Efficient siRNA selection using hybridization thermodynamics

Abstract

Small interfering RNA (siRNA) are widely used to infer gene function. Here, insights in the equilibrium of siRNA-target hybridization are used for selection of efficient siRNA. The accessibilities of siRNA and target mRNA for hybridization, as measured by folding free energy change, are shown to be significantly correlated with efficacy. For this study, a partition function calculation that considers all possible secondary structures is used to predict target site accessibility; a significant improvement over calculations that consider only the predicted lowest free energy structure or a set of low free energy structures. The predicted thermodynamic features, in addition to siRNA sequence features, are used as input for a support vector machine that selects functional siRNA. The method works well for predicting efficient siRNA (efficacy >70%) in a large siRNA data set from Novartis. The positive predictive value (percentage of sites predicted to be efficient for silencing that are) is as high as 87.6%. The sensitivity and specificity are 22.7 and 96.5%, respectively. When tested on data from different sources, the positive predictive value increased 8.1% by adding equilibrium terms to 25 local sequence features. Prediction of hybridization affinity using partition functions is now available in the RNAstructure software package.

Tiling genomes of pathogenic viruses identifies potent antiviral shRNAs and reveals a role for secondary structure in shRNA efficacy

“…4A, although the sensor score curves of two HIV strains and influenza A PR8 are largely overlapping, the curve of HCV shRNAs shifts to the lower efficacy end, suggesting some general sequence features that distinguish HCV from HIV and influenza A virus. We examined extensively 28 sequence features that were known to affect the efficacy of siRNAs (19), and found that five features could distinguish HCV from HIV and influenza A virus, as shown in Table S1, which likely explain the decreased efficacy of HCVtargeting shRNAs. All five features directly correlate with the GC content of the viral genome.…”

RNA Secondary Structure Analysis Using RNAstructure

“…It is available for free and can be run using a web server or downloaded to run locally on Windows, Macintosh OS X, or Linux. The program includes several algorithms, including secondary structure prediction by free energy minimization or maximum expected accuracy structure prediction (Lu et al, 2009;, a partition function for predicting base pair probabilities (Mathews, 2004), ProbKnot for predicting structures including pseudoknots (Bellaousov and Mathews, 2010), stochastic sampling from the Boltzmann ensemble (Ding and Lawrence, 2003), OligoWalk for predicting binding affinity of oligonucleotides to a complementary RNA target (Lu and Mathews, 2007;Lu and Mathews, 2008a;Lu and Mathews, 2008b;Mathews et al, 1999a), methods for predicting the structure of interacting sequences (Piekna-Przybylska et al, 2009), and methods for predicting conserved structures common to two or more sequences (Harmanci et al, 2007;Harmanci et al, 2008;Harmanci et al, 2009;Harmanci et al, 2011;Mathews, 2005;Mathews and Turner, 2002;Uzilov et al, 2006;Xu and Mathews, 2011).Basic Protocol 1 provides instruction for predicting RNA secondary structure with the RNAstructure web server. Alternative protocol 1 provides instructions for using the graphical interface to predict lowest free energy structures and base pairing probabilities.…”

Correlation between computed equilibrium secondary structure free energy and siRNA efficiency

“…al 44 that has been used by others in the field for similar purposes. 45 The original dataset collects 653 results from RNAi experiments reported in literature. The mRNAs reported belong to Homo sapiens (human), Mus musculus (house mouse), Streptomyces alboniger (bacteria), and artificial mRNA sequences.…”