Investigating the structural changes due to adenosine methylation of the Kaposi's sarcoma-associated herpes virus ORF50 transcript.

Kaposi's sarcoma-associated herpes virus (KSHV) is a human oncovirus. KSHV relies on manipulating the host cell N6-methyl adenosine (m6A) RNA modification pathway to enhance virus replication. Methylation within a RNA stem loop of the open reading frame 50 (ORF50) increases transcript stability via the recruitment of the m6A reader, SND1. In this contribution we explore the energy landscapes of the unmethylated and methylated RNA stem loops of ORF50 to investigate the effect of methylation on the structure of the stem loop. We observe a significant shift upon methylation between an open and closed configuration of the top of the stem loop. In the unmethylated stem loop the closed configuration is much lower in energy, and, as a result, exhibits higher occupancy.

Introduction

Kaposi’s sarcoma-associated herpes virus (KSHV) is a human oncovirus associated with Kaposi’s sarcoma, a highly vascular tumor of endothelial lymphatic origin, and several other AIDS-associated malignancies [1]. Like all herpesviruses, KSHV has a biphasic life cycle consisting of latent persistence and a lytic replication phase. Notably, both phases are required for KSHV-mediated tumorigenesis. Expression of a single KSHV-encoded protein, the replication and transcription activator (RTA) protein [2], is necessary and sufficient for the transition between latency and lytic replication, leading to the activation of the complete lytic cascade resulting in infectious virions.

RTA is encoded from the open reading frame 50 (ORF50)—and its expression is stimulated by various cellular cues such as plasma cell differentiation [3] and hypoxia [4]. RTA activates transcription of lytic genes by directly interacting with RTA-responsive elements (RREs) found in multiple lytic gene promoters, or indirectly via interactions with cellular transcription factors, particularly RBP-Jκ, AP-1, and Oct-1 [2].

N6-methyladenosine (m6A) is the most prevalent internal modification of eukaryotic messenger RNAs (mRNAs). Due to recent transcriptome-wide m6A mapping of multiple viruses, it is becoming evident that there is an interplay between m6A-decorated viral RNA and the host cellular m6A machinery, resulting in the modulation of viral replication output [5]. Several studies have demonstrated that the KSHV transcriptome is heavily m6A methylated [6]. The m6A sites are targets for protein recognition by so-called m6A reader proteins, with different proteins leading to a variety of biological fates, from promotion of RNA degradation [7, 8] to enhancement of translation [9]. In addition to direct recognition, indirect recognition is also possible via the so called m6A switch mechanism. Here, the methylation modification has been shown to destabilise and alter RNA structures allowing the recruitment of protein binding partners [10, 11].

In KSHV, m6A modification of the ORF50 RNA transcript leads to recruitment of the m6A reader Staphylococcal nuclease domain-containing protein 1 (SND1) [6]. SND1 binding to the ORF50 RNA stabilises the transcript, resulting in effective lytic replication. SND1 recruitment to ORF50 RNA is m6A dependent as binding is impaired upon depletion of the m6A methyltransferase, METTL3. Furthermore, depletion of SND1 results in destabilised RNA, lower levels of RTA and impaired lytic replication [6]. m6A modification of the ORF50 transcript occurs at a classical DRACH (D = A, G or U; R = A or G; and H = A, C or U), GGACU, motif situated in a 43mer hairpin. The hairpin is relatively unstable, due to a large number of unpaired nucleotides and resulting weak base pairing, and further destabilisation, likely associated with structural changes of the stem loop, is necessary for SND1 binding [6].

While it is likely that the m6A modification alters the RNA structure and as a result its binding affinity to SND1, it is not clear what structural change occurs, and how it allows the recruitment of a protein binding partner. An additional factor complicating our understanding of this process is the fact that only two-dimensional structural models are available for the RNA stem loop. Hence, simulations are valuable not only in identifying the changes between the methylated and unmethylated system, but furthermore in describing the structural ensemble in general. As the structural heterogeneity displayed by RNA stem loops and the associated slow dynamics complicate experimental and computational studies, an enhanced sampling approach may yield additional insight into the structural and kinetic properties of the stem loop, and the effect of the methylation. This information may be obtained by an explicit exploration of the energy landscape of the unmethylated and methylated stem loop. While this approach has not previously been applied to study epigenetic changes, it has been successfully used to study mutations in a different stem loop [12], as well as other non-canonical nucleic acid structures, and is known to produce insight into structural ensembles and the transitions between them [13, 14].

Here, we present the free energy landscapes for the unmodified and the m6A modified stem loop within the ORF50 transcript. We observe two main structural ensembles, differentiated by the orientation of A22, the m6A modification site. In one ensemble, the nucleotide is inside the stem loop, not accessible to potential binding partners (in-configuration), while in the other ensemble, it is pointing away from the stem loop (out-configuration). While the unmodified RNA shows a strong preference for the in-configuration, the m6A modification allows for a higher population of the out-configuration, through structural alterations based on changes in the intricate pattern of stabilising interactions. We further observe a key change in the lower part of the stem loop, where a large bulge can also stabilise or destabilise the stem loop. Both processes are interconnected, and hence required for binding, as observed in experiment [6].

Materials and methods

The energy landscapes of the unmodified and methylated stem loops were explored using the computational potential energy landscape framework [13, 14]. Below we give a brief summary of the simulation techniques in this work; a detailed guide to exploring energy landscape in this way can be found elsewhere [15]. In addition, S1 Text section A contains further detail, including discussions of certain choices within the modelling process.

The two-dimensional structures predicted by Baquero-Perez et al. were used as starting points with three leading U nucleotides at the 5′ end. This leading UUU motif means the full size of the simulated stem loop is 44 nucleotides, but we start the labelling at 0 to be consistent with previous work. From these two-dimensional structures, initial three-dimensional configurations were obtained from RNAComposer [16] (see S1 Text Fig A). RNAComposer allows the translation of the two-dimensional into a three-dimensional structure via fragment assembly. This structure exhibits the correct canonical interactions, but will not necessarily be the optimal structure with respect to non-canonical interactions and flexible regions, such as bulges. Therefore, basin-hopping (BH) global optimisation [17-19] was used to obtain low energy structures using physical modelling, optimising these remaining interactions. We conducted three sets of five BH runs. The first set used the unmodified structure from RNAcomposer as input. For the second set, we used the same structure but applied the m6A methylation. These two sets of basin-hopping runs were used to seed the energy landscape explorations. The final set was run for the unmodified set, but starting from an unfolded structure. This final set probed partially folded structures, but as the energy difference between them and the low energy folded structures was very large (around 80 to 100 kcal/mol), these structures are unlikely to be significant and we did not repeat this set of runs for the modified molecule. Each of the runs within the sets consisted of 150,000 BH steps, with grouprotation moves [20, 21] to create new configurations, and a convergence criterion for minima of 10−6 kcal mol−1 Å−1. The 100 lowest energy minima from each run where used to seed the databases to explore the energy landscapes by locating discrete paths.

Discrete pathsampling [22, 23] was employed to create a kinetic transition network [24, 25]. Transition state candidates were obtained using the doubly-nudged elastic band (DNEB) algorithm [26-28], and the actual transition states were located via hybrid eigenvector-following [29]. We obtain the free energies using the superposition approach with a harmonic approximation [30]. The rate constants are calculated using the new graph transformation (NGT) algorithm from the kinetic transition network [30, 31]. We represent the energy landscapes as disconnectivity graphs [32, 33], which faithfully represent the topography of the energy landscape (i.e. the folding funnels and their substructure) and the energy scale. In these graphs, a structure is associated to a point on the vertical axis according to its free energy. A vertical line is drawn upwards from each point. Lines are merged when the energy of all transition states connecting the structures is exceeded, which is drawn in a discrete manner to allow proper visualisation. The resulting horizontal ordering is such that structures with easier transitions are closest, and therefore faithfully shows the funnel structure of the energy landscape.

Throughout, the ff99 [34] force field with the Barcelona α/γ backbone modification [35] and the χ modification for RNA [36, 37] was used with implicit solvent (igb = 2 [38]). The parameters for the modified nucleotide were taken from the standard AMBER library [39].

Structures in the energy landscape are assigned to funnels and define structural ensembles. Low-energy structures in each ensemble are analysed using Barnaba [40] to detect base pairs, stacking and to compute all torsions in the backbone, sugar and pucker. For each structure, the overall number of canonical, non-canonical and stacking interactions is recorded for each nucleotide. All these values are then averaged to give the average local behaviour of the ensemble.

Results

The key results of this study are the free energy landscapes for the unmodified and the 22m6A-modified RNA stem loops. Disconnectivity graphs [32, 33] for these two systems at 310 K are shown in Figs 1 and 2, respectively. The three distinct structural ensembles associated with funnels are called A, B and C in the unmodified system, and the corresponding structural ensembles in the m6A-modified system are A*, B* and C*.

Fig 1

Free energy disconnectivity graph for the native loop.

The free energy disconnectivity graph at 310 K is shown for the unmethylated RNA stem loop. Representative structures are shown for each funnel, in which A22 is highlighted in red. Two main funnels are observed, one where A22 is orientated inside the loop (A and B) and another where A22 is pointing outwards (C). The former set of structures is much lower in free energy. The outwards orientation of A22 at the top of the stem loop is visible in the example structures shown on the graph for C, in contrast to the closed arrangement in A. The colouring scheme highlights the in/out-configuration of A22, and is based on ϕ, which is the dihedral angle formed by the basepair below A22, and the nucleotide itself, where values around 0 indicate an in configuration (green), and values close to ±180° are out-configurations (blue and red). Some structural variation is observed in both major funnels, resulting in the emergence of smaller subfunnels. These variations are mainly located in the lower parts of the stem loop, leading to different stem configurations (see for example A and B).

Fig 2

Free energy disconnectivity graph for the modified stem loop.

The free energy disconnectivity graph at 310 K for the methylated RNA stem loop exhibits significant changes compared to the unmethylated stem loop (see Fig 1). Representative structures are shown for each funnel, in which m6A22 is highlighted in red. The structural ensembles are preserved, but their relative energies are significantly altered. Firstly, the free energy of the out-configuration (C*) is lower, and in many cases comparable to the in-configurations. The set of lowest free energy structures is still an in-configuration (A*), but a large number of in-configurations (B*) are similar in energy to the out-configurations (C*), mainly due to changes in the lower part of the RNA stem loop.

In both landscapes, we see a broad separation of the structures into configurations where A22 is pointing inwards (in-configuration) and the nucleobase interacts with the surrounding nucleotides, and configurations where A22 is pointing away from the stem loop (out-configuration).

A significant difference is the relative energies of these structural ensembles. In both cases, the in-configuration is observed at the bottom of the free energy landscape. The out-configurations in the unmodified stem loop are between 22 and 30 kcal/mol higher in free energy. In contrast to this large gap, the methylation significantly lowers this energy gap to around 10 kcal/mol. In addition, multiple alternative stem configurations exist for the in-configuration, leading to substructure with some distinct subfunnels. For the unmodified system, these subfunnels are roughly 7 to 10 kcal/mol higher in free energy than the global minimum, while the m6A modification of A22 leads to more distinct substructure at slightly higher energies around 9 to 12 kcal/mol. A full discussion of the important structural features is given later. It should be noted here that the energy of the local minimum for the structure obtained originally from RNAComposer is about 70 kcal mol−1 higher than the global minima for the unmodified and the modified system. There are two reasons for this observation. Firstly, the structure is in an out-configuration, and therefore already significantly higher in energy than the lowest energy minima in the A and A* ensembles. Secondly, while the canonical base pairing is correct, due to the constraints from the two-dimensional structure, the non-canonical interactions and the flexible regions are not optimally arranged.

As a consequence of the change in relative energies of the structural ensembles, we predict a significant change in the transition rates between the different structures. All predicted rate constants and the associated equilibrium constants are given in Table 1. The transition from the in- to the out-configuration for A22 in the unmodified system is 10 orders of magnitude slower than the reverse process (1.535 ⋅ 10−10 s−1 vs. 1.427 s−1). Thus, we do not expect any significant population of the out-state at biologically relevant temperatures.

Table 1

Rate constants and equilibrium constants between A, B and C at 310 K.

System	Set of minima	kout←in (s−1)	kin←out (s−1)	K eq
A22	A + B ↔ C	1.535 ⋅ 10−10	1.427	9.30 ⋅ 109
m6A22	A* ↔ C*	4.588 ⋅ 10−15	2.943 ⋅ 10−8	6.41 ⋅ 106
m6A22	B* ↔ C*	2.262 ⋅ 10−8	2.943 ⋅ 10−8	1.30

Rate constants and the related equilibrium constants at 310 K for the unmodified (A22) and the modified (m6A22) system. For the methylation, the two different transitions are given, as described in the full text.

The picture for the m6A-modified stem loop is different, but also complicated by the topography of the energy landscape. For the unmethylated system, the substructure in the main funnel only consists of small, shallow subfunnels. In the modified system, the subfunnels containing structures with the in-configuration are more clearly separated. This separation arises from alterations in the lower stem loop. When considering a transition from A* to C*, the changes in the lower and upper stem loop therefore need to be treated as two distinct, but connected events. Considering a lowest minimum to lowest minimum transition (A* to C* in the disconnectivity graph), we still observe a significant bias towards the in-configuration, although the equilibrium constant is three orders of magnitude smaller (6.41 ⋅ 106 compared to 9.30 ⋅ 109). If we consider a transition including the higher energy minima for the in-configuration (B*) the forward and backward rates are nearly identical and we compute an equilibrium constant around unity. A key change in both cases is a significant slow down in the reaction rate from C* to A* and B* by eight orders of magnitude compared to C to A. This result means that the change in relative energies will lead to a significant population of the out-configuration in the A22-N6-methylated system.

Heat capacity curves show structural transitions

The changes in the rate and equilibrium constants will significantly impact the population of the different structural ensembles. A useful way to illustrate these changes, and further link them to an energy scale, is to consider the heat capacity curves for both systems. Peaks in heat capacity curves are associated with phase transitions between different states, and in the case of molecular ensembles may be interpreted as the transitions between different structural ensembles. Furthermore, the heat capacity is directly linked to the occupation probabilities of different configurations, and each peak in the curve can therefore be analysed in terms of increasing and decreasing occupation probabilities [41]. This analysis allows to identify two sets of structures, each one dominant on opposite sides of the phase transition (peak). As we go further from the peak on one side, the structure dominant on the other side become less and less likely to be observed, while at the peak the two structures co-exist with equal probability.

In Fig 3, the heat capacity curve for the unmodified stem loop is shown, alongside representative structures for the transitions obtained by the analysis introduced above. In these schemes, the structures on the left hand side of the arrows are the structures dominant below the peak, while the right hand side structure are the ones observed above. As the calculation of the heat capacity curve requires some approximations, and furthermore our simulations use implicit solvent, the curves should only be interpreted qualitatively, with the most important information coming from the difference between the two curves for the two systems. Some additional commentary is provided in the S1 Text, Section D.

Fig 3

Heat capacity curve for the unmodified RNA stem loop.

Heat capacity curve for the unmethylated RNA stem loop shows three distinct features. Analysing the contributions from local minima to the associated transitions [41] reveals a medium temperature transition between different configurations of the lower stem loop configuration (P1), and a high temperature transition between in- and out-configurations for A22 (P2). The shown structures are representative of the structural ensembles on either side of the transition. Where more than one structure is shown, this choice was made to provide a fair representation of the structural variation observed. For more detail on the shoulder observed for P2, see the S1 Text, section D and Fig D.

Two important transitions are observed, labelled P1 and P2 in Fig 3. P1 is the lowest energy feature, which is associated with a rearrangement in the lower stem region. This transition corresponds to higher occupancy of the higher energy structures in the main funnel on the landscape. The second feature, P2, is the transition from in to out.

We observe the same two transitions for the m6A-modified stem loop, as shown in Fig 4, also labelled as P1 and P2. Importantly we observe a distinct shift to lower energies for both transitions. We can associate the energy scale for the peaks with Boltzmann population proportions, and find that the P2 peak in the modified system is at energies accessible to around 26% of molecules, while in the unmodified system this percentage shrinks to only 7%. This shift is the key observation, as the transition associated with P2 is likely required for binding, and is significantly more likely in the modified system.

Fig 4

Heat capacity curve for the A22-methylated RNA stem loop.

Heat capacity curve for the A22-methylated RNA stem loop shows three distinct features similar to the unmodified stem loop (see Fig 3), but the peaks are shifted to lower temperatures. For reference, the peak positions in the unmodified loop are shown as dashed lines. The transition between different configurations of the lower stem loop configuration (P1) is now at temperatures well below room temperature, and the transition between in- and out-configurations for A22 (P2) at medium temperatures. The shown structures are representative of the structural ensembles on either side of the transition. Where more than one structure is shown, this choice was made to provide a fair representation of the structural variation observed. For more detail on the third peak at higher transition energies, see S1 Text, section D and Fig D.

Structural variations observed in the energy landscapes

Having established that the m6A-modified stem loop shows significant alterations in the occupation of the structural ensembles on the energy landscape, we will need to look at the structural ensembles more closely to identify the key changes introduced by the N6-methylation of A22, and how these changes alter the transition mechanism from the in- to the out-configuration.

The details of the local behaviour in the four structural ensembles A, C, A* and C* are given in Fig 5 where we can observe the variations in base pairing (canonical and non-canonical), stacking and pucker configuration for each nucleotide (details of the 3D structures are reported in S1 Text Fig B). The first global observation is that the canonical base pairing is well preserved across the four ensembles, with only one or two variations and with A exhibiting the most canonical pairs. Based on these canonical base-pairings we can identify secondary structural elements common to all ensembles. A first helical region is where pairing between nucleotides G4 to A7 with nucleotides U39 to U42 is observed (H1), with an additional base pair between U3 and A43 often occurring. A second helical region (H2) is more variable, but its core contains nucleotides G13 to C17 paired with G32 to C37, where either C33 or U34 are not paired. Two other key regions are the apical loop (L) sitting above the m6A-modification site containing nucleotides C23 to U27, and the bulge in the lower stem (B), which is formed by A8 to C11.

Fig 5

Local structural properties.

For each of the four low-energy ensembles, A, C, m6A A (A*) and m6A C (C*), we report the behaviour of each nucleotide in terms of number of canonical and non-canonical base pairing, independently of the partners, number of stacking interactions formed, and puckering conformation, as extracted from Barnaba software. Reported numbers are the average over the ensemble of structures in the corresponding funnel, computed for each nucleotide. Each panel is composed of an upper part for the native unmethylated system (A and C) and a lower part for the methylated system (A* and C*), separated by a horizontal dashed line. For puckering we express the values in terms of the classification in conformations instead of angular values (for this reason C3’ corresponding to an angle of 0 or 360 is repeated). Shaded regions represent the different secondary structure elements with helices in grey (H1 and H2), bulge (B) in pink, and apical loop (L) in yellow.

The overall puckering is also well preserved across the ensembles with only a few, but key, transitions from C3’-endo to C2’-endo in going from A to C and from A to A* and C*. These changes involve nucleotides that significantly rearrange from one ensemble to another by either switching base-pairing partner or swinging outward or inward with respect to the helical stems. Non-canonical pairing and stacking exhibit more variability across the ensembles with changes spread all across the structure. We can observe a loss of non-canonical pairing in going from A to C as well as in going from A* to C*, and to a lesser extent going from A to A*. The parameter showing the largest variability is stacking with many significant changes occurring in all parts of the molecule, including the two helical stems and the apical loop.

From all these observations we detail below the most significant structural changes across the four ensembles.

The in-configuration in the unmodified and m6A-modified stem loop

A comparison of the secondary structure of the unmodified and modified stem loop in their lowest energy ensembles (A and A*) is shown in Fig 6(a), highlighting some important changes. The first set of changes are located in the upper stem loop in helix H2. A triplet formed between the paired G16-U34 and C33 in the unmodified stem loop is altered in the modified stem loop, as the G16-U34 base pair is replaced by a G16-C33 canonical base pairing. Moreover, this change is accompanied by the base pairing of C18-G31 in the modified molecule. In addition, more non-canonical interactions in the apical loop L are observed for the methylated system, leading to a tightly packed loop, where C23 to U27 are all pointing inwards. Last but not least, the bulge B undergoes significant rearrangement, with U12 and A38 canonically paired in the modified system, replacing the non-canonical A8-A38 pairing in the unmodified system. As a result, the methylated molecule has C11 pointing out alongside C10. The helix H1 is less affected, and the only change is the existence of the additional non-canonical base pairing between A8 and A38 in the unmodified system on top of the helical stack.

Fig 6

Two-dimensional structures for the key structural ensembles.

Schematic two-dimensional structures are shown for the key structural ensembles, and their key features are highlighted and compared. Canonical base pairing is indicated in blue, and non-canonical base pairing interactions in yellow. A22 is highlighted in red, and other important residues are also highlighted, namely U34 (orange), U12 and A38 (both green). Key variations between structures are indicated with red dashed arrows, indicating the change in nucleobase orientation. The triplet formed by interaction between C33 and the G16-U34 basepair is indicated by a blue frame and important stacking in a red frame. The data used in this figure is derived from the ensemble properties, which are shown in Fig 5 in more detail. (a) Comparison of the lowest energy ensembles for the unmodified and m6A-modified stem loop. (b) Changes observed in the lower bulge for the in-configuration (A) and the out-configuration (C) of the unmodified system. (c) Changes in the upper stem loop between the in-configuration (A) and the out-configuration (C) for the unmodified system. (d) Changes in the upper stem loop going from the in-configuration (A*) to the out-configuration (C*) for the m6A-modified stem loop.

The changes between them might therefore be summarised as follows. The helical region H2 is significantly extended in the m6A-modified loop. The stacking in the modified stem loop is altered compared to the unmodified loop (see Fig 5). This modification is a result of the alteration of A22, such that m6A22-G28 is in a different configuration, with m6A22 sitting somewhat further outside the stacked nucleotides, likely due to the additional space requirement by the methyl group. This alteration allows stacking for G28 and m6A22, with alignment of more nucleotides along H2, leading to a larger stacked region including more paired bases, albeit at the cost of more strain in the backbone. This extension of the helical region impacts the lower stem, in particular the bulge B, and leads to C11 pointing outwards. This change in B is very similar to the change in this region observed in the out-configuration for the unmodified stem loop (see Fig 6B).

Changing from in- to out-configuration in the unmodified stem loop

The difference between the in- and out-configuration in the unmodified stem loop is characterised by changes in two main regions: the bulge B in the lower stem loop illustrated in Fig 6B, and changes in helix H2 and the apical loop L as shown in Fig 6C. The changes in B are C11 pointing outwards, and the non-canonical interaction between A8 and A38 being lost. As a result, we observe kinking between H1 and H2, and associated changes in the backbone. This change in B and at the lower end of H2 is also observed in higher energy structures with the in-configuration for A22, and can be followed by rearrangements at the top of H2, which are the loss of the G16-U34 base pair and the formation of the G16-C33 base pair instead. This alternative pairing leads to stacking from this new base pair up to the apical loop on top of C33. The stacking stabilises this configuration in which A22 can swing out. While this leads to a loss of contacts in the apical loop, we do not observe a clear tendency for all nucleotides in this region to change, and the stacking and non-canonical interactions are preserved for C23, U24 and G25.

Changes in the m6A modified system and the importance of changes in the lower bulge

As described above the changes observed in the lower stem loop for the unmodified system from A to C are largely similar to the changes observed going from the unmodified to the m6A-modified system (i.e. A to A*). However, there are two alternative configurations for the bulge region (one observed in A*, and the other in B* and C*). The stem loop is kinking in the bulge in the out-configurations, while in A* there is no kink. In fact, this change in structure is the difference between the ensembles A* and B*. The kinking is introduced by stacking between U9 and U12, leading to a significant change in the structure, but without the requirement to change other parts of the molecule. The new configuration will be higher in energy due to the introduced backbone strain in the bulge.

The structural changes associated with the transition to the out-configuration in the modified system are more modest than in the unmodified case (see Fig 6D). Two additional changes are observed in going from A* to C* in addition to the changes in the bulge discussed. The first one is located in the helical region H2, where the C18-G31 base pair is lost, changing the interactions within the helix somewhat and leading to some more stabilisation, likely due to the loss of the non-canonical interaction between m6A22 and G28 and the associated stacking. The second change is probably more interesting, as the nucleotides in the apical loop are all swinging out, similar to a fist that is transformed into an open hand, replacing the non-canonical interactions with stacking.

