Precision in Predicting Protein–Nucleic Acid Complexes: Establishing a Benchmark Data Set and Comparative Metrics

Protein–nucleic acid interactions are fundamental to biological processes and biotechnology, yet their computational prediction lags behind protein structure or protein–protein interaction modeling. This study introduces ProNASet, a benchmark data set of 100 experimentally resolved protein–nucleic acid complex structures, alongside a multidimensional evaluation framework using root mean square deviation (RMSD), TM-score, and local distance difference test (LDDT) metrics. We systematically evaluated four deep learning (DL) algorithms (AlphaFold3, Chai-1, HelixFold3, and Protenix) and two physically driven docking methods (HDOCK and HDOCK_NT). Our analysis revealed that physically driven methods significantly outperform current DL approaches in predicting protein–nucleic acid complex structures. The template-less HDOCK_NT demonstrated the highest success rate at 74.5% (using thresholds RMSD <2 Å, TM-score >0.9, and LDDT >0.6), compared to 63.8% for template docking and only 34.0% for the best-performing DL method, AlphaFold3. These results underscore the substantial need for improvement in DL methods for this specific task. The ProNASet benchmark provides a standardized testing platform, highlights intrinsic shortcomings in current DL models for capturing protein–nucleic acid interaction features, and guides the development of next-generation computational tools crucial for advancing genome editing and synthetic biology.