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5.2 Natural Ligands 95
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(a) Aminoglycosides
Neomycin B
(c) Tetracyclines
R = H; Tetracycline
R = Cl; Chlorotetracycline
Figure 5.1 Examples of natural molecules from classes described in Section 5.2, including
(a) aminoglycosides, (b) macrolides, (c) tetracyclines, and (d) native riboswitch ligands.
(b) Macrolides
Erythromycin A
(d) Riboswitch ligands
Pre Q
1
Pre Q
0
been investigated for use as broad-scale antibiotics, such as neomycin (Figure 5.1a);
however these natural antibiotics often run into challenges with resistance [18].
Resistance can occur through modications to the aminoglycoside itself by enzymes
that cause it to become inactive, as well as through mutations or modications
in the rRNA sequence that prevent binding of the ligands [18, 19]. Despite these
challenges, aminoglycosides remain a large class of antibiotics on market still today.
Besides targeting rRNA, aminoglycosides have been investigated as ligands against
a variety of other RNA structures. As an example, in a study investigating the hammerhead ribozyme RNA, neomycin B was found to have an anity on the same
order of magnitude as rRNA–neomycin interactions [20]. This work demonstrated
the non-specic nature of aminoglycosides showing that neomycin interacts with
a variety of RNA structures with similar anities. In another study investigating
aminoglycoside targeting of human immunodeciency virus (HIV) Rev response
element (RRE) RNA, a critical structure in HIV replication, aminoglycosides were
modied with acridine or made dimeric with the goal of increasing specicity for
RRE [21]. While high-anity aminoglycoside derivatives were identied, the novel
ligands did not show high specicity when compared to transfer RNA (tRNA) and
DNA [21]. While aminoglycosides have been critical in combatting bacterial infections, their propensity for developing resistance and lack of specicity make them
challenging molecules to continue to develop. Nevertheless, understanding their
complex recognition is still valuable to future RNA-targeting eorts.
5.2.2 Tetracyclines
Besides aminoglycosides, tetracyclines are another major class of rRNA-targeting
natural product antibiotics on the market (Figure 5.1c). In 2020, tetracyclines
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were the 4th most prescribed antibiotic class in the United States, composing
15.5% of outpatient prescribed antibiotics [22]. Tetracycline functions through a
similar mechanism as aminoglycosides by binding to the A-site in the ribosome
and disrupting protein synthesis [23, 24]. Tetracyclines also face challenges similar
to those of aminoglycosides, encountering antibiotic resistance through multiple
mechanisms, including eux [24]. While some synthetic derivatives have been
successful at circumventing resistance in Gram-positive bacteria, it is not an
eective treatment for all bacteria [24]. Tetracyclines are distinct from aminoglycosides in their structural features. They are composed of four fused rings and thus
have a more linear structure overall. This structure may allow for intercalation
in double-stranded RNA through stacking between RNA bases. Studies have
shown that tetracyclines have activity against various RNA viruses including HIV
and Japanese encephalitis virus (JEV), where minocycline was found to mitigate
infection and reduce the viral titer respectively [25, 26]. Tetracyclines have the
potential for broad applications, but as with aminoglycosides encounter diculties
with o-target eects from non-specic binding of RNA.
5.2.3 Macrolides
Macrolides are antibiotics that consist of 14-membered rings, with azithromycin, a
synthetic derivative of the naturally occurring macrolide erythromycin (Figure 5.1b),
being the second most prescribed antibiotic in 2020 [22, 27]. Macrolides have been
found to bind near the exit tunnel of nascent peptide chains in rRNA, leveraging
hydrogen-bonding and hydrophobic interactions to interfere with protein synthesis
in bacteria [28]. However, these molecules again suer similar challenges of antibiotic resistance, with the natural ligand erythromycin developing resistance through
multiple mechanisms [28, 29]. To combat this resistance, similar strategies of synthesizing near derivatives that can circumvent resistance mechanisms were developed
[28]. Additionally, macrolides were investigated for activity against non-ribosomal
RNA. In a study to evaluate the ability of antibiotics to target microRNAs (miRNAs)
and alter their processing, it was discovered that while the macrolides showed binding to the target miRNAs, they did not inhibit their processing while other antibiotics were potent inhibitors [30]. Together, macrolides and the previously described
aminoglycosides and tetracyclines represent some of the most explored scaolds for
RNA targeting due to their activity against bacteria and high clinical usage.
