Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with computational analysis have made it possible to accurately define such sequence recognition in a biophysically interpretable yet mechanism-agonistic way for individual TFs. The fact that such sequence-to-affinity models are now available for hundreds of TFs provides new avenues for predicting how the DNA binding specificity of a TF changes when its protein sequence is mutated. To this end, we developed an analytical framework based on a tetrahedron embedding that can be applied at the level of a given structural TF family. Using bHLH as a test case, we demonstrate that we can systematically map dependencies between the protein sequence of a TF and base preference within the DNA binding site. We also develop a regression approach to predict the quantitative energetic impact of mutations in the DNA binding domain of a TF on its DNA binding specificity, and perform SELEX-seq assays on mutated TFs to experimentally validate our results. Our results point to the feasibility of predicting the functional impact of disease mutations and allelic variation in the cell-wide TF repertoire by leveraging high-quality functional information across sets of homologous wild-type proteins.

Epigenetic DNA modification impacts gene expression, but the underlying molecular mechanisms are only partly understood. Adding a methyl group to a cytosine base locally modifies the structural features of DNA in multiple ways, which may change the interaction with DNA-binding transcription factors (TFs) and trigger a cascade of downstream molecular events. Cells can be probed using various functional genomics assays, but it is difficult to disentangle the confounded effects of DNA modification on TF binding, chromatin accessibility, intranuclear variation in local TF concentration, and rate of transcription. Here we discuss how high-throughput in vitro profiling of protein-DNA interactions has enabled comprehensive characterization and quantification of the methylation sensitivity of TFs. Despite the limited structural data for DNA containing methylated cytosine, automated analysis of structural information in the Protein Data Bank (PDB) shows how 5-methylcytosine (5mC) can be recognized in various ways by amino acid side chains. We discuss how a context-dependent effect of methylation on DNA groove geometry can affect DNA binding by homeodomain proteins and how principled modeling of ChIP-seq data can overcome the confounding that makes the interpretation of in vivo data challenging. The emerging picture is that epigenetic modifications affect TF binding in a highly context-specific manner, with a direction and effect size that depend critically on their position within the TF binding site and the amino acid sequence of the TF. With this improved mechanistic knowledge, we have come closer to understanding how cells use DNA modification to acquire, retain, and change their identity. (C) 2019 The Authors. Published by Elsevier Ltd.