RNA structure prediction using positive and negative evolutionary information

Elena Rivas

doi:10.1371/journal.pcbi.1008387

. 2020 Oct 30;16(10):e1008387. doi: 10.1371/journal.pcbi.1008387

RNA structure prediction using positive and negative evolutionary information

Elena Rivas ^1,^*

Editor: Tamar Schlick²

PMCID: PMC7657543 PMID: 33125376

Abstract

Knowing the structure of conserved structural RNAs is important to elucidate their function and mechanism of action. However, predicting a conserved RNA structure remains unreliable, even when using a combination of thermodynamic stability and evolutionary covariation information. Here we present a method to predict a conserved RNA structure that combines the following three features. First, it uses significant covariation due to RNA structure and removes spurious covariation due to phylogeny. Second, it uses negative evolutionary information: basepairs that have variation but no significant covariation are prevented from occurring. Lastly, it uses a battery of probabilistic folding algorithms that incorporate all positive covariation into one structure. The method, named CaCoFold (Cascade variation/covariation Constrained Folding algorithm), predicts a nested structure guided by a maximal subset of positive basepairs, and recursively incorporates all remaining positive basepairs into alternative helices. The alternative helices can be compatible with the nested structure such as pseudoknots, or overlapping such as competing structures, base triplets, or other 3D non-antiparallel interactions. We present evidence that CaCoFold predictions are consistent with structures modeled from crystallography.

Author summary

The availability of deeper comparative sequence alignments and recent advances in statistical analysis of RNA sequence covariation have made it possible to identify a reliable set of conserved base pairs, as well as a reliable set of non-basepairs (positions that vary without covarying). Predicting an overall consensus secondary structure consistent with a set of individual inferred pairs and non-pairs remains a problem. Current RNA structure prediction algorithms that predict nested secondary structures cannot use the full set of inferred covarying pairs, because covariation analysis also identifies important non-nested pairing interactions such as pseudoknots, base triples, and alternative structures. Moreover, although algorithms for incorporating negative constraints exist, negative information from covariation analysis (inferred non-pairs) has not been systematically exploited. Here I introduce an efficient approximate RNA structure prediction algorithm that incorporates all inferred pairs and excludes all non-pairs. Using this, and an improved visualization tool, I show that the method correctly identifies many non-nested structures in agreement with known crystal structures, and improves many curated consensus secondary structure annotations in RNA sequence alignment databases.

Introduction

Having a reliable method to determine the structure of a conserved structural RNA would be an important tool to be able to elucidate major biological mechanisms, and will open the opportunity of discovering new ones. Structure and biological function usually are closely related, as in the case of riboswitches where the structure dictates the biological function [1, 2], or the bacterial CsrB RNA which acts as a sponge to sequester the CsrA protein [3], or the 6S RNA which mimics the structure of a DNA promoter bound to the RNA polymerase to regulate transcription [4].

The importance of comparative information to improve the prediction of a conserved RNA structure has been long recognized and applied to the determination of RNA structures [5–13]. Computational methods that exploit comparative information in the form of RNA compensatory mutations from multiple sequence alignments have been shown to increase the accuracy of RNA consensus structure prediction [14–19]. Indeed, the majority (97–98%) of the ribosomal RNA (rRNA) secondary structure was inferred before the crystal structure was available using covariation analysis [20].

Several challenges remain in the determination of a conserved RNA structure using comparative analysis. There is ample evidence that pseudoknotted basepairs covary at similar levels as other basepairs, but most comparative methods for RNA structure prediction can only deal with nested structures. Identifying pseudoknotted and other non-nested pairs that covary requires having a way of measuring significant covariation due to a conserved RNA structure versus other sources.

Here, in addition to using positive information in the form of basepairs observed to significantly covary, it would also be advantageous to use negative covariation information in the form of basepairs that should be prevented from occurring because they show variation but not significant covariation. The use of both positive and negative information from covariation analysis can be compared to how chemical and enzymatic probing data is used to infer RNA structures, using propensities for modification of paired versus unpaired bases [21–25] to infer 2D [26–28] and 3D [29] RNA structure.

To approach these challenges, we have previously introduced a method called R-scape (RNA Structural Covariation Above Phylogenetic Expectation) [30] that reports basepairs that significantly covary using a tree-based null model to estimate phylogenetic covariation from simulated alignments with similar base composition and number of mutations to the given one but where the structural signal has been shuffled. Significantly covarying pairs are reported with an associated E-value describing the expected number of non-structural pairs that could have a covariation score of that magnitude or larger in a null alignment of similar size and similarity. We call these significantly covarying basepairs for a given E-value cutoff (typically ≤ 0.05) the positive basepairs.

The covariation measure used by R-scape takes into account any possible residue mutations, and does not restrict itself to changes compatible with standard Watson-Crick base pairs or G:U wobble pairs. Any nucleotide exchange, such as A:G in a Watson-Crick base pairs, or other mutations in non Watson-Crick base pairs contribute to the covariation score. R-scape uses the G-test covariation measure, as G-test reports the best sensitivity and specificity behavior in comparison to other covariation measures [30]. G-test has the same mathematical expression as mutual information (MI), but uses residue counts instead of frequencies. The effect of such change is that for two pairs of positions with the same MI, the pair with fewer gaps will have the larger G-test score.

In addition to reporting positive basepairs, that is, significantly covarying basepairs of any kind, R-scape has recently introduced another method to estimate the covariation power of a pair based on the mutations observed in the corresponding aligned positions [31]. Where a pair of position shows no significant covariation, this method allows distinguishing between two different cases: a pair that has too little sequence variation and may still be a conserved basepair, versus a pair with adequate sequence variation but where the variation is inconsistent with a covarying basepair. This latter case should be rejected as basepairs. We call these pairs with variation but not covariation the negative basepairs. In summary, positive basepairs that have significant covariation do not have to be consistent with standard Watson-Crick basepairs, and negative pairs that show variation but not covariation could include instances of consistent basepairs.

Here we combine these two sources of information (positive in the form of significantly covarying basepairs, and negative in the form of pairs of positions unlikely to form basepairs) into a new RNA folding algorithm. The algorithm also introduces an iterative procedure that systematically incorporates all positive basepairs into the structure while remaining computationally efficient. The recursive algorithm is able to find pseudoknots, other non-nested interactions, alternative structures and triplet interactions provided that they are supported by covariation. The algorithm also predicts additional helices without covariation support but consistent with RNA structure. Helices with covariation-supported basepairs tend to be reliable. Additional helices lacking covariation support are less reliable and need to be taken as speculative.

We use the alignments provided by the databases of structural RNAs Rfam [32] and the Zasha Weinberg Database (ZWD) [33] to produce CaCoFold structure predictions. The number of positive pairs (that is, significantly covarying basepairs proposed by R-scape) is constant for a given alignment and an annotated structure. Here we study the differences between the positive pairs incorporated into the CaCoFold structures versus those present in the annotated structures, comparing with structures derived by crystallography when possible.

Results

The CaCoFold algorithm

The new RNA structure prediction algorithm presents three main innovations: the proposed structure is constrained both by sequence variation as well as covariation (the negative and positive basepairs respectively); the structure can present any knotted topology, basepair of any type [34] (not just Watson-Crick), and include residues pairing to more than one residue; and all positive basepairs are always incorporated into a final RNA structure. Pseudoknots and other non-nested pairwise interactions, as well as alternative structures and tertiary interactions are all possible provided that they have covariation support.

