ArticleNucleic acids research2014
The use of interatomic contact areas to quantify discrepancies between RNA 3D models and reference structures.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A unified statistical potential reveals that amino acid stickiness governs nonspecific recruitment of client proteins into condensates.Protein science : a publication of the Protein Society · 2022Article
- Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.Nucleic acids research · 2018Article
- Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12.Proteins · 2018Article
- 3dRNAscore: a distance and torsion angle dependent evaluation function of 3D RNA structures.Nucleic acids research · 2015Article
- The CAD-score web server: contact area-based comparison of structures and interfaces of proteins, nucleic acids and their complexes.Nucleic acids research · 2014Article
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Authors and funding
2 authors.
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Abstract
Growing interest in computational prediction of ribonucleic acid (RNA) three-dimensional structure has highlighted the need for reliable and meaningful methods to compare models and experimental structures. We present a structure superposition-free method to quantify both the local and global accuracy of RNA structural models with respect to the reference structure. The method, initially developed for proteins and here extended to RNA, closely reflects physical interactions, has a simple definition, a fixed range of values and no arbitrary parameters. It is based on the correspondence of respective contact areas between nucleotides or their components (base or backbone). The better is the agreement between respective contact areas in a model and the reference structure, the more accurate the model is considered to be. Since RNA bases account for the largest contact areas, we further distinguish stacking and non-stacking contacts. We have extensively tested the contact area-based evaluation method and found it effective in both revealing local discrepancies and ranking models by their overall quality. Compared to other reference-based RNA model evaluation methods, the new method shows a stronger emphasis on stereochemical quality of models. In addition, it takes into account model completeness, enabling a meaningful evaluation of full models and those missing some residues.
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