Arabidopsis chloroplast transcriptome recognised by the natural editing factor CHLOROPLAST BIOGENESIS 19 (CLB19). We show that our synthetic editing factor specifically recognises the target sequence in in vitro binding assays. The designed factor is equally specific for the target rpoA site when expressed in chloroplasts and in the bacterium E. coli. This study serves as a successful pilot into the design and application of programmable RNA editing factors based on plant PPR proteins.Members of the pentatricopeptide repeat (PPR) protein family act as specificity factors in C-to-U RNA editing. The expansion of the PPR superfamily in plants provides the sequence variation required for design of consensus-based RNA-binding proteins. We used this approach to design a synthetic RNA editing factor to target one of the sites in the  RNA editing in plants is carried out by pentatricopeptide repeat (PPR) proteins. Royan et al construct an editing factor from synthetic PPR motifs and show that it can be expressed in plants and bacteria and edits the chosen target RNA with high specificity, demonstrating the potential of PPR proteins as programmable RNA editing factors. This process occurs in diverse forms across all kingdoms of life, including the deamination of adenosines to inosines (which are read as guanosines in the mRNA) by ADAR enzymes in animals2, the deamination of cytidines to uridines by APOBEC enzymes in animals3 and by PPR-DYW proteins in plants4, and the unknown mechanism of uridine modification to cytidine in an as yet unelucidated pathway present in some groups of plants 6. Recently, several tools have been developed to perform single-nucleotide editing of mRNA7. These tools utilise the ADAR deaminase domain as the base modification tool, with specificity encoded in a programmable guide RNA9. The reliance on RNA-guided enzymes, whilst facilitating design, has some drawbacks  and these artificial protein fusions have poor spatial accuracy around the desired target site due to flexibility in the linker region9. These limitations justify examining the possibility of redesigning natural protein-guided editing systems such as those found in plants.RNA editing is a term denoting a type of post-transcriptional modification of a transcribed RNA such that it differs from the sequence predicted from the genomic DNA10. Of particularly relevance to RNA editing are the PLS-class PPR proteins, which generally contain triplets of three different types of PPR motifs in a (P1-L1-S1)n-P2-L2-S2 arrangement11. These PPR arrays are followed by PPR-like E1 and E2 helix-turn-helix motifs, extending the characteristic repeating structure and a ~136 aa domain (named the DYW domain) with a conserved C-terminal D, Y, W tripeptide12. This DYW domain contains a cytidine deaminase-like active site and is necessary and sufficient for editing to occur18. PLS-class PPR protein-mediated editing has been characterised in several model plants, notably Arabidopsis, the moss Physcomitrella patens, rice, and maize4 but is far more prevalent in some other little-studied plants, including hornworts, lycophytes, and ferns23.RNA editing in plants is carried out by PPR proteins, a huge family of sequence-specific RNA-binding proteins with many roles in organellar RNA processing27. These specificity-determining interactions involve hydrogen bonding between the amino acids at the 5th and last positions in each motif and the aligned RNA base28. With minor variations this code can be used to describe and predict interactions of any of the P, L or S motifs with RNA26. This code has been employed to make many successful predictions about which editing factors target which sites in mitochondria and chloroplasts30. It has also been used in attempts to modify target recognition by editing factors. CLB19 is an A. thaliana chloroplast editing factor required for editing of the rpoA-78691 and clpP1-69942 sites31 that acts as the targeting factor in a complex with the DYW donor protein DYW2 and another editing cofactor, NUWA33. We previously showed that the target preference of CLB19 could be modified in vitro and in vivo34 although we were unable to make it entirely specific for one or other of the two target sites.Correlation of PPR protein sequences with their target sites has led to a proposed ‘code’ for identifying interactions of PPR motifs with an RNA base35. Such synthetic proteins have proved to be useful tools for understanding the structure of PPR proteins and demonstrating their potential as programmable RNA-binding proteins40. Recently, the first crystal structure of a designer PLS protein41 based on the consensus from genomic PLS sequences has revealed a similar structure to natural and synthetic P-class PPR proteins. This work suggests that the L-motifs within this synthetic protein are slightly misaligned with the P and S motifs, but this misalignment is rescued by the formation of a complex with the cofactor MORF9 to allow for effective RNA recognition41. MORF9 is one of 10 related proteins in Arabidopsis that have been identified as important RNA-editing cofactors in seed plants43, but until this crystal structure was published, it had been unclear how these proteins influence the process. MORF proteins are only one of several classes of cofactor theorised to form the ‘editosome’ in angiosperm organelles45. The apparent complexity of the editing process in angiosperms contrasts with its apparent simplicity in other plants where MORF proteins are absent and PPR editing factors are able to edit RNA on their own, as demonstrated by the reconstitution of editing in bacteria18 and in vitro17 with editing factors from Physcomitrella. These recent results are promising developments on the road to utilising PPR-RNA-editing factors as programmable biotechnological tools but to go further we need to be able to design new editing factors, which is facilitated by the use of synthetic proteins based on consensus motifs. We thus set out to construct a functional editing factor created from synthetic PPR motifs. As a test of functionality, we aimed to complement the well-studied clb19 mutant34. As a test of specificity, we aimed to target only one of the two editing events that are missing in clb19, thus attempting to design a synthetic editing factor that is more specific than the natural editing factor it is intended to replace. Here we demonstrate that a synthetic editing factor of this type can be expressed in plants and in bacteria, that it edits the chosen target RNA with high specificity, and that it is at least partially independent of plant-specific cofactors.The modular nature of PPR proteins, their large diversity, and the predictability of their interactions with RNA has prompted the design of synthetic PPR proteins based on consensus motifs, as first performed by3-P2-L2-S2-E1-E2-DYW . The P1-L1-S1 triplets were designed to be position-specific, which means that the first P1 motif is slightly different to the second or the third P1 motif, and likewise for the L1 and S1 motifs 3-PPR,41 dsn3PLS-DYW has additional domains at the C-terminus, but lacks the extensive cap and solvating helix present in (PLS)3-PPR that are derived from the N- and C-termini of ZmPPR1047 -L1-S13-PE. coli and purified by nickel affinity purification. The N-terminal tag was cleaved off with TEV protease and the dsn3PLS-DYW further purified by reverse nickel affinity purification to about 85% purity  3-PPR41 with optimal binding requiring at least 2 MORF molecules to each PPR molecule. The dsn3PLS/MORF9/RNA complex is clearly super-shifted with respect to the dsn3PLS/RNA complex. The formation of a dsn3PLS-DYW/MORF9/RNA complex was confirmed by analytical size-exclusion chromatography  Arabidopsis thaliana in a clb19-3 mutant background. The construct used for plant transformation encodes a 60 amino acid rubisco small subunit (RbcS) transit peptide from pea (Pisum sativum) in order to facilitate chloroplast localisation of the protein. The Arabidopsis CLB19 promoter and 5′ untranslated region (consisting of the 1 kb of DNA sequence from upstream of the start codon) was used to drive expression of the transgene. As a negative control, an almost identical construct was prepared, with the only difference being an inactivating mutation (E70A) in the DYW domain, which removes the conserved glutamate implicated as the catalytic residue in the deamination mechanism15. As a positive control, the native CLB19 sequence (codons 34–500) was cloned into an equivalent construct. Plants homozygous for the clb19-3 mutation are slow-growing and light-sensitive but were successfully transformed via Agrobacterium-mediated floral dipping. After hygromycin selection and growth, plants from the dsn3PLS-DYW lines displayed a greener phenotype than the clb19-3 mutant line or the dsn3PLS-DYW (E70A) line, but they were less healthy than those complemented with CLB19  showed an intermediate transcript abundance phenotype. Copious editing of both the rpoA-78691 and clpP1-69942 editing sites was observed in three biological replicates of the control line expressing CLB19 .As a sensitive test of physiological state, RNA-seq data was obtained from leaves of plants grown under relatively low light and analysed to quantify expression of chloroplast genes Fig. . In sampB19 Fig. . PartialThe transgene transcript abundance was quantified using the RNA-seq data to verify to what degree differences in editing could be explained by variations in expression level , which is a reported low-frequency site (12%) in wild-type Col-048, but which is edited at relatively high levels (~20%) in clb19 plants expressing transgenic CLB19  , and these in turn were used as negative controls for lines expressing CLB19 or dsn3PLS-DYW). Only one potential site, the intended target rpoA-78691, was found in lines expressing dsn3PLS-DYW (E70A), with 24 events out of 2654 reads (0.89%). As the E70A mutation should completely prevent the deaminase activity of this protein, this residual activity is, at first sight, surprising. However, CLB19 completely lacks this part of the DYW domain and still effectively induces editing at this site by recruiting other cofactors, so it is possible that the dsn3PLS-DYW (E70A) can do the same, albeit with much reduced efficacy. Besides the ycf3-43350 intron site already mentioned, twelve other sites were identified as potential off-target events induced by CLB19 , it is also significantly edited in clb19 (~2%) and dsn3PLS-DYW (E70A) plants (~2%), making it unlikely that it is a direct target of the introduced editing factors. The site 1502 is barely edited at all (0.03%) and at approximately equal levels in dsn3PLS-DYW, CLB19 and clb19 plants. Excluding these two probable ‘false positives’, the other potential off-target sites show similarity to the rpoA-78691 site  Fig. . This prB19 Fig.  and elevite Fig. . More deE. coli together with its target site, and optionally together