairs and aided by synergistic effects that enhance genome assembly, driving it to completion. Our structural evidence indicates that the interaction strength of segment pairs affects the spatial configuration of the gene segments, which appears to be preserved in mature virions. As our method quantified the interactions of whole influenza segments instead of identifying individual sequence-based interactions, our results can serve as a template to quantify the contributions of individual sequence motifs to selective packaging.