5.2.4 Native Riboswitch Ligands
Bacteria have unique feedback mechanisms for biosynthetic pathways that involve
metabolite binding to an RNA riboswitch molecule causing a conformational change
that alters gene expression (see Chapter 8 for further discussion on riboswitches).
Riboswitches consist of an aptamer domain that forms a ligand-binding pocket
and an expression platform. Upon ligand binding to the aptamer domain, conformational changes alter the expression platform to switch gene expression on or
o [31]. A recent survey identied over 50 functional riboswitch classes that have
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been experimentally validated [32]. Riboswitches may represent some of the most
well-regulated and specic RNA-ligand interactions in nature. Precise interactions
allow for distinguishing chemically similar ligands such that only one biomolecule
can switch on or o a given pathway in a biological system.
An example of this precise recognition is the abundant prequenosine1(PreQ1)
riboswitch. Three distinct riboswitch classes have been identied and evaluated
in bacteria for their recognition of PreQ1(Figure 5.1d), a chemical derivative of
guanine, and similar analogues, such as PreQ0[33]. These varying classes have
distinct RNA aptamer domains that specically recognize the various guanine
analogues with varying degrees of specicity [33]. In the case of the Class III PreQ
riboswitch there is a two order of magnitude preference for PreQ1over PreQ
where the ligands vary only by the reduction of a single nitrile to an amine in
PreQ1[33]. Furthermore, there are a variety of riboswitch classes that target diverse
molecular structures including cofactors, amino acids and sugars [32]. With such
unique recognition properties and interactions there is much we can learn from
understanding riboswitch recognition in targeting novel RNA structures.
Taking lessons from nature on selective RNA-ligand interactions may be the
key to understanding how to selectively target other RNA structures in the future.
While antibiotics may be prone to developing resistance and promiscuous RNA
binding, their chemical properties and modes of RNA recognition lend critical
insight into the design of more potent and selective ligands for novel RNA targets.
Antibiotics and riboswitch ligands show complex recognition through a variety of
interactions from intercalation to hydrogen-bonding or hydrophobic interactions.
In addition to native riboswitch ligands, selective ligands from synthetic and
high-throughput screening libraries have been discovered, including Ribocil-C targeting the avin mononucleotide (FMN) riboswitch [34], and compounds targeting
the PreQ1and ZTP riboswitches from high-throughput screening and synthetic
optimization, respectively [35, 36]. By understanding how ligands achieve specic
recognition of RNA sequence and structure we may nd more selective chemical
probes.
1
0
5.3 Commercial Ligands
With the limited number of proteins deemed druggable and the knowledge that a
majority of the human genome does not encode for proteins, the market for identifying novel RNA-binding molecules has grown signicantly in recent years [37–39].
The plethora of novel targets ledto an increase in industry-curated libraries designed
specically to target RNA [40, 41]. In addition to large industrial libraries, many academic institutions havealso designed and acquired drug-like libraries of compounds
with increased propensities for RNA binding [10, 42]. While these libraries have been
curated through a variety of methods, they have led to collections of molecules with
similar properties as observed through statistical analysis, machine-learning models, and sub-structure searches, reinforcing the existence of a privileged space for
RNA-targeting small molecules.
