Classical and machine learning algorithms for analysing complex DNA structures

Atomic force microscopy (AFM) is unique in its ability to image single molecules in liquid with sub-molecular resolution, without the need for labelling or averaging. This enables us to probe biomolecular structures in native-like states and examine conformational changes. For DNA, its innate flexibility enables compaction in the nucleus and processing by essential cellular machinery which drives a large range of these conformational changes and must be regulated to ensure cell survival. However, the large quantities of closed AFM filetypes limit the adoption of open-source tools developed by the image analysis community. The lack of AFM-specific automated analysis tools to process raw data and characterise conformation make high-throughput conformational analyses difficult and laborious.

I have developed AFMReader, an open-source Python file loader for the extraction of AFM images and metadata from proprietary file formats. I also developed TopoStats, a toolbox for; AFM-specific image processing, object identification, and characterisation of individual molecules. Key developments to this pipeline are a new height-biased skeletonisation algorithm, and quantification of overlapping DNA segments, enabling the accurate tracing of branched, crossing, and overlapping DNA structures.

This new automated tracing architecture enables the classification of DNA knots and catenanes produced by the Xer recombination system by extracting a pseudo 3D molecular backbone trace. I characterise DNA replication fork stalling by Lac-repressor protein and the Tur-Ter complex via calculation of the replicated and unreplicated DNA segment contour lengths. I show that this pipeline can be adapted to characterise a possible prebiotic RNA synthesis pathway via molecular backbone height profiles across samples conditions in a fixed location. Finally, I explore the feasibility of a deep learning variational auto-encoder to describe the conformational landscape of supercoiled DNA minicircles. These applications show the versatility of this new pipeline as a toolbox to help quantify and uncover the role of structure in DNA interactions.