Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5402_Библиотеки_им_академика_М_И_Перельмана
.pdf
References 25
https://t.me/med1917
23 Szikszai, M., Wise, M., Datta, A. et al. (2022). Deep learning models for RNA
secondary structure prediction (probably) do not generalize across families.
Bioinformatics 38 (16): 3892–3899.
24 Flamm, C., Wielach, J., Wolnger, M.T. et al. (2022). Caveats to deep learning
approaches to RNA secondary structure prediction. Front. Bioinf. 2: 835422.
25 Mathews, D.H., Disney, M.D., Childs, J.L. et al. (2004). Incorporating chem-
ical modication constraints into a dynamic programming algorithm for
prediction of RNA secondary structure. Proc. Natl. Acad. Sci. U.S.A. 101 (19):
7287–7292.
26 Deigan, K.E., Li, T.W., Mathews, D.H., and Weeks, K.M. (2009). Accurate
SHAPE-directed RNA structure determination. Proc. Natl. Acad. Sci. U.S.A.
106 (1): 97–102.
27 Wells, S.E., Hughes, J.M., Igel, A.H., and Ares, M. (2000). Use of dimethyl sulfate
to probe RNA structure in vivo. Methods Enzymol. 318: 479–493.
28 Simon, L.M., Morandi, E., Luganini, A. et al. (2019). In vivo analysis of
inuenza A mRNA secondary structures identies critical regulatory motifs.
Nucleic Acids Res. 47 (13): 7003–7017.
29 Manfredonia, I., Nithin, C., Ponce-Salvatierra, A. et al. (2020). Genome-wide
mapping of SARS-CoV-2 RNA structures identies therapeutically-relevant
elements. Nucleic Acids Res. 48 (22): 12436–12452.
30 Lan, T.C.T., Allan, M.F., Malsick, L.E. et al. (2022). Secondary structural
ensembles of the SARS-CoV-2 RNA genome in infected cells. Nat. Commun.
13 (1): 1128.
31 Rouskin, S., Zubradt, M., Washietl, S. et al. (2014). Genome-wide probing of
RNA structure reveals active unfolding of mRNA structures in vivo. Nature
505 (7485): 701–705.
32 Zubradt, M., Gupta, P. , Persad, S. et al. (2017). DMS-MaPseq for genome-wide or
targeted RNA structure probing in vivo. Nat. Methods 14 (1): 75–82.
33 Beaudoin, J.-D., Novoa, E.M., Vejnar, C.E. et al. (2018). Analyses of mRNA
structure dynamics identify embryonic gene regulatory programs. Nat. Struct.
Mol. Biol. 25 (8): 677–686.
34 Incarnato, D., Morandi, E., Simon, L.M., and Oliviero, S. (2018). RNA Frame-
work: an all-in-one toolkit for the analysis of RNA structures and posttranscriptional modications. Nucleic Acids Res. 46 (16): e97.
35 Mustoe, A.M., Lama, N.N., Irving, P.S. et al. (2019). RNA base-pairing complex-
ity in living cells visualized by correlated chemical probing. Proc. Natl. Acad. Sci.
U.S.A. 116 (49): 24574–24582.
36 Mitchell, D., Ritchey, L.E., Park, H. et al. (2018). Glyoxals as in vivo RNA struc-
tural probes of guanine base-pairing. RNA 24 (1): 114–124.
37 Weng, X., Gong, J., Chen, Y. et al. (2020). Keth-seq for transcriptome-wide RNA
structure mapping. Nat. Chem. Biol. 16 (5): 489–492.
38 Mitchell, D., Renda, A.J., Douds, C.A. et al. (2019). In vivo RNA structural prob-
ing of uracil and guanine base-pairing by 1-ethyl-3-(3-dimethylaminopropyl)
carbodiimide (EDC). RNA 25 (1): 147–157.
26 2 RNA Structure Probing, Dynamics, and Folding
https://t.me/med1917
39 Wang, P.Y., Sexton, A.N., Culligan, W.J., and Simon, M.D. (2019). Carbodi-
imide reagents for the chemical probing of RNA structure in cells. RNA 25 (1):
135–146.
40 Merino, E.J., Wilkinson, K.A., Coughlan, J.L., and Weeks, K.M. (2005). RNA
structure analysis at single nucleotide resolution by selective 2′-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127 (12): 4223–4231.
