Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements.

ABSTRACT: Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs

SUBMITTER: Oriol F 

PROVIDER: S-EPMC11167492 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature