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• W2890850847 abstract "•Over 52 million interactions between RNA motifs and small molecules were screened•Privileged RNA motifs that bind various chemotypes were identified•RNA binders are similar to known drugs but have key differences•Disease-to-gene-to-drug paradigm enabled production of an anti-HCV lead medicine Fortuitously, identifying RNAs that cause or contribute to disease can be deduced from sequencing; however, discovering compounds that target and modulate RNA biology is difficult, and purposefully targeting RNAs is even more so. We advanced a rational design approach by using RNA sequencing to identify rapidly disease-causing RNAs that have structures that can be targeted with small molecules. This work broadly defines the RNA motif binding preferences of drug-like small molecules to provide a first-in-class comprehensive dataset of molecular recognition patterns. The resulting interaction map was mined against a human pathogen's RNA genome to afford new lead inhibitors of viral replication by binding to a critical, yet previously undrugged site. These studies could represent a paradigm shift whereby rational design, rather than screening of millions of compounds, can be used to define lead therapeutic modalities against diseases, whether seasonal or prolonged threats to human health. Many RNAs cause disease; however, RNA is rarely exploited as a small-molecule drug target. Our programmatic focus is to define privileged RNA motif small-molecule interactions to enable the rational design of compounds that modulate RNA biology starting from only sequence. We completed a massive, library-versus-library screen that probed over 50 million binding events between RNA motifs and small molecules. The resulting data provide a rich encyclopedia of small-molecule RNA recognition patterns, defining chemotypes and RNA motifs that confer selective, avid binding. The resulting interaction maps were mined against the entire viral genome of hepatitis C virus (HCV). A small molecule was identified that avidly bound RNA motifs present in the HCV 3′ UTR and inhibited viral replication while having no effect on host cells. Collectively, this study represents the first whole-genome pattern recognition between small molecules and RNA folds. Many RNAs cause disease; however, RNA is rarely exploited as a small-molecule drug target. Our programmatic focus is to define privileged RNA motif small-molecule interactions to enable the rational design of compounds that modulate RNA biology starting from only sequence. We completed a massive, library-versus-library screen that probed over 50 million binding events between RNA motifs and small molecules. The resulting data provide a rich encyclopedia of small-molecule RNA recognition patterns, defining chemotypes and RNA motifs that confer selective, avid binding. The resulting interaction maps were mined against the entire viral genome of hepatitis C virus (HCV). A small molecule was identified that avidly bound RNA motifs present in the HCV 3′ UTR and inhibited viral replication while having no effect on host cells. Collectively, this study represents the first whole-genome pattern recognition between small molecules and RNA folds. RNA has diverse cellular functions and thus is an important target for small molecule chemical probes and lead therapeutics.1Guan L. Disney M.D. Recent advances in developing small molecules targeting RNA.ACS Chem. Biol. 2012; 7: 73-86Crossref PubMed Scopus (219) Google Scholar, 2Thomas J.R. Hergenrother P.J. Targeting RNA with small molecules.Chem. Rev. 2008; 108: 1171-1224Crossref PubMed Scopus (495) Google Scholar The most exploited RNA targets for small molecules are three-dimensionally folded riboswitches3Blount K.F. Breaker R.R. Riboswitches as antibacterial drug targets.Nat. Biotechnol. 2006; 24: 1558-1564Crossref PubMed Scopus (354) Google Scholar and ribosomes.4Tenson T. Mankin A. Antibiotics and the ribosome.Mol. Microbiol. 2006; 59: 1664-1677Crossref PubMed Scopus (123) Google Scholar, 5Schlunzen F. Zarivach R. Harms J. Bashan A. Tocilj A. Albrecht R. Yonath A. Franceschi F. Structural basis for the interaction of antibiotics with the peptidyl transferase centre in eubacteria.Nature. 2001; 413: 814-821Crossref PubMed Scopus (881) Google Scholar Both are bacterial in origin, and small molecules that target these structures are used clinically as antibiotics and as chemical probes to dissect RNA biology. Small molecule binding occurs via a vast array of complex interactions within the three-dimensional folds of the RNAs, akin to small molecule recognition of proteins. However, the vast majority of cellular RNAs have little defined tertiary structure but extensive secondary structure. Secondary structure is formed via various canonical (base pairs) and non-canonical pairings (internal loops, hairpins, bulges, and multibranch loops). There is a dearth of compounds targeting such RNAs that affect biological function because of limited information on small molecules that bind these RNA folds. Viral RNAs are a notable class of targets with extensive secondary structure. Studies involving antisense oligonucleotides (ASOs) and small molecules have shown that viral RNAs are indeed viable therapeutic targets.6Hermann T. Small molecules targeting viral RNA.Wiley Interdiscip. Rev. RNA. 2016; 7: 726-743Crossref PubMed Scopus (92) Google Scholar As small molecules have broad chemical space and can be derivatized via medicinal chemistry to improve potency and delivery,7Ohgushi M. Kuroki S. Fukamachi H. O'Reilly L.A. Kuida K. Strasser A. Yonehara S. Transforming growth factor beta-dependent sequential activation of smad, bim, and caspase-9 mediates physiological apoptosis in gastric epithelial cells.Mol. Cell. Biol. 2005; 25: 10017-10028Crossref PubMed Scopus (80) Google Scholar they are perhaps more medicinally suited for drugging RNAs than oligonucleotides. Currently the feasibility of drug development for disease-associated or disease-causing RNAs is limited by the lack of data defining small molecule-RNA secondary structure interactions. We previously developed a sequence-based lead identification strategy for identifying small molecules targeting RNA, dubbed Inforna.8Velagapudi S.P. Gallo S.M. Disney M.D. Sequence-based design of bioactive small molecules that target precursor micrornas.Nat. Chem. Biol. 2014; 10: 291-297Crossref PubMed Scopus (239) Google Scholar, 9Disney M.D. Winkelsas A.M. Velagapudi S.P. Southern M. Fallahi M. Childs-Disney J.L. Inforna 2.0: a platform for the sequence-based design of small molecules targeting structured rnas.ACS Chem. Biol. 2016; 11: 1720-1728Crossref PubMed Scopus (114) Google Scholar Inforna was enabled by a screening approach named two-dimensional combinatorial screening (2DCS) whereby a library of array-immobilized small molecules is incubated with a library of RNA motifs (secondary structures) commonly found in cellular RNAs.10Disney M.D. Labuda L.P. Paul D.J. Poplawski S.G. Pushechnikov A. Tran T. Velagapudi S.P. Wu M. Childs-Disney J.L. Two-dimensional combinatorial screening identifies specific aminoglycoside-RNA internal loop partners.J. Am. Chem. Soc. 2008; 130: 11185-11194Crossref PubMed Scopus (108) Google Scholar, 11Tran T. Disney M.D. Identifying the preferred RNA motifs and chemotypes that interact by probing millions of combinations.Nat. Commun. 2012; 3: 1125Crossref PubMed Scopus (55) Google Scholar By sequencing the RNAs that bind to a small molecule and are captured via 2DCS, the most avid interactions are defined, building an encyclopedia of privileged RNA motif-small molecule interactions that can inform drug design.12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar Much more data of this type are required to effectively target the myriad of disease-causing RNAs. Beyond identifying small molecule RNA binders, one major challenge in the discovery of small molecules directed at RNA is their “drug-likeness.” Aminoglycosides, the most commonly studied small molecules that target RNA, are highly charged, polar compounds and are considered very non-drug-like; ironically, they are important drugs used clinically. Herein, we used 2DCS to probe