A final comment is reserved for the configuration of m6A22. We observe m6A22 solely in its syn configuration. This configuration is lower in energy, as it prevents a steric clash, but prevents WC base pairing. We observe base pairing of m6A22 through its sugar edge, and hence it can adopt this configuration in both the in- and the out-configuration.

Discussion

The first question that needs to be answered with regard to the binding of SND1 to the ORF50 transcript is why this binding is not occurring in absence of the modification. Because no binding in absence of the modification is observed experimentally [6], and our analysis shows the key change is the change in the in/out-configuration of A22, we are led to the conclusion that the out-configuration is likely associated with binding, and so the accessibility of this configuration is important. While the out-configuration exists in the unmodified stem loop, the change in free energy between the in- and out-configurations compared to the m6A-modified stem loop significantly affects its occupancy. Aside from the occupancy, it is important to consider the stability of the structures, i.e. how easy it is for the molecule to escape the funnel. The high stability of the in-configuration and the comparatively low stability of the out-configuration result in a very fast transition from out to in, while the opposite transition is very slow. As a result the life time of the out-configuration is incredibly short, and the likelihood of transitions to it incredibly small, meaning its population is approximately zero. This observation is supported by the heat capacity curves and equilibrium constants calculated from the free energy landscapes. Hence, the most likely reason no binding is observed in the unmodified case is that the required RNA configurations are simply not available.

Changes in bulge in the lower stem are required for out-configurations

Given these observations, the next question is what is required for a transition to an out-configuration. A key observation is the absence of any out-configuration without a kinked bulge region, while such structures are lowest in free energy for the in-configuration. Likely therefore, the change in the structure lower in the stem is required to allow for changes in the configuration in and around the apical loop, where the GGACU binding motif is located. The large number of unpaired nucleotides in the bulge makes the arrangement of these nucleotides challenging, and generally at least one nucleotide sits outside the stem. The kinking in the bulge region reduces the interactions within the region and is associated with two nucleotides pointing away from the stem loop. However, this loss of interactions is associated with more flexibility and hence entropically favoured. The unkinked bulge is more stable due to the increased interactions, but as the arrangement of the nucleotides is difficult due to the crowding, it locks the stem loop structure. Hence, a change in the upper part of the stem loop is therefore linked to structural changes of the bulge region, allowing for more flexibility in the rest of the stem loop. Importantly this result matches experimental findings [6]. Further evidence for the importance of the bulge is that the absence of the bulge removes the bias in the unmethylated system. A detailed description of the energy landscape for a shortened sequence without the lower bulge is provided in S1 Text section C, with the disconnectivity for the shortened system provided in Fig C.

m6A22 destabilises the central helix H2 and alters the bulge structure

This interaction between the apical loop and the bulge through the helical region H2 naturally leads to the question how the N6-methylation of A22 alters this behaviour. As described in Results, the methyl group requires more space, and the nucleobase is therefore moving relative to the stacked bases below. m6A22 still forms a non-canonical base pair with G28, where the changed position of the base pair alters the stacking in the helical region all the way down to the bulge. The alteration in the base pairing and stacking interactions lowers the relative stability compared to the unmodified structures in the in-configuration. It further primes the bulge region for the required kinking, due to changes in the helical region. These structural changes establish a connection between the modification and the changes in the lower region of the stem loop. This connection means there is a requirement for the bulge to adopt a different structure to allow for the out-configuration, while the modification in the apical loop affects the bulge region, leading to a connection between these mechanisms. These intertwined mechanisms provide the structural explanation of how the stem loop is destabilised upon the m6A-modification, and how this process is linked to the lower stem loop and the RNA activation process for binding.

It should be noted at this point that these changes in H2 also affect the relative stabilities of A⋆ and B⋆. In Fig 2 this change can be seen in the representative structures shown. The orientation between H1 and H2 changes due to changes in the bulge, leading to a kink in B⋆, while A⋆ exhibits a fairly straight stem loop. These changes in the bulge require loss of the additional base pair between U12 and A38 in A⋆. This additional base pair and the related changes in H2 that have been described stabilise A⋆ compared to B⋆.

At this point, we can draw a comparison to the experimental findings by Baquero-Perez et al. [6], who provide a number of factors impacting the binding of SND1 to the ORF50 RNA transcript. Firstly, experimentally the system is metastable compared to other stem loops, and m6A-modification destabilises the system further. We observe these two features clearly in our physical modelling, namely through the existence of multiple competing structural ensembles and the changes of their relative stability as A22 is N6-methylated. Our modelling provides structural reasons for the destabilisation, and shows how the m6A-modification affects the structure of the stem loop. Furthermore, through the use of altered stem loops, it was highlighted that the lower region of the stem loop, i.e. the bulge is a key feature necessary for binding. Our model shows this link as well, and we can identify the connection of the configurations in the upper and lower parts of the stem loop through changes in stacking propagated by the helical region H2. Finally, we provide an explanation why binding is severely impaired in the unmodified stem loop.

The opening of the apical loop allows for binding

A last comment is reserved for the changes in the apical loop upon adopting the out-configuration. Not only is the change exposing m6A22 to the outside, but also C23 and U24, which are both part of the described binding motif GGACU for SND1. In addition, we observe the full opening of the loop, including C25 and U26. These nucleotides form a stack that is exposed, and can likely be recognised by other molecules, including SND1. This feature leads to an appearance of the apical loop like an outstretched hand, inviting interactions.

Conclusion

We have presented here a full investigation of the energy landscape of a 43mer stem loop of the KSHV ORF50 transcript containing a GGACU binding motif. We propose a structural explanation for the experimental observations that the m6A-modified ORF50 transcript binds to its protein partner, SND1, but does not bind if the adenine in position 22 is not N6-methylated. Our study suggests a change in the position of A22 in the methylated system with the base becoming exposed to the solvent in the modified system. This change is interconnected to other important structural modifications occurring in the loop: (a) with the bases of the apical loop also turning toward the solvent, instead of pointing inside the loop in a network of reciprocal interactions, and (b) alterations in the helix (H2) close to the modified nucleotide, which extend all the way to the central bulge separating the two helical regions of the stem-loop. These conclusions from our modelling analysis are in agreement with the experimental evidence suggesting that structural changes have to occur between the methylated and the native system for the binding to occur. In particular, our suggestion that a restructuring of the whole apical portion of the stem-loop (H2 and loop) is needed to accommodate for the methylated nucleotide is in agreement with the observation that the stem-loop is destabilised and is rearranged in the methylated system.