The method is named Cascade covariation and variation Constrained Folding algorithm (CaCoFold). Despite exploring a 3D RNA structure beyond a set of nested Watson-Crick basepairs, the algorithm remains computationally tractable because it performs a cascade of probabilistic nested folding algorithms constrained such that at a given iteration, a maximal number of positive basepairs are forced into the fold, excluding all other positive basepairs as well as all negative basepairs. Each iteration of the algorithm is called a layer. The first layer calculates a nested structure that includes a maximal subset of positive basepairs. Subsequent layers of the algorithm incorporate the remaining positive basepairs arranged into alternative helices.

From an input alignment, the positive basepairs are calculated using the G-test covariation measure with APC correction after removing covariation signal resulting from phylogeny, as implemented in the software R-scape [30]. G-test allows all possible nucleotide exchanges. The set of all significantly covarying basepairs is called the positive set. Positive pair can be of any type, and allow exchanges other than Watson-Crick (A:U, C:G or G:U). We also calculate the covariation power for all possible pairs [31]. The set of all pairs that have variation but not covariation is called the negative set. Negative pairs are highly variable and could include cases of Watson-Crick consistent pairs. Operationally, positive pairs have an E-value smaller than 0.05, and negative pairs are those with covariation power (the expected sensitivity of significantly covarying) larger than 0.95 and significance E-value larger than one. Non-significantly covarying pairs with an E-value between 0.05 and 1 are allowed (but not forced) to basepair regardless of power. All positive basepairs are included in the final CaCoFold structure, and all negative basepairs are forbidden to appear.

Fig 1 illustrates the CaCoFold algorithm using a toy alignment (Fig 1a) derived from the manA RNA, a structure located in the 5’ UTRs of cyanobacterial genes involved in mannose metabolism [35]. After R-scape with default parameters identifies five positive basepairs (Fig 1b), the CaCoFold algorithm calculates in four steps a structure including all five positive basepairs as follows.

(1) The cascade maxCov algorithm

The cascade maxCov algorithm groups all positive basepairs in nested subsets (Fig 1c). At each layer, it uses the Nussinov algorithm, one of the simplest RNA models [36]. Here we use the Nussinov algorithm not to produce an RNA structure, but to group together a maximal subset of positive basepairs that are nested relative to each other. Each subset of nested positive basepairs will be later provided to a folding dynamic programming algorithm as constraints. Fig 2 includes a detailed description of the Nussinov algorithm.

Fig 2 — (a) The Nussinov grammar implemented by the maxCov algorithm uses the R-scape E-values of the significantly covarying pairs, and maximizes the sum of -log(E-value). (b) The RBG model used by the first layer of the folding algorithm. (c) The G6X model used by the rest of the layers completing the non-nested part of the RNA structure. For the RBG and G6X models, the F nonterminal is a shorthand for 16 different non-terminals that represent stacked basepairs. The three models are unambiguous, that is, given any nested structure, there is always one possible and unique way in which the structure can be formulated by following the rules of the grammar.

The first layer (C0) finds a maximal subset of compatible nested positive basepairs with the smallest cumulative E-value. After the first layer, if there are still positive basepairs that have not been explained because they did not fit into one nested set, a second layer (C1) of the maxCov algorithm is performed where only the still unexplained positive basepairs are considered. The cascade continues until all positive basepairs have been grouped into nested subsets.

The cascade maxCov algorithm determines the number of layers in the algorithm. For each layer, it identifies a maximal subset of positive basepairs forced to form, as well as a set of basepairs not allowed to form. The set of forbidden basepairs in a given layer is composed of all negative pairs plus all positive pairs not in the current layer.

The cascade maxCov algorithm provides the scaffold for the full structure, which is also obtained in a cascade fashion.

(2) The cascade folding algorithm

For each layer in the cascade with a set of nested positive basepairs, and another set of forbidden pairs, the CaCoFold algorithm proceeds to calculate the most probable constrained nested structure (Fig 1d).

Different layers use different folding algorithms. The first layer is meant to capture the main nested structure (S0) and uses the probabilistic RNA Basic Grammar (RBG) [37]. The RBG model features the same basic elements as the nearest-neighbor thermodynamic model of RNA stability [26, 38] such as basepair stacking, the length of the different loops, the length of the helices, the occurrence of multiloops, and others. RBG simplifies some details of loops in the models used in the standard thermodynamic packages, such as ViennaRNA [26], Mfold [39], or RNAstructure [27] resulting in fewer parameters, but it has comparable performance regarding folding accuracy [37]. Fig 2 includes a description of the RBG algorithm.

The structures at the subsequent layers (S+ = {S1, S2, …}) are meant to capture additional helices with covariation support that do not fit into the main secondary structure S0. We expect that covariation in the subsequent layers will correspond to pseudoknots, and also non-nested tertiary contacts, or base triplets. The S+ layers add alternative helices (complementary or not) to the main secondary structure, for that reason instead of a full loop model like RBG, the S+ layers use the simpler G6 RNA model [40, 41] which mainly models the formation of helices of contiguous basepairs. Here we extend the G6 grammar to allow positive pairs that are parallel to each other in the RNA backbone, interactions that are not uncommon in RNA motifs. We name the modified grammar G6X (see Fig 2 for a description).

The RBG and G6X model parameters are trained on a large and diverse set of known RNA structures and sequences as described in Ref. 37. At each layer, the corresponding probabilistic folding algorithm reports the structure with the highest probability using a CYK dynamic programming algorithm on a profile sequence that contains information on the proportion of each nucleic acid in each consensus column of the alignment.

Because the positive residues that are forced to pair at a given cascade layer could pair (but to different residues) at subsequent layers, the CaCoFold algorithm can also identify triplets or higher order interactions (a residue that pairs to more than one other residue) as well as alternative helices that may be incompatible and overlap with other helices.

(3) Filtering of alternative helices

In order to combine the structures found in each layer into a complete RNA structure, the S+ structural motifs are filtered to remove redundancies without covariation support.

We first break the S+ structures into individual alternative helices. A helix is operationally defined as a set of contiguous basepairs. Basepairs disrupted by just a one or two residue bulge or by a 1x1 internal loop are still considered one single helix. Under this operational definition, a helix can consist of just one basepair. This is a slight misuse of the word “helix” which strictly speaking involves a spatial rotation and a translation between two basepairs. The algorithm makes sure that each basepair belongs to one and only one helix. A helix is arbitrarily called positive if it includes at least one positive basepair.

All positive alternative helices are reported. Alternative helices without any covariation support are reported only if they include at least 15 basepairs, and if they overlap in no more than 50% of the bases with another helix already selected from previous layers. This is an empirically determined cutoff that applies only to alternative helices without any covariation support. In our simple toy example, there is just one alternative helix. The alternative helix is positive, and it is added to the final structure. No helices are filtered out in this example (Fig 1d).

(4) Automatic display of the complete structure

The filtered alternative helices are reported together with the main nested structure as the final RNA structure. We adapted the program R2R to visualize the CaCoFold structure with all covarying basepairs annotated in green [42]. CaCoFold reports and draws a consensus structure for the alignment. Conserved positions display the residue identity color coded by conservation (red > 97%, black > 90%, and gray > 75%), otherwise a circle is displayed colored by column occupancy (red > 97%, black > 90%, gray > 75%, white > 50%).