a vast landscape of heterocyclic drug-like small molecule-RNA interactions to identify new chemotypes in small molecules that confer avid binding to RNA and elucidate their RNA motif-binding preferences (Figure 1). Various RNA motif libraries were probed for binding to microarray-immobilized small molecules, totaling over 52 million possible interactions, one of the largest screening campaigns to date. These studies defined a chemical code for recognizing RNAs with small molecules. For example, we found that aminopyrimidine chemotypes, a drug-like scaffold found in many clinically tested compounds including kinase inhibitors, bind avidly to RNA motifs. Importantly, the small molecules that bind RNA are indeed drug-like as their properties are similar to those found in US Food and Drug Administration (FDA)-approved drugs, increasing the potential of these compounds to target RNAs in vivo. Thus, decoding RNA with small molecules can be achieved with small molecules that have drug-like properties. Over 30,000 compounds from The Scripps Research Institute (TSRI) and the National Cancer Institute (NCI) small molecule libraries were inspected to identify members that contain an amine for site-specific conjugation onto aldehyde-functionalized microarrays. To reduce the number of compounds to a manageable number for screening, we used three small molecules known to bind toxic RNAs in cellulis and improve disease-associated defects (D6, 1a, and H1;13Disney M.D. Liu B. Yang W.-Y. Sellier C. Tran T. Charlet-Berguerand N. Childs-Disney J.L. A small molecule that targets r(CGG)exp and improves defects in fragile X-associated tremor ataxia syndrome.ACS Chem. Biol. 2012; 7: 1711-1718Crossref PubMed Scopus (82) Google Scholar, 14Pushechnikov A. Lee M.M. Childs-Disney J.L. Sobczak K. French J.M. Thornton C.A. Disney M.D. Rational design of ligands targeting triplet repeating transcripts that cause RNA dominant disease: application to myotonic muscular dystrophy type 1 and spinocerebellar ataxia type 3.J. Am. Chem. Soc. 2009; 131: 9767-9779Crossref PubMed Scopus (153) Google Scholar, 15Parkesh R. Childs-Disney J.L. Nakamori M. Kumar A. Wang E. Wang T. Hoskins J. Housman D.E. Thornton C.A. Disney M.D. Tran T. Design of a bioactive small molecule that targets the myotonic dystrophy type 1 RNA via an RNA motif-ligand database & chemical similarity searching.J. Am. Chem. Soc. 2012; 134: 4731-4742Crossref PubMed Scopus (115) Google Scholar, 16Kumar A. Parkesh R. Sznajder L.J. Childs-Disney J.L. Sobczak K. Disney M.D. Chemical correction of pre-mRNA splicing defects associated with sequestration of muscleblind-like 1 protein by expanded r(CAG)-containing transcripts.ACS Chem. Biol. 2012; 3: 496-505Crossref Scopus (66) Google Scholar Figure 1) as model compounds for refinement in two ways. First, the three small molecules were subjected to chemoinformatics analysis, generating three scaffolds or sub-structures, phenylguanidine (S1), benzimidazole (S2), and 2-phenyl-1H-imidazole (S3), which likely impart RNA binding affinity.17Labuda L. Pushechnikov A. Disney M.D. Small molecule microarrays of RNA-focused peptoids help identify inhibitors of a pathogenic group I intron.ACS Chem. Biol. 2009; 4: 299-307Crossref PubMed Scopus (34) Google Scholar Compounds that contain at least one of these scaffolds were then selected for further study. In the second refinement, D6, 1a, and H1 were used directly as query molecules in a chemical similarity search of TSRI's library; chemically similar structures (Tanimoto18Willett P. Similarity searching using 2D structural fingerprints.Methods Mol. Biol. 2011; 672: 133-158Crossref PubMed Scopus (112) Google Scholar > 0.25) were carried forward for screening (Figure S1). The average Tanimoto scores of selected compounds were 0.28 ± 0.05, 0.37 ± 0.08, and 0.37 ± 0.13 as compared with D6, 1a, and H1, respectively. The two refinements afforded 1,987 compounds that were commercially available. Notably, both the library of 1,987 compounds and the >30,000 from which they were selected are chemically diverse, as determined by using a Tanimoto analysis.18Willett