Fused in sarcoma (FUS) is an abundant RNA-binding protein, which drives phase separation of cellular condensates and plays multiple roles in RNA regulation. The RNA-binding ability of FUS protein is crucial to its cellular function. Here, our molecular simulation study on the FUS–RNA complex provides atomic resolution insights into the observations from biochemical studies and also illuminates our understanding of molecular driving forces that mediate the structure, stability, and interaction of the RNA recognition motif (RRM) and RGG domains of FUS with a stem–loop junction RNA. We observe clear cooperativity and division of labor among the ordered (RRM) and disordered domains (RGG1 and RGG2) of FUS that leads to an organized and tighter RNA binding. Irrespective of the length of RGG2, the RGG2–RNA interaction is confined to the stem–loop junction and the proximal stem regions. On the other hand, the RGG1 interactions are primarily with the longer RNA stem. We find that the C terminus of RRM, which make up the “boundary residues” that connect the folded RRM with the long disordered RGG2 stretch of the protein, plays a critical role in FUS–RNA binding. Our study provides high-resolution molecular insights into the FUS–RNA interactions and forms the basis for understanding the molecular origins of full-length FUS interaction with RNA. FET family of RNA-binding proteins (RBPs) aggregate in amyotrophic lateral sclerosis (ALS) and frontotemporal lobe degeneration (FTLD) , 2, two in vitro, where individual components can be purified and added to form one or few component-based reconstituted condensates, (ii) ex cellulo, where purified scaffold proteins are added to the cellular extract to form complex condensates that are similarly complex to the cellular condensates, and (iii) in cellulo, these are the physiological condensates that often contain many different proteins and RNA.Besides regulating RNA metabolism, FUS is also an important protein responsible for the formation of functionally important biomolecular condensates, which are membrane-less assemblies of biomolecules (proteins and protein–nucleic acid mixtures) formed because of their demixing from surrounding plasm by liquid–liquid phase separations and are now identified in all major compartments of cells—nucleus, cytoplasm, and mitochondria , 19. Nucet al.  long protein comprising a low-complexity region enriched with serine, tyrosine, glycine, and glutamine residues (SYGQ) at its N-terminal (1–165 AA), ordered RNA recognition motif , and zinc finger (419–454 AA) domains, separated by three Arg–Gly–Gly-rich RGG  domains. The region 166 to 269 AA can be further classified into a G-rich (166–222 AA) and RG/RGG-rich (223–268 AA) region; alternatively, the entire region from 166 to 269 is also called RGG1. The nuclear export signal (269–280 AA) and C-terminal PY-nuclear localization signal (502–526 AA) regions help in their cytoplasmic and nuclear localization . The RNA-binding pocket includes the surfaces of β-sheets 1, 2, and 3, the α1–β2 hairpin loop  conserved in the FET family proteins, the β2–β3 loop, and the C-terminal helical turn. A recent advanced sampling study of RRM with a 12mer ssRNA has established the importance of loop dynamics for RNA binding (β-sheet surface (β3) and the longer β-hairpin connecting α1 and β2, which is unique and distinct from a canonical RRM  . This study claims a shape specificity for the RRM domain and identifies a consensus motif of “NYNY”  sequence in the single-stranded loop of the stem–loop RNA as the recognition motif. The ZnF domain is another ordered nucleic acid–binding domain in FUS, which shows specificity for a GGU motif. The NMR structure of the ZnF domain in complex with a 5mer RNA of sequence UGGUG has been solved by Loughlin et al.  by the RRM–RGG2–ZnF construct of FUS expressing both shape and sequence specificities.Several studies have identified sequence and structural motifs in RNA that are recognized by FUS , 34. TheFig. 1C)  and a G-C) . A recen  and ZnF (>175 μM), show very weak affinity individually. Among the three disordered RGG regions, the RGG1 with 3 μM is the strongest, followed by RGG3 with 9 μM and RGG2 with 61 μM. However, when the two weak binding domains RRM and RGG2 are present together, the binding affinity shows a drastic increase to 2.5 μM. This is further enhanced to 1.9 μM when RGG1 is also included. We assume that such a major jump in binding affinity among the individual  and combined RRM–RGG2 (2.5 μM) constructs requires communication between the folded and disordered regions to bind RNA. Since the affinity data are biochemical in nature, structural binding geometry and interface behavior at the molecular scales are not apparent. Our study analyzes the interaction of the RRM domain with RNA and explores this hypothesis of a synergistic RNA-binding mechanism between RRM and RGG2 through all-atom MD simulations. Though the importance of FUS–RNA interaction has been well elucidated, the details of molecular interactions at the single-molecule level are still lacking. In this context, our study finds merit in exploring the characteristics of FUS–RNA interaction and also from the perspective of a varying number of RGG repeats. Also, given the recent findings establishing that the RGG regions interact with LCD in a condensate  and the RNA hairpin (B) remained within 2 Å for the initial 40 ns and starts fluctuating thereafter. After 60 ns, the minimum distance increases dramatically, indicating the dissociation of RNA from the core RRM  to 1021 Å3. Hence, it is evident that the core RRM is insufficient to bind RNA and that the residues beyond 368 have a crucial function. Therefore, despite the ambiguity between studies, we consider the RRM domain boundary at 377 AA to be the minimal region required to bind RNA. In addition, the selection of 377 AA is consistent with previous studies including the NMR structure solution investigation by Loughlin et al.  in triplicates, for 1 μs per replica, and all three simulations show a similar unstable behavior. The individual all-atom RMSDs for RRM in all replicates are shown in the bottom panel of Fig. S3A. Our analyses show that the structure of RRM domain itself is stable with RMSD variations of less than 6 Å in all three replicates. By calculating the RMSD of the RNA while superposing the RRM, the nature of RNA binding with respect to the stable RRM domain can be obtained. This RMSD  indicates the stability of the binding orientation of RNA relative to RRM, and a substantial variation up to 25 Å indicates that the binding of RNA is dynamic and unstable in all the three replicates. The distance between the center of mass (com) of RRM and RNA was monitored and reported in the upper panel of Fig. S3B. Variations of ∼5 Å (22.5 ± 5 Å) suggest a weak/flexible RNA binding. Though the RMSD and com–com distances indicate that the RNA binding is unstable, the minimum distance between the RRM surface and RNA hairpin is less than 3 Å  indicating that the recognition motif remains in contact with the RRM. Even though one of the three replicates show fluctuations in this minimum distance, at the end of the 1 μs simulation, the distance reduces up to 2.5 Å indicating that the interaction of FUS with the recognition motif of RNA is regained. In order to identify the specific regions of RRM interacting significantly with the RNA, we plotted a matrix of distances between every pair of residues in RRM and RNA averaged over the last 100 ns. In Fig. S3C, the interatomic distances of the three replicates are shown, and these distances decrease on a red to blue scale with brighter intensities representing tighter binding. Though we observe widely different dynamics of RNA, we also note from the average of all three replicates in Figure 2C that the distances between the RNA hairpin and specific regions of FUS like the β-sheet surfaces, KK loop, and C-terminal helix are well preserved with the NMR structure .Following the RRM core, we simulated the RRM–RNA complex  the β2–β3 loop with the AUUC motif, (iii) the KK loop with the major groove of the stem–loop junction, and (iv) the C-terminal helical turn with the RNA backbone (D). Consequently, the repositioning of RNA also disrupts interactions with the other regions of FUS. The interaction of the AUUC recognition motif with various regions of FUS was monitored, and the interacting residues with the lifetime of each interaction are listed in RRM even after simulations, albeit the lifetimes of few of these interactions like Arg328 and Arg372 are lesser. Particularly, the interactions of adenine and cytosine at the first and fourth positions of the recognition motif are lesser and varies from the NMR structure indicating a change in binding orientation of the RNA relative to the RRM. We report the variations in π–stacking or π–cation interactions with the RNA bases, the interacting pairs from the initial complex do not have very tight interactions. Altogether, the RNA binds weakly with the RRM domain even though the RNA motif AUUC demonstrates multiple strong contacts with the β-sheets of the RRM domain. Particularly, the boundary residues making up the C-terminal helix serve a crucial role in retaining the RNA close to the RRM domain.Structurally, the interaction of RNA with RRM can be classified based on the interacting regions as (i) the surfaces of backbone . The supbackbone D. ConseqA. The minimum distance between the β-sheet surface of RRM and the RNA hairpin during the 1 μs simulation of the mutant RRM–RNA complex shows no total dissociation of RNA (B). Though there was no dissociation, the interatomic distance matrix reveals a distinct pattern of RRM–RNA interaction when compared with the FUSRRM complex (C). While the distance between the KK loop and RNA stem increases, the distance between the KK loop and the RNA hairpin loop decreases indicating a rearrangement of the RNA. Based on these results where we witness severe rearrangements in the RNA binding pose leading to weaker binding, we hypothesize that the KK-loop mutation prevents initial recognition and binding of RNA or DNA. This is consistent with the experimental observation that mutations in the unique KK loop of FUS-RRM impair or drastically reduce the affinity for nucleic acid binding  are believed to be essential for RNA binding and subcellular localization. Moreover, mutational studies on the KK loop revealed similar chemical shifts for the mutant RRM–RNA/DNA complex and mutant-apo RRM, indicating that the mutation impairs nucleic acid binding . HoweverFigure 2n of RNA B. Though complex C. While  binding .et al.  were truncated at 380, 385, or 390, respectively, to model three different complexes with variable numbers of RGG repeats. Each of these three complexes were simulated for 1 μs in triplicates . The distance between the β-sheet surface of RRM domain and the RNA hairpin in RRM–RGG2–RNA complexes containing one (FUS380), two (FUS385), and three (FUS390) RGG repeats is shown in Figure 3B. The data in Figure 3B report the