Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies

Figures & data

Figure 1. Why target RNA with small molecules? (a) RNAs perform a myriad of functions, which are often mediated by structurally conserved motifs. The HIV-1 transactivation response (TAR), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stem loop 5 (SL5), and the flavin mononucleotide (FMN) riboswitch, whose secondary structures are shown, are a few examples of functional RNA motifs that are structurally conserved. (b) Many bioactive small molecules that act on RNA targets have now been identified. The equilibrium dissociation constant (K_d), 1.5 maximal effective concentration (EC_1.5) or median inhibitory concentration (IC₅₀) are provided. (c) Like the proteome, the transcriptome is likely to contain many druggable sites [2], as the ones depicted in red spheres. The druggable sites on the FMN riboswitch (PDB ID: 3 f4g) [3], add adenine riboswitch (PDB ID: 5swe) [4], simian retrovirus type-1 (SRV-1) pseudoknot (PDB ID: 1e95) [5], enterovirus 71 internal ribosomal entry sites (IRES) (PDB ID: 6xb7) [6], HIV-1 TAR (PDB ID: 1qd3) [7], UGGAA/UGGAA pentad in spinocerebellar ataxia type 31 (SCA31) [8] were calculated using Fpocket [9].

Table 1. List of programs for RNA secondary structure prediction.

Program	Approach	Description	References
CentroidFold	Knowledge based	Based on generalized centroid estimator	[27,28]
CentroidHomfold	Knowledge based	Employs automatically collected homologous sequence information	[29]
Context Fold	Machine learning	Based on feature-rich trained scoring models	[30]
CONTRAfold	Machine learning	Based on conditional log-linear models, a flexible class of probabilistic models, which generalize upon stochastic context-free grammars by using discriminative training and feature-rich scoring	[31]
Crumple	Knowledge based, ring graph approach	Generates all possible non-pseudoknotted folds for an RNA sequence, given optional constraints	[32]
CyloFold	Free energy minimization	Based on simulating a folding process in a coarse-grained manner by choosing helices based on established energy rules; Allows complex pseudoknots	[33]
GTFold	Free energy minimization	Fast and scalable RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers	[34]
IPknot	Competitive prediction	Predicts RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure; Also gives consensus secondary structure with pseudoknots when a multiple sequence alignment is given	[35]
KineFold	Exact stochastic clustering	Predicts RNA secondary structures including pseudoknots through long-time-scale RNA folding simulations, which follow the stochastic closing and opening of individual RNA helices	[36,37]
MC-Fold	Knowledge based	Based on the nucleotide cyclic motif, a first-order object representing nucleotide relationships in structured RNAs; Incorporates non-Watson–Crick base pairs and suites of such into a unified energetic framework	[38]
Mfold	Free energy minimization	Oldest and most well-known software; Determines the set of base pairs that gives the minimum free energy using dynamic programming	[39]
Pknots and Pknots RG	Free energy minimization	Generates the optimal minimum energy structure using standard RNA folding thermodynamic parameters and a few parameters for pseudoknots	[40,41]
RNAfold	Free energy minimization, circular folding	Predicts the minimum free energy structure using thermodynamic parameters; Adapts experimental constraints	[42,43]
RNAshapes	Abstract shape analysis	Searches the folding space using RNA abstract shape analysis and computes the probabilities of structures that share the same shape	[44,45]
RNAstructure	Free energy minimization	Predicts the minimum free energy structure and maximum expected accuracy structures, including pseudoknots; Adapts experimental constraints	[46,47]
SARNA-Predict	Simulated annealing	Predicts RNA secondary structures including pseudoknots based on simulated annealing	[48]
Sfold	Statistical sampling	Based on a sampling algorithm that incorporates comprehensive structural features and thermodynamic parameters	[49]
Sliding Windows & Assembly	Sliding window	Generates all possible helix structures in windows containing short pieces of the RNA sequence and assembles the helices using a dynamic programming algorithm	[50]
SPOT-RNA	Machine learning	Uses an ensemble of two-dimensional deep neural networks and transfer learning for base-pair prediction including noncanonical and non-nested base pairs stabilized by tertiary interactions	[51]
SwiSpot	Switching sequence	Predicts alternative secondary structures of riboswitches	[52]
UNAFold	Free energy minimization	Integrated collection of programs that simulate folding, hybridization and melting pathways	[53]
vsfold and vs subopt	Heuristic modeling with mapping and sequential folding	vsfold: Predicts RNA secondary structure including pseudoknots using an entropy model derived from polymer physics; vs_subopt: Computes suboptimal structures based on the free energy landscape derived from vsfold	[54–56]

Figure 2. Methods for identification of RNA targets. (a) SHAPE-MaP can be used to probe RNA secondary structures on a massively parallel scale. (b) MobyDick enables the analysis of RNA secondary structure predictions. When the analysis is completed, the MobyDick interface displays the locations, secondary structure, global population (P_Global), and stability (∆G) of the motifs.