P. Similarity searching using 2D structural fingerprints.Methods Mol. Biol. 2011; 672: 133-158Crossref PubMed Scopus (112) Google Scholar Furthermore, the starting library contains both N- and O-containing heterocycles and functional groups (25% of the compounds contain oxygen as part of a heterocycle or alcohol; 55% of the compounds have at least one oxygen as an aldehyde, ketone, ester, or amide). We also verified that the 1,987 small molecules screened have drug-like properties by comparing them with the compounds in DrugBank, a publicly available repository containing the properties of FDA-approved therapeutics.19Wishart D.S. Knox C. Guo A.C. Shrivastava S. Hassanali M. Stothard P. Chang Z. Woolsey J. Drugbank: a comprehensive resource for in silico drug discovery and exploration.Nucleic Acids Res. 2006; 34: D668-D672Crossref PubMed Scopus (2502) Google Scholar, 20Wishart D.S. Knox C. Guo A.C. Cheng D. Shrivastava S. Tzur D. Gautam B. Hassanali M. Drugbank: a knowledgebase for drugs, drug actions and drug targets.Nucleic Acids Res. 2008; 36: D901-D906Crossref PubMed Scopus (1966) Google Scholar, 21Knox C. Law V. Jewison T. Liu P. Ly S. Frolkis A. Pon A. Banco K. Mak C. Neveu V. et al.Drugbank 3.0: a comprehensive resource for 'omics' research on drugs.Nucleic Acids Res. 2011; 39: D1035-D1041Crossref PubMed Scopus (1491) Google Scholar The compounds were scored for lipophilicity22Hou T.J. Xu X.J. ADME evaluation in drug discovery. 2. Prediction of partition coefficient by atom-additive approach based on atom-weighted solvent accessible surface areas.J. Chem. Inf. Comput. Sci. 2003; 43: 1058-1067Crossref PubMed Scopus (46) Google Scholar, 23Viswanadhan V.N. Ghose A.K. Revankar G.R. Robins R.K. Atomic physicochemical parameters for 3 dimensional structure directed quantitative structure - activity relationships .4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally-occurring nucleoside antibiotics.J. Chem. Inf. Comput. Sci. 1989; 29: 163-172Crossref Scopus (1170) Google Scholar, 24Klopman G. Li J.Y. Wang S.M. Dimayuga M. Computer automated log p calculations based on an extended group-contribution approach.J. Chem. Inf. Comput. Sci. 1994; 34: 752-781Crossref Scopus (241) Google Scholar, 25Csizmadia F. TsantiliKakoulidou A. Panderi I. Darvas F. Prediction of distribution coefficient from structure .1. Estimation method.J. Pharm. Sci. 1997; 86: 865-871Abstract Full Text PDF PubMed Scopus (119) Google Scholar using LogP and LogD values.26Rutkowska E. Pajak K. Jozwiak K. Lipophilicity—methods of determination and its role in medicinal chemistry.Acta Pol. Pharm. 2013; 70: 3-18PubMed Google Scholar, 27Leo A. Hansch C. Elkins D. Partition coefficients and their uses.Chem. Rev. 1971; 71: 525-616Crossref Scopus (4298) Google Scholar, 28Barbato F. Caliendo G. Larotonda M.I. Silipo C. Toraldo G. Vittoria A. Distribution coefficients by curve fitting—application to ionogenic nonsteroidal antiinflammatory drugs.Quant. Struct. Act. Rel. 1986; 5: 88-95Crossref Scopus (18) Google Scholar Both values report partition coefficients for the ratio of unionized species in n-butanol to unionized species in water; LogD utilizes an additional algorithm to account for ionized species. The distribution pattern of the LogP and LogD values correlates well between our chemical library and DrugBank compounds, with most compounds having values between 1 and 4. Additionally, the small molecules studied herein and FDA-approved drugs followed similar distribution trends for molecular weight, lipophilicity, and rotatable bonds (Figure 2 and Table S2). To test the compounds for their ability to bind RNA, we conjugated them to a microarray surface that displays aldehydes and incubated them with radioactively labeled RNA motif libraries. The secondary structures displayed by the RNA libraries (Figure 3) were varied to define compound preferences, including hairpin libraries with five (1,024 unique members; A) or six randomized nucleotides (4,096 unique members; B); 3 × 2 nucleotide asymmetric internal loops (1,024 unique members; C), 3 × 3 nucleotide symmetric internal loops (4,096 unique members; D), and 4 × 3 nucleotide asymmetric internal loops (16,384 unique members; E). We have previously validated the secondary structures formed by members of RNA libraries by enzymatic mapping.8Velagapudi S.P. Gallo S.M. Disney M.D. Sequence-based design of bioactive small molecules that target precursor micrornas.Nat. Chem. Biol. 2014; 10: 291-297Crossref PubMed Scopus (239) Google Scholar, 12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar, 29Disney M.D. Childs-Disney J.L. Using selection to identify and chemical microarray to study the RNA internal loops recognized by 6′-N-acylated kanamycin A.Chembiochem. 2007; 8: 649-656Crossref PubMed Scopus (45) Google Scholar These discrete RNA motifs were selected because they are present in cellular RNAs. Because of the library-versus-library nature of these studies, they represent the largest screens completed to date, probing >52,000,000 interactions. Of the 1,987 compounds probed, 239 bound RNA (12.0%).Figure 4Chemical Diversity of Small Molecules that Bind RNA and Their Chemical PropertiesShow full caption(A) Heatmap of Tanimoto coefficients for each hit compound compared with every other hit.(B) Plots of various drug-like properties for hit compounds compared with the starting small molecule library. In general, the small molecules selected to bind RNA in this study have drug-like properties.View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Heatmap of Tanimoto coefficients for each hit compound compared with every other hit. (B) Plots of various drug-like properties for hit compounds compared with the starting small molecule library. In general, the small molecules selected to bind RNA in this study have drug-like properties. To remove compounds that non-selectively bind RNA motifs, we probed the 239 small molecules for binding to the RNA libraries in the presence of 1,000-fold excess bulk tRNA, affording 91 unique compounds (4.6%; Table S1). A final screen was then completed in which the 91 array-immobilized compounds were incubated with all five RNA motif libraries separately in the presence of oligonucleotide competitors F–J, d(AT)11 and d(GC)11. Oligonucleotides F–J mimic regions common to all library members, restricting binding to the randomized regions. (Note: oligonucleotide J was not used for hairpin selections.) After rigorous washing to remove unbound RNAs, bound RNAs were harvested and identified by RNA sequencing (RNA-seq). By completing selections under conditions of high oligonucleotide stringency (by use of excess competitor oligonucleotides), these studies identified small molecules that bound RNA motifs avidly. A challenge in the small molecule RNA-targeting area has been the development and identification of selective interactions between small molecules and RNAs. In myriad studies, 2DCS has defined selective RNA motif-small molecule interactions with varied affinities.11Tran T. Disney M.D. Identifying the preferred RNA motifs and chemotypes that interact by probing millions of combinations.Nat. Commun. 2012; 3: 1125Crossref PubMed Scopus (55) Google Scholar, 12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar, 30Velagapudi S.P. Pushechnikov A. Labuda L.P. French J.M. Disney M.D. Probing a 2-aminobenzimidazole library for binding to RNA internal loops via two-dimensional combinatorial screening.ACS Chem. Biol. 2012; 7: 1902-1909Crossref PubMed Scopus (27) Google Scholar, 31Velagapudi S.P. Seedhouse S.J. French J. Disney M.D. Defining the RNA internal loops preferred by benzimidazole derivatives via 2D combinatorial screening and computational analysis.J. Am. Chem. Soc. 2011; 133: 10111-10118Crossref PubMed Scopus (50) Google Scholar Using RNA-seq, a large sequencing dataset was obtained for each pool of RNAs that were specifically bound to each small molecule. RNA libraries A, B, C, D, and E had at least 12.1-fold (average: 133.1 ± 111.8), 6.7-fold (average: 80.8 ± 78.7), 9.9-fold (average: 103.5 ± 110.6), 7.2-fold (average: 75.7 ± 65.6), and 6.0-fold (average: 36.3 ± 31.6) coverage for each small molecule selection, respectively, as compared with the number of unique