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5.3.1 Industrial Libraries
The increasing demand for RNA therapeutics has led large companies to develop
libraries designed for RNA targeting or screening. For example, Merck performed
high-throughput screening of over 50,000 chemically diverse small molecules
against 42 RNA structures [40]. They then used the chemical properties of
molecules found to bind RNA in this preliminary screen to develop a smaller
RNA-focused library (∼3700 molecules). Within this focused library they observed
higher hit rates across RNA targets in a secondary screen, scaolds enriched in
selective ligands (Figure 5.2a), and the properties of the library were similar to
those of molecules previously published in R-BIND [40]. In a collaboration with
AstraZeneca, Disney and coworkers also performed a high-throughput screen
utilizing the properties of their Inforna database to develop a ∼2000-member library
from AstraZeneca’s collection of greater than two million compounds [41]. Similar
to the screen Merck performed, hit rates higher than those of general screening
campaigns were observed, and the properties of these ligands were similar to those
of both R-BIND and SMMRNA [3, 7, 41]. The mounting studies curating novel
libraries continually show an overlap in properties of the resulting hits, supporting
the existence of distinct properties that inuence RNA binding. Academic institutions have been simultaneously curating similar libraries and observing similar
trends.
5.3.2 Academic Libraries
While large industrial or pharmaceutical libraries are critical for understanding
the properties of RNA-targeting molecules and providing large datasets, they can
be inaccessible to academic institutions. Thus, some groups or institutions have
started their own library collections for RNA targeting, making the screening and
applications of these libraries more accessible to the eld through collaborations.
The Disney lab curated their Inforna database, which consists of a collection of
RNA-targeting small molecules known to bind specic RNA secondary structures [8, 9, 41]. The Inforna library has continually grown with the addition of
Substructures in commercial libraries
(a) (b)
Figure 5.2 Examples of enriched substructures identified in selective ligands from (a)
Rizvi and coworkers. Source: Rizvi et al. [40]/with permission of Elsevier; and (b) Yazdani
and coworkers. Source: Yazdani et al. [42]/with permission of John Wiley & Sons.
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new ligands upon screening of novel RNA-biased libraries, some of which come
from large sets of commercial ligands [41]. After multiple rounds of curation
through screening, the molecules within Inforna continue to show similar chemical
properties to each other and those of other RNA-targeted small-molecule libraries.
The Inforna database has been used to design selective and potent inhibitors of
a variety of RNA targets, showing its utility and ability to identify RNA specic
chemotypes in small-molecule ligands.
Recently, the Hargrove lab reported the design and application of the DRTL
[10]. The DRTL was curated by using a nearest-neighbor algorithm to identify
commercially available ligands that were similar in cheminformatic properties to
the ligands within R-BIND. The library was screened against four RNA targets, and
hit rates similar to other RNA-focused library screens were observed. While the
majority of the properties of DRTL molecules were not signicantly dierent from
R-BIND ligands, the scaolds or structures were unique as calculated by Tanimoto
dissimilarity scores [10]. This work demonstrated that using chemical properties
to dene RNA-binding ligands can allow for the discovery of novel RNA-binding
scaolds.
Additionally, similar work was performed by the Schneekloth lab to curate
a library of approximately 2000 RNA-binding molecules [42]. The Repository
of RNA Binders to Nucleic acids, or ROBIN, was developed through screening
24,572 diverse ligands from commercial libraries against 36 unique nucleic acid
targets [42]. In this work, machine-learning algorithms were used to identify
properties of the ROBIN molecules that are unique or enriched compared to
libraries of protein-binding molecules [42]. By comparing substructures of ROBIN
RNA-binding and non-binding molecules, the authors identied enriched substructures that were consistent with previously identied functional groups and
chemotypes (Figure 5.2b) [42]. This work allows for prediction of novel ligands
while further dening the chemical space of RNA-targeting small molecules, giving
the eld a basis to continue exploring RNA-specic chemical space through an
in-depth understanding of specic scaolds or properties that can be expanded
with further commercial mining or synthetic tuning.