In our analysis we are able to pinpoint the behaviour of key nucleotides undergoing significant structural changes between the two systems. These structural details open up the possibility of performing new experiments targeting specifically these fine structural details (such as chemical probing). If confirmed, our hypothesis would give a full structural picture of the two systems, the relationship between their structures, and their ability to bind. Moreover, the structure proposed for the modified system could be used for further modelling and experimental studies of the ORF50 system in the presence of the protein, or at least of the portion of the protein known to be involved in binding.

On a more general note, we apply here a new method to study the details of chemically modified RNA structures with a focus on the ensembles of possible alternative structures that the system might adopt. With systems as flexible and polymorphic as RNAs, where multiple structures are frequently observed for a given sequence, this approach is key to be able to correctly interpret experimental data and to link structures with experiments, gaining a coherent view of the system’s behaviour. We had applied this approach successfully in the past on a system for which several alternative experimental structures were resolved and for which mutational data was available, and in this work we report the first example of a study of the changes of the structural ensembles upon epigenetic modifications without any available experimental three-dimensional structures.

Additional information for the methodology, energy landscape for a shortened loop, and more detail on the CV curve.

Section A: Additional detail for the methodology. A comment on why the exploration of energy landscapes is desirable, and how it is practically achieved. Details on how to judge sampling convergence in general are also included, and how these points apply to the present study. Section B: Additional structural information and more detailed structural representations. Section C: Description of the energy landscape for a shortened sequence, including the used methodology, a brief discussion of the results, and a disconnectivity graph of the free energy landscape. Section D: Additional analysis of the CV curves. Description of the third peak in the CV curves. Figure A: Initial structure obtained from RNAcomposer. The two-dimensional structure from experiment and the corresponding three-dimensional structure obtained from it. Figure B: Structural close-ups. Comparison of the apical loop, the upper helix H2 and the bulge for A, C, A⋆ and C⋆. Figure C: Free energy disconnectivity graph at 310 K for the shortened unmethylated loop. Figure D: Example structures for the high temperature transition.

(PDF)

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14 Jan 2022

Dear Dr Röder,

Thank you very much for submitting your manuscript "Investigating the structural changes due to adenosine methylation of the  Kaposi’s sarcoma-associated herpes virus ORF50 transcript" (PCOMPBIOL-D-21-02101) for consideration at PLOS Computational Biology. As with all papers peer reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent peer reviewers. Based on the reports, we regret to inform you that we will not be pursuing this manuscript for publication at PLOS Computational Biology.

We greatly appreciate the significant importance of the research subject and the conclusions derived from the study. However, all the reviewers raised serious concerns about the validity of the main conclusions, and none of them recommended the publication of the manuscript. Particularly, reviewers questioned the potential bias of the result caused by the specific choice of the initial structures.

The reviews are attached below this email, and we hope you will find them helpful if you decide to revise the manuscript for submission elsewhere. We are sorry that we cannot be more positive on this occasion. We very much appreciate your wish to present your work in one of PLOS's Open Access publications.

Thank you for your support, and we hope that you will consider PLOS Computational Biology for other submissions in the future.

Sincerely,

Shi-Jie Chen

Associate Editor

PLOS Computational Biology

Arne Elofsson

Deputy Editor

PLOS Computational Biology

**************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Roder et al. investigate a stem-loop within ORF50 of KSHV by exploring the energy landscapes of the loop unmethylated and methylated at A22. They detail the structural differences between the unmethylated and methylated as well as between the in-configuration and out-configuration. The manuscript is well-written, and the science appears sound. My main concerns are as follows:

1. As I was reading the manuscript, I was hoping to see the sequence and a general secondary structure representation of the stem-loop being discussed. Looking at Fig. 6 was helpful, but this seemed way too late in the manuscript. I realize that the secondary structure between the unmethylated and methylated can be different as well as the secondary structure between the in- and out-configuration. But, having some visual to set the stage early would be helpful.

2. Three sets of BH runs were conducted. For one set, an unfolded structure was used as the starting structure. How did the structures resulting form the RNAComposer starting structure compare to the structures resulting from the unfolded structure? Would there be any benefit to starting with another unfolded (or random) structure?

3. I was hoping to see more detailed structures showing the atom-atom interactions. For example, line 133 states that A22 “interacts with the surrounding nucleotides.” Is it worth showing these interactions in a figure? Similarly, lines 235-239 discuss stacking in the unmodified system compared to the modified system and a different configuration of A22. Perhaps a detailed figure would help here too.

4. The authors conclude that the changes in the lower region and upper loop are connected (see “Changes in the bulge in the lower stem are required for out-configurations” section). What would happen if a smaller system was explored that consisted only of the upper loop (no bulge in the lower stem). If the conclusion is valid, no out-configurations would result. Can this be done to add validity to the conclusion?

5. While reading the “The in-configuration in the unmodified and m6A-modified stem loop” section, it wasn’t always obvious to me when the authors were referring to the unmodified versus the modified loop. Perhaps they could add some distinction there.

6. In Figs. 1 and 2, perhaps the authors could mention that A22 is colored red in the structures.

7. I was a little confused by the drawings in Figs. 3 and 4. In Fig. 4, I understand the two structures in equilibrium as represented by P1. I do not understand the equilibria where one structure is drawn on one side of the arrow and two structures were drawn on the other side. Is the one structure in equilibrium with both structures?

8. The authors didn’t mention the shoulder in Fig. 3 and the additional peak in Fig. 4. Any ideas what they represent?

9. In comparing Fig. 3 to Fig. 4, I did notice that the transition energy of P1 in Fig. 3 is very similar to the transition energy of P2 in Fig. 4. The same is true for the transition energy of P2 in Fig. 3 to the unlabeled peak in Fig. 4. Is there any significance to this? Similarly, in Fig. 4, why isn’t the first peak unlabeled, the second peak P1, and the third peak P2 (making the transition energies of P1 and P2 very similar in both Figs. 3 and 4)?