sequences within the corresponding RNA library. We have previously shown that at least 6-fold coverage is required to generate binding landscape maps.12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar RNA-seq data were then analyzed by high throughput structure-activity relationships through sequencing (HiT-StARTS)12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar to identify the privileged RNA motifs that bind each small molecule. In brief, the frequency of occurrence of a selected RNA in RNA-seq data was compared with the frequency of occurrence of the same RNA in RNA-seq analysis of the starting RNA library to account for biases arising during transcription and RT-PCR. This pooled population comparison affords the parameter Zobs, a metric of statistical confidence. A large, positive Zobs indicates a strong preference for binding the motif while a negative Zobs indicates a strong preference against binding the motif. A Fitness Score is assigned by normalizing the Zobs values to the most statistically significant small molecule-RNA interaction to 100. We previously studied the RNA-binding landscape of a series of substituted benzimidazoles that do not bind DNA. From these studies, we determined that a Zobs > 8 defined selective interactions; that is, high-affinity binding was observed to selected RNA motifs with Zobs > 8.12Velagapudi S.P. Luo Y. Tran T. Haniff H.S. Nakai Y. Fallahi M. Martinez G.J. Childs-Disney J.L. Disney M.D. Defining RNA-small molecule affinity landscapes enables design of a small molecule inhibitor of an oncogenic noncoding RNA.ACS Cent. Sci. 2017; 3: 205-216Crossref PubMed Scopus (52) Google Scholar We next analyzed the most privileged RNA motif binders and the most discriminated against RNA motifs (non-binders) for each compound selection by generating LOGOS from the highest and lowest 0.5% of Zobs scores (Supplemental Information).32Schneider T.D. Stephens R.M. Sequence logos: a new way to display consensus sequences.Nucleic Acids Res. 1990; 18: 6097-6100Crossref PubMed Scopus (2432) Google Scholar By comparing the LOGOS for related compounds, or DiffLogos,33Nettling M. Treutler H. Grau J. Keilwagen J. Posch S. Grosse I. Difflogo: a comparative visualization of sequence motifs.BMC Bioinformatics. 2015; 16: 387Crossref PubMed Scopus (41) Google Scholar structure-activity relationships (SARs) can be defined. Indeed, various hit compounds differ by a single functional group, including compounds 1 and 2 (Figure 5). Both compounds bind RNA libraries C, D, and E under conditions of high oligonucleotide stringency but do not bind RNA libraries A or B. Interestingly, both compounds prefer U-rich internal loops regardless of loop size or symmetry (3 × 2, 3 × 3, or 4 × 3) (Figure 5A). In the case of 2, LOGOS analysis reveals the potential preference for GU closing base pairs (if nucleotide 1 paired with 5; nucleotide 3 with nucleotides 4 or 5, etc.). In contrast, the RNA motifs most hindered from binding compounds 1 and 2 (discriminated against) are quite different (Figure 5B). Four compounds that share an indolylpyrimidine-2,4-diamine core bound members of A (5-nucleotide hairpin library). LOGOS analysis shows both similarities and differences, driven by substitution of the diamine (Figure 5C). For example, compounds 3 and 4 have strong preference for G in position 1; 3 and 5 prefer G > U ≫ A or C in position 4, while 6 prefers G ≈ U ≫ A or C and 4 prefers G > C ≫ A or U. Additional examples as well as LOGOS and DiffLogos analysis for all compounds are provided in Figure S3. Of the 26,624 possible sequences in the five RNA libraries, 1,215 sequences (4.6%) are unique for a single compound; that is, the sequence only appears in the highest 0.5% of Zobs values for one compound. We evaluated the most selective RNA motifs by searching for their sequences in the highest 0.5% and lowest 0.5% of Zobs values. A selective RNA motif will be enriched for only a single or a few compounds and discriminated against by many compounds. The most selective motifs for libraries B–E are (Figure 6): 5′CGAUUU3′ (discriminated against by nine