41 Spitale, R.C., Crisalli, P. , Flynn, R.A. et al. (2013). RNA SHAPE analysis in living
cells. Nat. Chem. Biol. 9 (1): 18–20.
42 Marinus, T., Fessler, A.B., Ogle, C.A., and Incarnato, D. (2021). A novel SHAPE
reagent enables the analysis of RNA structure in living cells with unprecedented
accuracy. Nucleic Acids Res. 49 (6): e34.
43 Busan, S., Weidmann, C.A., Sengupta, A., and Weeks, K.M. (2019). Guidelines
for SHAPE reagent choice and detection strategy for RNA structure probing
studies. Biochemistry 58 (23): 2655–2664.
44 Feng, C., Chan, D., Joseph, J. et al. (2018). Light-activated chemical probing of
nucleobase solvent accessibility inside cells. Nat. Chem. Biol. 14 (3): 276–283.
45 Costa, M. and Monachello, D. (2014). Probing RNA folding by hydroxyl radical
footprinting. Methods Mol. Biol. 1086: 119–142.
46 Adilakshmi, T., Lease, R.A., and Woodson, S.A. (2006). Hydroxyl radical foot-
printing in vivo: mapping macromolecular structures with synchrotron radiation.
Nucleic Acids Res. 34 (8): e64.
47 Strobel, E.J., Yu, A.M., and Lucks, J.B. (2018). High-throughput determination of
RNA structures. Nat. Rev. Genet. 19 (10): 615–634.
48 Homan, P.J., Favorov, O.V., Lavender, C.A. et al. (2014). Single-molecule
correlated chemical probing of RNA. Proc. Natl. Acad. Sci. U.S.A. 111 (38):
13858–13863.
49 Siegfried, N.A., Busan, S., Rice, G.M. et al. (2014). RNA motif discovery by
SHAPE and mutational proling (SHAPE-MaP). Nat. Methods 11 (9): 959–965.
50 Xiao, L., Fang, L., and Kool, E.T. (2022). Acylation probing of “generic” RNA
libraries reveals critical inuence of loop constraints on reactivity. Cell Chem.
Biol. 29 (8): 1341–1352.
51 Xiao, L., Fang, L., Chatterjee, S., and Kool, E.T. (2022). Diverse reagent scaolds
provide dierential selectivity of 2′-OH Acylation in RNA. J. Am. Chem. Soc.
https://doi.org/10.1021/jacs.2c09040.
52 Ramani, V., Qiu, R., and Shendure, J. (2015). High-throughput determination of
RNA structure by proximity ligation. Nat. Biotechnol. 33 (9): 980–984.
53 Lu, Z., Zhang, Q.C., Lee, B. et al. (2016). RNA duplex map in living cells reveals
higher order transcriptome structure. Cell 165 (5): 1267–1279.
54 Aw, J.G.A., Shen, Y., Wilm, A. et al. (2016). In vivo mapping of eukaryotic RNA
interactomes reveals principles of higher-order organization and regulation. Mol.
Cell 62 (4): 603–617.
55 Sharma, E., Sterne-Weiler, T., O’Hanlon, D., and Blencowe, B.J. (2016). Global
mapping of human RNA-RNA interactions. Mol. Cell 62 (4): 618–626.
56 Nguyen, T.C., Cao, X., Yu, P. et al. (2016). Mapping RNA–RNA interactome and
RNA structure in vivo by MARIO. Nat. Commun. 7 (1): 12023.
References 27
https://t.me/med1917
57 Ziv, O., Gabryelska, M.M., Lun, A.T.L. et al. (2018). COMRADES determines in
vivo RNA structures and interactions. Nat. Methods 15 (10): 785–788.
58 Cimino, G.D., Gamper, H.B., Isaacs, S.T., and Hearst, J.E. (1985). Psoralens as
photoactive probes of nucleic acid structure and function: organic chemistry,
photochemistry, and biochemistry. Annu. Rev. Biochem. 54: 1151–1193.
59 Nilsen, T.W. (2014). Detecting RNA-RNA interactions using psoralen derivatives.
Cold Spring Harb. Protoc. 9: 996–1000.
60 Spitale, R.C. and Incarnato, D. (2022). Probing the dynamic RNA structurome
and its functions. Nat. Rev. Genet. 1–19.
