RNA genes are ubiquitous in cell physiology, with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved RNAs of currently unknown function have been identified . Knowledge of 3D structure could can help elucidate the function of these RNAs but despite outstanding word using X-ray crystallography, NMR and cryoEM, structure determination remains low-throughput. RNA structure prediction in silico is a promising alternative. However, 3D structure prediction for large RNAs requires tertiary contacts between distant secondary structural elements that are difficult to infer with existing methods. Here, based only on sequences, we use a global statistical probability model of  co-variation to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical interactions with 65-95\% prediction accuracy. Importantly, we infer many long-range tertiary contacts, including non-Watson-Crick interactions. When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 to 10 Angstrom rmsd with potential for use with other constraints. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we present evolutionary couplings for 180 RNAs of unknown structure (available at URL  We anticipate that this approach will shed light on the structure and function of as yet less known RNA genes.
RNA genes are ubiquitous in cell physiology, but the vast majority of non-coding RNAs are poorly understood . Knowledge of 3D structure can help elucidate the function , but in silico 3D structure prediction for large RNAs requires tertiary contacts between distant secondary structural elements that are difficult to infer with existing methods. Using a global probability model of sequence co-variation , we predict contact with 65-90\% precision and capture many long-range tertiary contacts, including non-Watson-Crick interactions. These contacts allow all-atom blinded structure prediction with an accuracy of 6-10 rmsd. We present evolutionary couplings for 160 RNAs of unknown structure (available at URL and use these predictions to shed light on the mechanism of tRNA sensing by the T-box riboswitch, as well as the evolutionary history of the ribozyme RNase P. We anticipate that this approach will shed light on the structure and function of as yet less known RNA genes.
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with a diverse repertoire of known functions. In fact, the majority of the eukaryotic genome does not code for proteins, and thousands of conserved RNAs of currently unknown function have been identified
but the vast majority of non-coding RNAs are poorly understood
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of these RNAs but despite outstanding word using X-ray crystallography, NMR and cryoEM, structure determination remains low-throughput. RNA structure prediction in silico is a promising alternative. However,
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Here, based only on sequences, we use a global statistical
Using a global
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sequence
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to detect 3D contacts, in analogy to recently developed breakthrough methods for computational protein folding. In blinded tests on 22 known RNA structures ranging in size from 65 to 1800 nucleotides, the predicted contacts matched physical interactions with 65-95\% prediction accuracy. Importantly, we infer
, we predict contact with 65-90\% precision and capture
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When used as restraints in molecular dynamics simulations, the inferred contacts improve RNA 3D structure prediction to a coordinate error as low as 6 to 10 Angstrom rmsd with potential for use with other constraints. These contacts include functionally important interactions, such as those that distinguish the active and inactive conformations of four riboswitches. In blind prediction mode, we
These contacts allow all-atom blinded structure prediction with an accuracy of 6-10 rmsd. We
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and use these predictions to shed light on the mechanism of tRNA sensing by the T-box riboswitch, as well as the evolutionary history of the ribozyme RNase P.