Structural Analysis of Single-Point Mutations Given an RNA Sequence: A Case Study with RNAMute
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Structural Analysis of Single-Point Mutations Given an RNA Sequence: A Case Study with RNAMute Alexander Churkin1 and Danny Barash1, 2 1 Department 2 Genome
of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel Diversity Center, Institute of Evolution, University of Haifa, Israel
Received 2 May 2005; Revised 13 September 2005; Accepted 1 December 2005 We introduce here for the first time the RNAMute package, a pattern-recognition-based utility to perform mutational analysis and detect vulnerable spots within an RNA sequence that affect structure. Mutations in these spots may lead to a structural change that directly relates to a change in functionality. Previously, the concept was tried on RNA genetic control elements called “riboswitches” and other known RNA switches, without an organized utility that analyzes all single-point mutations and can be further expanded. The RNAMute package allows a comprehensive categorization, given an RNA sequence that has functional relevance, by exploring the patterns of all single-point mutants. For illustration, we apply the RNAMute package on an RNA transcript for which individual point mutations were shown experimentally to inactivate spectinomycin resistance in Escherichia coli. Functional analysis of mutations on this case study was performed experimentally by creating a library of point mutations using PCR and screening to locate those mutations. With the availability of RNAMute, preanalysis can be performed computationally before conducting an experiment. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
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INTRODUCTION
The secondary structure of an RNA molecule is a representation of the pattern complementary base pairings that are formed between nucleic acids, given an initial RNA sequence. The sequence, represented as a string of four letters, is a single strand consisting of nucleotides A, C, G, U that folds according to minimum energy consideration as a basic assumption. The secondary structure of RNAs is experimentally accessible, thus making its computational prediction a challenging problem that can be tested in the laboratory. The folding prediction problem of the secondary structure of RNAs has been an area of active research since the late 70’s (see [20] and other works, review available in [25]). Dynamic programming methods were developed in [15] (the NussinovJacobson algorithm) for computing the maximum number of base pairings in an RNA sequence. Energy minimization methods by dynamic programming [23, 24] have led to Zuker’s mfold prediction server [26] and the Vienna package [8]. An improvement in the success of these packages to predict an accurate folding comes from incorporating expanded energy rules [13], derived from an independent set of experiments, into the folding prediction algorithm. For sequences that are longer than approximately 150 nt, energy minimization methods may fail to reliably predict a secondary
structure from sequence alone. In those cases, an approach called comparative modeling [6] is preferable i
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