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. 2005 Feb 18;33(3):e30.
doi: 10.1093/nar/gni026.

siRNA target site secondary structure predictions using local stable substructures

Bret S E Heale  1 , Harris S Soifer, Chauncey Bowers, John J Rossi
Affiliations

siRNA target site secondary structure predictions using local stable substructures

Bret S E Heale et al. Nucleic Acids Res. .

Erratum in

  • Nucleic Acids Res. 2006;34(16):4653

Abstract

The crystal structure based model of the catalytic center of Ago2 revealed that the siRNA and the mRNA must be able to form an A-helix for correct positing of the scissile phosphate bond for cleavage in RNAi. This suggests that base pairing of the target mRNA with itself, i.e. secondary structure, must be removed before cleavage. Early on in the siRNA design, GC-rich target sites were avoided because of their potential to be involved in strong secondary structure. It is still unclear how important a factor mRNA secondary structure is in RNAi. However, it has been established that a difference in the thermostability of the ends of an siRNA duplex dictate which strand is loaded into the RNA-induced silencing complex. Here, we use a novel secondary structure prediction method and duplex-end differential calculations to investigate the importance of a secondary structure in the siRNA design. We found that the differential duplex-end stabilities alone account for functional prediction of 60% of the 80 siRNA sites examined, and that secondary structure predictions improve the prediction of site efficacy. A total of 80% of the non-functional sites can be eliminated using secondary structure predictions and duplex-end differential.

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Figures

Figure 1
Figure 1
The HIV TAR RNA was folded from a sequence derived from the pNL4-3 vector. The output of the secondary structure prediction is shown in the graph. The higher the relative accessibility score, the more accessible the site is predicted to be. Each point in the output graph represents a 21 nt stretch, with base 1 of the stretch corresponding to the position on the x-axis. For example, the red diamond represents nucleotide positions 6–26. The secondary structure predictions correspond to the known structure of TAR. For example, the 21 bp region starting from nucleotide 19 is the most accessible structure both in the graph of predicted accessibility and the known structure.
Figure 2
Figure 2
The excerpt is from our analysis of the published data. The percentage mRNA remaining is from Vickers et al. (33). As mentioned, effective sites are those which result in 55% or greater knockdown. For duplex energies, sites where the 5′ antisense end is 0.5 kcal/mol or more stable than the 3′ end are deemed unfavorable. The acceptable range for GC-content is 30–52%. And sites predicted to be accessible are those with a predicted relative accessibility of 0.55 or higher. The superscript letter ‘a’ represents the columns from Vickers et al. (33).
Figure 3
Figure 3
(a) The sequences of the siRNA duplexes used. (b) Average of FACS data for the amount of GFP signal relative to control. Data are the average from three 24-well plates with triplicate samples on each plate. Each plate was cotransfected with 0.1 μM EGFP vector and 20 nM in vitro transcribed siRNA. (c) Characteristics of sites. Comparing predicted versus knockdown. For example, site C was predicted to be an effective site as it had good duplex-end differential and predicted secondary structure accessibility. Site C produced 63.3% knockdown.
Figure 4
Figure 4
For sites 2, 4 and 5, we altered the sense strand of the siRNA duplex to reverse the duplex-end stabilities. For example, sites 2 and 5 had an A substituted for a U in the third position of the sense strand. This created an A–A mismatch in the duplex, weakening the stability of the 5′ antisense end (see Figure 3). Our results show that the changes in duplex-end stability reduced the effectiveness of the siRNA duplexes. However, for site 4, the change from unfavorable duplex energies to favorable duplex energies did not result in an increase in the effectiveness of the siRNA.
Figure 5
Figure 5
Relative knockdown of target mRNA. GAPDH used to normalize amount of target. Percentage knockdown measured by semiquantitative RT–PCR, example of gel inset (lanes are 100 bp ladder, sh1, sh2, sh3 and control, respectively. Upper panel is for target mRNA and the lower panel is for GAPDH). RT showed no signal (data not shown).
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