Improved asymmetry prediction for short interfering RNA s

In the development of RNA interference therapeutics, merely selecting short interfering RNA (si RNA ) sequences that are complementary to the m RNA target does not guarantee target silencing. Current algorithms for selecting si RNA s rely on many parameters, one of which is asymmetry, often predicted through calculation of the relative thermodynamic stabilities of the two ends of the si RNA . However, we have previously shown that highly active si RNA 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 si RNA 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 si RNA 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 si RNA activity over current approaches for predicting asymmetry. Going forward, we anticipate that this approach to si RNA asymmetry prediction will be incorporated into the next generation of si RNA selection algorithms.