Transcription factors (TFs), the core proteins of transcription initiating processes, regulate gene expression by binding to short genomic sequences, known as transcription factor binding site (TFBS), through defined DNA-binding domains (DBDs). These interactions are central to understand gene regulation, which underlies many cellular processes. Although many models exist for predicting transcription factor binding, none provide a comprehensive framework that accounts for the similarity of binding characteristics among TFs within the same DBD class. In fact, several of these families are severely under represented in most current analysis pipelines.

Our model, TFClassPredict, introduces a novel approach to identify transcription factor binding sites based on structural annotations of evolutionarily conserved DBDs. By leveraging canonical binding patterns, TFClassPredict provides high-confidence predictions essential for gene regulation analysis. Fine-tuned from the DNABERT model, TFClassPredict classifies DNA sequences across 23 classes of DBDs. TFClassPredict achieved strong performances, naturally aggregates predicted sites by DBD class, improving both interpretability and the balanced representation of all DBD classes. These results demonstrate that TFClassPredict constitutes a reliable, family aware framework for uncovering regulatory differences and for advancing comprehensive TF binding analyses.

Co-authors: Cigdem Hazal Timucin, Bendix Christian Harms, Inigo Vincente Hernandez, Ivan Bogeski, Tim Beißbarth, Martin Haubrock, Umut Akgül