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

Abstract

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.