Functional prediction of DNA/RNA-binding proteins by deep learning from gene expression correlations

Nucleic acid-binding proteins (NABPs) play central roles in gene regulation, yet their functional targets and regulatory programs remain incompletely characterized due to the limited scope and context specificity of experimental binding assays. Here, we present a deep learning framework that integrates gene co-expression-derived interactions with contribution-based model interpretation to infer NABP regulatory influence across diverse cellular contexts, without relying on predefined binding motifs or direct binding evidence. Replacing low-informative binding-based features with co-expression-derived interactions significantly improved gene expression prediction accuracy. Model-inferred regulatory targets showed strong and reproducible concordance with independent ChIP-seq and eCLIP datasets, exceeding random expectations across multiple genomic regions and threshold definitions. Functional enrichment and gene set enrichment analyses revealed coherent, cell type-specific regulatory programs, including cancer-associated pathways in K562 cells and differentiation-related processes in neural progenitor cells. Notably, we demonstrate that DeepLIFT-derived contribution scores capture relative regulatory importance in a background-dependent but biologically robust manner, enabling systematic identification of context-dependent NABP regulatory roles. Together, this framework provides a scalable strategy for functional annotation of NABPs and highlights the utility of combining expression-driven inference with interpretable deep learning to dissect gene regulatory architectures at scale.