5.4 Synthetic Ligands
Many eorts have been made to explore chemical space of RNA-targeted small
molecules by scaold-based synthesis, leading to a number of derivatives bearing
specic core structures (Figure 5.3). Dierent from screening existing commercial
libraries, which can lead to the identication of repeat hit molecules or promiscuous
ligands, examination of synthetic ligands could greatly extend the chemical space
being investigated and proactively modulate small-molecule-binding proles (e.g.
selectivity, cytotoxicity, pharmacokinetics) against specic RNA targets. There is a
plethora of reviews for RNA targeting discussing dierent classes of RNA targets
and various synthetic molecules that have been explored [43–47]. In this section,
representative small-molecule scaolds for RNA targeting will be introduced,
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(a) Benzimidazoles and purines
2,6-Diaminopurine
Isis-11
Targapremir-210
(c) Oxazolidinones, amilorides, and
diphenyl furans
IMB-73
DMA-135
DB60
(d) Multivalent ligands
(b) Naphthalenes, quinolines, and
quinazolines
NDl-β-CyD-1
β-CyD:
AbuSVPheQR
DPQ
Bisamidinium 2a
Figure 5.3 Examples of chemical ligands with core structures discussed in Section 5.4
highlighted in red, including, (a) benzimidazoles and purines; (b) naphthalenes, quinolines,
and quinazolines; (c) oxazolidinones, amilorides, and diphenyl furans; and (d) multivalent
ligands.
including multivalent ligands that assemble multiple modules into one structure to
achieve enhanced RNA-targeting eects.
5.4.1 Benzimidazoles and Purines
Benzimidazole ring systems exist extensively in many natural products and clinical
drugs, with structural similarity to purine bases (Figure 5.3a). The benzimidazole
scaold was rst identied to target RNA in a mass spectroscopy (MS)-based
high-throughput screening against the internal ribosomal entry site (IRES) IIa
subdomain in hepatitis C virus (HCV) [48]. The binding mode of lead benzimidazoles was identied with multiple orthogonal methods [49–51], which revealed a
signicant ligand-induced conformational change from an unliganded bent shape
to the linear conformation complexed with small molecules inhibiting viral translation. Relatedly, Hoechst dyes are nucleic-acid-staining molecules that contains
a bis-benzimidazole scaold. These molecules were found to aord high-anity
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binding and bioactivity toward multiple RNA targets, including self-splicing group
I intron as an antifungal [52, 53], repeat expansion RNAs in neurodegenerative
disease models [54, 55], and miRNAs with diverse secondary motifs implicated
in cancers [56, 57]. The MOA of these high-anity bis-benzimidazoles includes
interruption of essential folding pathways, restoration of functional proteins by
competitive binding, and modulation of miRNA biogenesis via Drosha or Dicer
ribonuclease-mediated processing [58].
Purine represents a frequently occurring nitrogen-containing heterocycle due
to its presence in adenine and guanine nucleobases. Synthetic purine derivatives
identied with RNA-targeting capabilities were mostly tested in recognition of
bacterial riboswitches, mimicking the cognate purine ligands binding to the
aptamer domain, such as the guanine- and adenine-specic riboswitches [59].
The advantages of adapting benzimidazoles and purines for RNA targeting might
include pharmacological properties, such as superior membrane permeability and
low cytotoxicity as they are structurally analogous to endogenous heterocycles.
5.4.2 Naphthalenes, Quinolines, and Quinazolines
The naphthalene-based RNA-targeting chemotype is usually found with naphthalene diimide (NDI), a type of known guanine-quadruplex (G4) binder (Figure 5.3b).
NDI-based compounds have been documented with high-anity and large planar
surface favored for threading-type intercalation against G4s, as well as great potential for chemical variability on naphthalene rings and imide nitrogens. Zou et al.
designed a di-substituted NDI with two conjugated β-cyclodextrins, which showed
improved specicity for the recognition of the parallel RNA G4 topology over a
hybrid G4 topology and double-stranded DNA (dsDNA) [60]. In this design, the
bulky cyclodextrin group disrupts the binding with dsDNA due to steric hindrance.
The preferred binding toward the parallel RNA G4 was explained by the orientation
of side chain loops, which allowed nucleobases in the loops to bind the cyclodextrin.
Loop nucleobases were not available for binding in the hybrid G4 topology, leading
to weaker anities.
