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Precision in Predicting Protein-Nucleic Acid Complexes: Establishing a Benchmark Data Set and Comparative Metrics.
Huizi Cui1, Yuxuan Wang1, Yu Fu1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin Road 2699, Changchun 130012, China.
Journal of Chemical Information and Modeling
|September 11, 2025
Summary
Physically driven methods outperform deep learning for predicting protein-nucleic acid structures. The new ProNASet benchmark highlights the need for improved deep learning (DL) algorithms in this critical area.
Area of Science:
- Computational biology
- Structural biology
- Bioinformatics
Background:
- Protein-nucleic acid interactions are vital for biological functions and biotechnology.
- Computational prediction of these interactions lags behind other molecular modeling tasks.
Purpose of the Study:
- Introduce ProNASet, a benchmark dataset for protein-nucleic acid complex structures.
- Evaluate current deep learning (DL) and physically driven methods for prediction accuracy.
Main Methods:
- Developed ProNASet with 100 experimentally resolved complex structures.
- Established a multidimensional evaluation framework using RMSD, TM-score, and LDDT.
- Systematically tested four DL algorithms and two physically driven docking methods.
Main Results:
- Physically driven methods significantly outperformed DL approaches.
- HDOCK_NT achieved the highest success rate (74.5%), surpassing template docking (63.8%) and DL methods (best 34.0%).
Conclusions:
- Current DL models show significant shortcomings in predicting protein-nucleic acid interactions.
- ProNASet provides a crucial benchmark for developing improved computational tools for genome editing and synthetic biology.
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