Abstract
Accurate quality assessment is critical for computational prediction and design of RNA three-dimensional (3D) structures, yet it remains a significant challenge. In this work, we introduce RNArank, a deep learning-based approach to both local and global quality assessment of predicted RNA 3D structure models. For a given structure model, RNArank extracts a comprehensive set of multi-modal features and processes them with a Y-shaped residual neural network. This network is trained to predict two intermediate 2D maps, including the inter-nucleotide contact map and the distance deviation map. These maps are then used to estimate the local and global accuracy. Extensive benchmark tests indicate that RNArank consistently outperforms traditional methods and other deep learning-based methods. Moreover, RNArank demonstrates promising performance in identifying high-quality structure models for targets from the recent CASP15 and CASP16 experiments. We anticipate that RNArank will serve as a valuable tool for the RNA biology community, improving the reliability of RNA structure modeling and thereby contributing to a deeper understanding of RNA function.
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Data availability
The authors declare that the data supporting the results and conclusions of this study are available within the paper and its Supplementary Information. The source data underlying Figs. 2–4 are provided in the Supplementary Data file. The test source data used in this study is available at Zenodo48 and our website (https://yanglab.qd.sdu.edu.cn/RNArank/benchmark/).
Code availability
The RNArank web server is available at: https://yanglab.qd.sdu.edu.cn/RNArank/. The source codes are available at Zenodo48 and GitHub (https://github.com/YangLab-SDU/RNArank/).
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Acknowledgements
This work is supported by the following funding sources: National Natural Science Foundation of China (NSFC T2225007, T2222012, 32430063, 62402075, 62501364), Postdoctoral Fellowship Program and China Postdoctoral Science Foundation (BX20240212, 2025M783122), the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300639), and Fundamental Research Funds for the Central Universities.
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J.Y. conceptualized and administered the study. X.L. designed and implemented the network. X.L., W.W., and J.Y. conducted the formal analysis. X.L., W.W., Z.D., and Z.W. performed data curation and generated decoys. Z.P. and W.W. co-supervised the study. All authors wrote and revised the manuscript.
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Liu, X., Wang, W., Du, Z. et al. Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps. Commun Biol (2026). https://doi.org/10.1038/s42003-026-09582-2
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DOI: https://doi.org/10.1038/s42003-026-09582-2
