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Consistent global structures of complex RNA states through multidimensional chemical mapping.

Cheng CY, Chou FC, Kladwang W, Tian S, Cordero P, Das R - Elife (2015)

Bottom Line: Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis.Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions.This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, Stanford University, Stanford, United States.

ABSTRACT
Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

No MeSH data available.


Related in: MedlinePlus

M2 analysis of the GIR1 lariat-capping ribozyme, performed during the fifth RNA-puzzles structure prediction trial.(A) M2 data set for 1M7 modification across 188 single mutations along the GIR1 ribozyme sequence. Mutants showing poor data quality are marked by red bars. (B) Z-score contact map extracted from (A). (C) Secondary structure prediction and (D) bootstrap support matrix using M2 data. In (B) and (D), the crystallographic secondary structure is overlaid as cyan circles.DOI:http://dx.doi.org/10.7554/eLife.07600.016
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fig3s7: M2 analysis of the GIR1 lariat-capping ribozyme, performed during the fifth RNA-puzzles structure prediction trial.(A) M2 data set for 1M7 modification across 188 single mutations along the GIR1 ribozyme sequence. Mutants showing poor data quality are marked by red bars. (B) Z-score contact map extracted from (A). (C) Secondary structure prediction and (D) bootstrap support matrix using M2 data. In (B) and (D), the crystallographic secondary structure is overlaid as cyan circles.DOI:http://dx.doi.org/10.7554/eLife.07600.016

Mentions: As a rigorous test, we applied MCM to model another RNA-puzzle before the release of its crystal structure, a 188-nucleotide lariat-capping ribozyme from Didymium iridis. As part of this blind challenge, we previously collected and shared M2 data (Figure 3—figure supplement 7) that supported literature models of the ncRNA's secondary structure and suggested two additional tertiary contacts creating a ‘ring’ around the ribozyme. Nevertheless, during the RNA-puzzle time window in the summer of 2012, modeling from M2 alone was uncertain, particularly in the long, kissing helical stacks P2.1 and P4–P6, which had no homologies to any experimentally solved structure (blue and green regions, Figure 3F). After developing MOHCA-seq but before the release of the lariat-capping ribozyme crystal structure, we completed the MCM pipeline by acquiring MOHCA-seq data for the ncRNA (Figure 3F), and the resulting map highlighted potential errors in these extended peripheral regions. We then blindly refined the peripheral regions based on the full MCM data (Figure 3F and Table 1), and the subsequent release of the ribozyme crystal structure (Meyer et al., 2014) confirmed that their accuracy significantly improved, from an original RMSD of 17.0 Å (original RNA-puzzle submission, M2/Rosetta) to a final RMSD of 11.2 Å (full MCM, i.e., M2/MOHCA-seq/Rosetta). The global accuracy of the models also improved, with a range of RMSDs over the full ribozyme from 7.6 to 8.9 Å, compared to our original submission, which turned out to have an overall RMSD accuracy of 9.6 Å. Taken together, these results rigorously demonstrated the ability of the full MCM pipeline to detect errors and to consistently and blindly refine ncRNA structures to 1-nm resolution or better throughout complex structures.


Consistent global structures of complex RNA states through multidimensional chemical mapping.

Cheng CY, Chou FC, Kladwang W, Tian S, Cordero P, Das R - Elife (2015)

M2 analysis of the GIR1 lariat-capping ribozyme, performed during the fifth RNA-puzzles structure prediction trial.(A) M2 data set for 1M7 modification across 188 single mutations along the GIR1 ribozyme sequence. Mutants showing poor data quality are marked by red bars. (B) Z-score contact map extracted from (A). (C) Secondary structure prediction and (D) bootstrap support matrix using M2 data. In (B) and (D), the crystallographic secondary structure is overlaid as cyan circles.DOI:http://dx.doi.org/10.7554/eLife.07600.016
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4495719&req=5

fig3s7: M2 analysis of the GIR1 lariat-capping ribozyme, performed during the fifth RNA-puzzles structure prediction trial.(A) M2 data set for 1M7 modification across 188 single mutations along the GIR1 ribozyme sequence. Mutants showing poor data quality are marked by red bars. (B) Z-score contact map extracted from (A). (C) Secondary structure prediction and (D) bootstrap support matrix using M2 data. In (B) and (D), the crystallographic secondary structure is overlaid as cyan circles.DOI:http://dx.doi.org/10.7554/eLife.07600.016
Mentions: As a rigorous test, we applied MCM to model another RNA-puzzle before the release of its crystal structure, a 188-nucleotide lariat-capping ribozyme from Didymium iridis. As part of this blind challenge, we previously collected and shared M2 data (Figure 3—figure supplement 7) that supported literature models of the ncRNA's secondary structure and suggested two additional tertiary contacts creating a ‘ring’ around the ribozyme. Nevertheless, during the RNA-puzzle time window in the summer of 2012, modeling from M2 alone was uncertain, particularly in the long, kissing helical stacks P2.1 and P4–P6, which had no homologies to any experimentally solved structure (blue and green regions, Figure 3F). After developing MOHCA-seq but before the release of the lariat-capping ribozyme crystal structure, we completed the MCM pipeline by acquiring MOHCA-seq data for the ncRNA (Figure 3F), and the resulting map highlighted potential errors in these extended peripheral regions. We then blindly refined the peripheral regions based on the full MCM data (Figure 3F and Table 1), and the subsequent release of the ribozyme crystal structure (Meyer et al., 2014) confirmed that their accuracy significantly improved, from an original RMSD of 17.0 Å (original RNA-puzzle submission, M2/Rosetta) to a final RMSD of 11.2 Å (full MCM, i.e., M2/MOHCA-seq/Rosetta). The global accuracy of the models also improved, with a range of RMSDs over the full ribozyme from 7.6 to 8.9 Å, compared to our original submission, which turned out to have an overall RMSD accuracy of 9.6 Å. Taken together, these results rigorously demonstrated the ability of the full MCM pipeline to detect errors and to consistently and blindly refine ncRNA structures to 1-nm resolution or better throughout complex structures.

Bottom Line: Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis.Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions.This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, Stanford University, Stanford, United States.

ABSTRACT
Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OH Cleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

No MeSH data available.


Related in: MedlinePlus