Quinoline derivatives have been identied for antitumor, antiviral, and antiinammatory use and are also known as DNA intercalators. The application of such
scaolds in the early studies of RNA targeting involved the design of helix-threading
peptides, where the intercalator module (e.g. quinoline) inserts into the base paired
nucleic acid duplex and the other substituents locate in the opposite groove of the
helix [61]. Gareiss and coworkers employed resin-bound dynamic combinatorial
chemistry to identify the rst compounds able to inhibit MBNL1 protein binding
to (CUG) repeat RNA in myotonic dystrophy type 1 (DM1) treatment [62]. The hit
compound with a quinoline moiety was further evolved into benzo[g]quinolines
and showed improved binding properties in vivo [63].
Like quinoline, quinazoline has a bicyclic aromatic structure with fused benzene and pyrimidine rings (Figure 5.3b). The potential of this scaold for RNA
binding can be traced from many studies. For instance, Lee and coworkers identied an amino-quinazoline compound from NMR-based fragment screening
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that showed binding to inuenza A virus promoter sequence with moderate
anity and inhibition of viral replication [64]. Inspired by the crystal structure
of a 2-amino-benzimidazole inhibitor targeting the IRES of HCV, Charrette and
coworkers designed several 2-amino-quinazoline compounds using a shape complementary strategy to increase pocket lling [65], indicating that structure-guided
shape complementarity could be a useful approach in RNA ligand optimization.
5.4.3 Oxazolidinones
Many promiscuous binding interactions of small molecules to RNA originate
from the densely charged RNA backbone, which can exert strong electrostatic
interactions and intercalation. Oxazolidinones are a type molecule with low
overall charge and a non-aromatic core that has shown binding to RNA, such as
linezolid and tedizolid antibiotics interacting with bacterial rRNA (Figure 5.3c)
[66, 67]. Besides rRNA, another well-studied RNA target recognized by oxazolidinones is the T-box riboswitch antiterminator RNA, which is an essential
regulatory element found in many Gram-positive bacteria. Maciagiewicz and
coworkers performed quantitative structure–activity relationship (QSAR) study
to understand C-5-substituted oxazolidinone-induced uorescence changes in
Förster resonance energy transfer (FRET)-labeled antiterminator RNA [68].
The results further support that hydrogen-bonding and hydrophobic properties
play signicant roles in ligand binding within scaolds lacking a strong positive
charge.
5.4.4 Amilorides
The structure of an amiloride is characterized by a pyrazine ring combined with
amino and acylguanidino groups that confer potential hydrogen-bonding capacities
(Figure 5.3c). The early studies on amilorides for targeting nucleic acids were
focused on the design of an abasic site (AP site) binder via pseudo-base pairing
due to the enrichment of hydrogen bond donor and acceptor atoms in the structure [69]. Stelzer and coworkers rst discovered 5′-(N,N )-dimethyl amiloride
(DMA) as a novel binder for HIV-1 transactivation response element (TAR) from an
ensemble-based virtual screening [70]. Following SAR studies on the C-5 and C-6
modications led to a series of DMA compounds with improved binding anities
and specicities against HIV-1 TAR [71]. In later research, Hargrove and coworkers
demonstrated the tunability of this scaold by targeting many other RNAs with
amiloride derivatives, including multiple HIV RNAs [72], enterovirus 71 (EV71)
IRES stem-loop II subdomain [73, 74] and 5′-untranslated regions of severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2) genome [75]. The latter studies
further highlighted the potential of amiloride derivatives as biologically active,
RNA-targeted antiviral compounds, specically against EV71 and SARS-CoV-2.
The compounds act by binding to RNA stem-loop motifs, altering their conformations and disrupting interactions or processes important for viral translation and
replication.
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5.4.5 Diphenyl Furan
Diphenyl furan (DPF) is generally functionalized with amidine-based side chains
attached to the phenyl rings (Figure 5.3c). Wilson and coworkers performed
extensive synthetic and biophysical studies on DPF’s potential in binding genomic
RNA of HIV, such as TAR and RRE [76–78]. The compounds displayed high anity
and selectivity against wild-type RRE or TAR, when compared to the poly(A•U)
duplex control or other mutants, and inhibited Tat-mediated transactivation in
HIV-infected cells [78]. Donlic and coworkers took a dierent perspective, investigating shape diversity of DPFs in the selective recognition of an RNA triple-helix
structure by synthesizing a set of para-, meta-, and ortho-DPF regioisomers that
occupy distinguished topological space [79, 80]. The synthetic ligands presented
diverse binding anities and selectivity against metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) triple helix, from uorescent titration, thermal
stability, and exonucleolytic degradation assays. DPFs, with feasible tunability
on multiple positions, also inspired other structures with amidine-functionalized
unfused aromatic ring systems, such as diminazenes [81].
