A global sampling approach to designing and reengineering RNA secondary structures
Name
Levin-2012-A global sampling ap.pdf
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Author(s)
Levin, Alexander
Lis, Mieszko
Ponty, Yann
O'Donnell, Charles William
Devadas, Srinivas
Berger, Bonnie
Waldispuhl, Jerome
Date Issued
August 2012
Journal
Nucleic Acids Research
Publisher
Oxford University Press
Citation
Levin, A. et al. “A Global Sampling Approach to Designing and Reengineering RNA Secondary Structures.” Nucleic Acids Research 40.20 (2012): 10041–10052. © 2012 Oxford University Press
Version
Final published version
Abstract
The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution Non-Commercial
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DOI of Published Version
https://doi.org/10.1093/nar/gks768
