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Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.
Takizawa, Hiroki; Iwakiri, Junichi; Terai, Goro; Asai, Kiyoshi.
Afiliação
  • Takizawa H; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Chiba 277-8561 Japan.
  • Iwakiri J; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Chiba 277-8561 Japan.
  • Terai G; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Chiba 277-8561 Japan.
  • Asai K; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Chiba 277-8561 Japan.
Bioinformatics ; 36(Suppl_1): i227-i235, 2020 07 01.
Article em En | MEDLINE | ID: mdl-32657400
ABSTRACT
MOTIVATION RNA folding kinetics plays an important role in the biological functions of RNA molecules. An important goal in the investigation of the kinetic behavior of RNAs is to find the folding pathway with the lowest energy barrier. For this purpose, most of the existing methods use heuristics because the number of possible pathways is huge even if only the shortest (direct) folding pathways are considered.

RESULTS:

In this study, we propose a new method using a best-first search strategy to efficiently compute the exact solution of the minimum barrier energy of direct pathways. Using our method, we can find the exact direct pathways within a Hamming distance of 20, whereas the previous methods even miss the exact short pathways. Moreover, our method can be used to improve the pathways found by existing methods for exploring indirect pathways. AVAILABILITY AND IMPLEMENTATION The source code and datasets created and used in this research are available at https//github.com/eukaryo/czno. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / RNA Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / RNA Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article