RNA adopts three-dimensional structures that play a crucial and direct role in its biological function. Assessing the real or predictive quality of a structure is at stake with the complex 3D possible conformations of RNAs.
Here we propose RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions
We hope this tool will help the automation of RNA 3D structures evaluation.
Zhanglab: a complete website to compute multiple scores, such as the GDT-TS scores.
BaRNAba: an implementation of the eRMSD and eSCORE.
DFIRE: an implementation of the DFIRE energy function.
RASP: an implementation of the RASP energy function.
rsRNASP: a Python implementation of the rsRNASP score.
OpenStructure: a C++ and Python implementation for structure analysis. It is used to compute TM-score and lDDT metrics.
References
How to cite State-of-the-RNArt
Clément Bernard, Guillaume Postic, Sahar Ghannay, Fariza Tahi RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model qualityBriefings in Bioinformatics, Volume 25, Issue 2, March 2024, bbae064https://doi.org/10.1093/bib/bbae064
Additional references:
The metrics that are used in the tools:
Davis, I. W., Leaver-Fay, A., Chen, V. B., Block, J. N., Kapral, G. J., Wang, X., Murray, L. W., Arendall, W. B., Snoeyink, J., Richardson, J. S., & Richardson, D. C.(2007). MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Research, 35(Web Server), W375–W383. https://doi.org/10.1093/nar/gkm216
Hajdin, C. E., Ding, F., Dokholyan, N. v., & Weeks, K. M. (2010). On the significance of an RNA tertiary structure prediction. RNA, 16(7), 1340–1349. https://doi.org/10.1261/rna.1837410
Parisien, M., Cruz, J. A., Westhof, É., & Major, F. (2009). New metrics for comparing and assessing discrepancies between RNA 3D structures and models. RNA, 15(10), 1875–1885. https://doi.org/10.1261/rna.1700409
Zok, T., Popenda, M., & Szachniuk, M. (2014). MCQ4Structures to compute similarity of molecule structures. Central European Journal of Operations Research, 22(3), 457–473. https://doi.org/10.1007/s10100-013-0296-5
Kliment Olechnovic, Eleonora Kulberkyte and Ceslovas Venclovas (2013). CAD-score: a new contact area difference-based function for evaluation of protein structural models. Proteins, 81:149–162. https://doi.org/10.1002/prot.24172
Kliment Olechnovic and Ceslovas Venclovas (2014) The use of interatomic contact areas to quantify discrepancies between RNA 3D models and reference structures. Nucleic Acids Res, 42:5407-5415 https://doi.org/10.1093/nar/gku191
Zemla A, Venclovas C, Moult J, Fidelis K. 1999. Processing and analysis of CASP3 protein structure predictions. Proteins3:22–29 https://doi.org/10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.0.CO;2-W
Miao, Z., & Westhof, E. (2017). RNA Structure: Advances and Assessment of 3D Structure Prediction. Annual Review of Biophysics, 46(1), 483–503. https://doi.org/10.1146/annurev-biophys-070816-034125
Mariani, V., Biasini, M., Barbato, A., & Schwede, T. (2013). lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics (Oxford, England), 29(21), 2722–2728. https://doi.org/10.1093/bioinformatics/btt473
The scoring functions that are used:
T. Zhang, G. Hu, Y. Yang, J. Wang, and Y. Zhou, “All-atom knowledge-based potential for RNA structure discrimination based on the distance-scaled finite ideal-gas reference state.”, J. Computational Biology, in press (2019).
Capriotti E, Norambuena T, Marti-Renom MA, Melo F. (2011) All-atom knowledge-based potential for RNA structure prediction and assessment. Bioinformatics 27(8):1086-93
Tan YL, Wang X, Shi YZ, Zhang W, Tan ZJ. 2022. rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation. Biophys J. 121: 142-156.