Figure 3. High-throughput screening approaches for identifying small molecule compounds that bind to RNA. (a) In fluorescence-based screening, a fluorescently labeled molecule – either the RNA target or an indicator – is used. Fluorescence resonance energy transfer (FRET) employs an RNA probe that is labeled with a fluorophore and a quencher. The FRET RNA probe is combined with the screening compounds and subjected to heating. The melting temperatures (Tm) of the RNA are measured using qPCR. Compounds that increase the Tm of the RNA bind to and stabilize the RNA. (b) Affinity-based screening methods do not require labeling of the RNA target. The automated ligand identification system (ALIS) is an indirect affinity-based screening method that involves the equilibration of the target RNA with a mixture of the screening compounds, size exclusion chromatography to purify the RNA–small molecule complexes, reverse-phase HPLC to dissociate the small molecules from the RNA, and mass spectrometry to identify the small molecule hits. (c) In small molecule microarray screening, the small molecules are immobilized onto microarray plates and used for screening against RNA targets. The hits are analyzed, e.g. by detecting increase in fluorescence if the RNA target is fluorescently labeled. (d) Fragment-based screening employs biophysical techniques, usually NMR, to identify low molecular weight compounds that bind to RNA targets. In ¹⁹F-NMR-based fragment screening, the 1D NMR spectra of mixtures of fluorinated compounds, before and after the addition of the RNA target, are taken. The hits are identified by looking for changes in the NMR signals of the fluorinated compounds; RNA-binding compounds exhibit more pronounced NMR signals after the addition of RNA. (e) In a typical DNA-encoded library (DEL) screening against RNA targets, the RNA is immobilized and incubated with small molecule compounds conjugated to a DNA barcode. The DEL members that bind to the RNA are eluted, and their identities are revealed after DNA amplification and sequencing. (f) Docking is the most widely used virtual screening approach. It requires that the three-dimensional structure of the RNA target be known so that the binding energies between the RNA and the screening compounds can be predicted in silico. (g) Most of the well-known RNA-targeted small molecules have been identified using phenotypic screening. In this method, the small molecules are evaluated for their ability to produce the desired phenotype within a cellular environment. The identity of RNA target is only revealed after molecular target identification and mechanistic studies.

Table 2. Selected screens conducted against RNA targets*.

Screening method	Target	Library size	Hit rate (%)	Affinity/potency	References
Fluorescence-based screening
FRET	HIV-1 Psi Stem Loop 3 (SL3)	2,643	3.0	Hit: IC50 = 4.5 μM (NSC260594)	[89]
FRET	TERRA	56	N/A	Hit: KD = 2.56 μM (CK1-14)	[90]
FRET (modified)	Group II Intron	10,000	0.16	Lead: Ki = 360 nM (intronistat B)	[91]
FRET	C9orf72 (G4C2)4 G-quadruplex	138	8.7	Hit: KD = 200–400 nM	[92]
TR-FRET	(CUG)12 RNA–MBNL1	320	9.0	Hit: IC50 = ~30–~150 μM	[93]
FID	C9orf72 (G4C2) repeat	132	2.3	Lead: Kd = 9.7–16 μM	[94]
FID	SMN2 exon 7 splice site stem-loop 2	304	54	Hit: EC50 = ~25 μM (PK4C9)	[95]
FID	C9orf72 (G4C2) repeat	40	25	Hit: IC50 = 0.9–12 μM	[96]
FID	EV71 IRES SLII	27	7.4	Hit: KD = 520 nM (DMA-135)	[6]
Affinity-based screening
ESI-MS	HCV IRES IIA	180,000	N/A	Hit: KD = ~100 μM (benzimidazole 1)	[97]
ALIS	FMN riboswitch	~53,000	0.12	Hit: KD = 130 nM (WG-3)	[21]
ALIS	Xist non-coding RNA	50,000	N/A	Hit: KD = 0.4 μM (X1)	[98]
Small molecule microarray (SMM)
SMM	HIV-1 TAR RNA	20,000	0.02	Hit: KD = 2.4 μM (analogue)	[99]
SMM	MALAT1 triple helix-forming sequence	26,229	0.7	Hit: KD = 2.3–6.1 μM	[100]
2DCS	HCV stem loops I–III	1,987	12	Hit: KD = ~400–~700 nM	[101]
Fragment-based screening
^1H NMR	Influenza A virus promoter	4,279	0.2	Hit: KD = 50.5 μM (DPQ)	[102]
^19F NMR	TERRA	355	5.6	Hit: KD = 120–1900 μM	[103]
DEL screening
DEL+2DCS	4,096 targets including pre-miRNA-27a	73,728	0.01	Hit: KD = 90 nM (compound 9, MST)	[104]
DEL	FMN riboswitch	46.3 billion	N/A	Hit: KD = 11.38 nM (HGC-1, 15.03 nM (HGC-2)	[105]
Virtual screening
Docking	HIV-1 TAR	181,000	N/A	Hit: KD<1 μM (EMSA)	[106]
Docking	Guanine riboswitch	2,615	N/A	Hit: KD = 80 μM (compound 27)	[107]
Docking	SARS-CoV Pseudoknot	80,000	N/A	Hit: IC50 = 0.45 μM (compound 43)	[23]
NMR+docking	HIV-1 TAR	~51,000	N/A	Hit: KD = 55 nM–122 μM	[108]
Docking	HIV-1 Rev response element stem loop IIB	N/A	N/A	Hit: IC50 = 6.8 μM (compound 6b)	[109]
Docking	DMPK CUG repeats	5	60	Hit: KD = 430 nM	[110]
Docking	DMPK CUG repeats	23	35	Hit: reduced nuclear foci at 40 μM concentration (compound 2–5)	[111]
Docking	HOTAIR	>200,000	N/A	N/A	[112]
Phenotypic screening
Luciferase reporter assay	SMN2	~200,000	1.0	Lead: EC1.5x = 4 nM (risdiplam)	[113,114]
Luciferase reporter assay	SMN2	1,400,000	<1.0	Hit: EC50 = 20 nM (branaplam)	[115]
Whole-cell phenotypic screening	FMN riboswitch	~57,000	N/A	Hit: KD = 16 nM (ribocil)	[20]

