Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.

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. Mor

SUBMITTER: Liu X 

PROVIDER: S-EPMC12923511 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature