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Rich RNA Structure Landscapes Revealed by Mutate-and-Map Analysis.

Cordero P, Das R - PLoS Comput. Biol. (2015)

Bottom Line: In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments.An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations.These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Program, Stanford University, Stanford, California, United States of America.

ABSTRACT
Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral infection [corrected]. We report herein that high-throughput chemical experiments can isolate an RNA's multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M2) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states' structures and populations. In an in silico benchmark on non-coding RNAs with complex landscapes, M2-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M2-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

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Landscape dissection of a random RNA sequence, the M-stable RNA.(a) M2 data, (b) REEFFIT fit, and (c) inference of the M-stable structural landscape, showing one helix, P1, present at almost 50% as well as numerous other helices (P2, P3, and P4) present at non-negligible fractions. (d-f) State medioids for (d) MST-A, (e) MST-B, and (f) MST-C, and comparison between reactivities predicted by REEFFIT and mutants designed to stabilize each state medioids.
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pcbi.1004473.g006: Landscape dissection of a random RNA sequence, the M-stable RNA.(a) M2 data, (b) REEFFIT fit, and (c) inference of the M-stable structural landscape, showing one helix, P1, present at almost 50% as well as numerous other helices (P2, P3, and P4) present at non-negligible fractions. (d-f) State medioids for (d) MST-A, (e) MST-B, and (f) MST-C, and comparison between reactivities predicted by REEFFIT and mutants designed to stabilize each state medioids.

Mentions: As a second test case with a previously unknown structural ensemble, we tested whether randomly generated or scrambled RNA sequences tend to fold into multiple disparate structures at equilibrium—a long-standing hypothesis fundamental to understanding RNA evolution, put forth by several in silico studies [35,36] and an experimental study that could not deconvolve the structures [37]. We carried out M2-REEFFIT for a randomly generated sequence, called here the M-stable RNA (Fig 6). Based on simulations, the structural ensemble of the construct was expected to consist mainly of a simple hairpin (P1 in MST-A in Fig 6d, see top triangle of the Fig 6c) but with at least two other structures becoming more stable than MST-A upon single mutations. The experimental M2 measurements were complex, with different mutants giving disparate protection patterns even in segments that appeared highly reactive (and seemingly unstructured) in the wild type RNA. As a first check on the number of states, REEFFIT fits assuming only 2 or 3 states missed many features observed in the data, including extended segments of changed chemical reactivity in several mutants (Fig J in S1 Text). However, the REEFFIT global ensemble fit successfully modeled the M-stable data and suggested an ensemble with many more weakly populated helices than RNAstructure’s estimate (compare bottom and top halves of Fig 6c). For visualization, we clustered these heterogeneous component structures into three states, MST-A, MST-B, and MST-C (see Fig 6d–6f and Fig Hd-Hf in S1 Text). Analogous to the case of the Tebowned switch, we tested the REEFFIT prediction of these alternative states by designing mutations to stabilize the medioid structures of each cluster (Fig 6d–6f). These mutants gave reactivities in agreement with predictions for MST-A and MST-C, supporting the inference of those structures. For MST-B, the state-stabilizing mutants gave reactivity profiles that did not exactly match each other, suggesting residual heterogeneity of structure; the profiles were nevertheless closer to the REEFFIT-predicted MST-B profile than wild type reactivities. These results corroborate the M2-REEFFIT model that the M-stable random RNA has a complex landscape with at least three structures, and likely significantly more heterogeneity, detectable upon unbiased nucleotide mutation.


Rich RNA Structure Landscapes Revealed by Mutate-and-Map Analysis.

Cordero P, Das R - PLoS Comput. Biol. (2015)

Landscape dissection of a random RNA sequence, the M-stable RNA.(a) M2 data, (b) REEFFIT fit, and (c) inference of the M-stable structural landscape, showing one helix, P1, present at almost 50% as well as numerous other helices (P2, P3, and P4) present at non-negligible fractions. (d-f) State medioids for (d) MST-A, (e) MST-B, and (f) MST-C, and comparison between reactivities predicted by REEFFIT and mutants designed to stabilize each state medioids.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4643908&req=5

pcbi.1004473.g006: Landscape dissection of a random RNA sequence, the M-stable RNA.(a) M2 data, (b) REEFFIT fit, and (c) inference of the M-stable structural landscape, showing one helix, P1, present at almost 50% as well as numerous other helices (P2, P3, and P4) present at non-negligible fractions. (d-f) State medioids for (d) MST-A, (e) MST-B, and (f) MST-C, and comparison between reactivities predicted by REEFFIT and mutants designed to stabilize each state medioids.
Mentions: As a second test case with a previously unknown structural ensemble, we tested whether randomly generated or scrambled RNA sequences tend to fold into multiple disparate structures at equilibrium—a long-standing hypothesis fundamental to understanding RNA evolution, put forth by several in silico studies [35,36] and an experimental study that could not deconvolve the structures [37]. We carried out M2-REEFFIT for a randomly generated sequence, called here the M-stable RNA (Fig 6). Based on simulations, the structural ensemble of the construct was expected to consist mainly of a simple hairpin (P1 in MST-A in Fig 6d, see top triangle of the Fig 6c) but with at least two other structures becoming more stable than MST-A upon single mutations. The experimental M2 measurements were complex, with different mutants giving disparate protection patterns even in segments that appeared highly reactive (and seemingly unstructured) in the wild type RNA. As a first check on the number of states, REEFFIT fits assuming only 2 or 3 states missed many features observed in the data, including extended segments of changed chemical reactivity in several mutants (Fig J in S1 Text). However, the REEFFIT global ensemble fit successfully modeled the M-stable data and suggested an ensemble with many more weakly populated helices than RNAstructure’s estimate (compare bottom and top halves of Fig 6c). For visualization, we clustered these heterogeneous component structures into three states, MST-A, MST-B, and MST-C (see Fig 6d–6f and Fig Hd-Hf in S1 Text). Analogous to the case of the Tebowned switch, we tested the REEFFIT prediction of these alternative states by designing mutations to stabilize the medioid structures of each cluster (Fig 6d–6f). These mutants gave reactivities in agreement with predictions for MST-A and MST-C, supporting the inference of those structures. For MST-B, the state-stabilizing mutants gave reactivity profiles that did not exactly match each other, suggesting residual heterogeneity of structure; the profiles were nevertheless closer to the REEFFIT-predicted MST-B profile than wild type reactivities. These results corroborate the M2-REEFFIT model that the M-stable random RNA has a complex landscape with at least three structures, and likely significantly more heterogeneity, detectable upon unbiased nucleotide mutation.

Bottom Line: In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments.An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations.These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Program, Stanford University, Stanford, California, United States of America.

ABSTRACT
Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral infection [corrected]. We report herein that high-throughput chemical experiments can isolate an RNA's multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M2) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states' structures and populations. In an in silico benchmark on non-coding RNAs with complex landscapes, M2-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M2-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

Show MeSH
Related in: MedlinePlus