ArticleJournal of chemical information and modeling2024
lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The trRosettaRNA server for RNA structure prediction.Nature protocols · 2026Review
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- Integrated experimental and AI innovations for RNA structure determination.Nature biotechnology · 2026Review
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
- AI meets RNA: revolutionizing structure prediction.Science China. Life sciences · 2025Article
- RNAdvisor 2: A unified platform for RNA 3D model quality assessment using metrics, scoring functions, and meta-metrics.Scientific reports · 2025Article
- RNAtive to recognize native-like structure in a set of RNA 3D models.Bioinformatics (Oxford, England) · 2025Article
- PARSEbp: Pairwise Agreement-based RNA Scoring with Emphasis on Base Pairings.bioRxiv : the preprint server for biology · 2025Article
- RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.bioRxiv : the preprint server for biology · 2025Article
- rsRNASP1: A distance- and dihedral-dependent statistical potential for RNA 3D structure evaluation.Biophysical journal · 2025Article
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Abstract
A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently available machine learning-based approaches. Here, we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root-mean-square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.
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