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.
authors.affiliation: Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA.
authors.forename: Amanda P
authors.initials: AP
authors.lastname: Malefyt
authors.forename: Ming
authors.initials: M
authors.lastname: Wu
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Vocelle
authors.forename: Sean J
authors.initials: SJ
authors.lastname: Kappes
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Lindeman
authors.forename: Christina
authors.initials: C
authors.lastname: Chan
authors.forename: S Patrick
authors.initials: SP
authors.lastname: Walton
mesh_terms: D000465:Algorithms; D015153:Blotting, Western; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics* / Q000378:metabolism; D017931:DNA Primers / Q000737:chemistry; D015195:Drug Design; D005453:Fluorescence; D020868:Gene Silencing; D049452:Green Fluorescent Proteins / Q000037:antagonists & inhibitors* / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000378:metabolism; D034622:RNA Interference; D012333:RNA, Messenger; D034741:RNA, Small Interfering / Q000235:genetics*; D013816:Thermodynamics; D014407:Tumor Cells, Cultured; D019892:eIF-2 Kinase / Q000037:antagonists & inhibitors* / Q000235:genetics
chemical_list: D017931:DNA Primers; D012333:RNA, Messenger; D034741:RNA, Small Interfering; C485184:enhanced green fluorescent protein; D049452:Green Fluorescent Proteins; D019892:eIF-2 Kinase
keywords: asymmetry; dsRNA-dependent protein kinase R; enhanced green fluorescent protein; short interfering RNA