Das, Rhiju 
ORCID: 0000-0001-7497-0972, Kretsch, Rachael C ORCID: 0000-0002-6935-518X, Simpkin, Adam J ORCID: 0000-0003-1883-9376, Mulvaney, Thomas ORCID: 0000-0002-4373-6160, Pham, Phillip ORCID: 0000-0002-0240-6384, Rangan, Ramya ORCID: 0000-0002-0960-0825, Bu, Fan ORCID: 0009-0000-1903-2270, Keegan, Ronan M ORCID: 0000-0002-9495-0431, Topf, Maya ORCID: 0000-0002-8185-1215, Rigden, Daniel J ORCID: 0000-0002-7565-8937 et al (show 2 more authors)
(2023)
Assessment of three-dimensional RNA structure prediction in CASP15.
Proteins, 91 (12).
pp. 1747-1770.
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Proteins, RNA, Computational Biology, Algorithms |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Mar 2024 16:32 |
| Last Modified: | 15 Mar 2024 19:52 |
| DOI: | 10.1002/prot.26602 |
| Open Access URL: | https://onlinelibrary.wiley.com/doi/10.1002/prot.2... |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3179500 |
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