Transcriptome-wide design of selective, bioactive small molecules targeting RNA
Methods and computer systems are described herein for identifying small molecules that bind to selected RNA structural features (e.g., to RNA secondary structures). Also described are compounds and compositions that modulate RNA function and/or activity.
1. A method for identifying a small molecule that binds to an RNA of the human transcriptome and modulates the amount thereof, said method comprising:
(a) determining the secondary structure or sequence of the RNA;
(b) selecting a small molecule from a listing of small molecules capable of binding to the secondary structure or sequence of the RNA;
(c) measuring the effect of the small molecule on the amount of the RNA and, optionally, measuring the binding affinity between the small molecule and the RNA;
(d) optionally, repeating steps (b) and (c) with at least one other small molecule from the listing;
wherein a small molecule is identified, when the small molecule modulates the amount of the RNA.
2. The method of claim 1 , further comprising amplifying the RNA, sequencing the RNA, or a combination thereof.
3. The method of claim 1 , wherein the secondary structure of the RNA comprises an internal loop, hairpin loop, a bulge, a bubble, or a branch, or any combination thereof.
4. The method of claim 3 , wherein the secondary structure of the RNA comprises an RNA motif selected from: an RNA symmetric internal loop, asymmetric internal loop, 1×1 internal loop, 1×2 internal loop, 1×3 internal loop, 2×2 internal loop, 2×3 internal loop, 2×4 internal loop, 3×3 internal loop, 3×4 internal loop, 4×4 internal loop, 4×5 internal loop, 5×5 internal loop, 1 base bulge, 2 base bulge, 3 base bulge, 4 base bulge, 5 base bulge, 4 base hairpin loop, 5 base hairpin loop, 6 base hairpin loop, 7 base hairpin loop, 8 base hairpin loop, 9 base hairpin loop, 10 base hairpin loop, multi-branch loop, or pseudoknot.
5. The method of claim 1 , wherein the listing comprises a structural description of each small molecule, a description of each small molecule by chemical formula, chemical name, a description of each small molecule structure, a description of each small molecule three-dimensional structure, a description of each small molecule three-dimensional atomic structure, or a combination thereof.
6. The method of claim 1 , wherein the listing comprises a description of bonds formed between RNA motifs and small molecules, a description of alignments for each structural feature of each RNA motif with each small molecule to which the RNA motif binds, a description of alignments for each structural feature of small molecule with each structural feature of the RNA motif to which the small molecule binds, of any combination thereof.
7. The method of claim 1 , wherein the RNA is a microRNA (miRNA), a pri-miRNA, an mRNA, a tRNA, a rRNA, or a small interfering RNA (siRNA).
8. The method of claim 7 , wherein the RNA is a microRNA (miRNA), a pri-miRNA, an mRNA, or an siRNA.
9. The method of claim 1 , wherein the listing is generated by combining a dataset of RNA secondary structures and RNA sequences with a dataset of small molecules and RNA secondary structures to which the small molecules bind.
10. The method of claim 9 , wherein the dataset of RNA secondary structures and RNA sequences comprises a dataset of RNA secondary structures and RNA sequences of all human precursor microRNAs (miRNAs).
11. The method of claim 10 , wherein the RNA secondary structures are RNA secondary structures predicted using a free energy minimization program.
12. The method of claim 9 , wherein the dataset of small molecules and RNA secondary structures to which the small molecules bind comprises a dataset obtained from a 2-Dimensional Combinatorial Screening (2DCS) assay of RNA secondary structures and small molecules.
13. The method of claim 9 , wherein the dataset obtained from 2DCS was statistically analyzed using a method called Structure-Activity Relationships Through Sequencing (StARTS) to identify and annotate or score the interactions between the small molecules and the RNA secondary structures.
14. The method of claim 9 , wherein the listing identifies about 1668 small molecules that bind to an RNA secondary structure.
15. The method of claim 14 , wherein the listing identifies about 26 RNA secondary structures that are located in either a Drosha or Dicer processing site of miRNAs implicated in a disease and that have been validated for modulation of the disease by oligonucleotides.
16. The method of claim 15 , wherein the RNA secondary structures that are located within either a Drosha or Dicer processing site are internal loops.
17. The method of claim 16 , wherein the listing identifies small molecules that bind to the internal loops.
18. The method of claim 1 , wherein the RNA is involved in a disease.
19. The method of claim 9 , wherein the listing is generated by a computer system comprising one or more computer processors and storage configured to compare a structured query dataset describing predicted RNA secondary structures based on RNA sequence with a structured dataset of identified RNA motif-small molecule pairs.
20. The method of claim 19 , wherein the computer system is further configured to assign one or more Z obs -scores to a RNA secondary structure identified to bind a small molecule using Equations (I) and (II):
φ
=
n
1
p
1
+
n
2
p
2
n
1
+
n
2
I
Z
obs
=
(
p
1
-
p
2
)
φ
(
1
-
φ
)
(
(
1
n
1
)
+
(
1
n
2
)
)
II
where
n 1 is the size of a population of structural features in RNA secondary structure identified to bind a compound (population 1);
n 2 is the size of a population of structural features from a larger library of RNA motifs (population 2);
p1 is the observed proportion of Population 1 displaying the feature, and
p2 is the observed proportion for Population 2 displaying the feature.
21. The method of claim 20 , wherein the computer system is further configured to sum the Z obs -scores to generate at least one ΣZ score for an RNA secondary structure when a Z obs -score indicates that a structural feature in the RNA secondary structure is distinct from the larger library of structural features (population 2) at a 95% confidence level.
22. The method of claim 21 , wherein the computer system is further configured to plot the ΣZ scores against measured binding affinities of the small molecule for the RNA to generate a ΣZ score—binding affinity plot, where the plot is fitted to an inverse first-order equation.