Two-dimensional combinatorial screening and the RNA Privileged Space Predictor program efficiently identify aminoglycoside-RNA hairpin loop interactions

Abstract

Herein, we report the identification of RNA hairpin loops that bind derivatives of kanamycin A, tobramycin, neamine, and neomycin B via two-dimensional combinatorial screening, a method that screens chemical and RNA spaces simultaneously. An arrayed aminoglycoside library was probed for binding to a 6-nucleotide RNA hairpin loop library (4096 members). Members of the loop library that bound each aminoglycoside were excised from the array, amplified and sequenced. Sequences were analyzed with our newly developed RNA Privileged Space Predictor (RNA-PSP) program, which analyzes selected sequences to identify statistically significant trends. RNA-PSP identified the following unique trends: 5'UNNNC3' loops for the kanamycin A derivative (where N is any nucleotide); 5'UNNC3' loops for the tobramycin derivative; 5'UNC3' loops for the neamine derivative; and 5'UNNG3' loops for the neomycin B derivative. The affinities and selectivities of a subset of the ligand-hairpin loop interactions were determined. The selected interactions have K(d) values ranging from 10 nM to 605 nM. Selectivities ranged from 0.4 to >200-fold. Interestingly, the results from RNA-PSP are able to qualitatively predict specificity based on overlap between the RNA sequences selected for the ligands. These studies expand the information available on small molecule-RNA motif interactions, which could be useful to design ligands targeting RNA.

Figure 1.

Secondary structures of the hairpin loop library (1, 4096 members) and competitor oligonucleotides 2–4. The boxed nucleotides highlight the randomized region; for simplicity, only this region is shown for the selected hairpins (Figure 6). The competitor oligonucleotides 2–4 were used in 5000 times excess over 1 to ensure RNA–ligand interactions occur within the randomized region.

Figure 2.

(A, top) Chemical structures of the azido-aminoglycosides used to study RNA–ligand interactions: 5 (kanamycin A derivative), 6 (tobramycin derivative), 7 (neamine derivative), 8 (neomycin B derivative). The 2-deoxystreptamine ring common to all four aminoglycosides is highlighted in blue. (A, bottom) Immobilization of 5–8 via 1, 3 Huisgen dipolar cycloaddition (14,21) on alkyne-displaying microarrays for 2DCS or conjugation to 5′-TAMRA (TMR, red ball) to study binding affinities. AmG refers to aminoglycoside. (B) Image of a microarray displaying compounds 5–8 that was hybridized with oligonucleotides from Figure 1 before (top) and after excision of the bound RNAs (bottom). Circles indicated the positions where the RNAs were excised. (C) Plot of the data for binding of ³²P-internally labeled 1 to array immobilized 5–8 in the presence of competitor oligonucleotides 2–4. Plots were normalized to the highest signal for binding 8.

Figure 3.

User interface for RNA-PSP. The control panel on the left-hand side of the screen allows control over search parameters including: (i) ability to define the random motif region by size and type; (ii) ability to define conserved regions in RNAs selected, providing a guide for automated searching and extracting of all random motif sequences from a file; (iii) automated or manual input of random motif sequences; (iv) ability to display both positive and negative Z-scores; and (v) ability to save the complete analysis to a text file.

Figure 4.

RNA-PSP program used to find and rank trends found within selected mixtures of sequences. (a) File containing complete DNA sequences with embedded selected hairpins is uploaded into RNA-PSP; (b) The randomized motif is identified within the RNA sequence; (c) Each random motif is located in the sequencing file, extracted, and stored as a member of Population 1. Each sequence is listed as a combination of A/U/G/C in order 5′ to 3′ in the randomized sequence (e.g. CUGGCA); (d) The entire library of all possible motifs is generated and stored as Population 2; (e) Populations are compared, and statistical Z-scores are calculated for all trends. Trends are then sorted and ranked according to Z-score; (f) Z-scores are manually converted to two-tailed p-values and statistically significant trends with >95% confidence level are displayed.

Figure 5.

Venn diagram of the statistically significant trends indentified in the RNA sequence space selected to bind the four aminoglycoside derivatives. Overlapping trends are shown in bold. The most statistically signifcant trend for the kanamycin A derivative, 5, is 5′UNNNC3′; for the tobramycin derivative, 6, 5′UNNC3′; for the neamine derivative, 7, 5′UNC3′; and for the neomcyin B derivative, 8, 5′UNNG3′.

Figure 6.

The secondary structures of a subset of the RNA hairpin loops that were selected to bind 5, 6, 7 and 8. The nucleotides shown are derived from the boxed region in 1 (Figure 1). The affinities (in nM) for the respective aminoglycoside are shown below the hairpin structure. Statistical analysis shows that kanamycin A binds 5′UNNNC3′ loops (A), tobramycin binds 5′UNNC3′ loops (B), neamine binds 5′UNC3′ loops (C), and neomycin B binds 5′UNNG3′ loops (D). Nucleotides highlighted in red have the statistically most significant trend for that aminoglycoside.

Figure 7.

Secondary structures of the hairpin loops tested to determine if cassette nucleotides contribute to binding affinity. 9 is a mimic of the cassette in the hairpin loop library (1, Figure 1) with a GAAA tetraloop closed by a CG pair. The dissociation constants (nM) to the minimized hairpin loop are given below the secondary structure of the hairpin. The dissociation constants (nM) to the original cassette are shown in parentheses.