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Bioinformatics Advance Access published online on February 26, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth157
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received October 6, 2003
Revised December 30, 2003
Accepted January 6, 2004

Article

Second eigenvalue of the Laplacian matrix for large predicting RNA conformational switch by mutation

Danny Barash 1*

1 1Genome Diversity Center, Institute of Evolution, University of Haifa, Mount Carmel, Haifa 31905, Israel

* To whom correspondence should be addressed. E-mail: dbarash{at}research.haifa.ac.il.


  Abstract

Motivation: Conformational switching in RNAs is thought to be of fundamental importance in several biological processes, including translational regulation, regulation of self-cleavage in viruses, protein biosynthesis, and mRNA splicing. Current methods for detecting bi-stable RNAs that can lead to structural switching when triggered by an outside event rely on kinetics, energetics, and properties of the combinatorial structure space of RNAs. Based on these properties, tools have been developed to predict whether a given sequence folds to a structure characterized by a bi-stable conformation, or to design mutli-stable RNAs by an iterative algorithm. A useful addition is in developing a local procedure to prescribe, given an initial sequence, the least amount of mutations needed to drive the system into an optimal bi-stable conformation.

Results: We introduce a local procedure for predicting mutations, by generating and analyzing eigenvalue tables, that are capable of transforming the wildtype sequence into a bi-stable conformation. The method is independent of the folding algorithms but relies on their success. It can be used in conjunction with existing tools, as well as being incorporated into more general RNA prediction packages. We apply this procedure on three well-studied structures. First, the method is validated on the mutation leading to a conformational switch in the spliced leader RNA from Leptomonas collosoma, a mutation that has already been confirmed by an experiment. Second, the method is used to predict a mutation that can lead to a novel conformational switch in the P5abc subdomain of the group I intron ribozyme in the Tetrahymena thermophila. Third, the method is applied on Hepatitis delta virus to predict mutations that transform the wildtype into a bi-stable conformation, a configuration assessed by calculating the free energies using folding prediction algorithms. The predictions in the final examples need to be verified experimentally, whereas the mutation predicted in the first example complies with the experiment. This supports the use of our proposed method on other known structures, as well as genetically engineered ones.

Availability: An eigenvalue application will be available in the near future attached to one of the existing tools.


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