Deep probabilistic models for analysing complex DNA structures in high-resolution atomic force microscopy images.

Rosalind Franklin's pioneering work to establish the atomic structure of DNA has underpinned much of our understanding of the 'molecule of life'. The compaction of genomic DNA into the nucleus results in significant topological stress and the formation of coiled, twisted and knotted DNA structures which impact cell viability, with ramifications from DNA replication to the activity of therapeutic agents in cancer and infection. The challenge of understanding how these complex DNA structures influence DNA processing has been fundamentally limited by the tools available.
High-resolution atomic force microscopy (AFM) is unique in its ability to provide quantitative information on DNA structure, function and kinetics in liquid with nanometre resolution without labelling or averaging [1], however the analysis of these datasets has until now relied on the eye of an experienced microscopist [2]. Despite the increasing size of datasets generated by AFM, automated analysis and/or machine learning techniques are not routinely applied.
Machine learning has driven step changes in our understanding of biological phenomena (e.g. AlphaFold). Deep learning using artificial neural networks has been applied to datasets produced with adjacent microscopies (notably cryo-EM in its resolution revolution), to solve previously inaccessible biological problems. Gaussian processes (GPs) are another important machine learning technique, useful in situations where data is less abundant and more is known about the behaviour of the system being modelled (e.g. DNA mechanics). We propose to use a combination of these and similar techniques, adapting and improving them in analysis of complex bio-AFM datasets.