Improved asymmetry prediction for short interfering RNAs

Amanda P. Malefyt, Ming Wu, Daniel B. Vocelle, Sean J. Kappes, Stephen D. Lindeman, Christina Chan, S. Patrick Walton

    Research output: Contribution to journalArticle

    • 4 Citations

    Abstract

    In the development of RNA interference therapeutics, merely selecting short interfering RNA (siRNA) sequences that are complementary to the mRNA target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the siRNA. However, we have previously shown that highly active siRNA sequences are likely to have particular nucleotides at each 5′-end, independently of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end-sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted on the basis of training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly and highly active sequences for enhanced green fluorescent protein and protein kinase R. Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.

    Original languageEnglish (US)
    Pages (from-to)320-330
    Number of pages11
    JournalFEBS Journal
    Volume281
    Issue number1
    DOIs
    StatePublished - Jan 2014

    Profile

    Small Interfering RNA
    Airway Obstruction
    Thermodynamics
    Clodronic Acid
    RNA Interference
    Green Fluorescent Proteins
    Protein Kinases
    Nucleotides
    Messenger RNA
    Community Psychiatry
    Alkynes
    Calculi

    Keywords

    • asymmetry
    • dsRNA-dependent protein kinase R
    • enhanced green fluorescent protein
    • short interfering RNA

    ASJC Scopus subject areas

    • Biochemistry
    • Cell Biology
    • Molecular Biology

    Cite this

    Malefyt, A. P., Wu, M., Vocelle, D. B., Kappes, S. J., Lindeman, S. D., Chan, C., & Walton, S. P. (2014). Improved asymmetry prediction for short interfering RNAs. FEBS Journal, 281(1), 320-330. DOI: 10.1111/febs.12599

    Improved asymmetry prediction for short interfering RNAs. / Malefyt, Amanda P.; Wu, Ming; Vocelle, Daniel B.; Kappes, Sean J.; Lindeman, Stephen D.; Chan, Christina; Walton, S. Patrick.

    In: FEBS Journal, Vol. 281, No. 1, 01.2014, p. 320-330.

    Research output: Contribution to journalArticle

    Malefyt, AP, Wu, M, Vocelle, DB, Kappes, SJ, Lindeman, SD, Chan, C & Walton, SP 2014, 'Improved asymmetry prediction for short interfering RNAs' FEBS Journal, vol 281, no. 1, pp. 320-330. DOI: 10.1111/febs.12599
    Malefyt AP, Wu M, Vocelle DB, Kappes SJ, Lindeman SD, Chan C et al. Improved asymmetry prediction for short interfering RNAs. FEBS Journal. 2014 Jan;281(1):320-330. Available from, DOI: 10.1111/febs.12599

    Malefyt, Amanda P.; Wu, Ming; Vocelle, Daniel B.; Kappes, Sean J.; Lindeman, Stephen D.; Chan, Christina; Walton, S. Patrick / Improved asymmetry prediction for short interfering RNAs.

    In: FEBS Journal, Vol. 281, No. 1, 01.2014, p. 320-330.

    Research output: Contribution to journalArticle

    @article{003428845344416c969697cb51db2916,
    title = "Improved asymmetry prediction for short interfering RNAs",
    abstract = "In the development of RNA interference therapeutics, merely selecting short interfering RNA (siRNA) sequences that are complementary to the mRNA target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the siRNA. However, we have previously shown that highly active siRNA sequences are likely to have particular nucleotides at each 5′-end, independently of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end-sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted on the basis of training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly and highly active sequences for enhanced green fluorescent protein and protein kinase R. Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.",
    keywords = "asymmetry, dsRNA-dependent protein kinase R, enhanced green fluorescent protein, short interfering RNA",
    author = "Malefyt, {Amanda P.} and Ming Wu and Vocelle, {Daniel B.} and Kappes, {Sean J.} and Lindeman, {Stephen D.} and Christina Chan and Walton, {S. Patrick}",
    year = "2014",
    month = "1",
    doi = "10.1111/febs.12599",
    volume = "281",
    pages = "320--330",
    journal = "FEBS Journal",
    issn = "1742-464X",
    publisher = "Wiley-Blackwell",
    number = "1",

    }

    TY - JOUR

    T1 - Improved asymmetry prediction for short interfering RNAs

    AU - Malefyt,Amanda P.

    AU - Wu,Ming

    AU - Vocelle,Daniel B.

    AU - Kappes,Sean J.

    AU - Lindeman,Stephen D.

    AU - Chan,Christina

    AU - Walton,S. Patrick

    PY - 2014/1

    Y1 - 2014/1

    N2 - In the development of RNA interference therapeutics, merely selecting short interfering RNA (siRNA) sequences that are complementary to the mRNA target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the siRNA. However, we have previously shown that highly active siRNA sequences are likely to have particular nucleotides at each 5′-end, independently of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end-sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted on the basis of training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly and highly active sequences for enhanced green fluorescent protein and protein kinase R. Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.

    AB - In the development of RNA interference therapeutics, merely selecting short interfering RNA (siRNA) sequences that are complementary to the mRNA target does not guarantee target silencing. Current algorithms for selecting siRNAs rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the siRNA. However, we have previously shown that highly active siRNA sequences are likely to have particular nucleotides at each 5′-end, independently of their thermodynamic asymmetry. Here, we describe an algorithm for predicting highly active siRNA sequences based only on these two asymmetry parameters. The algorithm uses end-sequence nucleotide preferences and predicted thermodynamic stabilities, each weighted on the basis of training data from the literature, to rank the probability that an siRNA sequence will have high or low activity. The algorithm successfully predicts weakly and highly active sequences for enhanced green fluorescent protein and protein kinase R. Use of these two parameters in combination improves the prediction of siRNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to siRNA asymmetry prediction will be incorporated into the next generation of siRNA selection algorithms.

    KW - asymmetry

    KW - dsRNA-dependent protein kinase R

    KW - enhanced green fluorescent protein

    KW - short interfering RNA

    UR - http://www.scopus.com/inward/record.url?scp=84891778982&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84891778982&partnerID=8YFLogxK

    U2 - 10.1111/febs.12599

    DO - 10.1111/febs.12599

    M3 - Article

    VL - 281

    SP - 320

    EP - 330

    JO - FEBS Journal

    T2 - FEBS Journal

    JF - FEBS Journal

    SN - 1742-464X

    IS - 1

    ER -