average from the three replicas, and we show the individual replica data in Fig. S4A. A minimum distance of <3 Å indicates that the RNA continues to interact with the RRM domain. As seen from the figure, RNA complexed with FUS385 and FUS390 have tighter binding than FUS380. Minimum distance plots between the β-sheet surface of RRM domain and the RNA hairpin show the same trends for the three replicas as well.The RGG2 spans residues 378 to 418 and contains five RGG repeats. A previous biochemical study by Schwartz et al.  determinet al.  and analC, and we show the data for the individual trajectories in Fig. S4B. The analyses clearly illustrate the difference caused by varying the number of RGG repeats. In FUS380, the distance between RNA and RRM increases, as indicated by the general decrease in RNA intensities. Nevertheless, the C terminus of RRM and RGG2 (370–380 AA) remains close to the RNA. On the other hand, the interatomic distances between RRM and RNA in FUS385 decrease significantly as evidenced by the strong intensities of RNA hairpin with the KK loop as well as the β-sheets. Notably, residues 370 to 380 remain securely bound to the RNA, whereas the residues 381 to 385 do not express any significant association with the RNA. Upon extending the RGG2 to include the third RGG repeat, in FUS390, the residues 370 to 390 are closer to the RNA hairpin and stem–loop junction. Similarly, the number of H-bonds between RGG2 and RNA increases proportionally to the number of RGG repeats. The number of H-bonds between RGG2 and RNA was calculated from the three independent simulations ), and a collective histogram is plotted in Figure 3D for all the three systems. The FUS380 complex shows a maximum of two H-bonds, whereas the FUS385 complex shows two additional H-bonds. Interestingly, FUS390 complex expresses the highest number of H-bonds (∼8) between RGG2 and RNA highlighting a significant shift in the interaction pattern with the addition of only one more RGG repeat.The FUS–RNA interactions were analyzed further in detail to understand the interaction by different regions of FUS and the effect of the number of RGG repeats on binding affinities. The interatomic distance matrices between the RRM and RNA, averaged over the last 100 ns of the triplicates for the three systems, are shown in Figure 3FUS380, and its stability increases with increasing lengths of RGG2. The stability of this C-terminal helix has a significant impact on the stability of RNA binding as evidenced by FUS380 , and we present the data as a histogram  to FUS390. The compositional bias of RGG2 is such that, three of the five RGG repeats are located within the first 13 AA, whereas ∼70% of the following stretch, 391 to 418 AA is made of Gly and only two Arg. Therefore, we propose to further classify the RGG2 into an RGG-rich region (378–390 AA) and a G-rich region (391–418 AA). The FUS418–RNA complex was modeled as a highly disordered structure containing all five RGG2 repeats. The modeling was performed by the sequential addition of three to five residues with 50 ns of restrained simulations at each step. In order to accommodate the extended structure, the length of the double-stranded stem of RNA was also extended by adding 10 base pairs. The modeling protocol is described in detail in the Figure 4A depicts the structure of the modeled system as well as the 1 μs simulated RRM–RGG2 construct. Similar to the other previous systems, the residue-specific interaction histogram for the FUS418–RNA complex was calculated and shown in Figure 4B. The interaction pattern of FUS418 is remarkably similar to that of FUS390, in which Arg and Lys predominate. Moreover, the number of interactions between Arg of RGG2 with RNA hairpin and Lys of RRM with RNA stem is higher than in other systems. In addition to these two residues, the Gly of RGG2 also exhibits several interactions specifically with the stem–loop junction of RNA. Notably, the interactions in FUS418 are uniformly distributed along the length of RNA, like Arg of RRM and RGG2 interacting with the RNA hairpin, Gly of RGG2 interacting with stem–loop junction, and Lys of RRM interacting with RNA stem, which maintains the structural integrity of RNA.The RRM domain has five arginines, three of which line the RNA-binding pocket. On the other hand, RGG2 (377–418 AA) is compositionally biased and contributes additional five arginines with 28 glycines. The significance of the addition of one RGG repeat in FUS390 and FUS418, Gly residues might have a crucial role in defining the RNA affinity of FUS418. The number and nature of these interactions might explain the augmented binding affinity of FUS418–RNA, and hence, the interactions are further classified as electrostatic, hydrophobic, and hydrogen bonds  and FUS418 (2.5 μM) is due to the addition of weak hydrophobic and H-bond interactions by the ∼28 Gly residues in RGG2. Though they are weak compared with the electrostatic interactions by Arg and Lys, they may collectively account for the increased RNA affinity of FUS418 over FUS390. Altogether, our study demonstrates that the number of RGG repeats has a direct effect on FUS–RNA interactions and affinity.Since Arg and Lys residues are involved in both en bonds . The resFUS418 simulation also allows us to explore the conformational landscape of an RNA-bound RGG. The heterogeneous conformations generated by our triplicate simulations were clustered from the 500 ns trajectory data using recently developed machine learning–based IDP-clustering method in our group. The method ingeniously combines the t-distributed stochastic neighbor embedding (t-SNE) projection data and kMeans techniques to cluster the highly heterogeneous IDP conformation ensembles into subgroups with homogeneous conformations  with the RRM–RGG2 construct is reported to improve the RNA-binding affinity to ranges close to wildtype. Hence, in order to understand the role of RGG1 in RNA binding, we modeled the RG/RGG-rich part of RGG1 (223–267 AA), also in an extended conformation, similar to RGG2. Modeling an additional intrinsically disorded region (IDR) stretch of ∼50 AA to the RRM–RGG2–RNA complex is a nontrivial exercise. The RGG1 was added to a conformation of FUS418 chosen based on the number of RGG2–RNA contacts. There are five RG/RGG repeats in the 223 to 267 AA range, which might add several interaction sites for the RNA to bind efficiently. The modeled structure is shown in Figure 6A. The RGG1–RRM–RGG2 construct with 59 mer RNA, referred hereafter as FUS223−418, was simulated in triplicates of 1 μs each. The three-dimensional structure of the simulated complex is also shown in Figure 6A depicting the wrapping of RGG1 with the double-stranded RNA stem and RGG2 with the spine of the RNA-hairpin. Furthermore, the interatomic distances, as well as the residue-wise interactions, were also calculated to understand the FUS–RNA interactions.The simulation of B clearly highlights the contacts formed by various regions of RGG1 and RGG2 with the entire length of RNA. The residues of RGG1 remain close to the RNA stem. In particular, the N terminus of RGG1 (230–250 AA) has high intensity with the ends of the RNA stem. RRM binds the RNA hairpin, whereas RGG2 is strongly in contact with the RNA stem–loop junction. In Figure 6C, we depict the RNA root mean square fluctuation along with a simulated APO RNA conformation and the conformation for the three replicas with RNA–protein complex. As compared with APO state, there is a noticeable drop in RNA root mean square fluctuation near the hairpin region when in complex with the protein. Moreover, the amino acid–wise interactions , as well as the position (RGG1 versus RGG2) of these RGG repeats, have a major influence on the binding of FUS with RNA.The three-dimensional structure of the simulated complex is also shown in Figure 6223–418. We identified three important residues 370-TRR-372 that interact with the RNA hairpin, based on our FUSRRM and FUS223-418 simulations as well as the NMR structure of RRM (PDB ID: 6GBM). We further explored the importance of boundary region by modeling several mutant complexes of the three residues , and simulated the systems to see whether the mutants caused disruption in the complex stability. Within 100 ns of our simulations, we clearly see that the Ala mutations disrupt the interaction between boundary residues and RNA, thereby destabilizing the FUS–RNA complex. The instability of the mutant complex is depicted in FUS is a complex protein expressing an intricate network of interactions using the disorderedness of RGGs along with the properties of RRM-like KK loop and the RRM–RGG2 boundary. Since we have highlighted the crucial role of boundary residues in RRM–RNA complex throughout our study, we further tested this hypothesis in our larger FUS construct, FUSet al.  and the simulated conformation of FUS418.The RRM domain of FUS is reported to express shape specificity, and accordingly, Loughlin et al.  proposedFUS390−RNAmut and FUS418−RNAmut complexes were modeled and simulated for a period of 1 μs, and the superimposition of initial and 1 μs simulated conformations is shown in A and B. The three-dimensional structure clearly shows that the binding of mutant RNA in FUS390−RNAmut is highly disrupted in contrast to the wildtype RNA in FUS390. Also, the single turn of the C-terminal helix in wildtype RNA complex is extended to include another turn leading to the reorientation of the RGG2 away from the RNA. These major conformational changes were not observed in any of the triplicate trajectories of wildtype RNA complex in FUS390 suggesting a weak interaction of mutant RNA with RGG2. The interatomic distance matrix calculated over the last 100 ns of mutant RNA complex simulations is shown in C and D. The FUS390−RNAmut shows slightly reduced intensities for RRM–RNA and much weaker intensities for RGG2–RNA indicating unstable RNA binding. Contrastingly, in the case of FUS418−RNAmut, the interatomic distance for RRM–RNA is very similar to wildtype RNA complex. Though the 378 to 390 AA remains close to RNA, the G-rich part of RGG2 (391–418 AA) reorients closer to the RNA hairpin. This is also shown in Figure 8D where the intensities are entirely absent for the G-rich part of RGG2.The FUS390−RNAmut and FUS418−RNAmut shows few conserved and several new interactions with the RRM domain (A and B). The mutation of U in the second position to A allows several additional interactions to form in both the mutant complexes, whereas none of the interactions from FUSRRM or NMR are conserved for C to G in the fourth position. The stacking interaction of U in the third position with Phe288 is still conserved along with hydrogen bonds with the backbone of Thr370 and Arg372. Apart from this, there are several new interactions with the β2–β3 loop , C-terminal helix , and Arg386 of RGG2. When compared with the wildtype complexes, it is clear that the RNA orientation in both the mutant complexes is different and the KK loop is devoid of any interactions. Our results from previous