The CaCoFold drawings of the (presented or predicted) consensus structures depict all nested and non-nested basepairs automatically (Fig 1f). For presented 3D structures, non Watson-Crick basepairs (regardless of whether they overlap or not) are annotated as non-canonical (“nc”), and Watson-Crick alternative helices as pseudoknots (“pk”). For a predicted CaCoFold structure, alternative helices are annotated as pseudoknots (“pk”) if they do not overlap with the main nested structure, or as triplets (“tr”) if they overlap. Thus, a CaCoFold alternative helix labeled “tr” includes at least one pair in which one the residues is also involved in a different pair. CaCoFold cannot distinguish whether an alternative helix labeled “tr” is a triplet or part of an alternative structure. Similarly, a CaCoFold alternative helix labeled as “pk” could also be part of an alternative fold, and it does not necessarily correspond to a helix composed only of Watson-Crick basepairs.

If R-scape does not identify any positive basepair, one single layer is defined without positive pairs and constrained only by the negative pairs, and one nested structure is calculated. Lack of positive basepairs results in a proposed structure without evolutionary support.

For the toy example in Fig 1, R-scape with default parameters identifies five positive basepairs. The CaCoFold algorithm requires two layers to complete. The first layer incorporates three nested positive basepairs. The second layer introduces the remaining two positive basepairs. The RBG fold with three constrained positive basepairs produces three helices. The G6X fold with two positive and three forbidden basepairs results in one alternative helix between the two hairpin loops of the main nested structure. In this small alignment there are no negative basepairs, and no alternative helices without covariation support have to be filtered out. The final structure is the joint set of the four helices, and includes one pseudoknot.

CaCoFold finds pseudoknots, triplets and other long and short-range interactions

For a realistic example of how CaCoFold works, we present in Fig 3 an analysis of transfer-messenger RNA (tmRNA). The tmRNA is a bacterial RNA responsible for freeing ribosomes stalled at mRNAs without a stop codon. The tmRNA molecule includes a tRNA-like structural domain, and a mRNA domain which ends with a stop codon. The tmRNA molecule is typically 230-400 nts [43], and its proposed structure includes a total of 12 helices forming four pseudoknots [44]. The core elements of the tmRNA structure are well understood, but the molecule has a lot of flexibility and is thought to undergo large conformational changes with the 4 pseudoknots forming a ring around a part of the small subunit of the ribosome [43].

We performed the analysis on the tmRNA seed alignment in Rfam (RF0023) which includes 477 sequences. The length of the consensus sequence is 354 nts, and the average pairwise percentage identity is 42% (Fig 3a). In step one, the covariation analysis on the input alignment results on 121 significantly covarying basepairs (Fig 3b). This analysis ignores the proposed Rfam consensus structure and performs the covariation analysis on the alignment alone. This result is in agreement with the covariation power estimated for this alignment, which expects to find on average 109 significantly covarying basepairs [31]. In the next step, the maxCov algorithm requires 6 layers to explain all 121 positive basepairs (Fig 3c). Next, the constrained folding of each of the 6 layers results on a total of 139 annotated pairwise interactions.

The covariation analysis also identifies 31,027 negative pairs (out of a total of 85,491 possible pairs for 414 columns analyzed), those are forbidden to form because they show variation but not covariation. In the final structure, 74 basepairs are not reported do to the forbidden negative pairs (Fig 3d). The final alternative helix filtering step reports: 5 pseudoknots, 3 triplets and 10 other covariations that are induced by coding constraints, which we describe in more detail below. All alternative helices have covariation support (Fig 3e).

The CaCoFold structure for the tmRNA is given in Fig 3f, and it includes the 12 helices and four pseudoknots [44]. It also proposes an additional helix (H13) with covariation support. We have not identified H13 in tmRNA crystal structures. Due to the amount of overlap between H13 and helix H2d, this could indicate the presence of two alternative competing structures.

In the helix H2d/helix H3 region, CaCoFold identifies three triplets, one of them (triplet 1) is confirmed by the structure derived from the tmRNA EM structure (13.6 Å) with PDB ID 3IZ4 [45]. A different triplet for which we do not find covariation signal has been previously proposed in that same region (marked with an asterisk in Fig 3g). This is a complex region with many 3D contacts as helix H2d interacts both with the PK1 and PK4 pseudoknots [43].

CaCoFold identified 10 additional interactions associated to the mRNA domain. These tend to occur between contiguous residues. These interactions are not related to the RNA structure and arise from coding constraints (more details in S6(c) Fig). We observe this kind of covariation in other coding mRNA regions, not just in tmRNA. Finally, CaCoFold reports one covariation between the first and second position of the stop codon in the mRNA domain. The U residue in the first position of the stop codon is invariant, so a covariation involving this reside should not occur. This spurious covariation arises from a misalignment of the stop codon in the Rfam seed alignment. A small rearrangement of the alignment in that region results in a conserved stop codon.

We compared the tmRNA CaCoFold structure to the structure predicted for the same alignment by RNAalifold, a ViennaRNA program for predicting a consensus structure [26]. S1(a) Fig shows the output of RNAalifold. RNAalifold does not predict pseudoknots or any other non-nested structure, and it only identifies 6 of the 12 helices in the tmRNA structure (Fig 3g). RNAalifold predicts 46 basepairs, but it does not assign confidence to the proposed basepairs. In S1(b) Fig, the covariation analysis of the tmRNA alignment shows that 45 of the 46 RNAalifold basepairs covary. But it also indicates that there are 76 other covarying basepairs not present in the RNAalifold structure (Fig 3b). CaCoFold brings together the basepair validation provided by the covariation analysis with a structure that incorporates all 121 inferred basepairs.

RNAs with structures improved by positive and negative signals

We have produced CaCoFold structures from the alignments provided by the databases of structural conserved RNAs Rfam [32] and ZWD [33]. Unlike the previous section where the proposed consensus structure was ignored, here we perform two independent covariation tests: one on the set of basepairs in the annotated consensus structure, another on the set of all other possible pairs (option “to improve an existing structure” in Methods). It is important to notice that because of this two-set analysis, CaCoFold builds on the knowledge provided by the alignments and the consensus structures of Rfam and ZWD. Using the positive and negative pairs obtained from the covariation test as constraints, the CaCoFold structure is then built anew.

One strength of the CaCoFold algorithm is that the structure is guided by significant covariation, and the final structure is viewed in the light of all covarying basepairs. For alignment with little or no significant covariation, CaCoFold behaves as the RBG model, which we have shown in benchmarks perform similarly to standard methods [37]. Because in the absence of covariation RNA structure prediction lack reliability and all methods perform comparably, we concentrate on the set of RNAs with covariation support.

Another strength of the CaCoFold algorithm is in incorporating all covariation signal present in the alignment into one structure. When the CaCoFold structure includes the same covarying pairs than the structure in the database, the differences between the two structures can only occur in regions not reliably predicted by either of the methods, thus we concentrate on the set of RNAs for which the CaCoFold structure has different covariation support than the annotated structure.

Because the set of positive pairs is constant and CaCoFold incorporates all of them, CaCoFold structures cannot have fewer positive pairs than the database consensus structures. Here we investigate the set of RNAs with CaCoFold structures with different (that is, more) covariation support than the annotated structures, and whether those differences are consistent with experimentally-determined 3D structures when available.

We identify 277 (out of 3,030) Rfam families and 105 (out of 415) ZWD RNA families for which the CaCoFold structure includes positive basepairs not present in the given structures. Because there is overlap between the two databases, in combination there is a total of 313 structural RNAs for which the CaCoFold structure has more covariation support than either the Rfam structure or the ZWD structure. Of the 314 RNAs, there are five for which the Rfam and ZWD alignments and structures are different (PhotoRC-II/RF01717, manA/RF01745, radC/RF01754, pemK/RF02913, Mu-gpT-DE/RF03012) and we include both versions in our analysis. In the end, we identify a total of 319 structural alignments for which the structure presented in the databases is missing positive basepairs, and CaCoFold proposes a different structure with more covariation support. In Table 1, we classify all structural differences into 15 types.

Table 1. CaCoFold structures with different covariation support than the structures provided with the structural alignments.

CaCoFold structures with different covariation support can only have more positive basepairs. (Left) The 319 structural RNAs (from the Rfam and ZWD databases combined) for which the CaCoFold structure has more covariation support are manually classified into 15 categories. Each RNA is assigned to one main type, although they can belong to others as well. Examples of types 1-11 are presented in S7 Fig. A full description of all 319 RNAs is given in the supplemental table.

Modifications introduced by the extra covariations in the CaCoFold structure		# RNAs
Type 1	Helix extended by additional covariations	23
Type 2	New helix with covariation support	12
Type 3	One helix completely modified	7
Type 4	New pseudoknot with covariation support	16
Type 5	New junction/internal loop or coaxial stacking	17
Type 6	Internal loop/multiloop reshaped by coaxial stacking	12
Type 7	Hairpin/internal loop covariations (often nonWC)	19
Type 8	Non Watson-Crick (not within a loop) covariations	24
Type 9	Base triples	28
Type 10	(Cross,Side)-covariations (see text)	30
Type 11	Possible alternative structures	6
Type 12	Additional covariations in SSU and LSU rRNA	6
Type 13	Covariations not supporting a secondary structure	3
Type 14	Misalignment introducing spurious covariations	2
Type 15	Low power; inconclusive	114
CaCoFold structures with different (i.e. more) covariation support		319

Open in a new tab

CaCoFold structures consistent with 3D structures

The set of 319 CaCoFold structures with more covariation support includes 27 RNAs that have 3D structures for representative sequences (out of a total of 97 families with 3D structures). We tested that for those RNAs (21 total, leaving aside 1 LSU and 5 SSU rRNA) the CaCoFold structure predictions are indeed supported by the 3D structures. Table 2 describes the 21 RNAs: 5S RNA, tRNA, 6S RNA, Group-II intron, two bacterial RNase P RNAs (A-type and B-type), tmRNA, two SRP RNAs (bacterial and archaeal), four snRNAs (U2, U3, U4, and U5), and eight riboswitches (FMN, SAM-I, Cobalamin, Fluoride, Glutamine, cyclic di-AMP, and YkoK leader). The comparison of the CaCoFold structures for those 21 RNAs to 3D structures are presented in Figs 4 and 5 and supplemental S2–S6 Figs.

Table 2. 21 RNAs with 3D structures and CaCoFold structures with different covariation support than the structures provided with the structural alignments.

Subset of 21/319 CaCoFold structures with more covariation support for which there is 3D structural information (not including the 6 rRNAs). We compare the 21 CaCoFold predicted structures to the 3D structures in Figs 4, 5 and Supplemental S2–S6 Figs. The associated “types” are described in Table 1.

21/319 RNAs with 3D structures
RNA	Rfam seed alignment	Types	Figure
RNase P RNA A-type [46]	RF00010	4,8	Fig 4a
SAM-I riboswitch [47]	RF00162	1,4,6	Fig 4b
U4 snRNA [48]	RF00015	2,5	Fig 4c
Cobalamin riboswitch [49]	RF00174	1,4,5	Fig 5a
tRNA [50, 51]	RF00005	1,8,9	Fig 5b
U2 snRNA [52, 53]	RF00004	11	Fig 5c
Bacterial SRP RNA [54]	RF00169	1	S2(a) Fig
cyclic di-AMP riboswitch [55]	RF00379	1	S2(b) Fig
YkoK leader [56]	RF00380	1	S2(c) Fig
5S rRNA [57]	RF00001	3,5	S3(a) Fig
FMN riboswitch [58]	RF00050	1,4	S3(b) Fig
ZPM-ZTP riboswitch [59]	RF01750	4,9	S3(c) Fig
Fluoride riboswitch [60]	RF01734	1,4	S4(a) Fig
Glutamine riboswitch [61]	RF01739	4	S4(b) Fig
Archaea SRP RNA [62]	RF01857	4	S4(c) Fig
RNase P RNA B-type [63]	RF00011	5	S5(a) Fig
Group-II intron (fragment) [64]	RF02001	5	S5(b) Fig
U5 snRNA [65]	RF00020	5,7,10	S5(c) Fig
Fungal U3 snoRNA [66]	RF01846	5	S6(a) Fig
6S RNA [4]	RF00013	8	S6(b) Fig
tmRNA [67]	RF00023	9,10,11,14	S6(c) Fig

Open in a new tab

Fig 5 — Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. **(a)** Relative to the Rfam structure, the Cobalamin riboswitch CaCoFold structure adds one pseudoknot and one Watson-Crick basepair defining a four-way junction between helices P1, P2, and P3, both confirmed by the *S. thermophilum* crystal structure [49]. It also adds more covariation support for helices P1 and P2. **(b)** In CaCoFold structures, alternative helices that do not overlap with the nested structure are annotated as pseudoknots (pk), otherwise they are annotated as triplets (tr). For structures obtained from a crystal structure, non Watson-Crick basepairs are annotated as non-canonical (nc) regardless of whether they are overlapping or not with the nested structure. The tRNA CaCoFold structure has been re-annotated manually to match the labeling of the *S. cerevisiae* phenylalanine tRNA 1EHZ crystal structure (1.93 Å) for all common basepairs [51]. Of the covarying pairs in the CaCoFold structure but not in the Rfam tRNA structure, five (depicted in blue) are confirmed by the 1EHZ structure as analyzed by RNAView. The sequence of the 1EHZ tRNA does not include the V loop, which appears in 16% of the 954 sequences in the Rfam tRNA seed alignment. Two covarying pairs (depicted in orange) appear to be the result of constraints other than RNA structure. The remaining six covarying pairs are labeled in black. Four basepairs identified in the 3D structure but not incorporated in the CaCoFold structure are depicted in brown. The annotation of the non Watson-Crick pairs with at least two H-bonds follows the nomenclature of [34] that reports the two edges of the nucleotides involved in the plain of the H-bonds. “W” stands for the Watson-Crick edge, “S” for the Sugar edge, and “H” for the Hoogsteen face; “c” and “t” stand for cis and trans respectively. WWc is a standard Watson-Crick basepairs. **(c)** In the U2 spliceosomal RNA, Stem IIa and Stem IIc, both with covariation support, are two alternative helices that compete to promote different splicing steps [53].

In Fig 4a, we show the RNase P RNA A-type where CaCoFold identifies the two pseudoknots, one of which (P6) is not in the Rfam consensus structure. RNase P RNA A-type also includes five long-range tertiary interactions [46, 68] highlighted in black and numbered “1” to “5” in Fig 4a. CaCoFold identifies covarying basepairs in two of those regions: a Y-R covarying pair (“tr_1”) in the P8/P14-hairpin-loop region “3”, and a R-R covarying pair (“tr_2”) in the P8/P18-hairpin-loop region “4”.

Fig 4b shows the SAM-I riboswitch where CaCoFold identifies the reported pseudoknot [47]. Other RNAs for which CaCoFold identifies unannotated pseudoknots with covariation support confirmed by crystallography include five riboswitches: the Cobalamin riboswitch [49] (Fig 5a), FMN riboswitch [58] S3(b) Fig, ZMP/ZTP riboswitch [59] S3(c) Fig, Fluoride riboswitch [60] S4(a) Fig, Glutamine riboswitch [61] S4(b) Fig, and the Archaeal SRP RNA [62] S4(c) Fig.

