Deleterious mutation prediction in the secondary structure of RNAs. Nucleic Acids Res 31:6578–6584

Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the ®eld of RNAs where very few homologs are avail-able at present, this analogy can serve as a precur-sor to investigate a deleterious mutation prediction approach that is based on RNA secondary struc-ture. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algo-rithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation predic-tion method, based on subdivision of the initial structure into smaller substructures and construc-tion of eigenvalue tables, is independent of the fold-ing algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo-switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control