De novo inference of thermodynamic binding energies using deep learning models of in vivo transcription factor binding

We introduce Affinity Distillation (AD), a method for extracting thermodynamic affinities de-novo from in-vivo immunoprecipitation experiments using deep learning. We show that neural networks modeling base-resolution in-vivo binding profiles of yeast and mammalian TFs can accurately predict energetic impacts of varying underlying DNA sequence on TF binding. Systematic comparisons between Affinity Distillation predictions and other predictive algorithms consistently show that Affinity Distillation more accurately predicts affinities across a wide range of TF structural classes and DNA sequences. Affinity Distillation relies on in-silico marginalization against many sequence backgrounds, resulting in a higher dynamic range and more accurate predictions than motif discovery algorithms. Moreover, we show that Affinity Distillation can learn differential paralog-specific affinities, thereby making it possible to more accurately reconstruct regulatory networks in cells. Chromatin immunoprecipitation (ChIP-nexus) data for Pho4 and Cbf1 in Saccharomyces cerevisiae