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

  • 6 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.

LanguageEnglish (US)
Pages320-330
Number of pages11
JournalFEBS Journal
Volume281
Issue number1
DOIs
StatePublished - Jan 2014

Profile

Small Interfering RNA
Thermodynamics
Thermodynamic stability
Nucleotides
RNA Interference
Protein Kinases
RNA
Messenger RNA

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. 2014 ; Vol. 281, No. 1. pp. 320-330
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