Also in the SAM-I riboswitch, CaCoFold identifies an apparent lone Watson-Crick A-U pair in the junction of the four helices which in fact stacks with helix P1 [47].

The SAM-I riboswitch [47] CaCoFold structure also includes additional covariations that further extend existing helices P2a, P3 and P4. Other RNAs for which CaCoFold identifies additional covarying pairs in helices supported by 3D structures are given in S2 Fig: Bacterial SRP RNA [54] S2(a) Fig, cyclic di-AMP riboswitch [55] S2(b) Fig, and YkoK leader [56] S2(b) Fig.

In Fig 4c, the U4 spliceosomal RNA shows two covarying pairs (labeled “1” and “3” in the figure) which define one additional helix relative to the Rfam structure, and help identify a kink turn RNA motif. Both the helix and the kink turn motif are confirmed by the crystal structure [48].

Four other examples of RNAs for which CaCoFold identifies key covarying residues missing in the Rfam structure that define important structural elements are described next. For RNaseP B-type in S5(a) Fig, one covarying basepair (labeled “1” in blue in the figure) in helix P15.1 identifies a new internal loop in the CaCoFold structure relative to the Rfam structure that is confirmed in the crystal structure [63].

For the Group-II intron fragment in S5b Fig, one additional R-Y covarying basepair (labeled “1” in blue) defines a three-way junction not present in the Rfam structure, and confirmed by the crystal structure [64].

For U5 snRNA [65] in S5(c) Fig, there is a new Y-Y covarying pair that modifies a hairpin loop. This non Watson-Crick interaction consists of a C:U pair exchanging to a U:C pair in 46% of the sequences, and it is annotated by the software RNAView as a stacked pair [69]. Stacked pairs do not belong to any of the major families of base pair types but are important in forming the RNA structure [69], and as a result they also covary.

For the Fungal U3 snoRNA S6(a) Fig, an additional R-Y covarying pair in the CaCoFold structure relative to the Rfam annotation (labeled with “1” in S6(a) Fig) allows to identify the characteristic boxB/boxC boxes of the snoRNA [66].

In Fig 5a, the CaCoFold structure for the Cobalamin riboswitch [49] includes a pseudoknot, a covarying pair identifying a multiloop with coaxial stacking, and additional covarying basepair in helices P1 and P2 all supported by the 3D structure [49]. CaCoFold also identifies other unreported covarying pairs in the internal loop between helices P7 and P8.

The tRNA CaCoFold structure derived from the Rfam seed alignment is given in Fig 5b. The variable loop (V loop) forms an additional helix only in type II tRNAs, but not in type I tRNAs [70]. About 15% of the tRNAs in the Rfam seed alignment are of type II, but the V loop basepairs are not annotated in the consensus structure. Under default parameters, R-scape does not analyzed the V loop positions as the number of gaps is above the cutoff of 75% gaps. By allowing the analysis of all positions, we observe three Watson-Crick basepairs in the V loop.

The tRNA CaCoFold structure also incorporates thirteen other long-range interactions not in the Rfam tRNA structure. We have used the crystal structure 1EHZ of the yeast phenylalanine type I tRNA at 1.93 Å resolution [51], and the RNAView annotation [69] of all its basepairs to produce a 3D-derived annotation of the consensus structure in the Rfam alignment. Five of the CaCoFold long-range interactions (depicted in blue) are confirmed by the 3D-derived structure, and for clarity, in Fig 5b they have been renamed to match the 3D structure.

CaCoFold identifies six additional covarying pairs not reported by RNAView on the 1EHZ tRNA crystal structure. The positive basepair (marked “1”) in the anticodon hairpin is a non-canonical basepairs that has been confirmed as a single hydrogen-bonded pair [71]. The “pk”, “pk_1” and “pk_2” interactions occur amongst the same residues. “pk_1” and “pk_2” have worse E-values than “pk” (0.007 and 0.00002 compared to 1e^{− 14}) and may be secondary covariations between the D and T loops. The “tr_2” triplet has been reported as specific to type 1 tRNAs (base triplet “g” in Ref. [70]). There are four additional CaCoFold covarying pair between backbone adjacent residues that happen in the three hairpin loops. tRNA pseudogenes, likely to be present in the Rfam seed alignment, may have induce spurious covariation in this analysis.

Finally, two CaCoFold covarying pairs (depicted in orange) in the anticondon (AC) loop appear to be the result of constraints other than RNA structure. One involves one anticodon residue and the discriminator residue in the acceptor stem. This anticodon/discriminator covariation results from the interaction of both residues with aminoacyl-tRNA synthetase [50].

In Fig 5c, the U2 spliceosomal snRNA describes a case of alternative structures. “Stem IIc” was originally proposed as possibly forming a pseudoknot with one side of Stem IIa, but was later discarded as non-essential for U2 function [52, 72]. But later, a U2 conformational switch was identified indicating that Stem IIa and Stem IIc do not form a pseudoknot but are two competing helices promoting distinct splicing steps [53]. Both helices are important to the U2 function, and both have covariation support.

5S rRNA S3(a) Fig shows the case of a region (the helix 4 and Loop E region) almost completely reshaped by the covariations found by CaCoFold, and in agreement with the 3D structure [57].

In addition to the coding mRNA signal in tmRNA S6(c) Fig, we have found another signal that produces non-phylogenetic covariations in the 6S RNA S6(b) Fig which regulates transcription by direct binding to the RNA polymerase [4]. The 6S RNA structure mimics an open promoter and serves as a transcription template. Synthesis of a 13 nt product RNA from the 6S RNA results in a structural change that releases the RNA polymerase. We do not find any covariation evidence for the alternative helix of “isoform 2” in Ref. 4 S6(b) Fig, but we observe one covariation between the U initiating the RNA product and the previous position. We hypothesize an interaction of the two bases with the RNA polymerase.

Other CaCoFold structures with more covariation support

Based on what we learned from the 3D structures, we manually classified the 319 RNAs with modified structures into 15 categories (Table 1). In Supplemental S1 Table, we report a full list of the RNA families and alignments with CaCoFold structures incorporating more positive covariation support, classified according to Table 1. In S7 Fig, we show representative examples of Types 1-12 amongst the RNAs with more covariation support but without 3D structures.

