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A full understanding of biomolecular function requires an analysis of both the dynamic properties of the system of interest and the identification of those dynamics that are required for function. We describe NMR methods based on metabolically directed specific isotope labeling for the identification of molecular disorder and/or conformational transitions on the RNA backbone ribose groups. These analyses are complemented by the use of synthetic covalently modified nucleotides constrained to a single sugar pucker, which allow functional assessment of dynamics by selectively removing a minor conformer identified by NMR from the structural ensemble.
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This content will become publicly available on May 8, 2027
RNAprecis: Prediction of full-detail RNA conformation from the experimentally best-observed sparse parameters
We address the problem of predicting high-detail RNA structure geometry from the information available in low-detail experimental maps. Here, low-detail refers to resolutions ≈ 2.5-3.5Å, where the location of the phosphate groups and the glycosidic bonds can be determined from experimental maps but all other backbone atom positions cannot. In contrast, higher-resolution maps allow high-detail determinations of all backbone atomic positions. To this end, we first create a gold standard dataset of highly curated, experimentally supported RNA suites. Second, we develop and employ a modified version of the previously devised algorithm MINT-AGE to learn clusters that are in high correspondence with the gold standard’s conformational classes of suites based on 3D RNA structure. Since some of the gold standard classes are of very small size, a new modified version of MINT-AGE is able to also identify very small clusters. Third, we create a new conformer prediction algorithm, RNAprecis, which assigns low-detail structures to newly designed 3D shape coordinates. Our improvements include: (i) learned classes augmented to cover also very low sample sizes and (ii) replacing distances from clusters by Bayesian posterior probabilities. On test data containing suites modeled as conformational outliers, RNAprecis shows good results suggesting that our learning method generalizes well. In particular, we show that the modified MINT-AGE clustering can more finely delineate between previously unseen suite conformer separations. For example, the0aconformer has been separated into two clusters seen in different structural contexts. Such new distinctions can have implications for biochemical interpretation of RNA structure.
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- Award ID(s):
- 2515765
- PAR ID:
- 10694400
- Editor(s):
- Kasson, Peter M
- Publisher / Repository:
- PLOS Computational Biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 22
- Issue:
- 5
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1014242
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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