Mapping the conformational landscape of DNA minicircles through atomic force microscopy and shape space analysis

The structural dynamics of DNA underpin essential biological processes, yet conventional structural biology methods often obscure conformational heterogeneity through ensemble averaging. Atomic force microscopy (AFM) provides single-molecule topographical maps capable of capturing both local and global variation, but extracting quantitative insight from these images remains challenging. Here, we introduce an automated framework that reduces AFM data to spline representations of the DNA backbone and applies cyclic Procrustes analysis to quantify shape similarity across ensembles. Using purified topoisomers of 339 bp DNA minicircles ranging from relaxed to highly negatively supercoiled, we resolved and measured the relative abundance of conformational states across the different topoisomers, capturing gradual transitions among open circles, compact conformations, and self-crossing structures that are invisible to techniques such as gel electrophoresis or cryoelectron microscopy (cryo-EM). We show that beyond quantification, Procrustes distances provide supervisory signals for neural network training, enabling feature extraction tuned to conformational geometry and supporting robust conformation classification of AFM images. Extending the same spline representation to molecular dynamics simulations allows experimental and computational ensembles to be directly compared, establishing a common shape-based framework for probing conformational variability. Together, these advances transform AFM from a descriptive imaging tool into a quantitative platform for mapping conformational continua, with broad applicability to DNA and other dynamic biomolecular systems.