Pseudouridine in a new era of RNA modifications

Zhao, Boxuan Simen; He, Chuan

doi:10.1038/cr.2014.143

“…These error ranges are similar or lower than uncertainties of empirically defined NN parameters for most motifs; original NN energy estimates for G--U dangling ends, single nucleotide bulges, and tetraloop free energies have been corrected by > 1 kcal/mol when revisited in detailed studies (25--28). Overall, computational approaches may be ready for calculations of new energetic parameters, including parameters for these uncertain motifs as well as for motifs involving non--standard nucleotides that are being found throughout natural coding and non-coding RNAs (29,30) or used to engineer new RNA systems (31,32). However, the predictive power of these methods has not been evaluated through tests on previously unmeasured nearest neighbor parameters.…”

Section: Introductionmentioning

confidence: 99%

Blind tests of RNA nearest neighbor energy prediction

Chou

¹

,

Kladwang

²

,

Kappel

³

et al. 2016

Preprint

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The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance among their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA's nearest-neighbor (NN) freeenergy parameters, but these methods have not been tested in predictive trials or on nonstandard nucleotides. Here, we present, to our knowledge, the first such tests through a RECCES-Rosetta (reweighting of energy-function collection with conformational ensemble sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values' systematic uncertainties. RECCES-Rosetta recovers the 10 NN parameters for Watson-Crick stacked base pairs and 32 single-nucleotide dangling-end parameters with unprecedented accuracies: rmsd of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For setaside test sets, RECCES-Rosetta gives rmsd values of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and 0.99 kcal/mol on seven stacked pairs involving nonstandard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol rmsd accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.RNA helix | ensemble prediction | simulated tempering | thermodynamics | blind prediction R NA plays central roles in biological processes, including translation, splicing, regulation of genetic expression, and catalysis (1, 2), and in bioengineering efforts to control these processes (3-5). These critical RNA functions are defined at their most fundamental level by the energetics of how RNA folds and interacts with other RNAs and molecular partners, and how these processes change upon naturally occurring or artificially introduced chemical modifications. Experimentally, the folding free energies of RNA motifs can be precisely measured by optical melting experiments, and a compendium of these measurements have established the nearest-neighbor (NN) model for the most basic RNA elements, including double helices with the four canonical ribonucleotides (6). In the NN model, the stability of a base pair is assumed to only be affected by its adjacent base pairs, and the folding free energy of a canonical RNA helix can be estimated based on NN parameters for each stacked pair, an initialization term for the entropic cost of creating the first base pair, and corrections for different terminal base pairs. Although next-NN effects and tertiary contacts are not treated in the NN mo...

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“…For example, original NN energy estimates for G-U stacked pairs, single-nucleotide bulges, and tetraloop free energies have been corrected by >1 kcal/mol when revisited in detailed studies (11,(25)(26)(27). Overall, computational approaches may be ready for calculations of new energetic parameters, including parameters for these uncertain motifs as well as for motifs involving nonstandard nucleotides that are being found throughout natural coding and noncoding RNAs (28,29) or used to engineer new RNA systems (30,31). However, the predictive power of these methods has not been evaluated through tests on previously unmeasured NN parameters.…”

mentioning

confidence: 99%

Blind tests of RNA nearest-neighbor energy prediction

Chou

¹

,

Kladwang

²

,

Kappel

³

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The predictive modeling and design of biologically active RNA molecules requires understanding the energetic balance among their basic components. Rapid developments in computer simulation promise increasingly accurate recovery of RNA's nearest-neighbor (NN) freeenergy parameters, but these methods have not been tested in predictive trials or on nonstandard nucleotides. Here, we present, to our knowledge, the first such tests through a RECCES-Rosetta (reweighting of energy-function collection with conformational ensemble sampling in Rosetta) framework that rigorously models conformational entropy, predicts previously unmeasured NN parameters, and estimates these values' systematic uncertainties. RECCES-Rosetta recovers the 10 NN parameters for Watson-Crick stacked base pairs and 32 single-nucleotide dangling-end parameters with unprecedented accuracies: rmsd of 0.28 kcal/mol and 0.41 kcal/mol, respectively. For setaside test sets, RECCES-Rosetta gives rmsd values of 0.32 kcal/mol on eight stacked pairs involving G-U wobble pairs and 0.99 kcal/mol on seven stacked pairs involving nonstandard isocytidine-isoguanosine pairs. To more rigorously assess RECCES-Rosetta, we carried out four blind predictions for stacked pairs involving 2,6-diaminopurine-U pairs, which achieved 0.64 kcal/mol rmsd accuracy when tested by subsequent experiments. Overall, these results establish that computational methods can now blindly predict energetics of basic RNA motifs, including chemically modified variants, with consistently better than 1 kcal/mol accuracy. Systematic tests indicate that resolving the remaining discrepancies will require energy function improvements beyond simply reweighting component terms, and we propose further blind trials to test such efforts.RNA helix | ensemble prediction | simulated tempering | thermodynamics | blind prediction R NA plays central roles in biological processes, including translation, splicing, regulation of genetic expression, and catalysis (1, 2), and in bioengineering efforts to control these processes (3-5). These critical RNA functions are defined at their most fundamental level by the energetics of how RNA folds and interacts with other RNAs and molecular partners, and how these processes change upon naturally occurring or artificially introduced chemical modifications. Experimentally, the folding free energies of RNA motifs can be precisely measured by optical melting experiments, and a compendium of these measurements have established the nearest-neighbor (NN) model for the most basic RNA elements, including double helices with the four canonical ribonucleotides (6). In the NN model, the stability of a base pair is assumed to only be affected by its adjacent base pairs, and the folding free energy of a canonical RNA helix can be estimated based on NN parameters for each stacked pair, an initialization term for the entropic cost of creating the first base pair, and corrections for different terminal base pairs. Although next-NN effects and tertiary contacts are not treated in the NN mo...

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“…7,35,36 Ψ formation is initiated at the cellular level by environmental cues and is thought to be an irreversible modification of RNA. 37 Mapping of Ψ modified RNA in conjunction with other functional studies have identified roles for Ψ in mRNA stabilization, intracellular transcript localization 38 and translation termination. 39 …”

Section: Rna Modificationsmentioning

confidence: 99%

Role of RNA modifications in brain and behavior

Jung

¹

,

Goldman

²

2018

Genes Brain and Behavior

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Much progress in our understanding of RNA metabolism has been made since the first RNA nucleoside modification was identified in 1957. Many of these modifications are found in noncoding RNAs but recent interest has focused on coding RNAs. Here, we summarize current knowledge of cellular consequences of RNA modifications, with a special emphasis on neuropsychiatric disorders. We present evidence for the existence of an “RNA code,” similar to the histone code, that fine-tunes gene expression in the nervous system by using combinations of different RNA modifications. Unlike the relatively stable genetic code, this combinatorial RNA epigenetic code, or epitranscriptome, may be dynamically reprogrammed as a cause or consequence of psychiatric disorders. We discuss potential mechanisms linking disregulation of the epitranscriptome with brain disorders and identify potential new avenues of research.

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Pseudouridine in a new era of RNA modifications

Cited by 71 publications

References 12 publications

Blind tests of RNA nearest neighbor energy prediction

Blind tests of RNA nearest neighbor energy prediction

Blind tests of RNA nearest-neighbor energy prediction

Role of RNA modifications in brain and behavior

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