In Type 1, the extra positive basepairs incorporated by CaCoFold extend the length of an already annotated helix, as in the TwoAYGGAY RNA (RF01731) and drum RNA (RF02958) examples. Type 2 includes cases in which several positive basepairs identify a new helix. We present the case of the Coronavirus 3’UTR pseudoknot, a pseudoknotted structure specific to coronaviruses, typically 54-62 nts in length found within the 3’ UTR of the N gene. The alignment for this RNA in the Rfam 14.2 Coronavirus special release (RF00165) has a consensus sequence of 62 nts, and it annotates two helices forming a pseudoknot [73]. The CaCoFold structure includes one additional third helix with 2 positive pairs and compatible with the pseudoknot. The existing chemical modification data for the Coronavirus 3’UTR pseudoknot does not rule out the presence of this additional helix [73]. Type 3 includes seven cases in which a helix without positive basepairs in the given structure gets refolded by CaCoFold into a different helix that includes several positive basepairs. For the RF03068 RT-3 RNA example, the original helix has no covariation support but the refolded helix has 8 positive basepairs. Type 4 describes cases in which positive basepairs reveal a new helix forming a pseudoknot. There are 16 of these cases, of which chrB RNA is an example. Type 5 and Type 6 are cases in which the additional positive basepairs refine the secondary structure, either by introducing new junctions (three-way or higher) or new internal loops (Type 5) or by adding positive basepairs at critical positions at the end of helices that help identify coaxial stacking (Type 6). For Type 6, we show a positive basepair in the DUF38000-IX RNA that highlights the coaxial stacking of two helices. Type 7 describes cases in which the extra positive basepairs are in loops (hairpin or internal). Types 5, 6 and 7 often identify recurrent RNA motifs [74], as in the case shown in S7 Fig, where an additional positive basepair identifies a tandem GA motif in the RtT RNA. Other more general non-Watson-Crick interactions are collected in Type 8, of which tRNA is an exceptional example where many non-Watson-Crick basepairs covary (see Fig 5(b)). The tRNA genes are highly variable in sequence, but they have a very conserved structure, and only a few positions do not covary, such as a more than 97% conserved U in the AC loop (depicted in red in Fig 5b). In S7 Fig we show another example of Type 8, Bacteroides-2 a putative RNA gene identified in the Bacteriodes genus [35], where CaCoFold finds three residues involved in three basepairs. Type 9 are putative base triplets involved in more than one positive interaction. In general, one of the positive basepairs is part of an extended helix, but the other is in general not nested and involves only one or two contiguous pairs. Type 10 includes a particular type of base triplet that we name cross-covariation and side-covariation. A cross(side)-covariation appears when two covarying basepairs i − j and i′ − j′ that belong to the same helix are such that two of the four residues form another covarying interaction. If the extra covarying pair involves residues in one side of the helix (i − i′ or j − j′), we name it a side-covariation (annotated “sc” in the graphical representation). If the residues are in opposite sides of the helix (i − j′ or j − i′), it is a cross-covariation (annotated “xc”). We have observed side covariations in tmRNA (Fig 3 and S6(c) Fig) and other mRNA sequences. In S7 Fig, we show an example of a helix with four cross-covariations. As an extreme example, the bacterial LOOT RNA with approximately 43 basepairs in six helices includes 28 cases of cross-covariations. Type 11 includes a few cases in which an alternative positive helix is incompatible with another positive helix. These cases are candidates for possible competing structures. The SSU and LSU ribosomal RNA alignments are collected in Type 12. These are large structures with deep alignments in which about one third of the basepairs are positive. For the LSU rRNA, CaCoFold finds between 8 (Eukarya) to 22 (bacteria) additional positive basepairs. Type 13 include just three cases for which the positive basepairs are few and cannot provide confirmation of the proposed structure. Type 14 identifies two cases in which the Rfam and ZWD alignments report different sets of positive basepairs. These suggest the possibility of a misalignment resulting in spurious covariations. Finally, Type 15 collects about a third (114/319) of the alignments for which CaCoFold identifies only one or two positive basepairs while the original structure has none. None of these alignments has enough covariation to support any particular structure. These alignments also have low power of covariation to decide whether there is a conserved RNA structure in the first place.

The R-scape covariation analysis and CaCoFold structure prediction including pseudoknots for all 3,016 seed alignments in Rfam 14.1 (which includes four SSU and three LSU rRNA alignments; ranging in size from SSU rRNA Archaea with 1,958 positions to LSU rRNA Eukarya with 8,395 positions) takes a total of 724 minutes performed serially on a 3.3 GHz Intel Core i7 MacBook Pro.

Discussion

The CaCoFold folding algorithm provides a comprehensive description and visualization of all the significantly covarying pairs (even if not nested or overlapping) in the context of the most likely complete RNA structure compatible with all of them. This allows an at-a-glance direct way of assessing which parts of the RNA structure are well determined (i.e. supported by significant covariation). The strength and key features of the CaCoFold algorithm are in building RNA structures anchored both by all positive (significant covariation) and negative (variation in the absence of covariation) information provided by the alignment. In addition, CaCoFold provides a set of compatible basepairs obtained by constrained probabilistic folding. The set of compatible pairs is only indicative of a possible completion of the structure. They do not provide any additional evidence about the presence of a conserved structure, and some of them could be erroneous as it is easy to predict consistent RNA basepairs even from random sequences.

CaCoFold is not the first method to use covariation information to infer RNA structures [14–19], but it is the first to our knowledge to distinguish structural covariation from that of phylogenetic nature, which is key to eliminate confounding covariation noise. CaCoFold is also the first method to our knowledge to use negative evolutionary information to discard unlikely basepairs. CaCoFold differs from previous approaches in four main respects: (1) It uses the structural covariation information provided by R-scape which removes phylogenetic confounding. The specificity of R-scape is controlled by an E-value cutoff. (2) It uses the variation information (covariation power) to identify negative basepairs that are not allowed to form. (3) It uses a recursive algorithm that incorporates all positive basepairs even those that do not form nested structures, or involve positions already forming other basepairs. The CaCoFold algorithm uses different probabilistic folding algorithms at the different layers. (4) A visualization tool derived from R2R that incorporates all interactions and highlights the positive basepairs.

Overall, we have identified over two hundred RNAs for which CaCoFold finds new significantly covarying structural elements not present in curated databases of structural RNAs. For the 21 RNAs in that set with 3D information (leaving aside SSU and LSU rRNAs), we have shown that the new CaCoFold elements are generally supported by the crystal structures. Those new elements include new and re-shaped helices, basepairs involved in coaxial stacking, new pseudoknots, long-range contacts and base triplets. Reliable CaCoFold predictions could accelerate the discovery of still unknown biological mechanisms without having to wait for a crystal structure.

We have found interesting cases of significantly covarying pairs where the covariation is not due to RNA structure, the tRNA acceptor/discriminator covariation (Fig 5b) or the coding covariations associated to the messenger domain of tmRNA (Fig 3, S6(c) Fig) are examples. These covariations do not interfere with the determination of the RNA structure, which usually forms during the first layers of the algorithm, as they are added by higher layers on top of the RNA structure. The CaCoFold visual display of all layered interactions permits to identify the RNA structure and to asses its covariation support, and may help proposing hypotheses about the origin of other interactions of different nature.

Even for RNAs with a known crystal structure, because the experimental structure may have only captured one conformation, CaCoFold can provide a complementary analysis, as in the case of the U2 spliceosomal snRNA presented here (Fig 5c). (Riboswitches also have alternative structures, but because Rfam alignments do not typically include riboswitch expression platform regions, we do not observe the alternatively structured regions of riboswitches in these data.).

CaCoFold improves the state of the art for accurate structural prediction for the many structural RNAs still lacking a crystal structure. This work provides a new tool for several lines of research such as: the study of significant covariation signatures of no phylogenetic origin present in messenger RNA, as those identified here in the tmRNA (Fig 3, S6(c) Fig); the study of the nature and origin of covariation in protein sequences; and the use of variation and covariation information to improve the quality of RNA structural alignments.

Materials and methods

Implementation

The CaCoFold algorithm has been implemented as part of the R-scape software package. For a given input alignment, there are two main modes to predict a CaCoFold structure using R-scape covariation analysis as follows,

To predict a new structure: R-scape --fold

All possible pairs are analyzed equally in one single covariation test. This option is most appropriate for obtaining a new consensus structure prediction based on covariation analysis in the absence of a proposed structure.

