Estimating energy parameters for RNA secondary structure predictions using both experimental and computational data

Shimpei Nishida, Shun Sakuraba, Kiyoshi Asai, Michiaki Hamada

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Computational RNA secondary structure prediction depends on a large number of nearest-neighbor free-energy parameters, including 10 parameters for Watson-Crick stacked base pairs that were estimated from experimental measurements of the free energies of 90 RNA duplexes. These experimental data are provided by time-consuming and cost-intensive experiments. In contrast, various modified nucleotides in RNAs, which would affect not only their structures but also functions, have been found, and rapid determination of energy parameters for a such modified nucleotides is needed. To reduce the high cost of determining energy parameters, we propose a novel method to estimate energy parameters from both experimental and computational data, where the computational data are provided by a recently developed molecular dynamics simulation protocol. We evaluate our method for Watson-Crick stacked base pairs, and show that parameters estimated from 10 experimental data items and 10 computational data items can predict RNA secondary structures with accuracy comparable to that using conventional parameters. The results indicate that the combination of experimental free-energy measurements and molecular dynamics simulations is capable of estimating the thermodynamic properties of RNA secondary structures at lower cost.

Original languageEnglish
Article number3370686
Pages (from-to)1645-1655
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume16
Issue number5
DOIs
Publication statusPublished - 2019 Sep

Keywords

  • MD simulation
  • RNA secondary structure predictions
  • base-pairing probability matrix
  • energy parameter

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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