sections have highlighted the importance of hydrophobic interactions by the Gly residues of the extended RGG2 (391–418 AA) to stabilize RNA. Hence, these interactions were further analyzed in FUS418−RNAmut to explore the importance of this extended RGG2 on RNA binding. The interaction histogram of mutant RNA with RRM and RGG2 of FUS418−RNAmut system detailing the contribution of each amino acid to RNA binding is shown in C and D.The mutated AAUG motif in M domain , A and Bβ-sheet surfaces, where the RNA hairpin is supposed to interact, are entirely devoid of Lys residues apart from the KK loop and β2–β3 loop. Even though FUSRRM was unable to stably bind the entire RNA, the recognition motif was interacting strongly. However, in the case of AAUG RNA mutant, the recognition motif loses several interactions with the β-sheets of RRM. The loss of these interactions with the RRM is clearly seen to be compensated by stronger electrostatic interactions with the Arg/Lys of both RRM and RGG2. Hence, it is clear that the interaction of RRM with the recognition motif in the mutant RNA is severely disrupted, nevertheless, parts of RGG2 were able to hold the RNA stem to still remain interacting with FUS.It is interesting to note that the mutation in the RNA motif recognized by the RRM domain clearly affects the binding and pattern of the remote interactions in the RGG2 loop, particularly with the G-rich half of RGG2 (391–418 AA) and its Gly residues. First, the Gly residues are not dominating the interactions. Several new interactions between Arg of RRM domain and the RNA stem–loop junction as well as the RNA stem are seen, which is very unique to the mutant RNA complex. Similarly, another unique interaction is seen between the RNA hairpin and Lys residues. It is worth mentioning here that the These observations also highlight the importance of RGG2 for RNA binding and the drastic enhancement of binding affinity because of the inclusion of a minimal number of RGG repeats. Collectively, it can be hypothesized that the specificity of RRM to RNA sequence/shape is required only for the initial recognition or localization, and thereafter, the interactions with RGG are stronger to overcome any loss in sequence/shape specificity. This highlights the synergistic effect and division of labor among the various regions of FUS protein, where the loss of interaction with one of the domains might be compensated by the gain of interactions with the other domains of FUS.et al. , better statistics and convergence as well as information on association/dissociation kinetics could be more faithfully achieved. Since the main purpose of our study is to explore the interaction pattern of an existing FUS–RNA complex, we believe (based on our convergence studies) that a sampling of 1 μs and three qualitatively similar behaving trajectories provides substantial information regarding the FUS–RNA interaction mechanisms.Another major issue in MD simulations of IDPs is the sampling of their conformational landscape. Being a protein without a defined three-dimensional structure, the conformational landscape of IDPs is very vast requiring a larger sampling. Previously, the structure and dynamics of IDPs like Tau proteins and LAF-1 , 48, 49 via Arg or Lys of RGG and Tyr of LCD. Similarly, our study identified the interaction of FUS RGG with RNA mediated by Arg, Lys, and Gly residues. Together, the Arg and Lys residues of RGG are crucial for the function of FUS since they interact with both LCD and RNA, thereby also facilitating RNA concentration–dependent phase separation of FUS. Further exploring the RGG–LCD and RGG–RNA interactions in the context of full-length FUS would greatly help in understanding the liquid–liquid phase separation of FUS.In this article, we have used large-scale  molecular simulations at an all-atom resolution to understand the atomic-level interactions in FUS–RNA complexes. Our study provides molecular-level mechanistic insights into observations from biochemical studies, and it has also illuminated our understanding of molecular driving forces that mediate the structure, stability, and interaction of RRM and RGG domains of FUS with a stem–loop junction RNA. Our simulation data clearly bring forth the very important role of the boundary region at the interface of RRM and RGG2, which seems to be central to the fidelity of the complex. This region is ambiguously classified as either RRM or RGG2 causing inconsistency in comparing the binding affinities among various experimental literature. We show that excluding this region in RRM or their mutations lead to dissociation of RNA, and this is an experimentally testable hypothesis. With FUS-RRM devoid of the classical recognition motifs seen in other RRMs, we believe that this boundary region between the folded and disordered domains gains importance as the anchor along with the earlier discovered noncanonical central KK loop. Our study also provides the structural biophysical rationale for why at least three RGG repeats are required in RGG2 to improve binding to the RNA. We find that the Arg residues in the first two RGG repeats are sterically constrained because of the persistence caused by the small helix at the start of RGG2. We also find that whatever the length of RGG2 is, the interactions are confined to the RNA hairpin and stem–loop junction only. The fourth and fifth RGG repeats in RGG2 do not significantly improve the binding strength. However, based on our data, the mild increase that was observed in experimental studies could be attributed to the hydrophobic interactions between glycines and RNA. The role of glycine residues in biomolecular interactions has been widely overlooked, yet we have observed an important role for these interactions in increasing the affinity of FUS–RNA complex. This is interesting from the point of view of bounds put on RGG repeats that maximizes their functional role. On the other hand, we find that once RGG1 is introduced from the N-terminal end of the RRM, RNA binding noticeably increases again. Flanking RGGs bind the entire RNA stem, and our simulations provide a very clear picture of the origins of the enhanced interactions. Interestingly, the nuclear export signal region connecting RGG1 with RRM does not express any interactions with the RNA, thereby facilitating the recruitment of other binding partners. Our data from RNA mutation simulations again provide experimentally testable hypotheses to establish the RNA sequence and structure specificity of FUS protein where we see specificity for NYNY motif. Mutation directly alters the RRM–NYNY interaction pattern, and as a result, we observe an indirect allosteric effect in the RGG2. Such adaptable interactions of FUS are mainly responsible for its promiscuous nucleic acid–binding property and minimal sequence specificity. RGGs are considered as the key component in an interaction network because of their ability to express intramolecular as well intermolecular interactions with other RGGs, disordered or folded proteins, and nucleic acids. Because of such high multivalency, RGG domains also contribute significantly to liquid–liquid phase separation and aggregation of proteins like FUS. Previous studies have highlighted weak interaction of RGG with the LCD of FUS and also with the RNA polymerase II in a condensate et al. . Other   first, followed by the modeling of the RGG1 into the system. After modeling each RGG, all harmonic restraints were removed, and the structures were simulated using the standard MD simulation protocol as explained previously.Computational modeling of IDP and IDR structures , 51 is a6SNJ ). In othx-axis and RNA on the y-axis. We have used a blue to red scale to color the residue pairs that are closest to farthest. We have used a cutoff of 20 Å to color the distances; hence, all residue pairs beyond 20 Å are shown in red. This distance ensures that all interacting residues and interaction types including electrostatic, π-, hydrogen bonds, and hydrophobic interactions are accounted for during the calculation. This matrix is helpful to identify the regions of FUS interacting with particular regions of RNA and the changes in binding orientation between the different simulated systems. The amino acid–wise interaction plots were calculated as an average of the last 100 ns of the three independent simulations. For the histogram of interacting residues, Cpptraj module of AmberTools20 was used to extract all pairs of residues between FUS and RNA present within a 6 Å distance that is maintained for at least 10% of the simulation period. The interactions by each RNA base were grouped on the interacting amino acids and the number of these interactions is plotted. Since all residue pairs within 6 Å are considered, the obtained number includes all types of nonbonded interactions like hydrogen bonds, electrostatic, π-, and hydrophobic interactions. Furthermore, these interactions were classified based on similar studies done previously  and the RNA hairpin . The minimum distance between any pair of atoms from these two groups is calculated, and this distance signifies the closest range that these two groups can achieve. By monitoring the changes in com–com distance and minimum distance, we can identify the RNA dynamics. Changes in minimum distance indicates dissociation of RNA. However, a stable minimum distance can also be accompanied with fluctuations in com–com distance, signifying disruptions in the binding orientation of RNA relative to RRM. The third distance metric, namely the interatomic distance matrix, is a matrix of distances between all the atoms of RNA and all the atoms of FUS averaged over 100 ns simulation. The matrix is plotted as a two-dimensional landscape with FUS residues on the eviously .MD simulation generates an ensemble of conformations representing the dynamics of biomolecules, and valuable insights could be derived by clustering these conformations. The clustering of an IDP ensemble is a challenging task because of the high conformational heterogeneity. Several clustering methods like hierarchal, vector quantization, and neural network are available to perform the clustering analysis. In our study, we use the nonlinear dimensionality reduction method called t-SNE coupled with the K-means method for clustering the highly heterogeneous IDP/IDR ensemble of FUS into subgroups of homogeneous conformations. Complete details about this method for clustering IDPs are available in the recent article from our group . The cluvia our laboratory GitHub link: codesrivastavalab/RNA-FUSAAMD. The files can also be accessed directly from the publicly available Figshare portal: https://doi.org/10.6084/m9.figshare.24155325.v1.Input files needed to initiate molecular simulations and full trajectory data of all simulations for all systems considered in this work are available on our server for download. The server data can be publicly accessed This article contains The authors declare that they have no conflicts of interest with the contents of this article.


While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism. All information about the structures, their energy and interconversion pathways between them can be calculated from knowledge of the EL. Insight can also be obtained on the influence of external factors such as ionic conditions, pH, temperature, presence of ligands and chemical changes in the sequence by considering the EL. Any experiment or simulation probes the EL directly or indirectly. Various methods do so in different ways, and often the EL is not directly mapped.The most common simulation method is molecular dynamics (MD) simulations. However, due to the broken ergodicity exhibited biomolecular ELs  along which the transitions will occur) is through the use of replica exchange MD simulations , the computational exploration of the EL is aided in two ways. Firstly, the CG smooths the EL see Fig. d, which The obvious shortcoming of any CG methodology is the loss of detail, due to the lower model resolution. In addition, the implicit nature of solvent and ions will impact the observed features. These drawbacks mean the entropy is not faithfully produced within coarse-grained simulations. However, the reduced complexity will allow the study of larger systems and larger scale rearrangements, providing otherwise inaccessible insights.Despite the fewer degrees of freedom, our coarse-grained MD simulations can still be expensive, with several days of CPU needed to achieve folding of a small molecule of 20–30 nucleotides, although we will be able to achieve much greater speed once the force field will be ported to parallel MD computing.In this section, we discuss a few illustrative applications of these methods, emphasising their complementary nature.et al., et al., et al., et al., et al., et al., et al., This example is a 47-nucleotide RNA, exhibiting an H-pseudoknot (two-interlacing strands) further stabilised by a triple helix (PDB ID 2K96). The system has been studied extensively experimentally  with the AMBER ff99 with the Barcelona et al., et al., et al., The HiRE-RNA simulations yielded the correct native state and identified a sensible folding pathway. Moreover, alternative states were observed, which constitute kinetic traps and are characterised by the formation of non-native secondary structures, leading to alternative folds. These results were in qualitative agreement with experimental evidence of the formation of metastable states and folding intermediates  , it was possible to exhaustively explore the EL  was characterised experimentally by different methods including X-ray crystallography  and further studied a set of mutations , convergence for such simulations is difficult to achieve. Nonetheless, the findings from the extended trajectories agree with the observations from path sampling. The clusters of structures corresponded well to the structures found in the main funnels of the EL. The agreement between the simulations enabled us to exclude a prominent role of structural water molecules or ions, which is not possible from the implicit solvent representation used in DPS. While DPS did not necessarily explore the high-energy portions of the landscape, which would require unfolding and potential refolding into different structures, the H-REX simulations did explore these regions. We would therefore expect H-REX to be able to depart more significantly from the initial structures and possibly find new structures with a full reorganisation of the molecule. In our simulations, however, we only located states already explored by DPS. The experimental evidence combined with the exploration of two different kind of simulations gives us some confidence that we have probably explored all the biologically relevant structures of the system.Two sets of H-REX simulations complemented the EL exploration with DPS, each starting from one of the observed crystal structures. Simulations were performed for 100 ns using 20 replicas, with a Hamiltonian coupling et al., et al., et al., et al., Another area of growing interest is the study of post-translational modifications. In a recent study, we investigated a post-translational methylation of an RNA hairpin  is found as well. In the unmethylated system, these structures are effectively inaccessible, while the methylation reduces the energy difference significantly, leading higher occupation of these states, which can recruit the m6A reader see .Fig. 5.EThese results highlight the importance of studies of mutations and chemical modifications, as the unmodified sequence might exhibit polymorphism, but it cannot be detected in experiment, while small alterations lead to significant changes in the EL, which may lead to detectable polymorphism.From these case studies, we can draw some general conclusions on the specificity of RNA folding and on what can constitute a profitable strategy to tackle larger and more complex systems. For all systems we have studied, even the simplest, we find a rugged EL, i.e. it is characterised by the presence of many locally stabilised structures, corresponding to the subsequent formation of local secondary structures. These configurations may be part of a single funnel, most likely for small systems, or, if they exhibit significant difference in their secondary structure, belong to competing funnels, a feature likely observed for larger RNA molecules.A successful strategy to investigate the possible structures is the combination of several simulation methods. Secondary structure predictions based on bioinformatics may already reveal some of the complexity of the RNA folding problem. If a single dominant structure emerges, it is likely that the EL is less complex and exhibits a single main funnel. In such cases, it is likely that canonical base pairs are adopted, and chemical probing may give further confidence in such predictions. Non-canonical interactions may be important to local structural details. Of course, in these scenarios, complexity may arise from additional effects, such as post-translational modifications.If, on the other hand, multiple secondary structures are proposed, a multiscale approach can be useful. Using a coarse-grained model, initial scouting of the EL can yield a survey of possible alternative structures, leading to an identification of the main folding funnels. Subsequent all atom simulations can then be used to investigate details on the EL, seeded by the structures obtained from the coarse-grained simulations.et al., et al., With this approach in mind, we recently started investigating the frameshifting pseudoknot of SARS-CoV-2, for which a structure is known experimentally but for which both experimental and simulation data suggest the existence of alternative structures , which can not yet be treated efficiently by traditional secondary structure prediction algorithms and that are only now becoming accessible in combined strategies using machine learning .” is quite large and the reviewer requests the author consider breaking this sentence down.4. In the introduction, this sentence requires at least two or more citation “The multiple hydrogen bond donor and acceptor sites of the nucleobases allow for a multitude of base pairs, which have been reported experimentally” in favor this claim.5. In the introduction, for the line “The small alphabet combined with the multitude of possible base pairs results in RNA folding being as or even more complex than protein folding.”, the authors does not explain what small alphabet mean. Furthermore, they claim RNA folding being just a complex or more than protein folding however this sentence does not have a citation in it’s favor.6. In the introduction, the authors do not explain what “minimal frustration” is. This needs to be defined as well mention what it means for a structure to have a higher frustration vs lower frustration. Without this definition the rest of their argument is hard to understand. Furthermore, while on the subject of protein folding a lot more recent papers have come out from the David Baker group (RoseTTAFold) and from DeepMind (AlphaFold2). The reviewer suggest the author explain computational protein folding those perspective.7. In the introduction, the sentence “The energy landscape contains the all information about the structures, their energy, and interconversion pathways between them.” seem to have been constructed strangely and the reviewer ask modification to this sentence. In the same paragraph, the reviewer is not quite sure what the authors mean by “Any experiment or simulation probes the energy landscape, though various methods do so in different ways, which can be explicit or implicit.” and asks the author to clarify this sentence in their text.8. In the introduction, the paragraph with the line “broken ergodicity exhibited biomolecular energy landscapes” is lacking details and the reviewer fails to see the point of this paragraph and how it connects to the next paragraph. Therefore, the reviewer request the author to rewrite this paragraph such that it can relate to why MD simultations for RNA are difficult.In summary the reviewer feels that the introduction is not strongly connected to the prespective that is being highlighted here and request the authors9. In the replica exchange section the opening sentence “As these simulations do not require the definition of collective variables (colvars), the sampling is unbiased.” is not introduced well and reviewer request this sentence be restructured. For example up until now, nothing is mentioned about colvars. Additionally, the reviewer is also confused how the sampling is unbiased if the potential energy is tempered with.10. This sentence “Importantly, entropic contributions for partially folded structures are captured in this approach” requires a citation.11. In the paragraphs about REST2 scheme, the authors explain the approach and gives with proteins of different sizes. Has there been no studies done with RNA? If yes, then the authors should also briefly explain that. If they explain it later in the manuscript they should mention that too. Else, the authors should mention that REST2 scheme has not been applied for studying RNA.12. By the time the reviewer reached section “Ratchet-and-pawl molecular dynamics and the Bias Functional approach”, the reiviewer has already forgotten what rMD stands for and had to scroll up to figure it out. The point, the reviewer is trying to make here is that it might be nice to re-introduce the abbreviation at the beginning of this section.13. The opening sentence “rMD simulations are based on introducing a soft history-dependent biasing force to enhance the generation of productive folding trajectories towards a given target structure” of the rMD section is not explained well and the reviewer ask the authors to explain this clearly. Additionally this sentence is not cited.14. Because the authors previously did not explain colvars mean for biological system such as proteins and RNA, their explanation using colvars also suffers in rMD section.15. This sentence “In RNA and protein folding simulations, one choice for the predetermined CV is obtained from the overlap of the instantaneous and the target atomistic contact map.” requires a citation.16. In the sentence “Therefore, with rMD it is only possible to obtain an approximate reconstruction of the folding EL” what is EL?17. Langevin dynamics needs to be cited.18. The shorthand “H-REX” has not been previously introduced or at the beginning of the section “Discrete pathsampling for RNA”19. This sentence “The energy landscape is coarse-grained into a set of local minima and transition states that connect them.” is not explained well.20. There are grammatical issues with this sentence “we can identified a discrete path consisting of a series of minima connected by transition states.”21. In the section Discrete pathsampling of RNA and for the sentence “which introduces a source of error.”, the authors didn’t give examples what source of error can cause to the simulation. Additionally this sentence needs citations in case the readers wanted to know more about.22. This sentence “More information and details on how the energy landscapes are explored can be found in various reviews” does not have a basis. The section is about Discrete pathsampling of RNA, however the last sentence of this section is very general. The reviewer suggest the authors to stay true to the section and come up with a better closing sentence.23. Could the authors point out what does “HiRE” in HiRE-RNA stand for?24. In the last paragraph of the section “Folding pathway of the human telomerase H-pseudoknot triple helix”, could the authors comment on how long it took  hours) for unbiased CG simulations and the rMD simulations in this particular case and additionally provide further guidance for readers for what sizes of systems can these simulations be used complementarily and beyond which it becomes impractical?25. The sentence “The hairpin is the binding site for an affector protein[cite], which is crucial to the important biological function of RNA 7SK” requires at least two or more citations as mentioned in square-brackets in the sentence. Comments to Author: It is an interesting overview of folding modelling literature of small(er) RNAs, focusing on the recent studies published by the authors. The paper is generally well written, the overall concept is smart, it has a good logical structure, and should be publishable. However, a major revision is suggested, as outlined below, as one of the parts is not in an acceptable form.Major criticismThe H-rex subchapter on pp. 3-4 is not realistic, specifically with its emphasis on the REST2 method. This part lacks any comments on the limitation of the methods. In addition, while the paper should be about RNA simulations, the paragraph is presently supported only by citations to protein or enzyme works which in addition appear to be self-citations by one of the authors. I have some doubts if proteins with 100-200 residues were convergently sampled by REST2. Nevertheless, unfortunately, a complete failure of the REST2 method for even the shortest RNA 8-mer tetraloops has been recently demonstrated on an aggregate millisecond time scale, 100+ microseconds per replica; no sign of a convergence , pp.2642-2656). There is clear lack of decorrelation in the replica ladder which even deepens once there is some folding event in the ladder. The folded continuous trajectories appeared to be pinned down at the bottom of the ladder. REST2 sampling might even deteriorate compared to plain simulations. Deeper theoretical roots of this can be found in Zuckerman et al., https://www.annualreviews.org/doi/abs/10.1146/annurev-biophys-042910-155255. So, it is a problem which should be known in the field but is often ignored. However, even for proteins the performance of REST2 has been very seriously questioned, Appadurai et al. https://www.nature.com/articles/s41467-021-21105-7 In this work other potential issues of REST2 are mentioned, affecting its sampling efficacy, such as cold solvent slowing down the conformational diffusion . In summary, the HREX paragraph is not in an acceptable form and should be rewritten completely, focused on the RNA RE works , pp.1578-1590 and several others in the literature; some literature search would be useful) and give a fair assessment of the limitations. I think the above-noted principal problems of REST2 may be responsible for some of the problematic sampling cases hinted in the other parts of the paper. Based on the recent works REST2 is most likely not very suitable method for RNA sampling, unless combined with some CV-class approaches. Even that can help only for specific systems where dimensionality reduction is possible by CVs.On the other hand the problematic efficiency of the explicit solvent atomistic methods highlights the need for the coarse-grained approaches, not mentioning the force-field limitations, making the story logical.Minor comments.In the DPS part of the paper, one point should be clarified for the reader. How much does the method depend on availability of the folded structures? I tend to think that the method is still not able to generally find unknown folds, if they are different and separated by substantial barriers from the funnel under investigation, i.e., I would assume there easily could be entirely missed parts of the landscape if prior information is not available. Could the authors briefly comment to it? Otherwise I agree it is a useful method to see the landscape structure, though there is the implicit solvent limitation . I assume the method can analyze roughness of a known funnel but I doubt it can find new funnels in case of major kinetic partitioning. Is that correct?The paper, albeit declared to be about RNA, is making several excursions to DNA quadruplexes (GQs). However, it may be somewhat excessive and even misleading. DNA GQs are a specific case of biopolymers where the kinetic partitioning originates from the interrelation between the folds and the syn-anti orientations of guanines. It is however not relevant to RNA GQs, as RNA GQs are all-anti and thus always all-parallel. The main source of frustration of the folding landscape in DNA GQs is thus not relevant for RNA GQs. Most recently it was reviewed in Sponer et al. https://www.sciencedirect.com/science/article/abs/pii/S0065774320300154, experimental view Grun&Schwalbe, Biopolymers 2021 DOI: 10.1002/bip.23477 As presently explained it is misleading or incomplete; the picture from DNA GQs cannot be extrapolated to RNA GQs and in fact, as far as I know, it does not have analogy in RNA folding. It is unique to DNA G-rich single strands due to the possibility of syn-anti partitioning which leads to numerous competing basins of attraction with different syn-anti patterns.In the Introductions the authors might note that RNAs massively interact with proteins, so they often co-fold with proteins and are re-modelled. And the hierarchical nature of RNA structure makes the RNA structure extremely complicated for predictions from the sequences, nice visualization of this problem is in Fig. 2 of Jaeger et al. NUCLEIC ACIDS RESEARCH 37 (1), pp.215-230. Proteins can be better predicted if they have domain folding.Another minor point for consideration.The text emphasizes the concept of native state which reads as synonym to a well folded structure. However, I think the authors might note disorder . In addition, one can think even about disordered state as being the native state, if it is the biochemically relevant ensemble. The intro is correct but somewhat too much based on traditional concepts reflecting the world of simple fast-folding proteins that have frustration-minimizing native basin and funnel folding.It appears that the Journal is imposing some quite strict requirements on the length of the paper, which may be responsible for some of the weaknesses of the present version noted above. I strongly recommend that the Editor gives the authors some flexibility regarding the length, if needed. MD technique is very complex field with countless methodological approaches, and very tricky reliability issues. MD works should be interpreted upon considering their limitations. It is in the interest of the readers, but ultimately also of the authors and the Journal. Otherwise the message could be confusing. (Myself I would not be capable to write it within such length limits). Comments to Author: Reviewer #1: It is an interesting overview of folding modelling literature of small(er) RNAs, focusing on the recent studies published by the authors. The paper is generally well written, the overall concept is smart, it has a good logical structure, and should be publishable. However, a major revision is suggested, as outlined below, as one of the parts is not in an acceptable form.Major criticismThe H-rex subchapter on pp. 3-4 is not realistic, specifically with its emphasis on the REST2 method. This part lacks any comments on the limitation of the methods. In addition, while the paper should be about RNA simulations, the paragraph is presently supported only by citations to protein or enzyme works which in addition appear to be self-citations by one of the authors. I have some doubts if proteins with 100-200 residues were convergently sampled by REST2. Nevertheless, unfortunately, a complete failure of the REST2 method for even the shortest RNA 8-mer tetraloops has been recently demonstrated on an aggregate millisecond time scale, 100+ microseconds per replica; no sign of a convergence , pp.2642-2656). There is clear lack of decorrelation in the replica ladder which even deepens once there is some folding event in the ladder. The folded continuous trajectories appeared to be pinned down at the bottom of the ladder. REST2 sampling might even deteriorate compared to plain simulations. Deeper theoretical roots of this can be found in Zuckerman et al., https://www.annualreviews.org/doi/abs/10.1146/annurev-biophys-042910-155255. So, it is a problem which should be known in the field but is often ignored. However, even for proteins the performance of REST2 has been very seriously questioned, Appadurai et al. https://www.nature.com/articles/s41467-021-21105-7 In this work other potential issues of REST2 are mentioned, affecting its sampling efficacy, such as cold solvent slowing down the conformational diffusion . In summary, the HREX paragraph is not in an acceptable form and should be rewritten completely, focused on the RNA RE works , pp.1578-1590 and several others in the literature; some literature search would be useful) and give a fair assessment of the limitations. I think the above-noted principal problems of REST2 may be responsible for some of the problematic sampling cases hinted in the other parts of the paper. Based on the recent works REST2 is most likely not very suitable method for RNA sampling, unless combined with some CV-class approaches. Even that can help only for specific systems where dimensionality reduction is possible by CVs.On the other hand the problematic efficiency of the explicit solvent atomistic methods highlights the need for the coarse-grained approaches, not mentioning the force-field limitations, making the story logical.Minor comments.In the DPS part of the paper, one point should be clarified for the reader. How much does the method depend on availability of the folded structures? I tend to think that the method is still not able to generally find unknown folds, if they are different and separated by substantial barriers from the funnel under investigation, i.e., I would assume there easily could be entirely missed parts of the landscape if prior information is not available. Could the authors briefly comment to it? Otherwise I agree it is a useful method to see the landscape structure, though there is the implicit solvent limitation . I assume the method can analyze roughness of a known funnel but I doubt it can find new funnels in case of major kinetic partitioning. Is that correct?The paper, albeit declared to be about RNA, is making several excursions to DNA quadruplexes (GQs). However, it may be somewhat excessive and even misleading. DNA GQs are a specific case of biopolymers where the kinetic partitioning originates from the interrelation between the folds and the syn-anti orientations of guanines. It is however not relevant to RNA GQs, as RNA GQs are all-anti and thus always all-parallel. The main source of frustration of the folding landscape in DNA GQs is thus not relevant for RNA GQs. Most recently it was reviewed in Sponer et al. https://www.sciencedirect.com/science/article/abs/pii/S0065774320300154, experimental view Grun&Schwalbe, Biopolymers 2021 DOI: 10.1002/bip.23477 As presently explained it is misleading or incomplete; the picture from DNA GQs cannot be extrapolated to RNA GQs and in fact, as far as I know, it does not have analogy in RNA folding. It is unique to DNA G-rich single strands due to the possibility of syn-anti partitioning which leads to numerous competing basins of attraction with different syn-anti patterns.In the Introductions the authors might note that RNAs massively interact with proteins, so they often co-fold with proteins and are re-modelled. And the hierarchical nature of RNA structure makes the RNA structure extremely complicated for predictions from the sequences, nice visualization of this problem is in Fig. 2 of Jaeger et al. NUCLEIC ACIDS RESEARCH 37 (1), pp.215-230. Proteins can be better predicted if they have domain folding.Another minor point for consideration.The text emphasizes the concept of native state which reads as synonym to a well folded structure. However, I think the authors might