Correlation between computed equilibrium secondary structure free energy and siRNA efficiency

We have explored correlations between the measured efficiency of the RNAi process and several computed signatures that characterize equilibrium secondary structure of the participating mRNA, siRNA, and their complexes. A previously published data set of 609 experimental points (with efficiency represented as percentage of remaining mRNA) was used for the analysis. While virtually no correlation with the computed structural signatures are observed for individual data points, several clear trends emerge when the ldquonoiserdquo is reduced by averaging over 10 bins of N ~ 60 data points per bin. The strongest of the trends is a positive linear (r² = 0.87) correlation between ln(remaining mRNA) and DeltaG_ms, the combined free energy cost of unraveling the siRNA and creating the break in the mRNA secondary structure at the complementary target strand region. At the same time, the free energy change DeltaG_total of the entire process mRNA + siRNA rarr (mRNA-siRNA)_Complex is not correlated with RNAi efficiency, even after the averaging. These general findings appear to be robust to details of the computational protocols, suggesting that, while straightforward analysis based on equilibrium secondary structure thermodynamics may not be directly applicable to the entire RNAi process, it is applicable to at least one of its key stages. The correlation between computed DeltaG_ms and experimentally observed RNAi efficiency can be used to enhance the ability of a machine learning algorithm based on a support vector machine (SVM) to predict effective siRNA sequences for a given target mRNA. Specifically, we observe modest, 3 to 7%, but consistent improvement in the positive predictive value (PPV) when the SVM training set is pre- or post-filtered to half the original size according to a DeltaG_ms threshold.