Relative Quantification of siRNA Strand Loading into Ago2 for Design of Highly Active siRNAs.

In RNA interference (RNAi), silencing is achieved through the interaction of double-stranded small interfering RNAs (siRNAs) with essential RNAi pathway proteins, including Argonaute 2 (Ago2). Based on these interactions, one strand of the siRNA is loaded into Ago2 forming the active RNA-induced silencing complex (RISC). Optimal siRNAs maximize RISC activity against the intended target and minimize off-target silencing. To achieve the desired activity and specificity, selection of the appropriate siRNA strand for loading into Ago2 is essential. Here, we provide a protocol to quantify the relative loading of individual siRNA strands into Ago2, one factor in determining the capacity of a siRNA to achieve silencing activity and target specificity.

Improved asymmetry prediction for short interfering RNAs.

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.