Professor Achillefs Kapanidis

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 support 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.

Abstract:

Most bacterial sigma factors (σ) contain a highly conserved structural module, the 'σ-finger', which forms a loop that protrudes towards the RNA polymerase active centre in the open complex and has been implicated in pre-organization of template DNA, abortive initiation of short RNAs, initiation pausing, and promoter escape. Here, we introduce a novel single-molecule FRET (smFRET) assay to monitor σ-finger motions during transcription initiation and promoter escape. By performing real-time smFRET measurements, we determine that for all promoters studied, displacement occurs before promoter escape and can occur either before or after a clash with the extending RNA. We show that the kinetics of σ-finger displacement are highly dependent on the promoter, with implications for transcription kinetics and regulation. Analogous mechanisms may operate in the similar modules present across all kingdoms of life.

Abstract:

The understanding of cellular mechanisms benefits substantially from accurate determination of protein diffusive properties. Prior work in this field primarily focuses on traditional methods, such as mean square displacements, for calculation of protein diffusion coefficients and biological states. This proves difficult and error-prone for proteins undergoing heterogeneous behaviour, particularly in complex environments, limiting the exploration of new biological behaviours. The importance of determining protein diffusion coefficients, anomalous exponents, and biological behaviours led to the Anomalous Diffusion Challenge 2024, exploring machine learning methods to infer these variables in heterogeneous trajectories with time-dependent changepoints. In response to the challenge, we present M3, a machine learning method for pointwise inference of diffusive coefficients, anomalous exponents, and states along noisy heterogenous protein trajectories. M3 makes use of long short-term memory cells to achieve small mean absolute errors for the diffusion coefficient and anomalous exponent alongside high state accuracies (>90%). Subsequently, we implement changepoint detection to determine timepoints at which protein behaviour changes. M3 removes the need for expert fine-tuning required in most conventional statistical methods while being computationally inexpensive to train. The model finished in the Top 5 of the Anomalous Diffusive Challenge 2024, with small improvements made since challenge closure.