compounds; privileged for one compound); 5′CCA3′/3′UC5′ (discriminated against by four compounds; privileged for one compound); 5′AAC3′/3′UAU′ (discriminated against by ten compounds; privileged for one compound) and 5′CCC3′/3′CCA5′ (discriminated against by nine compounds; privileged for one compound); and 5′UGGU3′/3′UGU5′ (discriminated against by nine compounds; privileged for one compound), respectively. Notably, there were no RNA motifs from library A that appeared in the highest 0.5% for few compounds and the lowest 0.5% for others. The most promiscuous binding sequence for RNA library A is 5′GGUGU3′ (n = 33 out of 38 total compounds) (Figure 6). Other 5-nucleotide hairpins were discriminated against by many compounds; that is, they do not bind, including 5′UUUUU3′ (n = 35; does not appear in the highest 0.5% Zobs values for any compound), 5′UCUUU3′ (n = 35; does not appear in the highest 0.5% Zobs values for any compound), and 5′UUUUC3′ (n = 36; does not appear in the highest 0.5% Zobs values for any compound) (Figure 6). Interestingly, all three sequences are U rich. There are also 6-nucleotide hairpins (derived from RNA library B) that accommodate binding to many small molecules (Figure 6): 5′CUAUAU3′ (n = 55 out of 65 compounds), 5′UAAGAG3′ (n = 56), 5′UACCUG3′ (n = 61), 5′AAAUAA3′ (n = 64), 5′UUAUAU3′ (n = 64), 5′UAAUAU3′ (n = 65), and 5′UGCGUG3′ (n = 65). Likewise, RNA motifs that do not generally form structures that bind the small molecules studied herein are (appear in the lowest 0.5% of Zobs values) 5′ACCCAU3′ (n = 54), 5′UCCAUU3′ (n = 43), 5′CACACU3′ (n = 44), 5′CUCAAU3′ (n = 46), and 5′UCCUAU3′ (n = 46) (Figure 6). Interestingly, the most promiscuous sequences from RNA library C are all predicted to fold into single nucleotide U bulges (Figure 6): 5′GUU3′/3′C_G5′ (n = 11 out of 32 compounds), 5′GGU(U)3′/3′CC_(A)5′ (n = 8; where invariant nucleotides from the cassette are in parentheses), 5′GCU(U)3′/3′CG_(A)5′ (n = 6), 5′GUA3′/5′C_U3′ (n = 6), and 5′GUU3′/3′C_A5′ (n = 6). RNAs 5′CGU3′/3′UU5′ (n = 8 compounds), 5′CGU3′/3′GU5′ (n = 7), and 5′CGA3′/3′GU5′ (n = 7) are the most highly discriminated against for binding (Figure 6). As observed for the other RNA libraries, there are internal loops derived from D that bind a wide range of small molecules (Figure 6). They include 5′ACA3′/3′CGG5′ (n = 46 out of 68 compounds), 5′GCU3′/3′CUG5′ (n = 46), 5′ACA3′/3′CGC5′ (n = 47), 5′ACU3′/3′CGU5′ (n = 47), 5′CAA3′/3′CGG5′ (n = 47), 5′GCU3′/3′CAC5′ (n = 47), 5′GGU3′/3′CCC5′ (n = 47), and 5′UAU3′/3′CCC5′ (n = 47). Many of these loops predicted to form loops with a U nucleotide opposite a C nucleotide. In contrast, 5′AAC3′/3′UAA5′ (n = 27), 5′ACA3′/3′CAA5′ (n = 30), 5′AAC3′/3′AAC5′ (n = 31), and 5′CAU3′/3′CAA5′ (n = 32) do not fold into structures that accommodate binding to the small molecules studied herein (Figure 6). The loops derived from E that bind the most number of compounds are 5′GCUU(U)3′/3′CGC(A)5′ (n = 12 out of 30 compounds), 5′GGUC3′/3′CCU5′ (n = 12), and 5′GUGG(U)3′/3′CGC_(A)5′ (n = 12) (Figure 6). The two most highly discriminated against loops selected from library 5 are 5′CGAU3′/3′GGG5′ (n = 12) and 5′CGUG3/′3′UGG5′ (n = 12) (Figure 6). Collectively, it appears that pyrimidine nucleotides provide scaffolds for binding to small molecules in the context of 6-nucleotide hairpins (derived from B), bulges (derived from C), and internal loops (derived from C–E), perhaps owing to their smaller size as compared with purines. This trend does not hold for 5-nucleotide hairpins (derived from A), which could fold into structures that are too small to accommodate binding. We measured the affinity of exemplar compounds that have inherent fluorescence. As shown in Table 1" @default.
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• W2890850847 title "A Massively Parallel Selection of Small Molecule-RNA Motif Binding Partners Informs Design of an Antiviral from Sequence" @default.
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• W2890850847 doi "https://doi.org/10.1016/j.chempr.2018.08.003" @default.
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