with MORF2. Based on the work by Oldenkott et al.18, the bacterial expression vector containing the dsn3PLS-DYW gene was modified to insert 39 bp regions covering either the A. thaliana rpoA-78691 or clpP1-69942 editing sites into the 3’ untranslated region of the dsn3PLS-DYW transcript. Expression of this modified dsn3PLS-DYW transcript including a downstream editing site was induced together with MORF2 co-expressed from pETM11, or as a negative control, unmodified pETM11 (which expresses human max dimerisation protein 1). The presence of either pETM11-based plasmid greatly reduces dsn3PLS-DYW expression, presumably through sequestration of T7 RNA polymerase as the effect is primarily transcriptional. RNA editing was assessed by sequencing of cDNA derived from total RNA of 1 mL aliquots of bacterial culture after 18 h growth post induction at 16 °C   construct were phenotypically distinguishable from untransformed clb19  samples also detected ‘off-target’ editing at numerous sites in plants expressing dsn3PLS-DYW and CLB19  or dsn3PLS-DYW (where ycf3-43350 is not edited). This site is specifically edited by CLB19, and according to our binding predictions . Beyond this trivial explanation, it is however possible that there is a fundamental functional difference between the MORF-requiring proteins from angiosperms and the MORF-independent proteins from bryophytes, lycophytes and ferns. We have speculated, based on characteristic differences in L motif sequences6, that the MORF-independent proteins have reduced specificity because their L-motifs are unable to distinguish between different bases. This is consistent with the moss editing factor results, where the off-target sites generally show any of A, C, G or U aligned with the L-motifs in their proteins18.The high-coverage RNA-seq that detected editing of B19 Fig. . Only on34. Promisingly for applications of synthetic editing factors, editing at off-target sites of dsn3PLS-DYW did not exceed 1.5% at any of the 9 sites we detected. Although eight of these events lead to non-synonymous changes in coding sequences 51, the exact role of these cofactors in editing has been unclear. They have been variously proposed to associate with N-terminal PPR motifs51 and/or C-terminal E motifs52, and to exist as monomers and/or homo- or heterodimers53. It was long uncertain whether they acted by enhancing RNA binding, by enhancing formation of larger protein complexes (the ‘editosome’) or by enhancing the editing reaction (reviewed in ref. 4). Our results are entirely consistent with those obtained with a different synthetic protein based on consensus P-L-S motifs41. Hopefully this confirmation that MORF proteins act by enhancing RNA binding by PPR editing factors helps remove some of the confusion concerning the role of MORF/RIP proteins in RNA editing.Binding of dsn3PLS-DYW to its target sequence in vitro and editing in vivo was strongly (but not completely) dependent on the presence of MORF proteins Figs.  and 5. M9. In this study, we have demonstrated the potential of a synthetic editing factor that can edit with high specificity. Natural PPR editing factors are extremely precise—editing almost always occurs at the 4th nucleotide 3’ of the nucleotide aligned with the S2 motif27. This is also the case for dsn3PLS-DYW, as all the off-target events observed are consistent with this positioning of the editing factor relative to the edited C; indeed, no editing was detected at adjacent C residues where this would have been possible (sites 49646 and 69992). Thus, it is reasonable to imagine that the specificity of ‘designer’ editing factors based on PPR-DYW scaffolds could ultimately exceed that of Cas13-ADAR fusions. On the other hand, designing the specificity of the PPR array remains complex due to the uncertain contributions of MORF cofactors and the C-terminal S2-E1-E2 motifs. Given that the Physcomitrella editing factors PPR56 and PPR65 can edit without MORF proteins present18, it should be possible to design a synthetic editing factor that does not require cofactors for optimal specificity and editing activity. Of particular interest in this context are the monotypic S motif arrays found in putative editing factors in lycophytes11, likely to be MORF-independent6.The currently favoured biotechnological tools for RNA editing, the REPAIR and RESCUE systems comprised of a base editor coupled to a deactivated Cas13, achieve a high degree of specificity via the short guide RNA complementary to the target RNA, but once bound, show undesired promiscuity, potentially resulting in off-target editing of any deaminable bases within a window of at least four nucleotides on either side of the editing siteIn conclusion, this work demonstrates the successful use of a synthetic PPR protein as an RNA-editing factor and lays the foundation for detailed structural and mechanistic studies into the mechanism of RNA editing. Designer PPR proteins represent an attractive multipurpose scaffold for targeted RNA binding, particularly as programmable RNA-editing factors.Arabidopsis thaliana clb19-3 T-DNA insertion line SALK_12375231. For general growth and propagation, seeds were germinated on 0.5 × MS plates (0.5 % sucrose) under dim light (40 µE m−2 s−1) in long day cycles  for 3 weeks. When the leaves started greening, seedlings were transferred to 100 µE m−2 s−1 light. In order to perform transformation by flower dipping, pale green seedlings were transferred to soil until they were able to produce flowers. For the phenotypic analysis in Fig. −2 s−1 light to enhance the phenotypic differences between the lines. For the RNA-seq experiments, leaves were harvested during the period of growth at 40 µE m−2 s−1.All plant genetics experiments performed in this study were undertaken on the Escherichia coli strains used in this study were DH5α  for cloning, BL21 (DE3)  for standard protein expression, and Rosetta 2 (DE3) (Novagen) for E. coli RNA-editing experiments.54 were a gift from Dr. Gunter Stier . The plant expression vector used was pCAMBIA1390 .Bacterial expression vectors pETM11 and pETM20https://www.idtdna.com) or Sigma-Aldrich , while labelled oligonucleotides were obtained from Integrated DNA Technologies .Primers and unlabelled oligonucleotides  and each category aligned separately to construct position-specific consensuses. Consensus sequences of the aligned motifs were constructed with the EMBOSS cons tool55 with plurality 0. The dsn3PLS-DYW protein was designed to start with a four amino acid Met, Gly, Asp, Ser cap, followed by three repeating modules of position-specific P1-L1-S1 triplets, a P2-L2-S2-E1-E2 module, and terminating in a consensus DYW domain .Domain sequences of putative DYW-subgroup editing factors from 38 land plant genomes containing three P1-L1-S1 motifs were obtained as previously describedNcoI and XhoI sites of the bacterial expression vector pETM2054 by Gibson assembly. Sequences encoding MORF2  and MORF9  were cloned into the NcoI and XhoI sites of the bacterial expression vector pETM1154 by standard subcloning. The dsn3PLS-DYW expression vector was transformed into E. coli strain BL21 (DE3), while the MORF2 and MORF9 expression vectors were transformed into E. coli strain Rosetta 2 (DE3). Strains were grown in LB medium at 37 °C, 200 rpm to an absorbance of 0.6–0.8 at 600 nm, induced with addition of 0.1 mM IPTG, with addition of 20 µM ZnSO4 to the dsn3PLS-DYW expressing cultures, and maintained at 16 °C, 200 rpm for 16–18 h. Bacterial pellets were resuspended in 40 mL of binding buffer  supplemented with 125 units of Benzonase Nuclease (Sigma). Cells were lysed on ice using an Emulsiflex C5 high-pressure homogeniser (Avestin) to a maximum operational pressure of 16,000 psi until the lysate was clarified. The soluble fraction was separated from the insoluble fraction by centrifugation at 24,000 × g for 45 min at 4 °C and passed through a 0.22 µm syringe filter (Merck Millipore) before purification. Filtered bacterial lysate in binding buffer containing recombinantly expressed proteins was loaded onto a 5 mL His-Trap HP column  and the protein was eluted using a gradient of imidazole (20–500 mM) on an NGC Quest Plus (Bio Rad Laboratories) FPLC instrument . 1 nM of pre-heated diluted RNA oligonucleotide was added to the sample and incubated for 30 min at ambient temperature before loading onto a 5% polyacrylamide gel, running at 100 V. After 30 min, the fluorescence was visualised on an Amersham Typhoon system , with an excitation wavelength of 488 nm and an emission wavelength of 520 nm.REMSAs were performed as described previouslyclb19−3 plants by floral dipping using Agrobacterium tumefaciens strain GV3101 RifR GentR. Total RNA was isolated with RNAzol RT reagent (Sigma) from 3.5-week-old seedlings (T3) grown on half-MS plates under dim light conditions (40 µmol m−2 s−2).The genes encoding dsn3PLS-DYW, dsn3PLS-DYW (E70A) or CLB19 (34–500) were cloned into pCAMBIA1390, along with the sequences for the native CLB19 promoter (−1000 to −1), the N-terminal chloroplast targeting peptide from pea RbcS, and a 4 × c-Myc epitope tag 5ʹ to the editing factor gene. Expression plasmids were transformed into 2-month-old clb19-3 and clb19-3 + dsn3PLS-DYW(E70A) lines) or three (clb19-3 + CLB19 and clb19-3 + dsn3PLS-DYW lines) independent libraries were made from 250 ng of total RNA treated with Turbo DNase  using an Illumina TruSeq Stranded Total RNA with Ribo Zero (Plant) library preparation kit. The sequencing run  was performed on an Illumina HiSeq4000 sequencer by Novogene . The read data is available from NCBI (BioProject ID PRJNA680434). Reads were first de-duplicated using clumpify  from the bbmap package (https://sourceforge.net/projects/bbmap/), then trimmed of adapters with bbduk (parameters: ktrim = r k = 23 mink = 11 hdist = 1 tpe tbo ftm = 5) and mapped to the Col-0 plastid genome (accession AP000423) with bbmap (parameters: ambiguous = random mappedonly = t). To ensure that reads mapping across the arbitrary linear ends of the reference genome were not lost, the reference was extended by 1 kb prior to mapping. For analysis of chloroplast transcript abundances, mapped read pairs overlapping annotated CDS features were counted using a script based on biojulia (https://biojulia.net) available at https://github.com/ian-small/pyrimid. Counts were normalised using a pseudo-reference approach taken from DESeq256. For analysing editing, strand-specific nucleotide counts were obtained using the same in-house package . PPR binding scores were calculated with PPRmatcher  using the Kobayashi scoring table (derived from ref. 26). For quantifying transgene transcripts, the de-duplicated, trimmed RNA-seq reads were mapped to Arabidopsis transcripts (TAIR10 annotations) supplemented with the relevant transgene sequence. The mapping and quantification were done with salmon 1.4.057, using the Arabidopsis nuclear, chloroplast and mitochondrial genomes as ‘decoys’.Two  according to the manufacturer’s instructions. For editing assays, pETM20:dsn3PLS-DYW plasmids containing the editing sites were transformed into Rosetta 2 (DE3) cells, or co-transformed with the empty pETM11 plasmid, pETM11-MORF9  or pETM11-MORF2 . Single colonies were used to inoculate 5 mL LB starter cultures with the appropriate antibiotics and grown at 37 °C, 200 rpm for 16 h. Two-hundred fifty microliters of the starter culture were then used to inoculate 50 mL LB expression cultures with the same antibiotics. The cultures were grown at 37 °C, 200 rpm to an OD600 of 0.5–0.6. Cultures were then cooled on ice for 10 min prior to supplementation with 0.4 mM ZnSO4, and induction with 0.4 mM IPTG. Cultures were incubated at 16 °C, 200 rpm for 18 h before harvesting. Total RNA was extracted from 1 mL of E. coli bacterial pellet using the Direct-Zol RNA extraction kit  according to the manufacturer’s instructions. Reverse transcription was performed on 2 µg of DNase-treated total RNA extract using Superscript™ III Reverse Transcriptase (Invitrogen) through random hexamer primers, according to the manufacturer’s instructions. Reverse transcription polymerase chain reaction (RT-PCR) was performed with Q5 DNA Polymerase (NEB) according to the manufacturer’s instructions, using the SynthSEQ.FOR and T7short primers. Sanger Sequencing of the RT-PCR products using the SynthSeq3.FOR primer was performed by Macrogen Inc. . Editing efficiency was determined from the raw sequencing chromatograms using the EditR webserver available at http://baseeditr.com/. For RNA-seq analyses, 500 ng of DNase-treated RNA was rRNA-depleted58 and then used for preparing sequencing libraries with the Illumina TruSeq Stranded total RNA library preparation kit as recommended by the manufacturer. The libraries were sequenced on an Illumina HiSeq4000 sequencer by Novogene. In all, 29–34 million 150 nt paired-end reads were obtained for each sample. The read data is available from NCBI (BioProject ID PRJNA680433). Analysis of the data was carried out as described above for the chloroplast RNA data, except that the reads were mapped to the E. coli BL21 genome (accession CP010816) and the relevant pETM11 and pETM20 constructs.p-values were corrected for multiple testing using statsmodels.stats.multitest.multipletests with the Simes-Hochberg procedure. Odds ratios were used to calculate and visualise differences in editing as they are an estimation of the ratio of editing activities in the two samples being compared. Odds ratios were calculated after adding a pseudocount of 0.5 to all observations to avoid division by zero. All experiments were replicated at least once (and in triplicate where shown). Replicates are biologically independent samples (independent transformants).Nucleotide count data was analysed statistically with a Fisher exact test as implemented in the Python scipy.stats package and the Peer Review FileSupplementary Information


The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5′ UTR; the reverse complement of the 5′ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3′ UTR. For eleven of these elements , modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’,   The COVID-19 outbreak has rapidly spread through the world, presenting an urgent need for therapeutics targeting the betacoronavirus SARS-CoV-2. RNA-targeting antivirals have potential to be effective against SARS-CoV-2, as the virus's RNA genome harbors conserved regions predicted to have stable secondary structures , some ofSeveral RNA regions in betacoronavirus genomes, including the 5′ UTR, the frameshift stimulating element (FSE), and 3′ UTR, feature RNA structures with likely functional importance. These regions include a series of five conserved stem–loops in the 5′ UTR, the FSE along with a proposed dimerized state, a pseudoknot in the 3′ UTR proposed to form two structures, and the hypervariable region in the 3′ UTR, which includes an absolutely conserved octanucleotide and the stem–loop II-like motif (‘s2m’). An NMR structure of stem–loop 2 in the 5′ UTR has been solved, adopting a canonical CUYG tetraloop fold . A crystet al. . Such work motivates our modeling of not just a single ‘native’ structure but an ensemble of states for SARS-CoV-2 RNA regions. As with HIV-1 TAR, many of the SARS-CoV-2 elements are unlikely to adopt a single conformation but instead sample conformations from a heterogeneous ensemble. Furthermore, transitions among these conformations may be implicated in the viral life cycle, as RNA genome regions change long-range contacts with other RNA elements or form interactions with viral and host proteins at different steps of replication, translation, and packaging. A possible therapeutic strategy is therefore to find drugs that stabilize an RNA element in a particular conformation incompatible with conformational changes and/or changing interactions with biological partners at different stages of the complete viral replication cycle. Consistent with this hypothesis, prior genetic selection and mutagenesis experiments stabilizing single folds for stem–loops in the 5′ UTR and the pseudoknot in the 3′ UTR demonstrate that changes to these RNA elements’ structural ensembles can prove lethal for viral replication  (de novo models for stem–loops 1 to 8 (SL1–8) in the extended 5′ UTR, the reverse complement of SL1–4 in the 5′ UTR, the FSE and its dimerized form, the 3′ UTR pseudoknot, and the 3′ UTR hypervariable region, along with homology models of SL2 and s2m. The use of Rosetta's FARFAR2 is motivated by extensive testing: FARFAR2 has been benchmarked on all community-wide RNA-Puzzle modeling challenges to date  . These s to date , achievi to date . For ourin vitro transcription with T7 TranscriptAid kits (Thermo Scientific). The resulting RNA was purified with Agencourt RNAClean XP beads supplemented with an additional 12% of PEG-8000 and quantified via NanoDrop. Owing to its longer length, the SL2–6 construct was subsequently size purified using a denaturing polyacrylamide gel , loaded in 80% formamide, run at 18 W for 35 min following 1 h of pre-running the gel prior to loading. A RiboRuler LR size standard (Thermo Scientific) was used. The correct-sized band was visualized using SyBr Gold (Invitrogen) and excised using a blue-light transilluminator, and the RNA was finally purified from the gel slice using a Zymo ZR-PAGE recovery kit.We collected chemical reactivity profiles for SL1–4 and SL2–6 of the 5′ UTR, the reverse complement of SL1–4, and the hypervariable region of the 3′ UTR. The DNA templates for the stem–loop 1–4 RNA were amplified from a gBlock sequence for the extended 5′ UTR, and the DNA template for the hyper-variable region was amplified from a gBlock sequence for the 3′ UTR. The SL2–6 construct was designed using the Primerize webserver  with bui2 to a final concentration of 10 mM in 15 μl, incubated at 50°C for 30 min, and then left at room temperature for 10 min. For chemical modification of folded RNA, fresh working stocks of 1-methyl-7-nitroisatoic anhydride (1M7) were prepared. For 1M7, 4.24 mg of 1M7 was dissolved in 1 ml of anhydrous DMSO. For a no-modification control reaction, 5 μl of RNase free H2O was added to 15 μl of folded RNA. Samples were incubated at room temperature for 15 min. Then, 5 μl of 5 M NaCl, 1.5 μl of oligo-dT Poly(A)Purist MAG beads (Ambion), and 0.065 pmol of 5′ fluorescein (FAM)-labeled Tail2-A20 primer were added . The reaction was incubated at 48°C for 45 min. The RNA was then degraded by adding 5 μl of 0.4 M NaOH and incubating the mixture at 90 °C for 3 min. The degradation reaction was placed on ice and quickly quenched by the addition of 2 μl of an acid quench solution . Bead-bound, FAM labeled cDNA was purified by magnetic bead separation, washed twice with 100 μl of 70% EtOH, and air-dried for 10 min. To elute the bound cDNA, the magnetic beads were resuspended in 10.0625 μl ROX/Hi-Di (0.0625 μl of ROX 350 ladder [Applied Biosystems] in 10 μl of Hi-Di formamide [Applied Biosystems]) and incubated at room temperature for 20 min. The resulting eluate was loaded onto capillary electrophoresis sequencers (ABI-3100 or ABI-3730) either on a local machine or through capillary electrophoresis (CE) services rendered by ELIM Biopharmaceuticals.https://github.com/ribokit/HiTRACE)  , followiIn separate high throughput experiments to probe RNA structures, Eterna players designed 3030 sequences for the Eterna Roll Your Own Structure Lab, including regions of the SARS-CoV-2 5′ UTR, FSE and 3′ UTR in Eterna Constructs 1–7 . A DNA l2O to 40 μl and then purified with 72 μl AMPure XP beads (Beckman Coulter), eluting into 20 μl of H2O.This pool of DNA oligonucleotides (360 ng) was amplified by emulsion PCR with Phire Hot Start II DNA-Polymerase. An oil-surfactant mixture was prepared containing 80 μl of ABIL EM90, 1 μl of Triton X100 and 1919 μl of mineral oil. The oil phase was vortexed for 5 min and kept on ice for 30 min. An aqueous phase was prepared containing 1× Phire Hot Start II buffer, 0.2 mM dNTPs, 1.5 μl of Phire II DNA polymerase, 2 μl of the T7 promoter primer, 2 μM of the reverse complement of the Tail2 sequence, and 0.5 mg/ml of BSA in final volume of 75 μl. An emulsion was prepared in a 1.0 ml glass vial by first adding 300 μl of the oil–surfactant mixture into the glass vial, vortexing at 1000 rpm for 5 min, and then adding 10 μl of the aqueous phase every 10 s until the final emulsion volume was 350 μl. The emulsion was transferred into PCR tubes and PCR was performed by denaturing at 98°C for 30 s, cycling with 98°C for 10 s, 55°C for 10 s and 72°C for 30 s for 42 cycles, and extending at 72°C for 5 min. The PCR reaction was purified by adding 100 μl of mineral oil, vortexing, and centrifuging at 13 000 g for 10 min, and then discarding the oil phase. The PCR products were degreased with diethyl ether and ethyl acetate and incubated at 37°C for 5 min. The reaction volume was adjusted with H2O. 15 pmol of RNA was added into 2 μl of 500 mM Na-HEPES, pH 8.0, denatured at 90°C for 3 min, and cooled down to room temperature for 10 min. 2 μl of 100 mM MgCl2 was added, and the reaction volume was brought to 15 μl with H2O. RNA was incubated at 50°C for 30 min. RNA was cooled down at room temperature for 20 min before being modified with 5 μl of 1M7 (8.48 mg/ml of DMSO) or left untreated for an untreated control sample. The reaction was left room temperature for 15 min, in final volume at 20 μl. The reaction was quenched with 5 μl of 500 mM Na-MES pH 6.0, the volume was adjusted to be 100 μl, and the reaction was purified with ethanol precipitation.DNA was transcribed with TranscriptAid T7 High Yield Transcription Kit (K0441), at 37°C for 3 h, treated with DNAse-I for 30 min, and purified with AMPure XP beads with 40% PEG at a 7:3 ratio of beads to PEG. RNA was eluted with 25 μl of H2O. The ligated product was sequenced on a Miseq for 101 cycles