61 Christy, T.W., Giannetti, C.A., Houlihan, G. et al. (2021). Direct mapping
of higher-order RNA interactions by SHAPE-JuMP. Biochemistry 60 (25):
1971–1982.
62 Van Damme, R., Li, K., Zhang, M. et al. (2022). Chemical reversible crosslinking
enables measurement of RNA 3D distances and alternative conformations in
cells. Nat. Commun. 13 (1): 911.
63 Houlihan, G., Arangundy-Franklin, S., Porebski, B.T. et al. (2020). Discovery
and evolution of RNA and XNA reverse transcriptase function and delity.
Nat. Chem. 12 (8): 683–690.
64 Christy, T.W., Giannetti, C.A., Laederach, A., and Weeks, K.M. (2021). Identi-
fying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper.
65 Serganov, A. and Nudler, E. (2013). A decade of riboswitches. Cell 152 (1):
17–24.
66 Kortmann, J. and Narberhaus, F. (2012). Bacterial RNA thermometers: molecular
zippers and switches. Nat. Rev. Microbiol. 10 (4): 255–265.
67 Tomezsko, P.J., Corbin, V.D.A., Gupta, P. et al. (2020). Determination of RNA
structural diversity and its role in HIV-1 RNA splicing. Nature 582 (7812):
438–442.
68 Morandi, E., Manfredonia, I., Simon, L.M. et al. (2021). Genome-scale deconvolu-
tion of RNA structure ensembles. Nat. Methods 18 (3): 249–252.
69 Olson, S.W., Turner, A.-M.W., Arney, J.W. et al. (2022). Discovery of a large-scale,
cell-state-responsive allosteric switch in the 7SK RNA using DANCE-MaP. Mol.
Cell 82 (9): 1708–1723.e10.
70 Yang, M., Zhu, P., Cheema, J. et al. (2022). In vivo single-molecule analysis
reveals COOLAIR RNA structural diversity. Nature .
71 Spasic, A., Assmann, S.M., Bevilacqua, P.C., and Mathews, D.H. (2018). Modeling
RNA secondary structure folding ensembles using SHAPE mapping data. Nucleic
Acids Res. 46 (1): 314–323.
72 Li, H. and Aviran, S. (2018). Statistical modeling of RNA structure proling
experiments enables parsimonious reconstruction of structure landscapes.
Nat. Commun. 9 (1): 606.
73 Yu, A.M., Gasper, P.M., Cheng, L. et al. (2021). Computationally reconstructing
cotranscriptional RNA folding from experimental data reveals rearrangement of
non-native folding intermediates. Mol. Cell 81 (4): 870–883.e10.
PLoS Comput. Biol. 17 (12): e1009632.
28 2 RNA Structure Probing, Dynamics, and Folding
https://t.me/med1917
74 Aviran, S. and Incarnato, D. (2022). Computational approaches for RNA struc-
ture ensemble deconvolution from structure probing data. J. Mol. Biol. 167635.
75 Mullard, A. (2017). Small molecules against RNA targets attract big backers.
Nat. Rev. Drug Discovery 16 (12): 813–815.
76 Warner, K.D., Hajdin, C.E., and Weeks, K.M. (2018). Principles for targeting
RNA with drug-like small molecules. Nat. Rev. Drug Discovery 17 (8): 547–558.
77 Childs-Disney, J.L., Yang, X., Gibaut, Q.M.R. et al. (2022). Targeting RNA struc-
tures with small molecules. Nat. Rev. Drug Discovery 21 (10): 736–762.
78 Hewitt, W.M., Calabrese, D.R., and Schneekloth, J.S. (2019). Evidence for ligand-
able sites in structured RNA throughout the Protein Data Bank. Bioorg. Med.
Chem. 27 (11): 2253–2260.
79 Padroni, G., Patwardhan, N.N., Schapira, M., and Hargrove, A.E. Systematic
analysis of the interactions driving small molecule–RNA recognition. RSC Med.
Chem. 11 (7): 802–813.
80 Brink, M.F., Brink, G., Verbeet, M.P., and de Boer, H.A. (1994). Spectinomycin
interacts specically with the residues G1064 and C1192 in 16S rRNA, thereby
potentially freezing this molecule into an inactive conformation. Nucleic Acids
Res. 22 (3): 325–331.
