De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding.

ABSTRACT: Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, ef

SUBMITTER: Alexandari AM 

PROVIDER: S-EPMC10197627 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature