Liu, P.; Wang, L.; Basnet, S.; Cheng, J.

Transcription factors (TFs) are central regulators of gene expression, and their selective recognition of genomic DNA underlies various biological processes. Experimental profiling of TF -- DNA interactions using chromatin immunoprecipitation followed by sequencing(ChIP-seq) provides high resolution maps of in vivoTF -- DNA binding but remains costly, labor-intensive, and inherently low-throughput, limiting their scalability across different transcription factors,cell types, and regulatory conditions. Computational modeling therefore plays an essential role in inferring TF -- DNA interactions at genome scale. However, most existing computational models rely solely on DNA sequence and chromatin features to predict TF -- DNA binding, neglecting TF-specific protein information. This omission limits their ability to capture protein-dependent binding specificity. Here, we present TFBindFormer, a hybrid cross-attention transformer that explicitly integrates genomic DNA features with TF specific representations derived from protein sequences and structures. By modeling protein-conditioned, position-specific TF -- DNA interactions, TFBindFormer enables direct learning of molecular determinants underlying DNA recognition. Evaluated across hundreds of cell-type-specific TFs and hundreds of millions of genome-wide DNA bins, TFBindFormer consistently outperforms DNA-only baselines, achieving substantial gains in both area under precision-recall curve(AUPRC) and area under receiver operating characteristic curve(AUROC). Together, these results demonstrate that integrating TF and DNA features via cross-attention enables TFBindFormer to serve as an effective and scalable framework for large-scale TF -- DNA binding prediction.