81 Sengupta, A., Rice, G.M., and Weeks, K.M. (2019). Single-molecule correlated
chemical probing reveals large-scale structural communication in the ribosome
and the mechanism of the antibiotic spectinomycin in living cells. PLoS Biol.
17 (9): e3000393.
82 Zeller, M.J., Favorov, O., Li, K. et al. (2022). SHAPE-enabled fragment-based
ligand discovery for RNA. Proc. Natl. Acad. Sci. U.S.A. 119 (20): e2122660119.
83 Fang, L., Velema, W.A., Lee, Y., Lu, X., Mohsen, M.G., Kietrys, A.M., and Kool,
E.T. (2022) Pervasive transcriptome interactions of protein-targeted drugs. https://
doi.org/10.1101/2022.07.18.500496 .
84 Guo, L.-T., Adams, R.L., Wan, H. et al. (2020). Sequencing and structure probing
of long RNAs using MarathonRT: a next-generation reverse transcriptase. J. Mol.
Biol. 432 (10): 3338–3352.
3
https://t.me/med1917
High-Resolution Structures of RNA
Lukas Braun1, Zahra Alirezaeizanjani2, Roberta Tesch2,and
Hamed Kooshapur
1
Bayer AG Pharmaceuticals, Research and Development, 13353 Berlin, Germany
2
Bayer AG Pharmaceuticals, Research and Development, 42113 Wuppertal, Germany
2
3.1 Introduction
Structural biology has become one of the cornerstones of modern drug discovery.
Knowing the binding pose of a small molecule with atomistic detail not only helps
to understand its mode of action but also makes rational improvements of the
compound more straightforward (structure-based drug design [SBDD]). Even in
the absence of ligands, structural insights provide a deeper understanding of a drug
target and facilitate the generation of new hypotheses. While this holds true for all
macromolecules, much of the past work has focused on determining protein structures. This is clearly reected in the number of deposited structures in the Protein
Data Bank (PDB) [1]: the number of RNA structures in the database is orders of
magnitude smaller than for proteins. This disparity can be partially attributed to the
longstanding belief that most RNAs are largely unstructured or at least too exible
for structure determination. In the 1970s, the crystal structure of phenylalanine
transfer RNA (tRNA) was the rst direct proof that RNAs can indeed adopt a stable,
complex three-dimensional (3D) fold [2–4]. Since then, an ever-growing number
of structures have been solved. These contributions have helped to understand
how ribozymes achieve their catalytic function, how small molecules regulate
riboswitches, and how single-point mutations alter the biological function of
noncoding RNAs, to name a few (Figure 3.1) [5–7]. This progress was only possible
by applying a broad arsenal of methods, often integrating data from multiple
experimental and computational approaches. Recent technological breakthroughs
have further helped to accelerate the advances in the eld. These improvements
come at a time when the rst-in-class, small-molecule splicing modier Risdiplam
was successfully brought to the market [8]. While the compound was discovered
through a target-agnostic phenotypic screen followed by ligand-based optimization,
retrospective structural studies have shown that it binds directly to RNA and
modulates its structure [9]. This proof-of-concept has sparked the interest of many
academic and industrial research groups in this modality and with it into RNA.
29
RNA as a Drug Target: The Next Frontier for Medicinal Chemistry, First Edition.
Edited by John Schneekloth and Martin Pettersson.
© 2024 WILEY-VCH GmbH. Published 2024 by WILEY-VCH GmbH.
30 3 High-Resolution Structures of RNA
https://t.me/med1917
1974
Phe
tRNA
(PDB 4TNA, X-ray)
Hammerhead ribozyme
(PDB 1MME, NMR)
1995
2008
FMN riboswitch-FMN
complex
(PDB 3F2Q, X-ray)
HIV-1 Core packaging signal
(PDB 2N1Q, NMR)
2016
FMN riboswitch-Ribocil
complex
(PDB 5KX9, X-ray)
2015
S-paRNA containing
functional SNP
(PDB 7SHX, NMR)
2021
Full length
Tetrahymena ribozyme
(PDB 7EZ0, cryo-EM)
2022
Figure 3.1 A selection of important RNA structures elucidated over the years using
different structural biology methods.
The COVID-19 pandemic has brought another boost for RNA structure. After the
outbreak, it was quickly realized that the genomic RNA of SARS-CoV-2 contains
highly conserved, functional structures that constitute potential drug targets.
In record time, a multitude of structures/models of dierent RNA segments was
determined using X-ray crystallography, nuclear magnetic resonance (NMR),
cryogenic electron microscopy (cryo-EM), and computational structure prediction
[10–12]. Due to this increased focus from a wider community, we will certainly see
an uptick in the number of RNA-containing structures over the next years. This not
only holds the promise of nding novel ways to treat a wide range of diseases but
also new insights into the molecular details of RNA biology.
Considering that RNA is made from only four building blocks, the multitude
of complex, intricate folds that it can adopt are truly astonishing. The aromatic
bases with their decorations of hydrogen bond donors and acceptors as well as the
charged phosphate groups and polar sugar moieties in the backbone give rise to
many possible interactions. The best known are certainly Watson-Crick (W-C) base
pairs in which the edges of complementary bases interact via matching hydrogen
bonds. The base-paring pattern is also referred to as the secondary or 2D structure.
Although W-C pairs are ubiquitous in structured RNAs, it is important to note
that they are not the main drivers of folding [13]. This role is attributed to stacking
interactions between the bases [13, 14]. The faces of these aromatic ring systems
are highly hydrophobic. Placing them on top of each other shields them from
the solvent and allows for favorable interactions with their π-orbitals [15]. Due
to this driving force, the ends of adjacent helices tend to stack on top of each
other (co-axial stacking). Likewise, it is often more favorable for unpaired bases in
junctions or (internal) loops to be buried into the structure. This can lead to kinks
3.2 X-Ray Crystallography 31
https://t.me/med1917
and distortions in the fold or bring sequentially distant regions of the molecule in
close spatial proximity [13, 14]. This tertiary structure is then further stabilized by
a multitude of polar and charged interactions. These include non-W-C base pairs,
base triples or even quadruples, extensive hydrogen bonding between the backbone
sugars, or charge-assisted hydrogen bonds with the phosphate groups.
In solution, biomolecules are best described as a dynamic ensemble of interconverting conformations. The relative population of each conformation is dictated
by the underlying free energy landscape [16]. Even lowly populated states with
short lifetimes can be functionally important. Some RNA sequences encode a free
energy landscape with few deep valleys leading to stable folds with slow transitions
between dierent states. Others have a atter landscape with rapid interconversion
of conformations. In general, RNAs are more exible than globular proteins [17].
The relative abundance of conformations in an RNA ensemble can be strongly modulated by cellular conditions (e.g. pH), binding partners, and posttranscriptional
modications. For RNA folding, the presence of mono- and divalent cations that
help to shield the strong electrostatic repulsion of the negatively charged backbone
is often required to stabilize the nal fold [18]. Within cells, most RNAs associate
with proteins and function as ribonucleoprotein complexes (RNPs). Protein binding
can also shift the population of a given state, as was shown for the HIV-1 RNA
(see Section 3.3). The ensemble view on RNA is important for a mechanistic
understanding of how it folds, functions, and binds to small molecules [19]. Since
most RNAs do not have catalytic activity, blocking of an active site is typically not
a path forward for drug design. Instead, a ligand could stabilize a conformation of
the ensemble that leads to the desired biological outcome.
Nowadays, structural biologists have a large toolbox for elucidating RNA structures and dynamics, each with their own strengths and limitations. In this chapter,
we provide an overview of the main experimental and computational methods
for determining RNA structures at (near) atomistic level and highlight key
achievements in the eld. Despite the essential role of RNA–protein interactions
in many biological processes, discussing RNPs would go beyond the scope of this
chapter. Therefore, we will focus on systems containing only RNA.
3.2 X-Ray Crystallography
X-ray crystallography, the oldest method in structural biology, delivered the rst
breakthrough in RNA structure determination: the structure of the yeast phenylalanine tRNA (tRNA
the cloverleaf secondary structure adopts an L-shaped conformation in 3D [2–4].
However,due to the dynamic nature of manyRNAs, the formation of well-diracting
crystals remains generally challenging. Crystallization is further impaired by the
uniformity of the charged phosphate backbone, which can impede the formation
of crystal contacts. To overcome these challenges, dierent engineering techniques
have been developed over the years. For a recent review see [20].
Phe
). In the 1970s, for the rst time, scientists were able to see that
32 3 High-Resolution Structures of RNA
https://t.me/med1917
A48
A85
OH OH
O
O
P
HO
OH
OH
(a) (b)
Natural ligand
FMN
A99
N
HN NH
FMN riboswitchG72
N
O
O
30°
G72
G72
G11
A85
A85
A48
A48
A99
A99
Substitutions
well tolerated
Ribocil-C
N
N
N
N
N
N
N
NH
N
N
N
R1
R1 = OH, NH
Ribocil-B
S
N
O
H
SBDD efforts
Few
substitutions
S
tolerated
N
O
H
Essential
hydrogen
bond
2
Figure 3.2 (a) X-ray structure of FMN riboswitch bound to the natural ligand FMN (left,
PDB: 3F2Q) and ribocil (right, PDB: 5C45). Interactions are highlighted as yellow dashes.
Distance between the piperidinyl ring of ribocil (light pink) and G11 (light green) guides the
addition of hydroxyl group in that region. (b) SAR profile of ribocil-B analogs leading to the
higher potent compound ribocil-C.
X-ray crystallography has been most successful for the structure determination
of riboswitches. Riboswitches are regions of mRNA that contain a ligand-binding
aptamer domain that senses small molecules (such as metabolites) and an expression platform that responds by undergoing a conformational change, thereby
modulating gene expression [21, 22]. The elucidation of diverse riboswitch structures has provided important molecular insights into RNA ligand recognition that
could be leveraged for the design of RNA-targeting drugs [23].
The presence of a ligand-binding site in riboswitches provides an opportunity for
developing small molecules that bind to these pockets and inhibit the biological
function of these regulatory elements. Ribocil is a member of a new class of antibiotics that modulates the bacterial avin mononucleotide (FMN) riboswitch [24].
In the crystal structure, ribocil adopts a constrained U-shaped conformation with a
keyinteraction between the pyrimidonyl oxygen and A48 and A99. When comparing
the FMN and ribocil-bound structures, FMN and ribocil overlap in terms of interaction with the RNA-binding pocket, but ribocil has additional stacking interactions
with other bases [6, 25] (Figure 3.2).
Moreover, the racemic mixture of ribocil was used during the crystallization
experiments but only one of the isomers ((S)-isomer, ribocil-B) was bound to RNA
based on the electron density map. This was further conrmed with the microbiological activity of the separated enantiomers. Following this, SBDD was applied
with the crystal structure of analogs to improve the inhibitory activity of ribocil-B.
Modications of the amino-pyrimidine moiety were more tolerated than those on
the thiophene ring, and a hydroxyl group attached to the piperidine ring allowed
additional interactions with G11. The medicinal chemistry eorts together with
the established structure–activity relationship (SAR) led to ribocil-C with eightfold
higher potency than the lead compound [25] (Figure 3.2).
X-ray crystallography has revealed another structural element that could have
an impact on RNA-targeted drug discovery: the triple helix [26, 27]. Specically,
3.2 X-Ray Crystallography 33
https://t.me/med1917
G41
C42
U43
Watson-Crick
side
Hydrogen bond pattern
(nucleobases)
C72
2.5 Å
A70
C+12
Hoogsteen
side
G71
2.5 Å
U11
Stem II
Triplex II
C-G pair
Triplex I
Stem I
MALAT-1 triple helix
Hydrogen bond pattern
(sugar-phosphate backbone)
Figure 3.3 X-ray structure of MALAT-1 ENE core (PDB: 4PLX). The A-rich tail is colored in
light yellow. Distances between the 2′-hydroxyl (light green spheres) of the double helix
and the backbone phosphate (light yellow spheres) of the A-rich strand are indicated.
The hydrogen bond network of U-A-U, C+-G-C, and C-G pairs is shown.
MALAT-1 (metastasis associated lung adenocarcinoma transcript 1) is one of
the most studied long noncoding RNAs (lncRNAs) and has been described as a
potential predictive biomarker for metastasis development in numerous cancers
[28]. The 3′-end of MALAT-1 contains a motif known as the expression and nuclear
retention element (ENE). The ENE stabilizes the RNA by inhibiting its degradation
through the formation of a triple helix with an A-rich tail.