*Abbreviations used: ALIS, automated ligand identification system; 2DCS, two-dimensional combinatorial screening; DEL, DNA-encoded library; DMPK, Dystrophia myotonica protein kinase, DPQ, 6,7-dimethoxy-2-(1-piperazinyl)-4-quinazolinamine; EMSA, electrophoretic mobility shift assay; ESI-MS, electrospray ionization mass spectrometry; EV71 IRES SLII, enterovirus 71 internal ribosome entry site stem loop II; FID, fluorescence indicator displacement; FMN, flavin mononucleotide; FRET, fluorescence resonance energy transfer; HCV, hepatitis C virus; HIV-1 TAR, human immunodeficiency virus type 1 transactivation response; HOTAIR HOX antisense intergenic RNA; K_D: equilibrium dissociation constant; MALAT1, Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1), MBNL1, muscleblind-like splicing regulator 1; NMR, nuclear magnetic resonance; pre-miRNA, precursor microRNA; SARS-CoV, severe acute respiratory syndrome coronavirus; SMM, small molecule microarray; SMN2, survival of motor neuron 2; TERRA, telomeric repeat-containing RNA; TR-FRET, time-resolved fluorescence resonance energy transfer

Figure 4. Biophysical techniques for confirming and validating hit compounds. (a) In an ITC experiment, the reference and sample cells should contain the same buffer because mismatched buffers lead to large background heats during the titration. Each peak in the raw data counts for one injection of the ligand solution, and the respective enthalpy is measured. The data is analyzed by fitting a sigmoidal curve to the released or absorbed enthalpy per mol plotted against the molar ratio. The binding affinity and stoichiometry can be determined from the fitted curve. (b) For SPR measurements, it is generally recommended to use low surface densities of the immobilized RNA and high ligand flow rate for accurate determination of kinetic constants. SPR causes a dip in the intensity of the reflected light at the sensor surface. A shift in the curve represents binding of the ligand to the RNA. An SPR sensogram can be divided into the association, steady state or equilibrium, and dissociation phases. (c) Since BLI experiments are conducted in uncovered plates and using small volumes, the entire experiment should be conducted swiftly (< 4 hours) to limit excessive evaporation, which can impact experimental results. In BLI, changes in the number of molecules bound to the biosensor tip shift the interference pattern that can be monitored in real-time. Association and dissociation are measured by dipping the sensor in solutions with or without the ligand, respectively. (d) Prior to MST assays, different assay conditions should be tested because the type of buffer, pH, and ionic strength can influence the thermophoretic movement of molecules and dramatically change the outcome. The RNA and ligand solutions are incubated and filled into a glass capillary. At a specific temperature, an infrared laser generates a temperature gradient, and RNA-small molecule complexes move slower in that gradient than unbound RNA. A binding curve can be generated from the difference between the fluorescence signals of the bound and unbound states, from which a binding constant can be derived. (e) The H1 and H3 protons of guanosines and uridines, respectively, as well as the H5–H6 crosspeaks of pyrimidines, can be perturbed after small molecule binding. By obtaining the ¹H and 2D ¹H −1H NMR spectra of the RNA target prior to and after the addition of small molecules, one can monitor these perturbations and determine if the small molecule binds to the RNA and which RNA residues bind the small molecule.