10. I was a little confused by Fig. 5. What do the pink and orange strips represent? I don’t understand the y-axis for the first three panels (i.e. what are 1, 0.5, and 0 in relation to canonical base pairing?). I was also confused by the fact that, in the top panel for example, why there is a 1 on the top half of the plot and a 1 on the bottom half of the plot. Similarly, in the bottom plot, there are four C3’-endo labels on the y-axis. Why?

11. In Fig. 6, can the blue and red frames be made darker and thicker? I also understood the “in-configuration” and “out-configuration” to refer to A22. In the caption for panel b, the authors use these terms to describe the lower bulge, which was confusing.

Minor corrections:

1. Line 11 – “Stimulated” is misspelled.

2. Line 16 – “Transcriptome” is misspelled.

3. Line 26 – “recruitment the” should be “recruitment of the.”

4. Line 32 – I am not familiar with DRACH. Is it an acronym?

5. Line 161 – “maybe” should be “may be.”

6. Line 183 – “We can associated” should be “We can associate.”

7. Line 205 – “Other key region” should be “other key regions.”

8. Line 227 – “non-canonical interaction” should be “non-canonical interactions.”

9. Fig. 5 – the “stacking” label is misspelled.

Reviewer #2: This manuscript titled “Investigating the structural changes due to adenosine methylation of the Kaposi’s sarcoma-associated herpesvirus ORF50 transcript” describes structural rearrangement in 43 mer stem-loop RNA due to m6A post-transcriptional chemical modification in its one of the bases. Authors have explored in and out configurations of A22 nucleotide in unmodified and 22m6A modified RNA structures. Authors have also revealed structural changes in stem-loops using the energy landscape framework. In order to obtain low energy structures author has used well known basin-hopping techniques and performed sufficient runs. Authors have also studied the kinetic transition network and its transition states using discrete path sampling and DNEB algorithm as well as hybrid eigenvector. Authors have studied rate constants and equilibrium constants of in and out the configuration of the unmodified and modified system, where they observed a significant number of out configurations in case of modified (m6A22) system. Authors have well explained that how m6A modification of A22 base would result in exposed nucleotide outwards that would be key for the highly specific interaction with reader protein such as SND1. This manuscript is well written, and mages were well organized and properly labelled. However, I have the following major and minor concerns:

1) The WT and m6 modified RNA stem-loop structures studied here using computational methods are modelled structures. Are there any experimentally determined structure (3D or even 2D probing based structure) available to make sure that we are starting with the right structure? My worry is that depending on the starting structure (for example different arrangement of initial base pairs in the structure) we may get a different result. On what basis is the starting structure of WT and m6A modified RNA different? Is there any experimental evidence for this available in the literature for the starting structures? Is there any experimental evidence as to which starting structure is more stable: WT vs m6A mutant?

2) Authors have used a previous study that reported that tudor SND1 protein is an m6A RNA reader essential for Kaposi sarcoma-associated herpesvirus as the justification for explaining the results here. That study however looked at m6A methylation and the role of SND1 as an m6A reader on a global scale using high throughput sequencing method. This study looked at things at a global scale on full-length RNA. The right study to compare the results would be when SND1 and the short stem-loop RNA (WT and m6A modified) would be used, showing the differences in binding. Is there any biochemical/biophysical evidence that showed that Tudor SND1 binds to the m6A modified stem-loop and not WT stem-loop RNA?

3) In a result, authors related structural changes with heat capacity curves modified and unmodified systems and well-explained energy scale with Boltzmann population proportions for P2 peak of the modified system and observed a higher percentage of molecules compared to unmodified system. In figure 4, there is a third peak on the right-hand side of P2 peak, which is at a higher transition energy state. Can the authors explain this third peak?

Minor comments

4) Page 2 (introduction): Is it “KSHV” or “KHSV” in the sentence, “Several studies have demonstrated the KHSV transcriptome is heavily m6A methylated.”?

5) Page 4 (last paragraph): the authors discussed the appearance of more distinct sub-funnels within in-configuration in the modified system due to changes in the lower stem-loop. However, there are alterations also present in the unmodified system but with no separate distinction of sub-funnels.

6) Table 1, Figure 1, 3, and 4: Is it “A23” or “A22”? It should be A22.

7) Page 5 (last paragraph line 183): “associated” can be “associate.”

8) Page 6 (last paragraph line 224): The pairing is non-canonical (GU base pair) instead of canonical.

Reviewer #3: The authors present a theoretical work addressing the effect of an m6A modification on the structural ensemble associated to a RNA stem loop. This modification is known to affect binding with a m6A reader (SND1). In this work, a significant effect of the methylation on the population of different states is observed. This effect is suggested to be responsible for the increased affinity of the methylated motif with SND1. The work is difficult to understand and, in my opinion, results are not correctly interpreted. As such, I think that publication is premature at this stage.

My main concern regards the results presented in Figures 1 and 2. The results are difficult to rationalize. Whereas I understand that the structure of the apical loop should be affected by the methylation, I don't understand how the structure of the lower part of the system could be correlated with the structure of the loop. This seems to be a key issue, since it is an important difference between the ensembles represented in Figure 1 and 2. The authors should provide an explanation. My suspect is that this is just a consequence of the random initialization of structures in the modified and non modified simulations.

The same problem emerges in Figure 6: how is the methyl group affecting the structure of the lower bulge (panel a)? There's no explanation for this, and I guess this is a random result.

A much more robust result could be obtained by using the same initial structures for the modified and not modified ensembles, just minimizing them separately with the modified / unmodified force fields. Are the authors doing this? As far as I understood, with the adopted procedure, it is extremely likely that randomness in the construction of the ensembles dominate the result.

Line 74-80: "Three sets [...] landscapes." I am not sure I understand what the authors did. Are the unfolded structures (third set) modified or not? Isn't this choice leading to a different number of initial modified vs unmodified models? What's the rationale of this choice? Are the authors just building a large database and picking the 100 lowest energy structures? How many initial structures (generated with RNA composer) were used?

Line 118-120: Taken literally, the authors are claiming that the methyl group induces an energy shift of 12 kcal/mol. There is no explanation for such a large difference.