note disorder . In addition, one can think even about disordered state as being the native state, if it is the biochemically relevant ensemble. The intro is correct but somewhat too much based on traditional concepts reflecting the world of simple fast-folding proteins that have frustration-minimizing native basin and funnel folding.It appears that the Journal is imposing some quite strict requirements on the length of the paper, which may be responsible for some of the weaknesses of the present version noted above. I strongly recommend that the Editor gives the authors some flexibility regarding the length, if needed. MD technique is very complex field with countless methodological approaches, and very tricky reliability issues. MD works should be interpreted upon considering their limitations. It is in the interest of the readers, but ultimately also of the authors and the Journal. Otherwise the message could be confusing. (Myself I would not be capable to write it within such length limits).Reviewer #2: Here the authors describe several computational methods commonly used to study RNA. They discuss Hamiltonian-replica exchange MD, ratchet-and-pawl MD, discrete path sampling and HiRE-RNA coarse-graining scheme. Next they discuss several case studies where these methods have been used. Finally they report the limitations of these methods and how multiple methods will better help understand experimental results on RNA polymorphism.This reviewer finds well written but however has few concerns and confusion regarding the author’s manuscript as listed below.1. The title is not very informative, and the reviewer suggests the author search for an informative title. Especially “methods for the greater good” add very little meaning to the title.2. In the introduction, the sentence “RNA folding was thought to be an easier problem to solve than protein folding.” requires a citation at the end of the statement.3. In the introduction the sentence “Moreover, large RNA architectures often present pseudoknots (non-nested arrangements of base pairs), which can not yet be treated efficiently by traditional secondary structure prediction algorithms and that are only now becoming accessible in combined strategies using machine learning .” is quite large and the reviewer requests the author consider breaking this sentence down.4. In the introduction, this sentence requires at least two or more citation “The multiple hydrogen bond donor and acceptor sites of the nucleobases allow for a multitude of base pairs, which have been reported experimentally” in favor this claim.5. In the introduction, for the line “The small alphabet combined with the multitude of possible base pairs results in RNA folding being as or even more complex than protein folding.”, the authors does not explain what small alphabet mean. Furthermore, they claim RNA folding being just a complex or more than protein folding however this sentence does not have a citation in it’s favor.6. In the introduction, the authors do not explain what “minimal frustration” is. This needs to be defined as well mention what it means for a structure to have a higher frustration vs lower frustration. Without this definition the rest of their argument is hard to understand. Furthermore, while on the subject of protein folding a lot more recent papers have come out from the David Baker group (RoseTTAFold) and from DeepMind (AlphaFold2). The reviewer suggest the author explain computational protein folding those perspective.7. In the introduction, the sentence “The energy landscape contains the all information about the structures, their energy, and interconversion pathways between them.” seem to have been constructed strangely and the reviewer ask modification to this sentence. In the same paragraph, the reviewer is not quite sure what the authors mean by “Any experiment or simulation probes the energy landscape, though various methods do so in different ways, which can be explicit or implicit.” and asks the author to clarify this sentence in their text.8. In the introduction, the paragraph with the line “broken ergodicity exhibited biomolecular energy landscapes” is lacking details and the reviewer fails to see the point of this paragraph and how it connects to the next paragraph. Therefore, the reviewer request the author to rewrite this paragraph such that it can relate to why MD simultations for RNA are difficult.In summary the reviewer feels that the introduction is not strongly connected to the prespective that is being highlighted here and request the authors9. In the replica exchange section the opening sentence “As these simulations do not require the definition of collective variables (colvars), the sampling is unbiased.” is not introduced well and reviewer request this sentence be restructured. For example up until now, nothing is mentioned about colvars. Additionally, the reviewer is also confused how the sampling is unbiased if the potential energy is tempered with.10. This sentence “Importantly, entropic contributions for partially folded structures are captured in this approach” requires a citation.11. In the paragraphs about REST2 scheme, the authors explain the approach and gives with proteins of different sizes. Has there been no studies done with RNA? If yes, then the authors should also briefly explain that. If they explain it later in the manuscript they should mention that too. Else, the authors should mention that REST2 scheme has not been applied for studying RNA.12. By the time the reviewer reached section “Ratchet-and-pawl molecular dynamics and the Bias Functional approach”, the reiviewer has already forgotten what rMD stands for and had to scroll up to figure it out. The point, the reviewer is trying to make here is that it might be nice to re-introduce the abbreviation at the beginning of this section.13. The opening sentence “rMD simulations are based on introducing a soft history-dependent biasing force to enhance the generation of productive folding trajectories towards a given target structure” of the rMD section is not explained well and the reviewer ask the authors to explain this clearly. Additionally this sentence is not cited.14. Because the authors previously did not explain colvars mean for biological system such as proteins and RNA, their explanation using colvars also suffers in rMD section.15. This sentence “In RNA and protein folding simulations, one choice for the predetermined CV is obtained from the overlap of the instantaneous and the target atomistic contact map.” requires a citation.16. In the sentence “Therefore, with rMD it is only possible to obtain an approximate reconstruction of the folding EL” what is EL?17. Langevin dynamics needs to be cited.18. The shorthand “H-REX” has not been previously introduced or at the beginning of the section “Discrete pathsampling for RNA”19. This sentence “The energy landscape is coarse-grained into a set of local minima and transition states that connect them.” is not explained well.20. There are grammatical issues with this sentence “we can identified a discrete path consisting of a series of minima connected by transition states.”21. In the section Discrete pathsampling of RNA and for the sentence “which introduces a source of error.”, the authors didn’t give examples what source of error can cause to the simulation. Additionally this sentence needs citations in case the readers wanted to know more about.22. This sentence “More information and details on how the energy landscapes are explored can be found in various reviews” does not have a basis. The section is about Discrete pathsampling of RNA, however the last sentence of this section is very general. The reviewer suggest the authors to stay true to the section and come up with a better closing sentence.23. Could the authors point out what does “HiRE” in HiRE-RNA stand for?24. In the last paragraph of the section “Folding pathway of the human telomerase H-pseudoknot triple helix”, could the authors comment on how long it took  hours) for unbiased CG simulations and the rMD simulations in this particular case and additionally provide further guidance for readers for what sizes of systems can these simulations be used complementarily and beyond which it becomes impractical?25. The sentence “The hairpin is the binding site for an affector protein[cite], which is crucial to the important biological function of RNA 7SK” requires at least two or more citations as mentioned in square-brackets in the sentence. Comments to Author: The paper is now OK for publication Comments to Author: Reviewer #1: The paper is now OK for publication No accompanying comment.


Intrinsic flexibility and structural modularity are two common features of RNA molecules. Although functionally crucial, RNA plasticity often represents a major complication in high-resolution structural studies. To overcome this problem, RNAs may be rigidified through the complexation with high-affinity partners such as Fab molecules. This approach has been previously used to characterize the DIR2-aptamer. However, possible perturbations induced by the insertion of the Fab binding site on the DIR2-aptamer conformational properties were not investigated. Here, using enhanced molecular dynamics simulations, we compared the dynamics of the DIR2 aptamer holding the Fab binding site with that of the parental sequence. Our results suggest that the L2-loop modification for the Fab recognition leads to a significant increase in local flexibility that also affects the mobility of distant regions. The trajectories provide clear indications of the groups and the interactions mediating the dynamics transfer in DIR2. The effectiveness of our approach in addressing RNA flexibility was further corroborated by showing its ability to reproduce the most important events affecting the NF-κB RNA aptamer upon dissociation from the partner. Therefore, REMD analyses, a rarely adopted technique to unravel the structural/dynamical properties of aptamers, could efficiently complement experimental data guiding the rational design of nucleic acid therapeutics.  The RNA plasticity allows a huge functional versatility but hampers the characterization. We provide a thorough picture of the structural and dynamic consequences caused by sequence modifications for an RNA aptamer. Our approach of modeling and theoretical simulations represents an ideal method for the rational design of nucleic acid therapeutics. The relative scarcity of structural information strongly limits our understanding of the RNA folding process as well as of its ability to adopt specific three-dimensional conformations that are frequently able to establish high-affinity interactions with diversified partners. One effective and used strategy to overcome this general issue is based on the structural characterization of aptamers following their complexation to high-affinity Fabs, by adapting a similar approach used for highly flexible proteins., In the case of aptamers, this procedure involves the modification of the aptamer sequence by the insertion of a structural motif that is specifically recognized by a Fab that operates as a crystallization chaperone.RNA has fundamental roles in life, as it regulates a plethora of cellular functions.This strategy has been successfully employed to structurally characterize DIR2, a fluorogenic RNA aptamer endowed with important potentialities in imaging techniques since it is able to activate fluorescence upon binding chromophore molecules. Indeed, DIR2 binds the chromophores Dimethylindole red (DIR) and oxazole thiazole blue (OTB) with nanomolar affinity, thus inducing the emission of red and blue fluorescence, respectively. The intrinsic flexibility of DIR2 impaired its ability to crystallize and perform high-resolution crystallographic studies.,Moreover, we also show that the method here illustrated can be reliably applied to gain insights into the structural basis of the aptamer-protein recognition process. In particular, using the well-characterized interaction between an RNA aptamer of the nuclear factor (NF)-κB transcription factor,The intrinsic dynamic and structural properties of the DIR2 aptamer and the impact that the insertion of the Fab recognition motif has on its properties were here investigated by performing REMD simulations to provide an enhanced sampling of the conformational ensemble of these intricate biomolecules. As detailed in the per residues that were computed on the entire 40 ns of REMD simulation time  these two secondary elements are closer than in ligand-free form (PDB code 6DB9). Nevertheless, the differences in the relative motif distances between the two aptamer variants do not impact the intrinsic flexibility of the region deputed to bind the fluorophore ligand, that corresponds to the L3 loop  folds in a fork-like architecture with a helix and two stem-loops oriented parallel to each other. Here, we performed an extensive conformational sampling of the two DIR2 aptamer variants (DIR2wt and DIR2fab) to measure the effect on the overall conformational space upon the insertion of the Fab binding motif within the RNA sequence. The MD simulation analysis clearly suggests that both variants retain the overall structure exhibited in the complex with the Fab. Nevertheless, present data indicate that DIR2wt and DIR2fab are endowed with remarkable flexibility. This finding well agrees with the experimental difficulties encountered in crystallizing DIR2 as an individual entity. In this general framework, a deep inspection of the trajectory structures also highlights small but significant differences in the overall flexibility of the two aptamers. In particular, the insertion of extra bases in the L2 loop of DIR2fab deputed to the Fab anchoring leads to a significant increase of the local flexibility that affects also some distant regions. Notably, these changes only marginally affect the binding site of fluorophores, in line with the similar affinity of these compounds for these two forms. The analysis of the interaction network of the two variants also provides a structural explanation for the increased flexibility of the aptamer bearing the insertion. Indeed, the modification of the L2 loop leads to the loss of a key intra-motif base connection (U24-G52) that is present in the parental aptamer and is missed in the DIR2fab  and a variant embodying a Fab3-6 binding motif (DIR2fab) that was ad hoc inserted into the aptamer sequence to facilitate its structural characterization. DIR2fab . The los,,Moreover, we explored the ability of this approach to deal with the identification of the structural basis of protein-aptamer recognition, which is a fundamental issue for the optimization of the effectiveness of these RNA molecules. Although the structure of several dozens of protein-aptamer complexes has been experimentally determined,,a priori screening of many variants of hit compounds to reduce drug development costs or for the rational design of nucleic acid therapeutics.In this context, it is important to note that for most therapeutic purposes, the initially identified aptamers are frequently heavily modified for several reasons.6DB9 and 1OOA, respectively).,The three-dimensional structure of the DIR2fab and NF-κB aptamers was extracted by the pdb solved structures, downloaded from the Protein Data Bank database  and previous protocols applied on nucleic acids and derivatives.,,http://virtualchemistry.org/remd-temperature-generator/,MD simulations were run using the amber14sb force field including the most recent parameters properly designed to describe RNA systems implemented for GROMACS 2020/5.0.5.https://github.com/idaaut/MolTher23 GitHub repository address.The data that support the findings of this study are available from the corresponding author, Ida Autiero, at the


Using single-vector delivery of the Streptococcus thermophilus Csm complex, we observe high-efficiency RNA knockdown (90–99%) and minimal off-target effects in human cells, outperforming existing technologies including short hairpin RNA- and Cas13-mediated knockdown. We also find that catalytically inactivated Csm achieves specific and durable RNA binding, a property we harness for live-cell RNA imaging. These results establish the feasibility and efficacy of multiprotein CRISPR-Cas effector complexes as RNA-targeting tools in eukaryotes.Robust and precise transcript targeting in mammalian cells remains a difficult challenge using existing approaches due to inefficiency, imprecision and subcellular compartmentalization. Here we show that the clustered regularly interspaced short palindromic repeats (CRISPR)-Csm complex, a multiprotein effector from type III CRISPR immune systems in prokaryotes, provides surgical RNA ablation of both nuclear and cytoplasmic transcripts. As part of the most widely occurring CRISPR adaptive immune pathway, CRISPR-Csm uses a programmable RNA-guided mechanism to find and degrade target RNA molecules without inducing indiscriminate  The bacterial Csm complex efficiently knocks down eukaryotic nuclear and cytoplasmic RNAs. However, RNAi can cause unintended cleavage of targets carrying partial sequence complementarity, especially when this complementarity occurs within the nucleating ‘seed’ region of the siRNA5. Furthermore, siRNAs are inefficient at targeting nuclear RNAs because the RNAi machinery localizes primarily to the cytoplasm7. Finally, RNAi is incompatible with certain eukaryotic model systems, including budding yeast that lacks RNAi machinery9 and zebrafish embryos that suffer from nonspecific developmental defects11. Thus, there has been ongoing interest in developing new RNA KD tools with higher specificity and broader targeting capability.The ability to alter RNA and protein levels in cells and organisms without making permanent changes to DNA has proven invaluable for both basic research and therapeutics. For the past two decades, targeted RNA knockdown (KD) in eukaryotes has been accomplished using RNA interference (RNAi), an approach whereby small interfering RNAs (siRNAs) direct endogenous Argonaute nucleases to cleave complementary target RNAs13, operate as programmable DNA or RNA nucleases16. Similar to RNAi, Cas nucleases use small RNAs, or CRISPR RNAs (crRNAs), to recognize nucleic acid targets via base-pairing complementarity. One such nuclease, Cas13, has gained attention as a new RNA-cleavage tool for use in eukaryotes19. However, unlike Argonaute proteins that cut only complementary RNAs in cis20, Cas13 also degrades nearby noncomplementary RNAs in trans21 30, which is now believed to be Cas13’s primary mode of defensive action against viral infection. Only recently, however, has evidence mounted that Cas13 exhibits trans-cleavage activity in eukaryotic cells, causing cytotoxicity and/or cell death35. The convolution of cis- and trans-cutting effects has made it difficult to interpret results obtained using Cas13, and call into question its utility as a tool for specific RNA KD.Clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated proteins (Cas), which comprise adaptive defense systems against infectious agents in prokaryotess21 Fig. . This is36, multisubunit Cas effectors have been harnessed only rarely as tools in eukaryotes (with few exceptions39) compared to single-subunit effectors, due to their component complexity. Nonetheless, the well-studied biochemical and structural properties of type III RNA-targeting CRISPR-Csm complexes make them of particular interest for potential transcript targeting tools. The multiprotein Csm complex comprises five subunits (Csm1-5) in varying stoichiometries and relies on an additional protein, Cas6, for processing the precursor crRNA47  synthase51 . Notably, all three catalytic activities are performed by independent domains of the Csm complex and can be individually ablated.Despite their higher prevalence in natureA47 Fig. . The crRA47 Fig. . Csm1 fue51 Fig. . The ssDcis within the crRNA:target complementary region and thus does not suffer from trans-cleavage activity40. Additionally, unlike Cas13, Csm-mediated RNA cleavage does not preferentially occur at a particular nt base 27 nor is directly influenced by sequence flanking the target 52. In this work, we demonstrate the utility of the Csm system as a highly efficient, specific and versatile RNA KD tool in eukaryotes.Csm is an attractive RNA KD tool over current methods. A self-contained system found only in prokaryotes, it can be orthogonally introduced into eukaryotes without intersecting host RNA regulatory pathways. Furthermore, unlike RNAi, it can be localized to the nucleus and used to target nuclear noncoding RNAs and pre-mRNAs. Compared to Cas13, Csm cleaves only in Streptococcusthermophilus for several reasons as follows: (1) it has been extensively characterized biochemically, structurally and in bacteria47, (2) it functions optimally at 37 °C, (3) it has been demonstrated to work in zebrafish embryos and human cell culture upon ribonucleoprotein (RNP) delivery54 and (4) it has fewer components than the analogous type III-B Cmr complex55. We began by verifying proper expression of each individual protein component (Csm1-5 and Cas6) in immortalized human embryonic kidney (HEK293T) cells. Proteins were human codon optimized, N-terminally FLAG-tagged for detection and expressed from a cytomegalovirus promoter. While RNAi operates in the cytoplasm where mRNAs mainly reside, we chose to localize Cas6 and each Csm component to the nucleus through the addition of an N-terminal SV40 nuclear localization signal so as to target nuclear RNAs and pre-mRNAs before export. Following transient transfection, Western blot  mRNA in a GFP-expressing HEK293T cell line. Seven plasmids individually expressing Csm1-5, Cas6 and either a GFP-targeting or nontargeting crRNA from a U6 promoter were cotransfected into cells, and GFP fluorescence assayed by flow cytometry 48 h post transfection  expression of each protein from separate promoters or (2) expression of all proteins from a single bidirectional promoter separated by 2A peptides Fig. . We also56), which may not accurately reflect abundance at the RNA level. We thus sought to target endogenous transcripts and assay RNA KD directly. We chose to target a panel of three nuclear noncoding RNAs  and eight cytoplasmic mRNAs   Fig.  of varyiD4) Fig. , testingD4) Fig.  and 2a,cD4) Fig. . These rTo examine KD kinetics, we repeated the above RT-qPCR experiment for two of the RNA targets (XIST and BRCA1) across a 5-d time course. KD peaked 2–3 d post transfection and waned thereafter Fig. , as mighTo corroborate RNA KD with an orthogonal method, we performed RNA fluorescent in situ hybridization (FISH) for all three nuclear noncoding RNAs, which are easily visualized and display characteristic morphologies. HEK293T cells were transfected with Csm plasmid carrying a GFP reporter (to identify transfected cells) and either a targeting or nontargeting crRNA and assayed by RNA FISH after 48 h  to examine the potential off-target effects of Csm-mediated KD in cells. For comparison with other established KD technologies, RNA-seq was also performed for Cas13 (RfxCas13d) and RNAi (short hairpin RNA (shRNA))-mediated KD using crRNAs/shRNAs targeting the same complementary sequence 61 Fig. . We alsotrans-cleavage activity35. Type III systems do not exhibit trans-activity40 and are thus poised to offer robust RNA KD without toxicity. To check this, we tracked cell proliferation/viability using the WST-1 assay across a time course after transfecting cells with targeting or nontargeting Csm, Cas13 or shRNA constructs  and specificity (~10-fold fewer off-targets than Cas13), outperforming competing RNA KD technologies. More notably, KD was not accompanied by detectable cytotoxicity, unlike Cas13-based methods that suffer from inherent trans-cleavage activity35.Here we have shown that the type III-A Csm complex from StCsm was shown to be effective at depleting GFP or viral RNA upon delivery of bacterially purified RNP into zebrafish embryos or human cells, respectively54. While demonstrating proof of principle, RNP delivery of multisubunit CRISPR-Cas effectors is not ideal for several reasons as follows: (1) it is often difficult and short-lived compared to DNA-delivery methods, (2) the RNP may be unstable and prone to disassembly and (3) for every new crRNA, the entire RNP must be repurified from bacteria or reconstituted from individually purified subunits in the proper ratio. We have overcome these hurdles of multicomponent CRISPR-Cas systems by encoding all necessary parts in a single deliverable plasmid.Recently, 32. This effector is interesting from an evolutionary and structural