The Energy Landscape of DNA-Binding Proteins Along the Genome

Abstract Reconstructing the energy profile of DNA-binding proteins along the genome requires an algorithm that quantifies efficiently the binding free energy. We assembled a dataset of protein structures and DNA binding sites, together with their binding energies, and used it to train a machine-learning algorithm that learns a latent invariant representation of the protein interface and of the DNA sequence, combining them to predict the free energy. After validating the method, we used it to determine the energy profile of a single-domain transcription factor (PU.1) sliding on mammalian chromosomes, predicting its binding regions and quantifying the statistical properties that determine their stability and their kinetic accessibility.