ArticleRNA (New York, N.Y.)2019
What is the best reference state for building statistical potentials in RNA 3D structure evaluation?
Article in RNA (New York, N.Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- TiRNA: a coarse-grained method with temperature and ion effects for RNA structure folding and prediction.Nucleic acids research · 2026Article
- rsRNASP1: A distance- and dihedral-dependent statistical potential for RNA 3D structure evaluation.Biophysical journal · 2025Article
- RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.Briefings in bioinformatics · 2024Article
- RNA 3D Structure Prediction: Progress and Perspective.Molecules (Basel, Switzerland) · 2023Review
- Predicting 3D structures and stabilities for complex RNA pseudoknots in ion solutions.Biophysical journal · 2023Article
- cgRNASP: coarse-grained statistical potentials with residue separation for RNA structure evaluation.NAR genomics and bioinformatics · 2023Article
- FebRNA: An automated fragment-ensemble-based model for building RNA 3D structures.Biophysical journal · 2022Article
- rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation.Biophysical journal · 2022Article
- Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement.Nature communications · 2021Article
- RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.Frontiers in bioinformatics · 2021Article
- Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement.Frontiers in molecular biosciences · 2021Article
- Representations of protein structure for exploring the conformational space: A speed-accuracy trade-off.Computational and structural biotechnology journal · 2021Article
- RNA 3D Structure Prediction Using Coarse-Grained Models.Frontiers in molecular biosciences · 2021Review
- Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway.RNA (New York, N.Y.) · 2019Article
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6 authors.
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Abstract
Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walk-chain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.
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