Improved asymmetry prediction for siRNAs

In the development of RNA interference (RNAi) therapeutics, merely selecting short, interfering RNA (siRNA) sequences that are complementary to the messenger RNA (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 stability 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, independent 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 based on 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 (EGFP) and protein kinase R (PKR). 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.