Deep Learning-Based Classification of Histone-DNA Interactions Using Drying Droplet Patterns

Developing scalable and accurate predictive analytical methods for the classification of protein-DNA binding is critical for advancing our understanding of molecular biology, disease mechanisms, and a wide spectrum of biotechnological and medical applications. It is discovered that histone-DNA interactions can be stratified based on stain patterns created by the deposition of various nucleoprotein solutions onto a substrate. In this study, a deep-learning neural network is applied to categorize polarized light microscopy images of drying droplet deposits originating from different histone-DNA mixtures. These DNA stain patterns featured high reproducibility across different species and thus enabled comprehensive DNA categorization (100% accuracy) and accurate prediction of their respective binding affinities to histones. Eukaryotic DNA, which has a higher binding affinity to mammalian histones than prokaryotic DNA, is associated with a higher overall prediction accuracy. For a given species, the average prediction accuracy increased with DNA size. To demonstrate generalizability, a pre-trained CNN is challenged with unknown images that originated from DNA samples of species not included in the training set. The CNN classified these unknown histone-DNA samples as either strong or medium binders with 84.4% and 96.25% accuracy, respectively.