for read 1 and 51 cycles for read 2. Sequencing data were analyzed using the MAPseeker software, freely available for non-commercial use at https://eternagame.org/about/software.Reverse transcription was performed using SuperScript III RTase (Thermo Fisher). RNA was added into a reaction mix of 1× First strand buffer, 5 mM DTT, 0.8 mM dNTPs and 0.6 μl of SS-III RTase (Thermo Fisher), and 1 μl of 0.25 μM primer .Chemical reactivity from Manfredonia et al. , Huston   or starting a new cluster. We make available up to 50 models from each of the 10 lowest energy clusters in the resulting FARFAR2-SARS-CoV-2 dataset to help efforts in virtual screening that take advantage of ensembles. We note that these models and their frequency in clusters are not necessarily an accurate representation of the thermodynamic ensemble obtained by the RNA due to biases in Rosetta FARFAR2 sampling and inaccuracies in the Rosetta all-atom free energy function. Nevertheless, the models offer a starting point of physically realistic conformations for virtual ligand screening and more sophisticated approaches to thermodynamic ensemble modeling.For each system, we generated large model sets using the Stanford high performance computing cluster Sherlock and the Open Science Grid . For sysFor each RNA segment, we carried out Rosetta modeling using the secondary structure proposed in the literature . We addiHomology modeling with FARFAR2 for the 5′ UTR SL2 and the 3′ UTR stem–loop II-like motif (s2m) was carried out using the approach outlined in ref. . For theSimulations that are sufficiently converged produce multiple occupancy clusters, which signal that FARFAR2 sampling is able to discover lowest energy states, as evaluated in Rosetta's all-atom energy function. Runs with only single occupancy clusters would need more computer power to discover lowest energy states. In Table https://github.com/DasLab/FARFAR2-SARS-CoV-2. Large model sets with the top 5% of models for each simulation are included at the PURL repository: https://purl.stanford.edu/pp620tj8748.For each simulation, we additionally report a ‘convergence’ estimate in Table https://github.com/DasLab/FARFAR2-Apo-Riboswitch.To guide the development of virtual screening approaches using the SARS-CoV-2 FARFAR2 models, we additionally compiled similarly prepared representative models for ten small-molecule binding RNA aptamers using previously generated decoy sets . This darna_denovo application to combine the loop region from the identified structure with the coordinates for the remaining nucleotides that had been obtained from the top-scoring FARFAR2 structure. These models including loop motifs from JAR3D are available in the Github repository: https://github.com/DasLab/FARFAR2-SARS-CoV-2.For the 5′ UTR, reverse complement of the 5′ UTR, frameshift stimulating element, and the 3′ UTR, we searched for matches to 3D RNA motifs from previously solved RNA structures using JAR3D . In partWe identified candidate small-molecule binding pockets in the RNA constructs from the FARFAR2-SARS-CoV-2 dataset, running fpocket  with defin vitro or in cells , with the top-scoring structures depicted in et al.  than the RMSD predicted by the convergence of the de novo models. Interestingly, the cryo-EM study showed clear density for the stem–loop surrounding the slippery-site sequence predicted from our de novo modeling. In addition, after these de novo models for the FSE were generated, the cryo-EM structure of the FSE on a mammalian ribosome was solved by Bhatt et al. , we generated over 25 000 models using the secondary structure depicted in Figure de novo models of RNA’s that are known to bind small molecules. Recent RNA-puzzles blind prediction trials have shown that FARFAR2 models in combination with conservation information enable manual identification of ligand binding sites in 3D models of bacterial ‘riboswitch’ aptamers (S-adenosyl methionine (SAM), glycine, cobalamin, 5-hydroxytryptophan, the cyclic dinucleotides c-di-AMP (the ydaO riboswitch), the alarmone nucleotide ZMP (AICAR monophosphate), glutamine and guanidinium. Each RNA was modeled without ligands present during fragment assembly or refinement, to mimic the protocols that would be used in virtual drug screening, in which modeling of apo RNA structures are used for computational docking of ligands.Use of the above SARS-CoV-2 RNA 3D models for virtual screening would be aided by a benchmark of analogous aptamers . To guidThree of these model sets for SAM-I, SAM-I/IV and SAM-IV made use of homology to previous riboswitch structures’ ligand binding sites —that is, independent modeling runs did not converge to similar low energy structures. It is possible that these RNA elements do not have well-defined 3D structures in solution unless bound tightly to partners such as the SARS-CoV-2 replicase complex. Alternatively, or in addition, our modeling methods and currently available computational power are not well-suited to regions of this size. While additional sampling may alleviate this problem, these regions have more ing site . Howevering site ,17, and ing site .de novo modeling, we felt that it was important to provide analogous models of RNAs of known structure. Virtual screening approaches appear poised to make good use of computational models of RNA, but have so far made only limited use of de novo predicted models . FARFAR2-SARS-CoV-2 models are included at gkab119_Supplemental_FileClick here for additional data file.


Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches. RNA genome is an essential part of RNA viruses. In particular, non-enveloped ones convey a direct relationship between capsid proteins and RNA molecules, which was the genesis of several research routes on the assembly, disassembly and delivery functionalities of RNA inside proteinaceus capsids ,10,11,12The article comprises several sections, starting with a short description of the method, including the steps for the subdomain reconstruction process. Subsequently, we test the method with the input from the STMV secondary structures proposed by Weeks and co-workers , namely,The SPlit and conQueR model (SPQR) is a coarse-grained representation  of RNA, i with respect to a reference frame located at the origin of nucleobase j with its orientation. The rescaled vectors s, composed of In order to enforce a particular geometry on a set of nucleotides, we perform SMC simulations, which are constant temperature Monte Carlo (MC) simulations with additional energy restraints. These simulations minimize the ructures . The ERMconstant . In thisconstant ,38. The An additional restraint can be exerted on the radius of gyration, which has the following harmonic form:Finally, an important feature of the method is the use of soft repulsive or attractive energy terms, which are designed to put away colliding coarse-grained nucleotides or to reestablish broken bonds, which allow to easily relax assembled fragments into a more realistic conformation.Within our method, the secondary structure is given as an input, where we decompose the structure into two type of subdomains: junctions and stem loops. The latter can be composed of one or more sequential complementary double helices, which can be connected by internal loops or be closed by a hairpin.We proceed to generate models for each stem loop as shown in Similarly, the size of the stem is increased by exerting the secondary structure restraints on additional unpaired nucleotides, which belong to the hairpin loop. Here, we have empirically determined that the size of such a hairpin loop has to be at most 4 nucleotides. This procedure is necessary in order to prevent the entanglement of the hairpin with itself and with the stem. At the end, the artificially introduced additional pairs are released from their restraint, and the structure is subsequently relaxed with the original secondary structure.T such that Each of these simulations is performed with temperature For larger stem loops, the procedure is illustrated in The junctions are generated as ring-like structures, which are composed of strands connected by a closing base pair, where a stem loop is anchored see c. A set Finally, e models ,33,35.Note also that our fragment assembly procedure could, in principle, be applied to other RNA coarse-grained models ,41,42,43The three-dimensional in virio and in vitro models of the STMV genome are shown in An optimal determination of the 3D encapsidated structure of the RNA genome goes beyond the capabilities of the current RNA–protein interaction modeling, which was discussed and conceptualized under different hypotheses, specifically for virus assemblies ,45,46,47https://doi.org/10.5281/zenodo.5035769). Such an approach is complex by itself; however, the offered ability of our method to discern between molecular interactions can be used to test physical assumptions, and guide the interpretation of experiments and possibly the direction of new experiments. On top of this general method improvements, a robust parametrization of the underlying RNA-protein molecular interactions would consequently permit the modification and/or control of the capsid status from assembled to disassembled or vice versa, which are awaited features for further applications in nanomedicine.Our results show a structural 3D domain reconstruction of the RNA genome from viral secondary structures, and propose a way to control the quality of the model based on further microscopic information and/or modeling hypotheses. There are several routes and assumptions involved in the quality control of those 3D reconstructions. Here, we classify them in two main routes, namely, high-resolution microscopic information and novel computational/theoretical models. High-resolution microscopic information, such as cryoelectron microscopy ,49,50, pWe have presented a new computational method for the 3D reconstruction of the complete RNA genome of virus particles. The RNA genome of the STMV was tested systematically with our algorithm, where we demonstrated the possibility of generating reasonable conformations of the three-dimensional structure of the RNA genome with coarse-grained efficient models (1 CG bead ≈ 30 atoms per nucleotide). This method includes several features, such as the radius of gyration topological constraints, or diverse restraints applied to RNA stems. In addition, our method can be easily extended and combined with further experimental information and/or computational/theoretical models. We have further shown an example of additional microscopic analysis with the five-fold hairpins distribution disambiguation for the STMV, based on the additional X-ray structural information. Finally, we have also discussed examples on how to not only further test the novel microscopic information, but to combine it with theoretical and semi-empirical models to elucidate further mechanisms of virus capsid assembly, disassembly and RNA delivery.


