Conference Paper

Maximum expected accurate structural neighbors of an RNA secondary structure

Lab. pour la Rech. en Inf. (LRI), Univ. Paris-Sud XI, Orsay, France
DOI: 10.1109/ICCABS.2011.5729865 Conference: IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011, Orlando, FL, USA, February 3-5, 2011
Source: DBLP


Since RNA molecules regulate genes and control alternative splicing by allostery, that is, by switching between two distinct secondary structures, it is important to develop algorithms to predict RNA conformational switches. It has recently emerged that RNA secondary structure can be more accurately predicted by computing the maximum expected accurate (MEA) structure, rather than the minimum free energy (MFE) structure. The MEA structure S has maximum score 2 Σ (i, j)ϵs Pi, j + Σi unpaired qi, where first sum is taken over all base pairs (i, j) belonging to S, and the second sum is taken over all unpaired positions in S, and where pi, j [resp. qi] is the probability that i, j are paired [resp. i is unpaired] in the ensemble of low energy structures. Results: Given an arbitrary RNA secondary structure S0, for an RNA nucleotide sequence a = a1,. . . , an, we say that another secondary structure S of a is a k-neighbor of S0, if the base pair distance between S0 and S is k. Here we describe the algorithm RNAborMEA, which for an arbitrary initial structure So and for all values 0 ≤ k ≤ n, computes the secondary structure MEA(k), having maximum expected accuracy over all k-neighbos of S0. We apply our algorithm to the class of purine riboswitches.

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Available from: Peter Clote, May 07, 2014
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    ABSTRACT: Many non-coding RNAs (ncRNAs), such as riboswitches, can fold into alternate native structures and perform different biological functions. The computational prediction of an ncRNA's alternate native structures can be conducted by analyzing the ncRNA's energy landscape. Previously, we have developed a computational approach, RNASLOpt, to predict alternate native structures for a single ncRNA by generating all possible stable local optimal (SLOpt) stack configurations on the ncRNA's energy landscape. In this paper, in order to improve the accuracy of the prediction, we incorporate structural conservation information among a family of related ncRNA sequences to the prediction. We propose a comparative approach, RNAConSLOpt, to produce all possible consensus SLOpt stack configurations that are conserved on the consensus energy landscape of a family of related ncRNAs. Benchmarking tests show that RNAConSLOpt can reduce the number of candidate structures considered compared with RNASLOpt, and can predict ncRNAs' alternate native structures accurately. Availability: Source code and benchmark tests for RNACon-SLOpt are available at