RNA genes are ubiquitousin 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 RNAgenes .
Non-coding RNAs are ubiquitous , but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA .
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RNA genes are ubiquitousin cell physiology
Non-coding RNAs are ubiquitous
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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
discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models
R
, we predict contact with 65-90\% precision and capture many long-range tertiary contacts, including non-Watson-Crick interactions . These
to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted
R
structure prediction with an accuracy of 6-10 rmsd. We present evolutionary couplings for
3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in
R
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 RNAgenes
non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA