ArticleNAR genomics and bioinformatics2023
cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- Spectral Graph Features for Reference-free RNA 3D Quality Assessment.bioRxiv : the preprint server for biology · 2026Article
- Quality assessment of RNA 3D structure models using deep learning and intermediate 2D maps.Communications biology · 2026Article
- TiRNA: a coarse-grained method with temperature and ion effects for RNA structure folding and prediction.Nucleic acids research · 2026Article
- PARSEbp: pairwise agreement-based RNA scoring with emphasis on base pairings.Bioinformatics advances · 2026Article
- 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
- rsRNASP1: A distance- and dihedral-dependent statistical potential for RNA 3D structure evaluation.Biophysical journal · 2025Article
- Semi-supervised segmentation of RNA 3D structures using density-based clustering.Computational and structural biotechnology journal · 2025Article
- lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.Journal of chemical information and modeling · 2024Article
- lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.bioRxiv : the preprint server for biology · 2024Article
- State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction.NAR genomics and bioinformatics · 2024Article
- Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.Journal of chemical theory and computation · 2024Article
- Article
- RNA 3D Structure Prediction: Progress and Perspective.Molecules (Basel, Switzerland) · 2023Review
- Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding.Molecules (Basel, Switzerland) · 2023Review
- Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions.Biophysical journal · 2023Article
- FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures.Biophysical journal · 2022Article
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5 authors.
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Abstract
Knowledge-based statistical potentials are very important for RNA 3-dimensional (3D) structure prediction and evaluation. In recent years, various coarse-grained (CG) and all-atom models have been developed for predicting RNA 3D structures, while there is still lack of reliable CG statistical potentials not only for CG structure evaluation but also for all-atom structure evaluation at high efficiency. In this work, we have developed a series of residue-separation-based CG statistical potentials at different CG levels for RNA 3D structure evaluation, namely cgRNASP, which is composed of long-ranged and short-ranged interactions by residue separation. Compared with the newly developed all-atom rsRNASP, the short-ranged interaction in cgRNASP was involved more subtly and completely. Our examinations show that, the performance of cgRNASP varies with CG levels and compared with rsRNASP, cgRNASP has similarly good performance for extensive types of test datasets and can have slightly better performance for the realistic dataset-RNA-Puzzles dataset. Furthermore, cgRNASP is strikingly more efficient than all-atom statistical potentials/scoring functions, and can be apparently superior to other all-atom statistical potentials and scoring functions trained from neural networks for the RNA-Puzzles dataset. cgRNASP is available at https://github.com/Tan-group/cgRNASP.
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Registered trials
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