Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning.

ABSTRACT: Sequence-specific interactions of transcription factors (TFs) with genomic DNA underlie many cellular processes. High-throughput in vitro binding assays coupled with machine learning have made it possible to accurately define such molecular recognition in a biophysically interpretable way for hundreds of TFs across many structural families, providing new avenues for predicting how the sequence preference of a TF is impacted by disease-associated mutations in its DNA binding domain. We developed a method based on a reference-free tetrahedral representation of variation in base preference within a given structural family that can be used to accurately predict the effect of mutations in the protein sequence of the TF. Using the basic helix-loop-helix (bHLH) and homeodomain (HD) families as te

SUBMITTER: Liu S 

PROVIDER: S-EPMC12392098 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature