Spectral Decomposition for the Search and Analysis of RNA Secondary Structure

Abstract

Scales in RNA, based on geometrical considerations, can be exploited for the analysis and prediction of RNA structures. By using spectral decomposition, geometric information that relates to a given RNA fold can be reduced to a single positive scalar number, the second eigenvalue of the Laplacian matrix corresponding to the tree-graph representation of the RNA secondary structure. Along with the free energy of the structure, being the most important scalar number in the prediction of RNA folding by energy minimization methods, the second eigenvalue of the Laplacian matrix can be used as an effective signature for locating a target folded structure given a set of RNA folds. Furthermore, the second eigenvector of the Laplacian matrix can be used to partition large RNA structures into smaller fragments. An illustrative example is given for the use of the second eigenvalue to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs.