Template-guided selection of RNA ligands using imine-based dynamic combinatorial chemistry

Article abstract of DOI:10.1039/d0cc00266f

This study establishes the applicability of imine-based dynamic combinatorial chemistry to discover non-covalent ligands for RNA targets. We elucidate properties underlying the reactivity of arylamines and demonstrate target-guided amplification of tight

Full text of DOI:10.1039/d0cc00266f

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Template-guided selection of RNA ligands using imine-based
dynamic combinatorial chemistry
Aline Umuhire Juru,a Zhengguo Cai,a Adina Jan,a and Amanda E. Hargrove*a
Received 00th January 20xx,
Accepted 00th January 20xx
DOI: 10.1039/x0xx00000x
This study establishes the applicability of imine-based dynamic structure or dynamics. Miller and co-workers demonstrated the
combinatorial chemistry to discover non-covalent ligands for RNA utility of this approach by using disulfide exchange to generate
targets. We elucidate properties underlying the reactivity of a dynamic resin-bound library of 11,325 members that has led
arylamines and demonstrate target-guided amplification of tight to the selection of bioactive ligands for RNA targets in HIV and
binders in an amiloride-based dynamic library.
myotonic dystrophy.19 One limitation of this method has been
the use of disulfide chemistry, which requires replacement of
the exchanging bond with a bio-isostere.30
RNA molecules are increasingly recognized and pursued as
novel targets that would expand the scope of “druggable”
space, and several promising RNA-binding small molecules have
been discovered.1-4 Successful approaches to RNA ligand
discovery have included scaffold-based synthesis, screening of
general and RNA-biased libraries, sequence- and structure-
based design, ensemble-based virtual screening, modular
assembly of multivalent ligands, fragment-based screening, and
dynamic combinatorial chemistry.2, 5-24
While successful in specific cases, many of these approaches
suffer from technical challenges that limit generalizability. In
the case of microarray-based screening, potential ligands can be
missed if surface-immobilization greatly affects their binding to
RNA. Screening methods that involve labeling RNA with a
fluorophore are limited by the potential impact of the label on
the RNA conformational landscape. Indicator displacement
assays provide a label-free solution-based alternative, however,
these assays are limited to small RNA constructs. Computational
prediction of ligands based on the RNA secondary structure
motifs,25 while promising for some systems, has not been
developed for RNAs with highly folded binding pockets. Finally,
structure-based design is difficult due to inherent challenges
with solving three-dimensional structures of large complex
RNAs at high resolution and the importance of dynamics in
recognition, though advances are rapidly being made.26
To develop a complementary method that addresses some
of the limitations of current approaches to RNA-ligand
discovery, we worked within the area of template-guided ligand
selection by DCC. We expanded on work previously done in this
area by introducing imine exchange chemistry (Fig. 1A).31 Imine
exchange also allows access to diverse ligands as researchers
can utilize the large selection of amines and aldehydes or
ketones that are commercially available.32 Rayner and co-
workers used imine-based DCC to conjugate nucleosides at the
3’ end of an RNA construct, supporting the compatibility of
imine-based DCC with RNA.33 Recently, Dash and co-workers
showed the promise of imine-based DCC for nucleic acids by
targeting G-Quadruplex DNA.34 However, the target in this work
was conjugated on a magnetic nanoplatform, a format that has
potential to affect ligand binding by altering the conformational
landscape of the target, especially in the case of RNA that has a
highly dynamic nature.35 Our efforts to adapt imine-based DCC
for RNA targets were thus focused on developing a general,
solution-based method that applies to many different systems.
To determine the applicability of solution imine-based DCC
to RNA systems we sought to identify the types of amines
amenable to DCC under certain conditions, the amplification
factors that can be expected, and how the relative amplification
factors relate to the relative binding affinities of the library
members. As a model system, we used an aldehyde analogue of
an amiloride-based scaffold shown to be tunable for differential
binding to multiple RNAs (Fig. 1C).36, 37 A tunable scaffold is
particularly desirable for these studies because it has the
potential to generate both tight and weak binders. Three RNA
constructs were chosen for this study (Fig. 1B): the HIV-1
transactivation response element (TAR) for which the amiloride
scaffold was initially optimized, the related RNA HIV-2 TAR, and
stem IIB of the HIV-1 Rev response element (RRE-IIB).38 These
RNAs make an excellent model system for RNA-small molecule
Dynamic combinatorial chemistry (DCC), on the other hand,
is a uniquely poised approach as it allows tandem in situ
synthesis and screening of a large diverse library of ligands27-29
and has the potential to allow targeting of RNAs with a wide
range of complexity without a priori knowledge of RNA
a. Department of Chemistry, Duke University, 124 Science Drive, Durham, NC 27705,
USA. E-mail: amanda.hargrove@duke.edu
† Electronic Supplementary Information (ESI) available: The methods employed,
additional figures and tables, spectroscopic and chromatographic data, and binding
curves. See DOI: 10.1039/x0xx00000x
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DOI: 10.1039/D0CC00266F
Fig. 1. Model system for DCC studies. (A) Representation of imine exchange.
Addition of a reducing agent locks the imine library into stable secondary amines;
(B) Structures of RNA constructs; (C) Aldehyde analogue of the amiloride scaffold.
interactions as they have been extensively studied in efforts to
discover novel anti-HIV therapeutics, and their biological roles
are well understood.
Our studies began by selecting amine building blocks suited
for a rapid DCC-based method. We chose to first focus on
aromatic amines because RNA ligands (not based on
aminoglycosides) tend to have fewer sp3 centers compared to
40
protein ligands.39,
Twenty-four commercially available
aromatic amines were selected and tested for imine formation
in buffer at pH 6.3 (Fig. 2A and S1). Since our DCC procedure
would include the addition of a reducing agent (NaBH3CN) to
convert imine library members into secondary amines that are
stable to water-based analysis methods,27 amine reactivity was
tested under these conditions using reductive alkylation as a
proxy for imine formation (Fig. 3A). Fifteen amines showed high
reactivity (>90% aldehyde consumption), while the remaining
amines showed no reactivity (Fig. 2). To understand the basis of
this differential reactivity, we compared the calculated HOMO
energies of the amines and found that the reactive amines
generally had higher HOMO energies than the unreactive ones
(Fig. 2B), while no trend was observed with pKa values (Fig. S2).
This result is consistent with the study by Lehn and co-workers
which showed that incorporation of amine HOMO energies
improved modeling of the stability of imines in aqueous media
while pKa alone was poorly correlated to the imine formation
constant.41 Of note, all the unreactive amines contain a nitrogen
atom in the same ring carrying the NH2 group (Fig. 2). For these
amines, the poor reactivity may derive from diminished
electron density around the NH2 group as suggested by the
preferential protonation of the ring nitrogen.42 The correlation
of the energy prediction to experimental reactivity facilitates
library design as it allows for the filtering of commercial amines
and the purchase of only those expected to be reactive.
Fig. 2. Selection of amine subunits. (A) Structures of amines tested for reactivity
(orange: unreactive; blue: reactive); (B) Relationship between amine reactivity and
their HOMO energies. For reactive amines (reactivity index = 1), HPLC analysis showed
>90% consumption of aldehyde and presence of the secondary amine. For unreactive
amines (reactivity index = 0), no secondary amine formation was observed. See Fig. S1
for reaction conditions.
aldehyde, and the RNA in buffer (pH = 6.3) at ambient
temperature (~21 ˚C) for 20 hours (Fig. 3A). Dimethylsulfoxide
(DMSO) was used at 5% v/v to enhance ligand solubility while
maintaining biologically-relevant conditions given the known
susceptibility of RNA conformations to high DMSO content.43
A
reducing agent (NaBH3CN) was added to convert imine library
members into secondary amines that are stable to water-based
analysis methods (See ESI for detailed procedures).27 Negative
control reactions (no RNA) were run in parallel under the same
conditions. The slightly acidic conditions were chosen because
we observed that reductive amination was significantly higher
yielding at pH 6.3 compared to pH 6.9 and pH 7.4 (Fig. S4). After
incubation, the reactions were analysed by electrospray
ionization mass spectrometry (ESI-MS) with single ion
monitoring, which provides high sensitivity and resolution
compared to commonly HPLC analysis. The peak area of each
compound in RNA-containing reactions was compared to that
in the negative control to give a measure of how its abundance
changes in the presence of RNA.
Of the 15 reactive amines, six were chosen for the DCC proof
of concept study. Seven amines were excluded based on
anticipated low buffer solubility of the resulting amiloride
analogues (Fig. S3). Given the chemical similarity between
amines
k and o and between amines u and w, we decided to use
only one amine in each pair. In both cases, the analogue with
higher nitrogen count (
chances of solubility.
k and u) was chosen to increase the
As shown in Fig. 3B, differential amplification and
suppression were observed. Compounds 3k and 3l were
amplified in the presence of RNA, suggesting that these
DCC experiments with HIV-1 TAR, HIV-2 TAR and RRE-IIB
were performed by incubating the six amines, the amiloride
2 | J. Name., 2012, 00, 1-3
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TAR, HIV-2 TAR and RRE-IIB using the TOPRO-1 fluorescent
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DOI: 10.130k39/D0CC00266F
, 3l and 3u
indicator displacement assay.44 Compounds
displaced TOPRO-1 twice as strongly as compounds 3q and 3r to
all three RNAs, consistent with the higher amplification in DCC
(Fig. 3C, Table S8). A CD50 value could not be recorded for 3v
since the binding was too weak for convergence during curve
fitting (Fig. S10–S12), consistent with suppression in DCC. Both
the aldehyde and alcohol were substantially weak binders, with
the aldehyde showing somewhat stronger displacement at high
concentrations compared to the alcohol (Fig. S10–S12).
Therefore, the increase in alcohol by-product in DCC may be a
result of decreased reaction efficiency of the aldehyde with the
amines when it is bound to RNA.45 These results show that
imine-based DCC can effectively discriminate between binders
and non-binders for a given RNA target and that the secondary
amines resulting from the incorporation of a reducing agent still
generally reflect the behaviour of the underlying dynamic
library of imines. We will note, however, that the binding
affinities of library members did not perfectly correlate to the
DCC amplification pattern. These discrepancies likely reflect the
difficulty of discriminating between highly similar compounds
within a narrow range of affinities (26–91 µM; Table S8).
To further verify these results, we used surface plasmon
resonance (SPR) as an orthogonal assay to test the binding of
compounds 3k
displacement assay, compounds 3k
–
3u to HIV-1 TAR. Similar to the TOPRO-1
3l, and 3u bound
,
considerably stronger than 3q (Fig. S13), further supporting that
amplification in imine-based DCC corresponds to stronger
binding of the library member.
In this work, we establish the applicability of imine-based
DCC to RNA systems. We showed that the method is versatile,
as amplification of binders was observed for multiple RNAs with
structure and sequence diversity. Of note, this method is highly
sensitive as differentiation was observed among compounds
with highly similar binding affinities (Fig. 3C). Interestingly, the
three compounds with higher affinity (3k, 3l and 3u) were
derived from amines containing two aromatic rings, supporting
the potential for identification of properties advantageous for
RNA-binding using imine-based DCC. Furthermore, the use of
highly reactive amines enables considerably faster
identification of ligands compared to previous imine-based DCC
studies.27 Studies to understand conditions under which
heteroaromatic amines and alkylamines can be used are
underway as well as the expansion of the library to increase
chances of selectivity and begin applying imine-based DCC to
large complex RNAs. In particular, we are investigating the use
of imine-based DCC to expedite the discovery of multivalent
ligands through dialdehyde or diamine linkers. We expect this
methodology to be a valuable addition to current approaches of
targeting RNA particularly because it does not require large
library synthesis or knowledge of high-resolution structure.
We would like to thank Dr. Brittany Morgan (University of
Michigan) and Jordan Forte (Wake Forest School of Medicine)
for their input in the initial selection of amines, Dr. Peter Silinski
(Director, Analytical Services, Duke University) for optimizing
the ESI-MS method used for DCC reaction analysis and Dr. Brian
Watts (Manager of Duke DHVI BIA Core) for assistance with SPR
Fig. 3. DCC and TOPRO-1 assay with HIV-1 TAR, HIV-2 TAR and RRE-IIB. (A) DCC
reaction scheme. The aldehyde and amines were incubated in the presence or
absence of RNA in buffer (20 mM BisTris, 25 mM NaCl, 1 mM MgCl2, 0.1 mM EDTA,
pH = 6.3, 5% v/v DMSO). (B) Percent change of the peak area of each compound in
the presence of RNA relative to the no-RNA control reaction (average ± standard
deviation of three independent experiments). (C) TOPRO-1 displacement assay of
library members with HIV-1 TAR, HIV-2 TAR and RRE-IIB. CD50: Competitive dosage
for 50% reduction in fluorescence signal. Error bars: standard deviation.
compounds bound more strongly than the other library
members to each RNA. On the other hand, compound 3q shows
marked suppression, followed by compounds 3r and 3v. These
compounds are expected to have the lowest affinity for the
RNAs amongst all library members. Lastly, compound 3u shows
slight amplification, suggesting a moderate binding affinity
compared to 3k and 3l. An unexpected increase in alcohol by-
product from aldehyde reduction was also observed in the
presence of RNA.
To test whether the observed amplification pattern indeed
corresponded to binding affinity, the library members were
independently synthesized and evaluated for binding to HIV-1
This journal is © The Royal Society of Chemistry 20xx
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17. J. Lee, Y. Bai, U. V. Chembazhi, S. Peng, K. Yum, L. M. Luu, L. D.
experiments. We thank Duke University undergraduate
students Noelia Boldizsar and Oluwafikemi Faleye for assistance
in synthesizing library members, Shantal Jayawickreme for
synthesizing RRE-IIB RNA, and members of the Hargrove Lab for
valuable discussion. The authors acknowledge financial support
from Duke University, the U.S. NIH (U54 AI150470), the NSF
Career Award (CAREER 1750375) and the Prostate Cancer
Foundation Young Investigator Award. A.U.J. acknowledges
additional support from the Lews Siegel Fellowship from the
Pratt School of Engineering, Duke University.
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