standpoint in that it appears to have arisen from fusion of the canonical type III subunits into one large polypeptide. While simpler to introduce into eukaryotes, Cas7–11’s demonstrated RNA KD efficiency was only 25–75% for most targets (without enriching for transfected cells), making it somewhat less practical as a tool. Meanwhile, two new reports of naturally occurring and engineered high-fidelity Cas13 variants claim to have mitigated trans-cleavage activity (and thus cytotoxicity) while preserving on-target activity57—although a mechanistic explanation for this remains unclear. Cas7–11 and high-fidelity Cas13s await further characterization before widespread use.More recently, a single-protein type III effector, Cas7–11, was characterized and used for RNA KD in eukaryotes6. However, the increased stability of these oligos often leads to unexpected off-target hybridization and cytotoxic effects. Aside from long ncRNAs, nuclear targeting may prove useful for the study of other ncRNA species such as eRNAs, tRNAs, rRNAs, circRNAs, miRNAs and snoRNAs. For instance, it will be interesting to see whether targeting introns containing miRNA or snoRNA clusters enables their degradation before processing/maturation, or whether targeting particular exons alters the abundance of mRNA splice isoforms.A key advantage of our approach over RNAi is the ability to target transcripts in the nucleus. We were able to achieve >95% KD for three biologically significant nuclear ncRNAs . Nuclear RNAs are notoriously difficult to KD, often requiring expensive chemically modified antisense oligos to direct RNase H-mediated cleavageAnother advantage of our system is its ease of multiplexing. Multiple spacers can be cloned into the CRISPR array and processed into individual crRNAs by Cas6. This allows for pooled screening, either by encoding crRNAs against multiple targets at once or encoding multiple crRNAs against the same target. The latter may enable robust KD on the first try without the need to individually screen multiple crRNAs against a target. An unexpected observation was the titratable nature of KD with increasing spacer length. This may allow for easy tunability of KD  when studying concentration-dependent effects of gene products.65. The observed variability in KD efficiency from one crRNA to another may in part be explained by differences in target site accessibility due to local RNA secondary structure or protein occupancy66. A more large-scale analysis must be performed to determine optimal spacer design criteria, and to test how different factors  influence KD efficiency.Csm-mediated RNA KD appears to be robust. We were able to achieve significant KD for nearly all targets tested, with at least one of three crRNAs per target yielding >90% KD. Because, like other RNA-targeting CRISPR-Cas systems, Csm does not have any PAM requirement for target site selection, the only criteria we used were that the target be a unique sequence in the human transcriptome and the spacer avoid stretches of ≥5 consecutive Ts, which might cause premature Pol III transcriptional termination within the crRNA sequenceWe showed that fluorescently tagged, catalytically inactivated Csm can be used for live-cell RNA visualization. By fusing GFP to the most abundant subunit (Csm3), we were able to achieve multivalent display (≥3x GFP per complex), which may offer unique advantages over single-subunit effectors such as Cas13. Beyond GFP, other proteins of interest may be fused to the various Csm subunits to achieve assembly or tethering at a desired stoichiometric ratio. Thus, as a multisubunit complex, Csm offers the benefits of split-protein systems without the engineering effort. Catalytically inactivated Csm might also be useful for disrupting RNA structural motifs or RNA-protein interactions without manipulation at the DNA level.67, leading to the activation of various downstream effectors51. These effectors range from RNases to DNases, proteases and transcription factors71. cA molecules and reliant pathways are currently not known to exist in eukaryotes and thus could be introduced in an orthogonal manner. By bringing type III systems to eukaryotes, we have paved the way for co-introduction of related trans-effectors that can be activated in an RNA sequence-dependent manner .HEK293T, HEK293T-GFP and HEK293T-GFP/RFP cells (UC Berkeley Cell Culture Facility) were grown in medium containing DMEM, high glucose, GlutaMAX supplement, sodium pyruvate (Thermo Fisher Scientific), 10% FBS (Sigma), 25 mM HEPES pH 7.2–7.5 (Thermo Fisher Scientific), 1× MEM nonessential amino acids (Thermo Fisher Scientific), 1× Pen/Strep (Thermo Fisher Scientific) and 0.1 mM βME (Thermo Fisher Scientific) at 37 °C with 5% COS. thermophilus strain ND03 (NCBI). Protein sequences were human codon optimized using online tools (GenScript), synthesized as gene blocks (IDT), modified using PCR and cloned into custom eukaryotic expression vectors (derived from pUC19) by Golden Gate assembly, Gibson assembly (NEB), or Gibson assembly Ultra (Synthetic Genomics). Plasmids were verified by Sanger or whole-plasmid sequencing. All cloning was performed in Stbl3 Escherichia coli (Thermo Fisher Scientific) to prevent recombination between repetitive sequences. crRNA/shRNA sequences are provided in Supplementary Table CRISPR-Cas/Csm sequences were derived from 6 HEK293T cells were transfected with 2.5 μg plasmid DNA using 7.5 μl FuGENE HD transfection reagent in 6-well plates. Following transfection, cells were grown for 48 h to allow plasmid expression and RNA KD to occur, unless otherwise stated.According to the manufacturer’s instructions, 1 × 10Cell fluorescence was assayed on an Attune NxT acoustic focusing cytometer (Thermo Fisher Scientific) equipped with 488 nm excitation laser and 530/30 emission filter (eGFP), and 561 nm excitation laser and 620/15 emission filter (mCherry). Data were analyzed using Attune Cytometric Software v5.1.1 and FlowJo v10.7.1.Cells were sorted by fluorescence on a Sony Cell Sorter SH800Z (100 μm sorting chip) equipped with 488 nm excitation laser and 525/50 emission filter (eGFP), and 561 nm excitation laser and 600/60 emission filter (mCherry). Data were analyzed using Sony Cell Sorter Software v2.1.5.Target–CtGAPDH)Targeting crRNA – (CtTarget–CtGAPDH)NT crRNA). No-RT and no-template controls were run alongside all RT-qPCR experiments.Total cell RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific) as per the manufacturer’s instructions. Genomic DNA was removed using TURBO DNase (Thermo Fisher Scientific). After inactivating TURBO DNase with DNase Inactivating Reagent, 1 μg DNase-free RNA was reverse transcribed using SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) with random primers (Promega) as per manufacturer’s instructions. qPCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad) in a CFX96 Real-Time PCR Detection System (Bio-Rad). Gene-specific primer pairs used to detect mature transcripts are listed in Supplementary Table The WST-1 assay was used to quantify cell viability and proliferation. Cells transfected with Csm, Cas13 or shRNA plasmids were grown in 96-well plates until the indicated timepoints, incubated with WST-1 reagent (Sigma) at 37 °C for 1 h as per manufacturer’s instructions, and absorbance measured using a Cytation five microplate reader (BioTek Instruments) at 450 nm with 600 nm reference.For wide-field fluorescent imaging, cells were observed on a Zeiss Axio Observer Z1 inverted fluorescence microscope, equipped with 63/1.4 NA oil DIC and 100×/1.4 NA oil Ph3 Plan Apochromat objective lenses, ORCA-Flash4.0 camera (Hamamatsu) and ZEN 2012 software. Images represent max-intensity z-projections and were generated using ZEN 2012 (Zeiss) and FIJI (ImageJ) software. For live-cell imaging, cells were grown on chambered 1.5 coverglasses (Nunc Lab-Tek 2) in medium lacking phenol red (Thermo Fisher Scientific) and imaged directly on the inverted fluorescent microscope.−1 human Cot-1 DNA (Thermo Fisher Scientific)). Hybridization was performed in a humidified chamber at 37 °C overnight. After being washed once in 25% formamide/2× SSC at 37 °C for 20 min and three times in 2× SSC at 37 °C for 5 min each, cells were mounted for wide-field fluorescent imaging. Nuclei were counter-stained with Hoechst 33342 (Life Technologies).Cells were grown on glass coverslips and rinsed in PBS. They were fixed in 4% paraformaldehyde for 10 min at room temp and then permeabilized in PBS/0.5% Triton X-100 for 10 min. Cells were dehydrated in a series of 70%, 80%, 90% and 100% ethanol for 5 min each. Labeled oligo probe pool  was added to hybridization buffer containing 25% formamide, 2× SSC, 10% dextran sulfate and nonspecific competitor  for 1 h, incubated with primary antibody in blocking buffer for 1 h, washed three times with PBS/0.05% Tween-20 for 5 min each, incubated with dye-conjugated secondary antibody in blocking buffer for 1 h at room temp and washed three times again with PBS/0.05% Tween-20 for 5 min each. Cells were mounted for wide-field fluorescent imaging and nuclei were counter-stained with Hoechst 33342 (Life Technologies).g for 10 min, protein concentration in the supernatant was measured (Pierce BCA Assay Kit). 20–50 μg protein lysate was denatured in 1× Laemmli buffer at 95 ºC for 10 min and resolved by SDS-PAGE. Protein was transferred to Immun-Blot LF PVDF membrane (Bio-Rad). The membrane was blocked with blocking buffer (PBS/0.05% Tween-20 containing 5% milk) for 1 h at room temp, incubated with primary antibody in blocking buffer overnight at 4 ºC, washed three times with PBS/0.05% Tween-20 for 5 min each, incubated with dye-conjugated secondary antibody in blocking buffer for 1 h at room temp and washed three times again with PBS/0.05% Tween-20 for 5 min each. Protein bands were visualized on an LI-COR Odyssey CLx with Image Studio v5.2 software using 700 nm and 800 nm channels.Cells were washed once with PBS and lysed in cold RIPA lysis buffer ). Lysate was sonicated (Qsonica Q800 Sonicator) in polystyrene tubes at 50% power setting, 30 s on/30 s off for a total sonication time of 5 min at 4 ºC. After removing debris by centrifugation at 16,000For Western blot, the following primary antibodies were used: mice anti-FLAG  and rabbit anti-GAPDH ; the following secondary antibodies were used: IRDye 680RD goat antimouse , IRDye 800CW goat antirabbit . For immunofluorescence, the following primary antibodies were used: mice anti-FLAG ; the following secondary antibodies were used: Alexa Fluor 555 goat antimouse .−1 proteinase K) at 55 °C for 2 h and genomic DNA extracted with phenol-chloroform. The CKB locus was amplified from genomic DNA by PCR  gene locus with BWA MEM (v0.7.17) and PCR duplicates were removed with Picard Tools (v2.21.9). Mismatches and indels at each position were tabulated with Pysamstats (v1.1.2).Reads were aligned to the Total cell RNA was extracted using TRIzol Reagent (Thermo Fisher Scientific). PolyA mRNA was isolated using NEBNext PolyA mRNA Isolation Module (NEB), as per the manufacturer’s instructions. Strand-specific cDNA libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kit for Illumina, as per the manufacturer’s instructions. Libraries were sequenced by Novogene, Genewiz or UCSF Center for Advanced Technology on a NovaSeq6000 with a 150 bp paired-end run configuration to a depth of ~30 million reads each, with one biological replicate per sample.Reads were assessed for sequencing quality with FastQC (v0.11.9), then adapters and low-quality bases were trimmed with CutAdapt (v4.1). Samples were aligned to the GRCh38 reference genome (GENCODE Release 39) with STAR (v2.7.10a) and uniquely mapped reads were used to generate a strand-specific count matrix with featureCounts (v2.0.3). EdgeR (v3.36.0) calcNormFactors was used to normalize samples according to seven housekeeping genes  before calculating RPKM. Transcripts with a ≥2-fold change relative to EV control were considered upregulated or downregulated.All graphs display the mean and standard deviation of three biological replicates. For RNA-seq analysis, no statistical parameters were applied given one biological replicate.Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41587-022-01649-9.Supplementary InformationSupplementary Figs. 1–4 and Supplementary Tables 1 and 2.Reporting Summary