Improved asymmetry prediction for short interfering RNAs

• Published in: The FEBS journal 2014-01, Vol.281 (1), p.320-330
• Main Authors: Malefyt, Amanda P., Wu, Ming, Vocelle, Daniel B., Kappes, Sean J., Lindeman, Stephen D., Chan, Christina, Walton, S. Patrick
• Format: Article
• Language: English
• Subjects: Algorithms; asymmetry; Blotting, Western; Carcinoma, Non-Small-Cell Lung - genetics; Carcinoma, Non-Small-Cell Lung - metabolism; DNA Primers - chemistry; Drug Design; dsRNA‐dependent protein kinase R; eIF-2 Kinase - antagonists & inhibitors; eIF-2 Kinase - genetics; enhanced green fluorescent protein; Fluorescence; Gene Silencing; Genetic engineering; Green Fluorescent Proteins - antagonists & inhibitors; Green Fluorescent Proteins - genetics; Humans; Lung Neoplasms - genetics; Lung Neoplasms - metabolism; Pharmaceutical sciences; RNA Interference; RNA, Messenger; RNA, Small Interfering - genetics; RNA-protein interactions; short interfering RNA; Studies; Thermodynamics; Tumor Cells, Cultured
• ISSN: 1742-464X; 1742-4658
• DOI: 10.1111/febs.12599

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. Identifying highly active siRNAs is important for developing RNA interference therapeutics. Here, we describe an algorithm for predicting highly active siRNA sequences based solely on two asymmetry parameters, end sequence nucleotides and thermodynamic stabilities. Use of these parameters combined improves the prediction of siRNA activity over current asymmetry approaches when tested on enhanced green fluorescent protein and protein kinase R.