Studying the interactions between aggregation-prone proteins and molecular chaperones using single-molecule fluorescence-based techniques
There are a number of cellular mechanisms that act to maintain the folded and functional state of the proteome, including the highly conserved molecular chaperone proteins. Molecular chaperones act to prevent protein aggregation and comprise a diverse range of sub-classes; these include the small heat shock proteins (sHsps), which function independently of ATP, and the Hsp70 system which are ATP-dependent. Molecular chaperones are inherently heterogeneous and dynamic proteins, and protein aggregation itself is also extremely heterogeneous. Single-molecule techniques have emerged as a powerful tool to study chaperone function and the interaction of chaperones with their client proteins; this is largely owing to their capacity to visualise rare or dynamic features in individual proteins that are typically masked in ensemble-averaging techniques. The work in this thesis aimed to utilise fluorescence-based single-molecule techniques, in particular total internal reflection fluorescence (TIRF) microscopy, to observe and quantify the interactions between molecular chaperones and their clients.
This work first involved the development of an analysis workflow to calculate the number of subunits per protein oligomer from single-molecule photobleaching trajectories. Previous methods to calculate subunits within protein assemblies via single-molecule photobleaching were typically comprised of highly manual and subjective elements. In this work a generalizable machine-learning tool, called py4bleaching, was developed and optimised to identify photobleaching trajectories which are appropriate for further analysis. This machine-learning tool was integrated into an automated analysis workflow which can be easily implemented to analyse data output from a range of experimental designs. Py4bleaching was subsequently used throughout this work to quantify the number of protein subunits from single-molecule data.
History
Year
2024Thesis type
- Doctoral thesis