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SHAPE-enabled fragment-based ligand discovery for RNA

Significance RNA molecules encode proteins and play numerous regulatory roles in cells. Targeting RNA with small molecules, as is routine with proteins, would create broad opportunities for modulating biology and creating new drugs. However, this opportunity has been difficult to realize because creating novel small molecules that bind RNA, especially using modest resources, is challenging. This study integrates two widely used technologies, SHAPE chemical probing of RNA and fragment-based ligand discovery, to craft an innovative strategy for creating small molecules that bind to and modulate the activity of a structured RNA. The anticipated impact is high because the methods are simple, can be implemented in diverse research and discovery contexts, and lead to realistic druglike molecules.


Statistical test for a difference in modification rates of a given nucleotide
The screening analysis requires statistical comparison of the modification rate of a given nucleotide in the presence of a fragment as compared to its absence. For each nucleotide the number of modifications in a given reaction is a Poisson process with a known variance; the statistical significance of the observed difference in modification rates between two samples can therefore be ascertained by performing the Comparison of Two Poisson Counts test 1 . That is, if m1 modifications of a tested nucleotide were counted among n1 reads in sample 1 and m2 modifications were counted among n2 reads in sample 2, the tested null hypothesis predicts that among all the counted modifications (m1 + m2), the proportion of modifications in sample 1 will be p1 = n1/(n1 + n2). The Z-test of this hypothesis is: If the Z value exceeds a specified significance threshold, the tested nucleotide is taken to be statistically significantly affected by the presence of the test fragment.

Minimizing the multiple testing problem
For each fragment, the Z-test has to be performed on a large number of nucleotides comprising the RNA sequence, increasing the probability of false positives. While the numbers of false positive assignments of SHAPE reactivity per nucleotide can be minimized by raising Z significance threshold, this approach would reduce the sensitivity of the screen (meaning it would reduce the ability to detect weaker binding ligands). To reduce the number of Z-tests performed, such tests were applied only to nucleotides in the region of interest, rather than to all nucleotides in the RNA screening construct. For the dengue motif of the RNA, the region of interest was positions 59-110; for the TPP motif, the region of interest was positions 100-199.
The number of Z-tests was reduced further by omitting nucleotides with low modification rates in both samples. The threshold for considering a nucleotide to have a low modification rate was 3 set at 25% of the plate-average modification rate, which was computed over all nucleotides in all 96 wells of a given plate. Z-tests were performed only on those nucleotides that, in at least one of the two compared samples, had the modification rate exceeding this 25% threshold.

Minimizing effects of uncontrolled factors on nucleotide reactivity
Ideally, the only difference between conditions in two compared samples would be the presence of a fragment in one sample but not in the other. Testing negative-control samples against each other can be used to gauge the prevalence of uncontrolled factors that might introduce acrosssample variability in nucleotide modification rates. For example, if the Z significance threshold is set at 2.7, in the absence any such factors, the Z-test applied to pairs of negative-control (no fragment) samples should, theoretically, identify differentially reactive nucleotides with a probability P = 0.0035. However, when the Z-test was applied to pairs of negative-control samples selected at random from the 587 negative-control samples tested in the primary screen, the actual probability was 90 times higher with P = 0.32. Thus, there was statistically significant variability in SHAPE reactivities at individual nucleotides in the absence of fragments.
Although the majority of replicates shared essentially the same profiles, there were a substantial number of replicates with dissimilar profiles; some coefficients of determination were as low as 0.85. Applying the Z-test to dissimilar negative-control samples generated large numbers of cases were nucleotides were falsely classified as differentially reactive. To avoid this outcome, each sample was compared to the five most highly correlated negative-control samples. Z-tests applied to such selective pairs of negative controls with a Z significance threshold of 2.7, resulted in identification of differentially reactive nucleotides with a probability P = 0.067.
This probability is about 20 times higher than the theoretical P = 0.0035 indicating that there is variability in sample processing. Some of this variability scales equally across the reactivities of all the nucleotides of all RNAs in a sample. This variability can be removed by scaling down the overall reactivity in the more reactive sample so as to match the overall reactivity in the less reactive sample. Such scaling was performed by (i) computing for each nucleotide in the RNA sequence the ratio of its modification rate in the more reactive sample to that in the less reactive sample and (ii) dividing the modification rates of all the nucleotides in the more reactive sample by the median of the ratios obtained in step (i). Such scaling of correlation-maximized pairs of negative-control wells reduced the probability of finding nucleotide hits to P = 0.030, 9-fold higher than the theoretical probability. Thus, false-positive identification of fragments will occur, 4 as indeed occurs in all high-throughput screening assays, and we distinguished actual fragment hits from non-ligand variations by replicate SHAPE validation and by direct ligand binding measurement using ITC.

Maximizing the ratio of true-to-false ligand detections
Since an effective ligand is expected to affect modification rates of multiple nucleotides in the target RNA, a fragment was recognized as a hit only if the number of nucleotides with reactivity different from that in the negative control exceeded a defined threshold, which was set to 2.
Second, since we are looking for relatively robust effects of fragments on the RNA, small relative differences in reactivity of a nucleotide, even if statistically significant, were excluded from the total count of differentially reactive nucleotides. In practice, the minimal accepted difference was set to 20% of the average: where r1 and r2 are the nucleotide modification rates in two samples. Third, a given sample was tested against the five negative-control samples with which it was most highly correlated. All five tests were required to find the test sample altered relative to the negative-control sample.
Finally, the sensitivity and specificity of the screen were controlled by the choice of Z significance threshold. Evaluation of samples containing fragments and all negative-control samples was performed at multiple Z significance threshold settings. For each such setting, the false-positive fraction (FPF) was computed as a fraction of the negative-control samples that were found to be altered, and the ligand fraction (LF) was estimated by subtracting FPF from the fraction of altered samples containing a fragment. The balance between LF and FPF was quantified by their ratio, LF/FPF. The best balance (LF/FPF ≈ 1.3) for the TPP riboswitch RNA was achieved with Z significance threshold in the range between 2.5 and 2.7, at which 0.022 > FPF > 0.014. For the dengue pseudoknot, the best balance (LF/FPF ≈ 4) was achieved with Z significance threshold in the range between 2.5 and 2.65, at which 0.007 > FPF > 0.005.   9 Figure S1. Screening construct design. (A) RNA sequence, colored by individual components.
(B) Secondary structure of the RNA-sequence barcode in the context of its self-folding hairpin. Figure S2. SHAPE profiles for non-hit, hit, and nonspecific hit fragments. Mutation rate traces corresponding to fragment-exposed and no-ligand control traces are in solid colors (red, yellow and black) and in black outline, respectively. Nucleotides determined to be statistically significantly different in fragment versus no fragment samples are denoted by green triangles.
Mutation rate traces for the same fragments are shown schematically in Fig. 2.