This release is available in French.
A team of bioinformaticians at the Universit de Montral (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) from sequence data, providing new tools to understand the role of this important class of cellular regulators.
The folding of a single-stranded RNA molecule is determined by the interactions between its constituent nucleotides. The classical approach to RNA modelling 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 Franois 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, to propose a radically different approach to model RNA structure. Their idea: assemble the structure in silico starting from motifs that combine all the possible interactions between a nucleotide and its neighbors.
The researchers implemented a first algorithm, MC-Fold, that systematically assigns the different motifs to each segment of the sequence and selects the most probable pair based on its frequency in known structures. A second algorithm, MC-Sym, then assembles the set of selected motifs, taking into account the constraints that are found in known structures.
"We introduced a new first-order o
|Contact: Christian Lanctt|
University of Montreal