The crystal structure of MALAT-1 ENE revealed dierent structural elements,
specically a bipartite triple helix that is interrupted by a C-G pair leading to
the formation of independent triple helices (triplex I and II) [29]. These helices
are comprised of U-A-U triples that have a combination of W-C and Hoogsteen
base pairs. Triplex I has an additional triple interaction formed by C+-G-C and
is stabilized by a C-G pair (C72-G41). Moreover, mutations in C-G and C+-G-C
lead to an increase in the RNA decay, indicating an important functional and
structural role for these base pairs. The disruption of the U-A-U triple also changes
the distance prole between the backbone phosphate of the A-rich tail and the
2′hydroxyl of the double helix (represented as spheres in Figure 3.3). This distance
prole is important for minimizing the electrostatic clashes in the sugar-phosphate
backbone [29].
Since MALAT-1 has been implicated in dierent malignancies, the triple helix
structure is a potential drug target. The available crystal structure presented an
opportunity for identifying MALAT-1 ligands through virtual screening methods
and studying the potential binding mode of new drug molecules One example is
the work of Le Grice and collaborators that combined small-molecule microarray
(SMM) screening, biophysical, and computational methods to identify chemotypes
that aect the stabilization of the MALAT-1 ENE triplex [30]. The most promising
compounds that were identied bind MALAT-1 ENE triplex but do not aect
NEAT1, a lncRNA with a similar triple helix structure. Docking of these compounds on the RNA showed preference for distinct regions. In addition, saturation
transfer dierence (STD) NMR conrmed the binding of one of the hit compounds
34 3 High-Resolution Structures of RNA
https://t.me/med1917
to MALAT-1 ENE triplex but not to other known triple helices. The compounds
identied in this work not only aect the levels of MALAT-1 in a cellular context
but also modulate previously identied downstream targets. However, the main
challenge is understanding the biological mechanisms of poly(A) protection by the
formation of the triplex structure. Nevertheless, the increasing knowledge of ENE
structures and its presence in equivalent motifs from other RNAs [31] together with
the discovery of new chemical tools could lead to new drug discovery programs.
3.3 NMR Spectroscopy
NMR spectroscopy is a highly versatile technique that provides atomic-resolution
insight into structure, dynamics, and interactions of biomolecules and has played a
pivotal role in RNA structural biology. Since the rst solution structure of an RNA
molecule (a 12 nt hairpin) reported in 1990 [32], several hundred NMR structures
of RNA in the free form and in complex with various binding partners have been
reported. Currently, around one-third of RNA-only structures deposited in the PDB
are obtained by NMR spectroscopy.Further, NMR can readily report on base pairing
and determine the secondary structures of RNA.
Despite the great success, structure determination of RNA by NMR remains
challenging [33]. Compared to proteins that are made of 20 amino acids, RNA is
composed of only four nucleotides with similar chemical structures; this leads to
lower chemical shift dispersion and overlap of NMR signals. In addition, there is
a lower density of protons in RNA and they typically form extended structures;
thus, the number of intramolecular contacts that can be obtained is limited. These
challenges are exacerbated in large RNAs (>50 nt) where the NMR signals are
broadened owing to slower tumbling of larger molecules in solution. As of now,
the average size of NMR-derived RNA structures is 29 nt, the largest RNA structure
determined by NMR is 155 nt, and fewer than ten structures above 100 nt have been
deposited in the PDB.
Several approaches have been developed for reducing signal overlap and simplifying NMR spectroscopy of large RNAs. One strategy is “divide-and-conquer” in
which the RNA is divided into domains that are studied separately. This is only
applicable when the conformation of the domains is maintained in the context of
the intact RNA. As an example, for determining the structure of the 77 nt (∼25 kDa)
domain II of the hepatitis C virus (HCV) internal ribosome entry site (IRES),
the RNA was divided into two subdomains that were analyzed individually for
assigning the chemical shifts (CS) and obtaining short-range distance information based on nuclear Overhauser eect (NOE) and other local restraints. These
were then combined with long-range residual dipolar coupling (RDC) restraints
obtained from the full-length RNA [34]. For cases where divide-and-conquer is not
applicable, segmental labeling is the method of choice. Here isotopically labeled
and unlabeled regions of an RNA are ligated to form the full-length RNA that is
only partially labeled and hence visible in the NMR spectra [35]. Although sample
preparation can be laborious, such labeling strategies can drastically improve
Соседние файлы в папке Библиотека им академика М.И. Перельмана