Deleterious mutation prediction in the secondary structure of RNAs

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 field of RNAs where very few homologs are available at present, this analogy can serve as a precursor to investigate a deleterious mutation prediction approach that is based on RNA secondary structure. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algorithms 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 prediction method, based on subdivision of the initial structure into smaller substructures and construction of eigenvalue tables, is independent of the folding 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.