As a result, we suppose that hole localization within the SSU can affect intersubunit rotation (ratcheting) and SSU head swiveling, in agreement with the scenario of electronic coordination of ribosome operation. We anticipate that our findings will improve the understanding of the translation process and advance molecular biology and medicine.Despite several decades of research, the physics underlying translation—protein synthesis at the ribosome—remains poorly studied. For instance, the mechanism coordinating various events occurring in distant parts of the ribosome is unknown. Very recently, we suggested that this allosteric mechanism could be based on the transport of electric charges (electron holes) along RNA molecules and localization of these charges in the functionally important areas; this assumption was justified using tRNA as an example. In this study, we turn to the ribosome and show computationally that holes can also efficiently migrate within the whole ribosomal small subunit (SSU). The potential sites of charge localization in SSU are revealed, and it is shown that most of them are located in the functionally important areas of the ribosome—intersubunit bridges, Fe Translation—protein synthesis at the ribosome—is necessary for life of all organisms and proceeds in similar way in all domains of life. Accordingly, understanding of this process is of great importance from both the fundamental and practical points of view. However, although the ribosome structure has been recently resolved in detail , the phyJ) to their neighbors so that an extended π-conjugated system is formed. It was also shown that hole localizes at certain sites of tRNA, inducing conformational changes of the latter. However, charge transport and localization within the very ribosome were not addressed.Electronic (as opposed to ionic) conductivity of nucleic acid molecules (DNA and RNA) is enabled by the π-stacking of their nucleobases, which can provide significant overlapping of the π-conjugated electron systems of the latter. Such π-stacking is often observed in DNA and helices (stems) of non-coding RNAs, e.g., tRNA and ribosomal RNA (rRNA) . Neverth4S4 cluster, the function of which is still unknown , wster see a, presumin EF–Tu ; we suggThe other two potentially important sites of localization are G1405 and G1416, which reside in h44—the longest single helix in SSU that stretches from the bottom of the head to the bottom of the body and forms several intersubunit bridges with LSU see d 23,44],4423,44]Like our previous studies of charge transport in RNA molecules ,24, this4S4 cluster using conventional electrochemical reactions and monitoring the fluorescence of the luminophore attached near the localization site. We expect that this fluorescence will be quenched when the cluster becomes reduced .Certainly, the suggested mechanism of electronic regulation of the ribosome operation is to be tested experimentally. For instance, photoluminescence quenching, EPR, electrochemiluminescence, and electrochemical studies capable of monitoring the charge carrier motion and localization, as well as mutational analysis, could be useful. A possible experiment could feature a luminophore attached to the rRNA in the vicinity of the one of the predicted hole localization sites see  and a mo4S4 clusters are incorporated [Interestingly, very recently, it was observed that in a rather different system—RNA-dependent RNA polymerase of the SARS-CoV-2 coronavirus, the culprit of continuing COVID-19 pandemic—two Ferporated . The rolrporated . We suggrporated ,22 and prporated . The posrporated ,20,21,374S4 cluster, and the pivot enabling conformational changes of SSU. We suppose that holes localization at these sites can be a part of the mechanism coordinating intersubunit rotation (ratcheting) and SSU head swiveling with other events occurring during translation. We anticipate that our findings will facilitate further studies of possible charge transport within the ribosome, improve the understanding of the physics underlying translation and advance the molecular biology and biophysics.We have shown that electron holes can efficiently migrate within the ribosomal SSU and localize at certain sites. Specifically, most of the nucleotides in the SSU have large hole transfer integrals with their neighbors, resulting in considerable transfer rates between them. Nonuniform energy landscape induces hole localization, and at least six of the potential localization sites are situated in the vicinity of the functionally important areas of the ribosome—several intersubunit bridges, the Fe


TUT4 and the closely related TUT7 are non-templated poly(U) polymerases required at different stages of development, and their mis-regulation or mutation has been linked to important cancer pathologies. While TUT4(7) interaction with its pre-miRNA targets has been characterized in detail, the molecular bases of the broader target recognition process are unclear. Here, we examine RNA binding by the ZnF domains of the protein. We show that TUT4(7) ZnF2 contains two distinct RNA binding surfaces that are used in the interaction with different RNA nucleobases in different targets, i.e that this small domain encodes diversity in TUT4(7) selectivity and molecular function. Interestingly and unlike other well-characterized CCHC ZnFs, ZnF2 is not physically coupled to the flanking ZnF3 and acts independently in miRNA recognition, while the remaining CCHC ZnF of TUT4(7), ZnF1, has lost its intrinsic RNA binding capability. Together, our data suggest that the ZnFs of TUT4(7) are independent units for RNA and, possibly, protein-protein interactions that underlay the protein’s functional flexibility and are likely to play an important role in building its interaction network. TUT4/Zcchc11  is a non-templated U-specific polymerase that, together with the related and partially redundant protein TUT7, plays a key role in organismal development. TUT4(7) is essential for oocyte development  and the TUT4(7) plays important roles in different stages in development and in adults these are linked to its uridylation of a broad range of RNA targets that differ in structure and sequence, including mRNAs  but alsoThe best studied function of TUT4(7) is the regulation of the biogenesis of the tumour suppressor Let-7 miRNA. TUT4(7) acts in combination with the protein adaptor Lin28 to recognize and poly(U)ridylate the Let-7 miRNA precursor, leading to its degradation by the exonuclease DIS3L2 ,12. TUT4In addition to recognizing pre-miRNA, hTUT4(7) has been shown to interact with and regulate poly-uridylate mRNAs(7) ,2,8,9, mWhile structural and molecular work from a number of groups has provided an account of TUT4(7) interactions with the hLin28 protein and the Let-7 pre-miRNA target, we have a limited understanding of how hTUT4(7) recognizes the diverse set of cognate RNAs. It seems likely that the single-stranded, sequence specific ZnF domains play a key role in this recognition. ZnF2 has been shown to recognize RNA in the poly-Uridylated targets in a very specific structural setting, but how ZnF1 and ZnF3 interact with the RNA targets is unclear. In this work, we ask what is the role of TUT4(7) ZnF domains in RNA recognition.15N-correlation spectrum of the domain displays 29 well-dispersed peaks. These correspond to 29 backbone amide resonances, which is the expected number for ZnF2, and indicates that the construct folds into a single, structured, conformation in solution  tail and increases processivity in pre-Let-7 poly-Uridylation. However, it is not clear whether this domain, which flanks the NTD2 catalytic domain, plays a wider role in recognizing and recruiting different RNA targets. Interestingly, ZnF2 directly engages in RNA recognition when NTD2 is bound to the UU di-nucleotide but not when a U1-derived RNA is bound instead. We therefore examined whether ZnF2 can fold and recognize RNA independently from NTD2, and what its base sequence specificity is. As a first step, we tested the fold and stability of the free ZnF2 using NMR. The solution . Then, w surface .d values in the micromolar range. As NMR operates at micromolar concentrations of protein, it is ideally suited to report on the interactions. Next, we investigated de novo the nucleobase preference of hTUT4 ZnF2 using NMR Scaffold Independent Analysis (SIA), a method that can be used to define the nucleobase preference of isolated RNA-binding domains at each of the bound positions [d; however, comparison of the shift of peaks at equivalent protein to RNA ratios can be used as a proxy to evaluate the bound protein molar fraction. This indicated that CUGA and CGUA both interact with the protein, but with significantly lower affinity than CGGA , in agreement with the SIA results .The resonance shifts in the NNNN titration indicate that the protein interacts with RNA with Kositions  (Supplemositions  (Supplem results . BecauseOne important question is whether the TUT4(7) ZnF2 interacts with the CGGA RNA and the UU RNA in the TUT7 complex, using the same surface. In addition to providing information on affinity and specificity, our NMR Chemical Shift Perturbation (CSP) data allow us to define the ZnF2 surface that mediates the interaction with the CGGA RNA, provided we can assign the individual NMR cross-peaks to the amide resonances of specific amino acids. In order to do this, we assigned the backbone resonances of ZnF2 using a standard set of NMR experiments  and mappInterestingly, while we have no molecular data on the interaction of TUT4(7)  in complex with a G-rich sequence, high resolution structural data and biophysical data exists on the interaction of double CCHC ZnFs and short G-rich sequences. In particular, both high resolution structural information and CSP data are available on the interaction between the ZnF di-domain of hLin28 and the pre-Let-7 RNA target sequence. In this di-domain unit, the second ZnF interacts with a four nucleotide stretch within a larger RNA, with extensive contacts made with two central Gs whose individual mutation results in a drop in the affinity of the interaction . We therTwo important questions are whether ZnF2 interaction with a short G-rich sequence recapitulates the one with a physiological RNA target(s) and, relatedly, whether the domain cooperates with hTUT4 ZnF1 and ZnF3 domains in this interaction. First, we explored the affinity and kinetics of ZnF2 interaction with the well-characterized Let7i miRNA and, compared them with those obtained for the interaction with a short RNA containing the SIA isolated sequence  using BiNext, we want to understand whether ZnF2 and ZnF3 cooperate to create a stronger or more long-lasting interaction. CCHC ZnF domains have been shown to recognize RNA as a di-domain unit. However, the linker between the two domains is typically of a few amino acids. TUT4(7) ZnF2 is separated from the neighbouring ZnF3 domain by a long (~ 40 amino acids) linker. Further, although we have previously shown that ZnF3 selectively binds G-containing RNAs , the rol15N-correlation NMR spectra of the ZnF2, ZnF3 and ZnF2-3 constructs  in the uridylation of Let-7 miRNA precursor molecules has been studied in detail, but it is unclear how the protein(s) would recognize other ssRNA targets, including a set of mature ss miRNA molecules regulating immune response, growth hormone and development . Here, wOur data show that hTUT4 ZnF1 and ZnF2 are independent folding units. However, while ZnF1 does not bind RNA, ZnF2 recognizes ssRNA with significant specificity. Importantly, ZnF2 does not recognize the U nucleobase that has been shown to make specific contacts with two amino acid side chains in a larger hTUT7 protein construct . These cd, and a higher affinity in the cell is likely reached by multiple synergistic interactions.Many of TUT4(7) G-rich targets, including its miRNA targets, are regulated by adding a single Uridine . In our in vivo. Such binding may take place and, for example, help orient the RNA in the larger TUT4 complex. Rather, the decoupling of the two domains indicates that, contrary to what is observed for hLin28, target recognition by hTUT4 ZnFs is mediated by binding of individual domains that recognize very short RNA sequences. This is consistent with the overall molecular context being more important for hTUT4-RNA than for hLin28-RNA recognition, and with hTUT4 recognizing a broad range of RNA targets.CCHC ZnF domains have been shown to recognize RNA sequences as part of di-ZnF structural units . AlthougOur data on hTUT4 ZnF domains, reveal a context-dependent RNA recognition mode for hTUT4(7) ZnF2 and lead to a target selection model where the individual CCHC ZnF domains of hTUT4(7) can play multiple roles by stabilizing the nascent poly(U) chain, recruiting G-rich targets and possibly orienting these targets in the larger protein complex. How the latter would take place will require high-resolution structural information on a complex between the large hTUT4(7) CM module and a G-rich RNA target. Importantly, ZnF2 is not coupled to ZnF3 in binding the miRNA target, and the affinity and kinetics of the interaction are typical of individual protein domain binding short RNA sequences, rather than of a CCHC ZnF di-domain. It seems likely that, as for many other RNA binding proteins, higher affinity is reached by multiple inter-molecular interactions and, in this context, the role of ZnF1, which does not bind RNA and yet has a conserved interaction surface, may be to mediate some of the complex’s protein-protein contacts. Overall, our data paint a picture of complexity, where the different CCHC ZnF domains of TUT4(7) play very different roles and contribute to creating a network of interactions that define the multi-faceted function of this protein. Dissecting the interaction network using functional and molecular studies in different settings will represent a key step of understanding this global role.6‐tag) followed by a cleavable tobacco etch virus (TEV) site and whose expression is under the control of a T7 promoter. Primers were designed to introduce the mutation F1360S/V1368V into CCHC-ZnF3. Point mutations were introduced into the construct by amplification of the plasmid using overlapping complementary primers with the mutation of interest inserted at the centre of the oligonucleotides. Following PCR amplification parent DNA was removed by DpnI digestion. The five constructs  were expressed and purified using the protocols below.hTUT4 zinc finger constructs CCHC‐ZnF1 (903‐931), CCHC‐ZnF2 (1293‐1321), CCHC‐ZnF2-3 (1293‐1382) and CCHC-ZnF3 (1354–1382)  were cloned from human cDNA into a pET‐M11 vector  to obtai15N and, if required, 13C-labelled protein samples, the protein was expressed in M9 minimal media containing respectively (15NH4)2SO4 as the only nitrogen source and 13C6‐D‐glucose as the only carbon source. E. coli BL21(DE3) cells were transformed with the pET-M11 relevant plasmid and grown overnight at 37°C. The overnight culture was diluted to an OD600 of 0.1 in fresh media and cells were grown to an OD600 of 0.6 before the temperature was reduced to 22°C and protein expression induced with 0.5 mM IPTG. Protein expression was overnight at 22°C, and cells were harvested by centrifugation and stored at ‐80°C.In order to obtain 2PO4, 10 mM Tris‐HCl pH 8.0, 8 M urea) and the lysate was centrifuged at ~7700 x g for 30 minutes. The supernatant was loaded on a Ni-NTA column and purified by immobilized metal ion affinity chromatography (IMAC). In more detail, 4 ml of Ni‐NTA resin per litre of cell culture was packed into a gravity flow column and equilibrated with 10 column volumes (CV) denaturing buffer. The clarified cell lysate was loaded onto the column and the flow through was collected and reloaded onto the column to enhance binding of the expressed protein. The protein was eluted with denaturing buffer pH 4.5 . The eluted protein was dialysed against 4 litres of equilibration buffer  to reduce the urea content in the buffer. TEV protease was then added to a final concentration of 2.5 μM to cleave the His6‐tag. The reaction mixture was dialysed for a further 16 hours for cleavage to take place. Both protease and cleaved tag were separated from the protein by a reverse IMAC column (Ni‐NTA). The cleavage reaction mixture was loaded into a gravity flow column packed with Ni‐NTA resin equilibrated with 10 mM Tris pH 8.0, 10 mM Imidazole, 200 mM NaCl, 2 mM β‐mercaptoethanol, 10 μM ZnCl2. The flow through was collected and reloaded onto the column for a second time and the flow through from the second column was concentrated using a Vivaspin concentrator and further purified using a Hiload 16/60 Superdex 75 gel filtration column. Fractions containing pure protein as assessed by SDS-PAGE were pooled and concentrated before being dialysed into a final buffer of 10 mM Tris‐HCl pH 7.4, 100 mM NaCl, 0.5 mM TCEP and 10 μM ZnCl2 and concentrated in a Vivaspin to the final storage concentration. Protein concentration was determined from the absorbance at 280 nm and the molecular weights of the different constructs confirmed by mass spectrometry.Cells were sonicated in denaturing buffer pH 8.0  were purchased from Horizon Discovery, and deprotected as advised. RNA concentration was determined from the 260 nm absorbance of the sample.RNA oligonucleotides  hTUT4 samples were prepared as described and concentrated to ~200 μM. NMR experiments were recorded at 25°C using a Bruker Avance NMR spectrometer operating at 600 MHz 1H frequency. Measurements were made in 90% H2O/10% D2O. A 13C15N labelled hTUT4 CCHC‐ZnF2 sample was used to acquire a HNCA, HN(CO)CA [2 buffer conditions. Data were processed using Topspin 3.7 and assignment of protein resonances was done on CcpNmr analysis [Labelled (HN(CO)CA , HNCACB HN(CO)CA , HN(CO)CHN(CO)CA  and HNCOanalysis .15N‐labelled hTUT4 ZnF2-3 sample to obtain T1 and T2 values. Experiments were performed on a Varian Inova NMR spectrometer operating at 800 MHz 1H frequency. 20, 200, 400, 700, 1000, 1500 ms delays and 10, 20, 40, 70, 100, 150 ms delays were used for T1 and T2 measurements, respectively. Standard relaxation values were determined for each residue by fitting an exponential decay to the peak volume over the course of the data collected. Residues were excluded where overlap in the signals prevented accurate measurement on peak volume.Relaxation experiments  were recThe hTUT4 ZnF2 peaks used in the di-domain relaxation to calculate the correlation time in that domain are C1295, R1296, V1297, G1299, G1302, H1303, Y1304, M1305, D1307, K1310 and R1311 . In the hTUT4 ZnF3 case the residues used were C1360, F1361, I1362, G1364, D1365, A1366, G1367 and H1368 .2 buffer conditions at 25°C. 15N HSQC NMR spectra were recorded during the titration of a 50 μM protein sample with increasing at each concentration of the different RNAs . Data were processed using Topspin 3.7 (Bruker) and a value for the 1H and 15N Chemical Shift changes was calculated for each residue applying the following equation: ^2-4x) equation. Kd were calculated for individual residues and then merged to obtain the global Kdan average of all individual Kd per residue [The K residue .2, sodium azide and RNasin Plus. This was aliquoted out into 180 μM samples and RNA pools added where required at a protein to RNA ratio of 1 to 4. Samples were transferred to 3 mm NMR tubes and placed in a rack. Samples were automatically loaded into the spectrometer using the Bruker Sample Jet and 1H‐15N SOFAST‐HMQC spectra were recorded for each of the samples. NMR data were recorded on a Bruker Avance NMR spectrometer at 700 MHz. Spectra were processed using NmrPipe and data analysed using the weighted average chemical shift changes (for HN and N) with each RNA pool for a subset of shifting residues being measured and normalized with respect to the highest shift. The average normalized value across the subset of peaks is taken to give a comparative score of binding preference.70 μM hTUT4 ZnF2 stock was prepared in 100 mM NaCl, 10 mM Tris pH 7.4 buffer containing 0.5 mM TCEP, 10 μM ZnCl2, 0.5 mg/ml bovine serum albumin (BSA) on an Octet Red 96 instruments  operating at 25°C. 5ʹ-Biotinylated 5ʹ-btnUUUGGA-3ʹ and Let7i miRNA variations  (1 ng μL−1 solutions) were immobilized on streptavidin-coated biosensors and incubated with varying concentrations of hTUT4 ZnF2 and ZnF2-3 (1–8 μM). Equilibrium and kinetic parameters were extracted by the analysis of the observed association rate constant versus ligand concentration using the program Anabel, as described [BLI experiments were performed in 10 mM Tris pH 7.4, 100 mM NaCl, 0.5 mM TCEP, 10 μM ZnClescribed .All sequence alignments were carried out using the programs T-Coffe  and EMBOClick here for additional data file.


The approach and understanding developed represents a new roadmap for design of supramolecular drugs to target RNA structural motifs across biology and nucleic acid nanoscience.RNA targeting is an exciting frontier for drug design. Intriguing targets include functional RNA structures in structurally-conserved untranslated regions (UTRs) of many lethal viruses. However, computational docking screens, valuable in protein structure targeting, fail for inherently flexible RNA. Herein we harness MD simulations with Markov state modeling to enable nanosize metallo-supramolecular cylinders to explore the dynamic RNA conformational landscape of HIV-1 TAR untranslated region RNA (representative for many viruses) replicating experimental observations. These cylinders are exciting as they have unprecedented nucleic acid binding and are the first supramolecular helicates shown to have anti-viral activity  MD simulations and Markov state modeling explore induced fit binding of metallo-helicates to bulges in dynamic TAR RNA, reproduce experimental data, show how RNA conformational flexibility is reduced, and give mechanistic insight into insertion. Infectious disease represents one of the greatest current threats to humans as demonstrated by the frequency of recent lethal viral outbreaks: 4 out of the 10 greatest threats identified by the World Health Organization are viral related. While vaccines offer long-term eradication or suppression, they are bespoke to the disease and their development and implementation across a global population is slow. There is therefore a pressing need for a new generation of drugs that could hold an emerging disease at bay while bespoke solutions are created; broad-acting anti-viral agents having different molecular designs and molecular targets, offering a diverse platform that maximizes the potential preventative effect against new diseases.1–5  of the infectious agents are of particular interest with RNA increasingly recognized as a druggable target.6,7 The rapid emergence of infections, and subsequent rapid evolution of viral genetic sequences, means that drugs that target a specific sequence are unsuitable. However, agents that target a specific nucleic-acid structure could be much more interesting. In particular, the untranslated regions (UTR) at both 3′ and 5′ ends of many viral genomes are not only highly structured but often share common structural elements7–10 that are functionally essential and so conserved as the virus evolves (drifts) genetically.10,11 Indeed, structure-affecting mutations in the UTR have been used to create live attenuated or inactivated vaccine strains.12,13 UTRs have been mostly studied in RNA viruses, such as HIVs,7,10,14,15 coronaviruses,16–18 dengue,11,12,19,20 zika21 and other flaviviruses22 and, in every studied case, functional involvement of the UTR has been shown in either initiation of replication16,19 (by recruiting proteins or by direct interaction with the ribosome) or regulation of the replication cycle. The most studied example is the retrovirus HIV-1 which contains a bulge in the first stem loop of the 5′ UTR of its RNA genome,23–30 the structure and dynamics of which are crucial for initiation of viral replication. Similar bulges are found in UTRs of other RNA viruses including coronaviruses and SARS-COV-2. These UTR structures represent exciting potential anti-viral targets.Modern drug research tends to focus primarily on the protein targets as the effectors of disease. However, to target broad classes of disease, drugs that target the nucleic acids4,31–34 The molecular structural information needed for such recognition is not yet available for most viruses, and crystal structures of drugs bound to RNA structures are rare (and not necessarily representative); new molecular-level understanding of such binding is a critical need. Structural studies on RNA are further complicated by the inherent flexibility of RNA molecules, which requires an understanding of their dynamics not just their ground state conformation. Consequently, simple molecular docking will not suffice; by contrast molecular dynamics potentially allows the energy landscape and structural flexibility to be probed. Herein we employ molecular dynamics to explore in detail, for the first time, a nano-scale drug inserting into a bulge in a UTR viral RNA, replicating experimental observations and gaining fundamental new insight into the dynamics of the RNA and of the drug entry process; crucial intelligence to inform design of new UTR-structure-targeting drugs. The nano-scale drugs studied are supramolecular cylinders, which not only have unprecedented RNA bulge-binding ability but are the first in class of metallo-supramolecular architectures to show potent anti-viral activity in cellular assays.35 There is a growing interest in the application of metallo-supramolecular architectures in biology.36–41Structure-based recognition of RNA (and DNA) by drugs is still very much in its infancy.42,43 It is also unique in threading through an RNA cavity, interacting with all of the internal structure. These cylinders also bind bulge structures in RNA, prevent TAT protein from recognizing the binding site in the TAR sequence of HIV35,44 and arrest HIV replication in mammalian cells.35 The strong evidence of binding and in-cell efficacy, makes this an ideal test-bed to investigate whether molecular dynamics simulations can identify the processes that underpin the kinetics of targeting highly flexible RNA strands. At the same time, it provides a suitable challenging size of drug, and one with large, nanoscale, 3-dimensional molecular surfaces whose match and strong binding to the 3D shape of RNA structural motifs should collapse the RNA's conformational landscape to a non-functional (impotent) state. The cylinder exists in two enantiomeric forms, both of which bind RNA bulges. Experimental X-ray crystal structures are also available for unbound cylinders;45,46 the calculated DFT structures herein are almost identical.As a suitable UTR structure for our studies we chose HIV-1 TAR RNA which is both experimentally well described and representative of wider viral UTR structural motifs. As a drug we chose a nanoscale metallo-supramolecular cylinder because it is unique as a nano-drug that has previously been crystallographically characterised when bound within an RNA cavity (a perfect three-way junction (3WJ)) .42,43 It47–52 been found to be unsatisfactory – over such timescales they induced structures not seen experimentally. With longer simulations being available, the conformational space sampled can deviate further from the absolute minimum energy point and explore the importance of non-covalent interaction dynamics as pi-stacking and hydrogen bonding.53–57 However new forcefields50–52 have become available and we show now that the Rochester-Mathews forcefield51,53 can be used to simulate RNA over long timescales, reproducibly, not only for free RNA but for drug–bound complexes. The Rochester-Mathews forcefield is publicly available giving it the potential to be accessed and implemented by all. It uses the same underpinning level of DFT theory as that applied to metal-containing cylinder coordination compounds creating an overall consistency. Moreover, there are ways to accurately model NMR ensembles of RNA structures without the need of extensive MD simulations.58 Collectively we accumulated over 200 μs of simulated time; such long and data-rich simulations on a flexible RNA system, brought new challenges in analysis. We address these by applying Markov state modeling59,60 to the problem and show that this enables us to identify stable and metastable conformations among the millions of frames.For multi-microsecond simulations, classical MD forcefields describing the dynamics of both RNA and DNA have until very recently60 and Markov State Modelling (MSM). This involves reducing the dimensionality by choosing appropriate features of the simulation and identifying macrostates of each simulations using MSM and extracting those metastable structures with Perron-cluster cluster analysis (PCCA). Those extracted structures and the whole simulation are also presented in the Leontis–Westholf61 nomenclature using Barnaba.62 A detailed explanation of this workflow is included in ESI.‡Overall we have performed 123 simulations of at least 1 μs and up to 10 μs, overall ∼200 μs including several shorter runs with varying initial conditions. To analyse the vast volume of data, over 200 000 000 coordinate frames, we employ the PyEmma workflow50–52 to conserve structural features of viral stem-loop RNAs , and to establish the effectiveness of our approach to analysis, we first explored the dynamics of poliovirus stem loop (pdb: 2GRW)63 coxsackievirus stem loop (pdb: 1RFR)64 and HIV2-TAR (pdb: 1AJU)65 RNA with no bound drugs. The simulations reliably reproduced NMR observations for the stem loops (including regions of non-Watson–Crick pairing) and the predicted effect of a small bound ligand on the HIV2 TAR. Indeed for poliovirus stem-loop, the MD simulations reveal and explain features that are observed in the NMR structural data, but have not previously been satisfactorily captured in the deposited conformations, and for HIV2-TAR shows how the ligand-free RNA structure deviates from the conformation of the bound state, demonstrating the effect a binding molecule can have on an RNA structure: a detailed analysis of these free RNA simulations is included in ESI.‡To confirm the ability of the forcefield1ANR) structures. In an effort to avoid introducing biases and acceleration methods to the simulation we chose to explore the conformation landscape by starting simulations from different local minima as described in the original HIV-1 TAR NMR solution structure.66 There are 20 NMR solutions proposed and we started from five such minima . For each of these higher energy solutions a 2 μs simulation retained the characteristics consistent with the NMR structure and did not deviate into unnatural (loosely bound) conformations. From each starting point similar features can be observed as the simulation proceeds which indicates that the forcefield can reproduce transitions within the landscape of a few μs per solution. These unbiased MD simulations capture the conformational changes of the RNA across the energy landscape for the first time, and clearly reveal the variation possible in the RNA structure and the range of conformations sampled (and which a drug could encounter and sample). Importantly, time-lagged independent component analysis (TICA) of the trajectories  creating a transient folding up of the second stem. Such a folding was not observed in the HIV-2 TAR simulation.cis or trans WC/Hoogsteen to A35.The loop region is characterised by limited stacking between bases and common WC pairing between C30 and G34. Transient non-WC pairing can include C30 Examining the runs starting from the different local energy minima; the first simulation starting from 1anr1 identified 3 distinct states, that can be recognised even by the PCA analysis. All are energetically and conformationally close together as seen by the RMSD and ERMSD. PCCA analysis shows one to be in much higher occupancy, clearly the ground state. A second simulation also starting from 1anr1 sampled a wider conformational space. Base pairing of stems was retained although stacking between C19:G43 and A20:U21 was not, although it is observed in the NMR. After that, stacking does continue all the way to the loop. At the loop a few different conformations were sampled that mostly gave rise to the different MSM states identified. C30 base pairs with either G33 or G34. In the bulge region U40 is stacked strongly with C39 but not always to C41 and transient, short lived pairing takes place between all bulge residues and either of C39 and U40, with pairing types including both sugar and Hoogsteen edges as well as in the trans position. MSM analysis gave 6 different states.The simulation starting from the third NMR solution, 1anr3, yielded 5 local minima in the TiCA projections and CK test allowed for 5 states in MSM analysis. Overall, stacking and pairing throughout the stems is conserved and transient pairing within the bulge region is similar to that of the previous run. Most importantly the second state is very reminiscent of the ground state.et al.67 In that work, 3 nucleotide bulges are observed to adopt multiple conformations; we replicate these multiple conformations in our simulations  has been discussed by Orlovsky The seventh, 1anr7, and twelfth, 1anr12, structures are quite different from the ground state and this brings challenges for the simulation: specifically, the loss of A helix structures which is characterised by the overall elongation of G17 to G33 distance can be testing to any forcefield. Nevertheless, starting from 1anr7, the stacking and pairing remains consistent. PCA identified 2 states whereas TiCA suggested 6 states and the CK test is also passed with 6 states. The first 4 states are reminiscent of the ground state with different loop configurations, namely sugar to Hoogsteen between C30 and A35, or less often trans WC to Hoogsteen. In the other two states, U25, which generally points outside the bulge can create temporary long-range interactions with loop residue G33.Starting from 1anr12, which is also very elongated with a sharp backbone kink in the bulge area, also retrieved most of the properties of the ground state. Pairing and stacking remain consistent for the stems. In the loop the common C30 to G34 pairing is stable along with a transient Hoogsteen to sugar between A35 and C30. In the bulge region stacking between C39 and U40 is strong and most of the transient non traditional base pairings are also seen. PCA revealed 2 states whereas TiCA revealed 5.The results demonstrate that the forcefield can satisfactorily retain characteristics of the structure as described by the NMR experimental constrains.1anr structure, there are some TAR RNA structures with various different bound drugs, and so for comparison we also explored as a starting point one such structure 68 from which we had removed the drug. The structure, after removing the ligand, has some differences with the 1anr structure: pairing on the stems is the same, but stacking is disturbed before the bulge, probably since U23 is WC paired with A27.In addition to the unbound 52 behaved well for every case of RNA molecular dynamics, even in cases outside the ground state of the structure in question.When using this as the starting point for a 2 μs simulation, the loop folded back onto the bulge (from which the ligand had been removed) forming interactions from U23 and C24 to A35, and the stem remained folded for much of the simulation. The bulge stacking did not return to the transient pairings seen in the earlier simulations. PCA analysis of the simulation revealed 3 states and TiCA 6, which was also passed the CK test on with MSM with the sixth state being ground state of this run. The simulation demonstrates how ligand binding can modify the structure and dynamics of the TAR RNA and again highlights that docking, while a useful guide, may miss key features and opportunities. The Rochester forcefield3,52 We initially undertook simple docking calculations as described in methods using all 20 structures from pdb; 1anr TAR RNA NMR study. The results are dominated by different forms of bulge region binding. While the two enantiomers do show slightly different binding energies, the Autodock Vina69 as other docking software  as other docking software tends to underestimate the electrostatic contribution when a charged molecule is involved. Nevertheless the docking scores are high compared to other small molecule drugs assessed by this method reflecting the larger available surface of the cylinder.Disney has recently used docking to screen libraries of small molecules binding to RNAs including TAR.It is interesting to compare the results of docking with overall results of subsequent MD simulations. In particular in the MD simulations, capping of the open terminal bases is a transient, but relatively stable (more than 2 μs) location seen with both enantiomers. Although only a local minimum in the interaction of cylinders with TAR it highlights the limitations of docking in targeting nucleic acids because, across all 20 NMR solutions of TAR RNA, the terminal bases are coplanar only in one (the ninth). Consequently only in this structure solution does the docking reveal the end capping as a potential binding site. So docking outcomes are constrained by the rigid RNA structure(s) used in the docking, whereas in reality – as we shall see – RNAs are highly fluxional and dynamic molecules that access much structural space. Thus while such simple docking studies are valuable for high throughput screening they might be more suited to small molecules where the molecule is less likely to have a major effect on RNA conformation. For the larger cylinders the size of the binding surface means that induced conformational change is more likely and so more sophisticated MD can offer