A Global Sampling Approach to Designing and Reengineering RNA
i “rnaensign” — 2017/7/1 — 22:02 — page 1 — #1 i i i Published online ?? Nucleic Acids Research, ??, Vol. ??, No. ?? 1–11 doi:10.1093/nar/gkn000 Aglobalsamplingapproachtodesigningandreengineering RNA secondary structures 1,2, 1, 3 1 1 Alex Levin ⇤,MieszkoLis ⇤,YannPonty ,CharlesW.O’Donnell ,SrinivasDevadas , 1,2, 1,2,4, Bonnie Berger †,J´erˆomeWaldisp¨uhl † 1 Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA. and 2 Department of Mathematics, MIT, Cambridge, MA 02139, USA. and 3 Laboratoire d’Informatique, Ecole´ Polytechnique, Palaiseau, France. and 4 School of Computer Science, McGill University, Montreal, QC H2A 3A7, Canada. Received ??; Revised ??; Accepted ?? ABSTRACT mechanisms (1), reprogramming cellular behavior (2), and designing logic circuits (3). The development of algorithms for designing artificial Here, we aim to design RNA sequences that fold into RNA sequences that fold into specific secondary structures specific secondary structures (a.k.a. inverse folding). Even has many potential biomedical and synthetic biology in this case, efficient computational formulations remain applications. To date, this problem remains computationally difficult, with no exact solutions known. Instead, the solutions difficult, and current strategies to address it resort available today rely on local search strategies and heuristics. to heuristics and stochastic search techniques. The Indeed, the computational difficulty of the RNA design most popular methods consist of two steps: First a problem was proven by M. Schnall-Levin et al. (4). random seed sequence is generated; next, this seed is One of the first and most widely known programs for progressively modified (i.e. mutated) to adopt the desired the RNA inverse folding problem is RNAinverse (5).
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