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 journalArticle

    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
    JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
    DOIs
    Publication statusAccepted/In press - 2018 Mar 9

    Keywords

    • base-pairing probability matrix
    • Biological system modeling
    • Computational modeling
    • Data models
    • Energy measurement
    • energy parameter
    • Gold
    • MD simulation
    • Protocols
    • RNA
    • RNA secondary structure predictions

    ASJC Scopus subject areas

    • Biotechnology
    • Genetics
    • Applied Mathematics

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