greater insight into the interaction. Crucially, while the docking showed bulge region binding, bulge insertion by the cylinder was not observed.1ANR solutions examined earlier.To examine the interaction between TAR and the cylinders, simulations (112) started with the cylinder  in the simulations' timescale. Consequently a single simulation would fail to explore all binding sites and conformations. Instead we take the quite different approach of using multiple simulations (1–10 μs) from different starting points which allows the cylinder to explore a much greater range of RNA conformations and to encounter multiple potential binding sites. By combining this with Markov state modelling analysis we are now able to explore effectively the dynamic conformational landscape of the TAR RNA – cylinder complex.en route, of which some represent local minima with longer residence times (though still transient) and are identified from the MSM analysis  reproduces this binding as captured by the crystal structure we extracted the G17:C45 bases and the cylinder from a frame of the longest lived position and we then optimised that structure at the ssb-d-D3/LANL2DZ ) and superimposed it on the binding mode extracted from a crystal structure. The overlap (Often the cylinder (both enantiomers) found a local minimum, which it occupied for at least 1 μs at a time, and in which it capped the terminal G17:C45 bases . Some RN overlap  is extreM enantiomer on average is longer than for the P implying that the M enantiomer may have a higher affinity for the grooves although the kinetics were not adequately sampled to quantify difference.The cylinder is commonly observed exploring the RNA grooves, primarily the groove of the first stem. The residence time for the The cylinder can take advantage of unpaired open bases of the loop and interact transiently there , but this is less commonly observed in the simulation compared to other locations. Loops are a common feature in RNA structures  but seem not to be a particular target for the cylinder, consistent with our experimental observations.35,44), with M and P being very similar in their preference for this location. RNA conformations that involve the loop bridging to the bulge (U25) can be stabilised for longer (compared to free TAR), with the cylinder sitting on top of the bridge or mediating stacking. In the absence of the bridge, the cylinder can also sit between the bulge and the opposite RNA strand, in a position in which it opens up the base pairing protecting the TAT binding site. In the case that the cylinder sits on the bulge nucleotides, it stabilises the transient base pairing and dislocates the counter ions that would normally reside there which leads to an overall elongated structure of the RNA with minimal helicity.The cylinder is most frequently found around or on the bulge  in the s72,73 Given that the bulge-area is the most frequent location for the cylinder prior to bulge-insertion, it seems likely that this region could also be a preferred area of binding for other dinuclear complexes that cannot insert inside the bulge; for example differently shaped metallo-helices have been reported to not remain bound to TAR in electrophoresis,74 in contrast to the bulge-inserting cylinders herein,35,44 and might be more loosely associated outside the bulge.In this context it is noteworthy that Keene and Collins have explored the binding of a dinuclear ruthenium polypyridyl agent (but of quite different shape to the cylinders) to a TAR-like RNA and proposed that it might bind around the groove near the bulge.M and P enantiomers, insertion into the bulge is observed; once in the bulge the cylinder is strongly bound and remains there. In this unique binding mode, the cylinder sits in a V-shaped cleft  and C39–G26. The cylinder starts to slide around placing its pyridyls into the bulge; these pyridyls initially encounter the sugar of U23. U25 and C24 swing back and forth with U25 also encountering the pyridyls and transiently stacking with pyridyls as does A22. The crucial point of insertion involves the cylinder stacked with G26–C39, twisting around and inserting through the centre of the bulge . It doesThe MSM analysis of this entry process shows just two principal states; once the cylinder has moved from its location just outside and starts to open and enter the bulge, the energy landscape drops rapidly down into the final position where the cylinder is fully inserted and where it remains .P enantiomer from the same starting position ]2+, which doesn't intercalate into duplex RNA, can bind at RNA mismatch sites,75 where it is proposed to do so by insertion, with extrusion of the mispaired bases. The organic intercalator ethidium has been proposed to bind one-base bulges in RNA,76 and metal complexes bearing a ‘phi’ intercalator suggested to bind near the TAR bulge from cleavage experiments, though that is not yet well understood at a structural level.77–80 This insertion of a three-dimensional structure represents a unique and exciting approach to target RNA structures.This bulge insertion is a fascinating illustration of how a three dimensional nano-size agent might target the interior of an RNA structural feature, not by hydrogen bonding to the bases but rather by using its external pi-surfaces to recognize the surfaces inside the structure. To that extent the structure resembles a three-dimensional version of intercalation, and in that context it is notable that Barton has shown that the ‘light-switch’ intercalator [Ru(bpy)81,82The simulations suggest that the binding interaction between the cylinder and the TAR-RNA should be characterised as an “induced fit” interaction, meaning that the cylinder does not recognise the bulge cavity in the traditional lock-key manner but rather it induces the precise conformation of the RNA. This complicates the free energy landscape estimation. Although we do get an idea of the landscape using TiCA and PCA we do not believe that the space is sufficiently sampled and therefore MSM probabilities only reflect the sampled space. Mmpbsa techniques cannot be used as removing the cylinder from the final complex exposes a large hydrophobic cavity and an RNA structure that is not in a minimum. Therefore in this paper we have focused on the kinetics and mechanics of the binding process and not on the free energy estimation of the binding. However, in other systems, metadynamics and transition path sampling (TPS) have previously been applied to study the interaction of metal complexes with nucleic acids and proteins.84 becke97-d,85 and TPSSh86 with D3 dispersion correction87 for the first two and D3BJ for the last with of Def2-SVP basis set. The optimisation was performed under tight driver criteria and increased grid to xfine settings for convergence. Partial charge distribution on atomic positions was calculated with the ESP module under overall restrain of charge. Visualisation of the charge distribution at the surface was done in VMD 1.9.2 . Exhaustiveness was set to 1000. Additional docking to just the terminal bases, specifying the docking box to the first 3 base pairs showed that only 2 out of the 20 solutions allowed for capping-mode docking.Autodock vina89 that generated parameters for the metal centres at the wB97XD9/6-31G*86 level of theory using Gaussian09 (ref. http://parmed.github.io/ParmEd/html/index.html).Parametrisation of supramolecular cylinder: already DFT optimised geometries of the cylinders were split into 5 residues  that were fed to MCPB.pyn09 ref.  as well 66 was achieved with pdb2gmx program of GROMACS 2019.2  were analysed to remove periodic boundary conditions and rotations using gromacs' trjconv program. After removing the water the trajectories were analysed with pyemma2.5.6 and pyemma 2.5.7,We also explored simulations for the ruthenium cylinder  in place of the iron cylinder. The ruthenium cylinder behaved analogously in its binding, though its movement was slower due to the increased molecular mass.60 The workflow involves principal component analysis, time dependent component analysis, and Markov state modeling and Perron cluster cluster analysis.To analyse the simulations and identify different micro-states on the energy landscape of each run, we followed the Pyemma workflow.To identify the best features to apply the workflow to, we explored a variety of potential different features to see which best captured the kinetic variance that occurred during the simulations:1. Position of centre of mass (COM) of each residue is a low dimensional and relatively efficient way to capture different states, including simulations that involve one or more cylinders.2. Taking advantage of the fact that each residue has an atom named N3, which is away from the backbone, we created a matrix of distances between these N3 atoms, which although high in dimensionality captures nearly all the kinetic variance. For the cylinder simulations, we also added the distances of the metal ions (Fe or Ru) and the resulting matrix can capture adequately the kinetics of the system during the simulation.3. The distances between the phosphorus atoms in the backbone.Of these approaches 2 proved the most useful and was applied to all the simulations.http://www.emma-project.org/latest/index.html. Lag times for MSM model were chosen from the convergence at timescales of identified processes. Only models that used all of the states and could pass the Chapman–Kolmogorov test were continued to Perron-cluster cluster analysis (PCCA)which led to extraction of states with certain probability and structure in pdb format. Not all simulations were long enough to produce an appropriate Markov state model, and it should be noted that the Markov state models as used here are meant to describe or sum up the particular simulations and not the whole system.For each simulation, Principal Component Analysis (PCA) was carried and the projections between the first 4 PCs are plotted, followed by time-lagged independent component analysis (TICA) for lag times 1 to 5000 steps. The lag time for which the fewer number of TICA dimensions were necessary to capture 95% of the kinetic variance was chosen for further analysis. The number of clusters was chosen by examining the convergence with regards to VAMP2 as described the original paper and 62 all long production molecular dynamics runs, as well as states identified by PCCA, were analysed using barnaba resulting in 2D Leontis/Westhof classification61 of base interactions as well as E-RMSD as defined by barnaba software, RMSD and J-couplings.The extracted state and the full length of the simulation were analysed with Barnaba:This study provides an unprecedented platform to inform design of agents that target different important RNA structural motifs found in nucleic acid nanoscience and biology, such as this bulge cavity present in the UTR of many different viruses. We show that MD simulations, in conjunction with Markov state modeling, allow the dynamic conformational landscape of RNA to be probed and thus different and more relevant binding modes and capabilities of a potential drug to be identified; by contrast, docking to rigid RNA structures is not sufficient to guide such drug designs. The simulations provide crucial new information, not readily accessible by experiment: they show insertion of the cylinders into the cavity of the RNA bulge in a similar binding to that seen for RNA 3-way junctions; they not only provide insight into the ultimate bound structure but also its wider effect on RNA conformation reducing the RNA conformational flexibility once the cavity is bound; and, for the first time, they provide insight about the molecular mechanism through which a drug might enter a cavity in the RNA UTR, involving stacking on and sliding down bases and base pairs. Together these new molecular insights and the combined modelling and analysis approaches that have enabled them and can be more widely applied, will transform understanding of how to create supramolecular drugs that insert effectively into RNA cavities and can guide new designs against a spectrum of critical RNA viruses that threaten human well-being.DFT calculations, MD simulations and MSM analyses were designed and undertaken by LM. MJH conceived the project, which was supervised by MJH and IS. All authors analysed the data and discussed the results, and MJH and LM drafted the paper. All authors commented on the manuscript and contributed to the final version of the manuscript.There are no conflicts to declare.SC-012-D1SC00933H-s001SC-012-D1SC00933H-s002SC-012-D1SC00933H-s003SC-012-D1SC00933H-s004SC-012-D1SC00933H-s005