The structures in Figs 1, 3 were obtained using this option.
To improve a existing structure: R-scape -s --fold

This option requires that the input alignment has a proposed consensus structure annotation. Two independent covariation tests are performed, one on the set of proposed base pairs, the other on all other possible pairs. The CaCoFold structure is built anew using the positive and negative basepairs as constraints.

The structures in Figs 4, 5 and Supplemental S2–S7 Figs were obtained using this option.

Extracting the RNA structure from a PDB file

The software is capable of obtaining the RNA structure from a PDB file for a sequence homolog to but not necessarily represented in the alignment, and transforms it to a consensus structure for the alignment.

For a given PDB [75] file, we use the software nhmmer [76] to evaluate whether the PDB sequence is homologous to the aligned sequences. If the PDB sequence is found to be a homolog of the sequences in the input alignment, we extract the RNA structure from the PDB file (Watson-Crick and also non-canonical basepairs and contacts) using the program RNAView [69]. An Infernal model is built using the PDB sequence and the PDB-derived RNAView structure [77]. All input sequences are then aligned to the Infernal PDB structural model. The new alignment includes the PDB sequence with the PDB structure as its consensus structure. We use the mapping of each sequence to the PDB sequence in this new alignment to transfer the PDB structure to the sequence as it appears in the input alignment. From all individual structures, we calculate a PDB-derived consensus structure for the input alignment. The R-scape software can then analyze the covariation associated with the PDB structure mapped to the input alignment.

For example, the PDB structure and covariation analysis in Fig 5b for the tRNA (RF00005) was derived from the PDB file 1EHZ (chain A) using the options:

R-scape -s --pdb 1ehz.pdb --pdbchain A --onlypdb RF00005.seed.sto

The option --pdbchain <chain_name> forces to use only the chain of name <chain_name>. By default, all sequence chains in the PDB file are tested to find those with homology to the input alignment. The option --onlypdb ignores the alignment consensus structure. By default, the PDB structure and the alignment consensus structure (if one is provided) would be combined into one annotation.

Availability

A R-scape web server is available from rivaslab.org. The source code can be downloaded from a link on that page. A link to a preprint version of this manuscript with all supplemental information and the R-scape code is also available from that page.

This work uses R-scape version 1.5.4. The distribution of R-scape v1.5.4 includes external programs: FastTree version 2.1.10 [78], Infernal 1.1.2 [77], hmmer 3.3 [79]. It also includes modified versions of the programs RNAView [69], and R2R version 1.0.6.1-49-g7bb81fb [42]. The R-scape git repository is at https://github.com/EddyRivasLab/R-scape.

For this manuscript, we used the databases Rfam version 14.1 (http://rfam.xfam.org/), the 10 new families and 4 revised families in Rfam 14.2, and ZWD (114e95ddbeb0) downloaded on February 11, 2019 (https://bitbucket.org/zashaw/zashaweinbergdata/). We used program RNAalifold from the ViennaRNA-2.4.12 software package [26].

All alignments used in the manuscript are provided in the supplemental S1 File.

Supporting information

S1 Fig. tmRNA structure predicted by RNAalifold and covariation analysis.

(a) The RNAalifold predicted consensus structure output for the tmRNA Rfam seed alignment (RF00023) obtained using default parameters. The RNAalifold structure consists of 46 basepairs, and it annotates (at least partially) 6 of the 12 helices in the structure [44]: 2 (a,b,d), 3, 5, 6, 9, and 10 (a,b,c), see Fig 3g. (b) The covariation analysis of the RNAalifold structure indicates that 45 of the 46 RNAalifold basepairs have covariation support (shown in green). It also identifies 76 other basepairs with covariation support not in the proposed RNAalifold structure. The display of all 121 positive pairs can be seen in Fig 3f. (Columns with more than 75% gaps have been removed from the display.).

(PDF)

Click here for additional data file.^{(372.6KB, pdf)}

S2 Fig. CaCoFold structures confirmed by known 3D structures (part 3/7).

Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. The three RNAs are examples of CaCoFold structures with more covariation support in the form of more positive basepairs to helices already present in the consensus Rfam structures. (a) SRP RNA. The SRP complex 2XXA PDB X-ray diffraction structure has 3.94 Å resolution [54]. The PDB-derived consensus structure was obtained as described in Methods. (b) For the cyclic di-AMP riboswitch, the region around helix P4 is highly variable in the Rfam alignment, and none of the proposed structures has covariation support. The displayed CaCoFold structure showing helix P4 was obtained using a consensus reference sequence (instead of the default profile sequence). The rest of the structure has covariation support and remains invariant. (c) For the YkoK leader, there are two additional basepairs labeled “1” and “2” in helices P5 and P6 respectively confirmed by the crystal structure [56].

(PDF)

Click here for additional data file.^{(2.8MB, pdf)}

S3 Fig. CaCoFold structures confirmed by known 3D structures (part 4/7).

Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. (a) The 5S rRNA CaCoFold structure remodels Helix 4 (six basepairs) and Loop C (two basepairs) in agreement with the crystal structure [57]. A Y-R covarying basepair in Loop B is not described in the 3D structure. (b) The FMN riboswitch CaCoFold structure identifies a confirmed 2-basepair pseudoknotted helix, and one covarying pair in helix P2 that is different than in the 3D structure [58]. (c) The covarying pseudoknot identified by CaCoFold in the ZPM-ZTP riboswitch is confirmed by the Fusobacterium ulcerans X-ray diffraction structure (2.82 Å) [59].

(PDF)

Click here for additional data file.^{(2MB, pdf)}

S4 Fig. CaCoFold structures confirmed by known 3D structures (part 5/7).

Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. All three cases (a) Fluoride riboswitch (b) Glutamine riboswitch (c) Archeal SRP are examples of CaCoFold structures with more covariation support in the form of a new helix forming a pseudoknot all confirmed by the 3D structures.

(PDF)

Click here for additional data file.^{(662.4KB, pdf)}

S5 Fig. CaCoFold structures confirmed by known 3D structures (part 6/7).

Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. (a) An additional covarying pair introduces a new internal loop in the B-type RNase P RNA confirmed by Ref. 63, Fig 4a. (b) An additional covarying pair introduces a new three-way junction in the Group-II intron D1D4-3 fragment [64]. (c) In the U5 snRNA, an additional Y-Y covarying pair that modifies a hairpin loop is confirmed by the S. pombe spliceosomal RNA cryo-EM structure 3JB9 (3.60 Å) [65].

(PDF)

Click here for additional data file.^{(830.3KB, pdf)}

S6 Fig. CaCoFold structures confirmed by known 3D structures (part 7/7).

Structural elements with covariation support introduced by CaCoFold relative to the Rfam annotation and corroborated by 3D structures are annotated in blue. (a) The U3 snoRNA CaCoFold structure adds a covarying pair closing the boxB/boxC of the snoRNA [65]. (b) 6S RNA covarying pair at the RNA synthesis initiation site not associated to RNA structure [4]. (c) Side-covariation in the mRNA-like domain of tmRNA not due to RNA structure.

(PDF)

Click here for additional data file.^{(2MB, pdf)}

S7 Fig. Examples of RNAs without a 3D structure for which the CaCoFold structure has more positive basepairs (green shading) than the structure given by the corresponding database.

We provide examples of differences corresponding to Types 1 to 11. A description of all different types is given in Table 1.

(PDF)

Click here for additional data file.^{(919.4KB, pdf)}

S1 File. Supplemental material.

Includes alignments, and source code.