Line 133-138 and 293: I think there is a logical flaw here. The authors write the text as if the rates were a consequence of the free energy differences between the local minima. This is not correct: the rates are indeed a consequence of the differences between the local minima and the transition states. The rate between forward and backward transition rates then is the reason for the observed population. For instance, at line 293 the authors write that "the high stability of the in configuration [...] result in a very fast transition rate". This is logically incorrect.

Line 290: According to the prediction of the authors, the out configuration does NOT exist in practice in the unmodified stem loop (22-30 kcal/mol implies a negligible population).

Table 1: I cannot understand how a difference of 22-30 kcal/mol (see line 118) or of 10 kcal/mol (see line 120) can result in the equilibrium constants reported in the table.

Line 158 "A useful way". I cannot see how this representation can be useful. As far as I understand, this heat capacity is not related to anything that can be measured experimentally.

Figure 5: there's no explanation for the labels in the last panel. Why is C3'-endo repeated?

Lines 364-374: As far as I understand from this text, the experiment suggests that there is a conformational change driven by methylation. It does not suggest that the lower bulge should be affected by the methylation. So, this finding, which is puzzling (see above), is not validated in any way.

Minor issues:

Line 11 Typo ("Stimualted")

Line 98: I would add a reference rather than showing the AMBER keyword (igb=2)

Line 201: "were" -> "where"

In several places, the authors write A23 instead of A22 (can be found with text search).

Figure 5: staking -> stacking

Figure 5, caption: "Barbnaba" -> "Barnaba"

--------------------

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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21 Mar 2022

Submitted filename: ReviewsPLOSComputBiol.pdf

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12 Apr 2022

Dear Dr Röder,

Thank you very much for submitting your manuscript "Investigating the structural changes due to adenosine methylation of the  Kaposi’s sarcoma-associated herpes virus ORF50 transcript" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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Arne Elofsson

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Roder et al. investigate a stem-loop within ORF50 of KSHV by exploring the energy landscapes of the loop unmethylated and methylated at A22. They detail the structural differences between the unmethylated and methylated as well as between the in-configuration and out-configuration. The authors adequately addressed my concerns related to the original submission. Upon reading the revised manuscript, I have a few minor concerns:

On line 33, when the authors use a footnote to define DRACH, perhaps they could include that R = A or G.

On line 70, “information contain further detail” should be “information contains further detail.”

On lines 75 and 211 and in SI section S2 (three times) “figure” should be capitalized.

On line 149, “orders of magnitudes slower” should be “orders of magnitude slower.”

On line 266, “alternation leads changes” needs to be corrected.

On line 290, “asides from the changes” needs to be corrected.

On line 337 when the authors refer to the SI, perhaps they could direct the reader to section S3.

On line 342, “described in the results section” can be “described in Results.”

On line 368, “explanation while binding” needs to be corrected.

Figs. S3 and S4 aren’t mentioned in the main text. See below for more about Fig. S4.

In the caption to Figs. 3 and 4, the authors should refer the reader to SI section S4 and Fig. S4 for further information about the shoulder (Fig. 3) and additional peak (Fig. 4).

In the SI, “BARRIER find lower” should be “BARRIER finds lower.”

In the SI, “shorten stem loop” should be “shortened stem loop.”

In the caption to Fig. S3, “disconnectivity” is spelled incorrectly.

Reviewer #2: I had raised a few major concerns in the first review of this paper. One of the concern I had asked for the basis on which the selection of starting RNA structure was made. Authors have explicitly explained that the experimental 2D structure of the RNA was used to generate the 3D model of RNA using RNAcomposer. The results suggest that WT in-configuration is more stable. In my second major concern, I had asked if there is any experimental evidence for binding of tudor SND1 to m6A modified stem-loop RNA and not with the WT stem-loop RNA. Authors have explained this point by citing the published experimental results. In an another question, I had asked for an explanation for the occurrence of third peak at higher transition energy state in figure 4. Authors have explained that this third peak corresponds to the loss of structural features, such as stacking and base pairing, and is associated with a transition form folded to partially folded structures

Therefore, overall authors have satisfactorily explained my concerns.

Reviewer #3: The authors replied to my questions. It turned out that most of my doubts were related to unclear explanations. To be fair, I still do not understand some of the presented results and I would encourage the authors to clarify them for the benefit of the readers:

1. When comparing Fig 1 and Fig 2, I see two major effects: m6A stabilizes C wrt B and m6A stabilizes A wrt B. The former makes sense (I still do not understand how the change can be this large, but I trust the authors). The second I suspect is due to the randomness of the basin hopping algorithm. If this is correct, please comment this in the caption.

2. If I understand correctly, the discrepancy between the large free energy differences and the relatively small Keq (Table 1) is a missing entropic effect associated to configuration count. If this is correct, please explain this in more detail in the paper. Readers will implicitly associate free energies with populations and macrostates, whereas here the authors are reporting free energies for micro states, and the number of these states is different in different macro states. Please also report an estimate in the count of the number of states that could justify this discrepancy.

3. Related to the previous point, is the horizontal density of points in Figs 1 and 2 uniform? If so, can I deduce that basin hopping is NOT able to report the correct number of states that would be required to correctly compute populations of macro states? Again, a comment on this issue would help the reader.

Finally, one small issue that I didn't mention in my first review is that it might be interesting to know if m6A is in syn or anti conformation in the in- and out- state. In general, m6A is expected to be anti when WC paired and syn when non WC paired (see e.g. https://pubmed.ncbi.nlm.nih.gov/25611135/). Here, in the in-state it is forming a non canonical pair with G28 (Fig. 6c). It would be interesting to know if this pairing requires A22 to flip to the least stable anti conformation or not. The analysis should be straightforward and the result could be useful.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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22 Apr 2022

Submitted filename: ReviewsPLOSComputBiol_round2.pdf

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28 Apr 2022

Dear Dr Röder,

We are pleased to inform you that your manuscript 'Investigating the structural changes due to adenosine methylation of the  Kaposi’s sarcoma-associated herpes virus ORF50 transcript' has been provisionally accepted for publication in PLOS Computational Biology.

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18 May 2022

PCOMPBIOL-D-21-02101R2

Investigating the structural changes due to adenosine methylation of the  Kaposi’s sarcoma-associated herpes virus ORF50 transcript

Dear Dr Röder,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

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