摘要Accurately quantifying protein-DNA interactions(PDIs)is critical for understanding biological processes and facilitating drug design.However,the inherent flexibility of nucleic acids limits the availability of experimentally determined structures of PDI complexes,posing a significant challenge for training reliable scoring functions(SFs).To address this,we developed PDIScore,a novel deep learning-based SF for PDI prediction.PDIScore utilizes a comprehensive graph representation to capture nucleotide flexibility,employs a scalable GraphGPS architecture with BigBird linear global attention to handle large interaction interfaces,and leverages Mixture Density Networks(MDNs)to model residue-nucleotide distance distributions.PDIScore was trained on a self-collected dataset of～7000 protein-nucleic acid complex structures and validated on three rigorous test sets for evaluating its screening,docking,and ranking capabilities.The results illustrated that PDIScore significantly outperformed existing methods:it achieved the best screening power on the screening set(e.g.,EF1％=14.13,AUROC=0.82 using AlphaFold3 structures),the highest docking success rate on the docking set(48.94％top1),and superior ranking capability on the ranking set(PCC=0.50).Case studies demonstrated PDIScore's ability to elucidate biological mechanisms(e.g.,adenovirus transcription,SOCS1 regulation)and its interpretability at the nucleotide level for identifying key interaction sites.PDIScore represents a robust,generalizable tool with significant potential for advancing PDI-related research and therapeutic design.