Structural alphabet of RNA may advance RNA nanotechnology

We’ve written before that progress in understanding the folding of RNA molecules could lead to building molecular machinery from RNA—an RNA nanotechnology. Because RNA is a chemical cousin of DNA, with slightly different properties from DNA, the Watson-Crick base pairs that constitute the molecular recognition code for DNA nanostructures are supplemented by other significant interactions, thus giving RNA molecules a wider range of structural and functional properties, including mimicking protein enzymes in some cases. A developing understanding of these non-Watson-Crick interactions places RNA nanotech on a firmer foundation. led us to a press release from the Université de Montréal (UdeM): “The structural alphabet of RNA“:

UdeM bioinformaticians make an important discovery: a very small number of motifs of 8 nucleotides or less are sufficient to reconstitute the RNA structures that are found in experimental databases

A team of bioinformaticians at the Université de Montréal (UdeM) report in the March 6th edition of Nature the discovery of a structural alphabet that can be used to infer the 3D structure of ribonucleic acid (RNA) molecules from sequence data.

With the growing recognition of the importance of small RNAs in cellular metabolism, the ability to predict accurate RNA structures from sequence data has become an important research goal. Thanks to the work of François Major, principal investigator at the Institute for Research in Immunology and Cancer of the UdeM and professor in the Department of Computer Science and Operations Research, and Marc Parisien, a graduate student in his laboratory, a significant step has been made towards this goal.

Unlike its DNA cousin, which is made of two complementary strands that are wrapped around each other in a monotonous double helix, RNA molecules are made of single strands that can fold into an array of complex structures.

The structure of an RNA is determined in large part by the pairing of its constituent nucleotides over short regions of the molecule. Until now, RNA structure has usually been modelled by looking for the most stable combination of such paired regions. The classical approach, however, suffers from an important limitation: it only takes into account the canonical Watson-Crick interactions A:U and G:C, that is those where the nucleotides are facing each other.

The non-canonical Hoogsteen and sugar interactions, those where the nucleotides are side by side or on top of each other, are not taken into account by conventional modelling algorithms. The result can be incomplete or erroneous models which can mislead researchers.

The attempt to remedy this problem led Major and Parisien to propose a radically different approach to model RNA structure. Their idea was to assemble the structure in silico starting from motifs that combine all the possible interactions between a nucleotide and its neighbors. While working on this approach, the UdeM bioinformaticians made an important discovery: a very small number of motifs of 8 nucleotides or less are sufficient to reconstitute the RNA structures that are found in experimental databases.

The research was published in Nature (abstract). The RNA modelling tools these researchers developed are available on the Internet at

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