5.4.6 Multivalent Ligands
Multivalency is a prevalent strategy adopted by many biological interactions, including molecular recognition at cell surfaces. The application of multivalent ligands in
RNA recognition can greatly enhance binding anity and specicity by introducing multiple modules into one molecule to realize additive or cooperative eects.
The design of multivalent ligands involves identication of monomeric recognition
modules and optimization of the intervening linker(Figure 5.3d). Many of the multivalent ligands designed so far aimed to target RNAs with repeat expansions, as these
targets have repeated secondary motifs that can be captured by specic monomeric
chemical probes. In 2008, Miller et al. identied the rst non-nucleic acid-based
ligands that targeted r(CUG) repeat RNA via a dynamic combinatorial chemistry
(DCC) method [62]. While optimization of the linker usually becomes the essential
part of assembling multiple binding moieties into an eective chemical probe, the
cell permeability of the multivalent ligands should also be considered during rational design. Zimmerman et al. incorporated bisamidinium groove binders into the
linker of an oligomeric ligand to mimic polycationic and amphiphilic characteristics of the cell-penetrating peptide (CPP) structure [82]. The synthetic multivalent
ligand showed good cell permeability and capacity to recognize U–U mismatches in
r(CUG) repeat RNA with low toxicity in both cell and mice models.
5.5 Computational Tools for the Exploration
of Chemical Space
Eorts to extend the currently known chemical space of RNA-targeted small
molecules have used diverse approaches, often with a computational focus. Various
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N
H
N
H
Similarity search
O
O
O
N
H
O
OH
N
H
O
10
0
–10
Principal component 2
–10
Principal component 1
Principal component analysis
Input
molecule
PC1
0 10
Input features
PC2
Machine-learning tools
Latent space
Latent space
Deep generative models
200
0
Predicted
–200
–250 0 250
Measured
Reconstructed
molecule
N
O
H2N
O
Molecular docking and virtual screening
Figure 5.4 Potential tools to explore larger chemical space in the future: cheminformatics
analysis (e.g. PCA), machine-learning tools, similarity search, deep generative models, and
structure-based molecular docking and virtual screening.
cheminformatic approaches including similarity search, principal component
analysis (PCA), and sophisticated machine-learning methods have been deployed
for identifying more chemotypes (Figure 5.4). With high-resolution RNA structural data, molecular docking and virtual screening, a wider range of chemical
space can be eciently explored. By advancing HTS data in combination with
high-performance computing units, de novo ligand design using generative
chemistry methods may be achieved.
5.5.1 Similarity Searches and Principal Component Analysis
Chemical similarity search algorithms and metrics are computational methods
used to identify and compare molecules based on their structures and properties.
Collections of known RNA-binding molecules could provide valuable information
and guide our search toward more potent compounds via various similarity metrics.
Cheminformatic tools could help interpret such information and translate it into
chemically readable formats. PCAs reveal the most relevant molecular features
by decreasing the dimensionality of the chemical space being investigated, thus
enabling similarity search of novel structures in a predened feature space. Such
chemical searches have proven successful in constructing an eective RNA-biased
library [40, 83] starting with several known active structures. Multiple algorithms
have been employed for searching similar compounds with a reference molecule
or a library, including Tanimoto similarity, nearest neighbor search, and substructure search. Depending on what endpoint is being investigated, the similarity
metrics used in such chemical searches can also vary, from molecular descriptors
in distance-based metrics to molecular ngerprints in bit vector-based metrics
and structural overlap in 3D space [84]. Often, the type of similarity metrics used
impacts the quality of resulting compounds more strongly than the search algorithm
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