Table 3. Techniques to assess engagement of small molecules to RNA targets.

Technique	Description	References
Chem-CLIP	Steps: Preparation of the Chem-CLIP probe: Two functional units are appended with the small molecule–a chemical cross-linking unit that allows the small molecule to attach to RNAs and a purification unit (biotin) for easy isolation of the bound RNA Incubation of cells with the Chem-CLIP probe Isolation and purification of bound RNAs RT-qPCR or RNA-seq to identify the bound RNAs	[146,149–157]
C-Chem-CLIP	By using C-Chem-CLIP, one can determine the RNA bound not by the compound but by the cross-linking and purification units. Cells are incubated with the Chem-CLIP probe and the competing non-cross-linking parent compound. RNAs bound by the parent compound are depleted in the pull-down.	[146,150–153,155]
Chem-CLIP-Map-Seq	Chem-CLIP-Map is an extension of the Chem-CLIP technique. It was developed for repeat sequences since it is difficult for RT to read through the repeats and to PCR amplify. Chem-CLIP-Map is performed in the following manner: Traditional Chem-CLIP experiment Digestion of bound RNAs with antisense oligonucleotide and RNase H Analysis of digested fragments using RT-qPCR	[150,152]
Global proteomics	Global proteomics can be used to study the effects of RNA-targeted small molecules on proteins. This can be performed by incubating the cells with the small molecules, then harvesting and analyzing the proteins using LC-MS/MS.	[156]
PEARL-seq	PEARL-seq employs photoaffinity labeled probes to assess engagement of a small molecule to its intended RNA target and identify other RNAs it binds to. The technique is carried out by: Preparing small-molecule compounds with photoaffinity labels (photoprobes) Addition of the photoprobes to RNA extracted from cells Cross-linking, biotinylation, and fragmentation of the RNA Avidin capture, reverse transcription, and cDNA elution Second adapter ligation PCR amplification and sequencing	[158,159]
RiboSNAP	RiboSNAP is a technique that uses small molecules capable of cleaving an RNA target to profile the binding of small molecules directly from total RNA obtained from cells. There are three variations of RiboSNAP. In the first variation, the small molecule is appended with a light-activatable cleavage module (e.g. N-HPT). After irradiation with light, the cleavage module produces radicals that cleave the target RNA. The second variation uses RIBOTACs – cleavage-inducing probes comprised of RNA-binding and nuclease-recruiting units – to target RNAs and activate RNase L, leading to the degradation of the bound RNAs. The third variation was developed to attenuate the issues associated with light-activatable cleavage modules (limited tissues to which light can be applied) and RIBOTACs (complex cleavage patterns). For a cleavage module, this variation uses bleomycin A5, which, when appended with the RNA-binding small molecule, allows for RNA-specific cleavage.	[152,160]

*Abbreviations used: Chem-CLIP, chemical cross-linking and isolation by pull down; C-Chem-CLIP, competitive Chem-CLIP; N-HPT, 1-hydroxy-6-thioxo-1,6-dihydropyridine-2-carboxylic acid; LC-MS/MS, liquid chromatography with tandem mass spectrometry; RiboSNAP, small molecule nucleic acid profiling by cleavage applied to RNA; RIBOTAC, ribonuclease-targeting chimeras; RNA-seq, RNA sequencing; RT, reverse transcriptase; RT-qPCR, RT quantitative polymerase chain reaction

Figure 5. Comparison of the techniques for the determination of the 3D structures of RNA–small-molecule complexes. (a) X-ray crystallography can provide atomic resolution structures for RNAs of any size but requires a large amount of sample that must be crystallized. The crystal structure of the FMN riboswitch in complex with the compound, BRX1151 (red), is shown [22]. (b) Unlike X-ray crystallography, solution-state NMR structures provide information about the dynamics of the RNA target, but it also requires large amounts of sample and is not suited for solving the 3D structures of large RNAs. The solution structure of the UGGAA-UGGAA RNA pentad in complex with the naphthyridine carbamate dimer (NCD, red) is shown [8]. (c) In recent years, the resolutions of cryo-EM structures of RNAs are improving, but it is still only suitable for large RNAs or RNA–protein complexes. Cryo-EM requires only a small amount of sample, and the obtained structures are closer to the RNA’s native state than the X-ray structures. The cryo-EM structure of the SAM IV riboswitch–SAM (red) complex is shown [161].

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