(GZ)

Click here for additional data file.^{(88.8MB, gz)}

S1 Table

(PDF)

Click here for additional data file.^{(109.1KB, pdf)}

Acknowledgments

I thank the Centro de Ciencias de Benasque Pedro Pascual in Benasque, Spain, where ideas for this manuscript were developed. Special thanks to Ioanna Kalvari and Anton Petrov for their assistance with the Rfam database, Zasha Weinberg for assistance with the database ZWD and the program R2R, and Sean R. Eddy for comments.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

The author received no specific funding for this work.

References

1. Mironov AS, Gusarov I, Rafikov R, Lopez LE, Shatalin K, Kreneva RA, et al. Sensing small molecules by nascent RNA: a mechanism to control transcription in bacteria Cell. Cell. 2002;5:747–756. 10.1016/S0092-8674(02)01134-0 [DOI] [PubMed] [Google Scholar]
2. Winkler WC, Nahvi A, Breaker RR. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature. 2002;419:952–956. 10.1038/nature01145 [DOI] [PubMed] [Google Scholar]
3. Babitzke P, Romeo T. CsrB sRNA family: sequestration of RNA-binding regulatory proteins. Current Opinion in Microbiology. 2007;10(2):156–163. 10.1016/j.mib.2007.03.007 [DOI] [PubMed] [Google Scholar]
4. Chen J, Wassarman KM, Feng S, Leon K, Feklistov A, Winkelman JT, et al. 6S RNA mimics B-form DNA to regulate Escherichia coli RNA polymerase. Mol Cell. 2017;68(2):388–397.e6. 10.1016/j.molcel.2017.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Holley RW, Apgar J, Everett GA, Madison JT, Marquisee M, Merrill SH, et al. Structure of a ribonucleic acid. Science. 1965;14:1462–1465. 10.1126/science.147.3664.1462 [DOI] [PubMed] [Google Scholar]
6. Noller HF, Kop JA, Wheaton V, Brosius J, Gutell RR, Kopylov AM, et al. Secondary structure model for 23S ribosomal RNA. Nucl Acids Res. 1981;9:6167–6189. 10.1093/nar/9.22.6167 [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Gutell RR, Weiser B, Woese CR, Noller HF. Comparative anatomy of 16S-like ribosomal RNA. Prog Nucl Acids Res Mol Biol. 1985;32:155–216. 10.1016/S0079-6603(08)60348-7 [DOI] [PubMed] [Google Scholar]
8. Pace NR, Smith DK, Olsen GJ, James BD. Phylogenetic comparative analysis and the secondary structure of Ribonuclease P RNA—A review. Gene. 1989;82:65–75. 10.1016/0378-1119(89)90031-0 [DOI] [PubMed] [Google Scholar]
9. Michel F, Westhof E. Modelling of the three-dimensional architecture of group I catalytic introns based on comparative sequence analysis. J Mol Biol. 1990;216:585–610. 10.1016/0022-2836(90)90386-Z [DOI] [PubMed] [Google Scholar]
10. Brown JW, Haas ES, James BD, Hunt DA, Pace NR. Phylogenetic analysis and evolution of RNase P RNA in Proteobacteria. J Bacteriol. 1991;173:3855–3863. 10.1128/JB.173.12.3855-3863.1991 [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Haas E, Morse D, Brown J, Schmidt F, Pace N. Long-range structure in ribonuclease P RNA. Science. 1991;254(5033):853–856. 10.1126/science.1719634 [DOI] [PubMed] [Google Scholar]
12. Williams KP, Bartel DP. Phylogenetic analysis of tmRNA secondary structure. RNA. 1996;2:1306–1310. [PMC free article] [PubMed] [Google Scholar]
13. Michel F, Costa M, Massire C, Westhof E. Modeling RNA tertiary structure from patterns of sequence variation. Meth Enzymol. 2000;317:491–510. 10.1016/S0076-6879(00)17031-4 [DOI] [PubMed] [Google Scholar]
14. Gutell RR, Power A, Hertz GZ, Putz EJ, Stormo GD. Identifying constraints on the higher-order structure of RNA: continued development and application of comparative sequence analysis methods. Nucl Acids Res. 1992;20:5785–5795. 10.1093/nar/20.21.5785 [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Akmaev VR, Kelley ST, Stormo GD. Phylogenetically enhanced statistical tools for RNA structure srediction. Bioinformatics. 2000;16:501–512. 10.1093/bioinformatics/16.6.501 [DOI] [PubMed] [Google Scholar]
16. Knudsen B, Hein J. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucl Acids Res. 2003;31:3423–3428. 10.1093/nar/gkg614 [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Bindewald E, Hayes R, Yingling YG, Kasprzak W, Shapiro BA. RNAJunction: a database of RNA junctions and kissing loops for three-dimensional structural analysis and nanodesign. Nucl Acids Res. 2008;36:D392–D397. 10.1093/nar/gkm842 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Kiryu H, Kin T, Asai K. Rfold: an exact algorithm for computing local base pairing probabilities. Bioinformatics. 2008;24:367–373. 10.1093/bioinformatics/btm591 [DOI] [PubMed] [Google Scholar]
19. Bernhart SH, Hofacker IL, Will S, Gruber AR, Stadler PF. RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics. 2008;9:474 10.1186/1471-2105-9-474 [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Gutell RR, Lee JC, Cannone JJ. The accuracy of ribosomal RNA comparative structure models. Curr Opin Struct Biol. 2002;12:301–310. 10.1016/S0959-440X(02)00339-1 [DOI] [PubMed] [Google Scholar]
21. Deigan KE, Li TW, DH DHM, Weeks KM. Accurate SHAPE-directed RNA structure determination. Proc Natl Acad Sci U S A. 2009;6:97–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Zarringhalam K, Meyer MM, Dotu I, Chuang JH, Clote P. Integrating Chemical Footprinting Data Into RNA Secondary Structure Prediction. PLOS ONE. 2012;7:e45160 10.1371/journal.pone.0045160 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Washietl S, Hofacker IL, Stadler PF, Kellis M. RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction. Nucl Acids Res. 2012;40:4261–4272. 10.1093/nar/gks009 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Sükösd Z, Swenson MS, Kjems J, Heitsch CE. Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions. Nucleic acids research. 2013;41(5):2807–2816. 10.1093/nar/gks1283 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Eddy SR. Computational Analysis of Conserved RNA Secondary Structure in Transcriptomes and Genomes. Annu Rev Biophys. 2014;43:433–456. 10.1146/annurev-biophys-051013-022950 [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Lorenz R, Bernhart SH, Siederdissen CHZ, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:1748–7188. 10.1186/1748-7188-6-26 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Xu ZZ, Mathews DH. Experiment-assisted secondary structure prediction with RNAstructure: methods and protocols. Methods in Molecular Biology. 2016;1490:163–176. 10.1007/978-1-4939-6433-8_10 [DOI] [PubMed] [Google Scholar]
28. Swenson MS, Anderson J, Ash A, Gaurav P, Sükösd Z, Bader DA, et al. GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops. BMC Research Notes. 2012;5::341 10.1186/1756-0500-5-341 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Parisien M, Major F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature. 2008;452:51–55. 10.1038/nature06684 [DOI] [PubMed] [Google Scholar]
30. Rivas E, Clements J, Eddy SR. A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs. Nature Methods. 2017;14:45–48. 10.1038/nmeth.4066 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Rivas E, Clements J, Eddy SR. Estimating the power of sequence covariation for detecting conserved RNA structure. Bioinformatics. 2020; 10.1093/bioinformatics/btaa080 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Kalvari I, Argasinska J, Quinones-Olvera N, Nawrocki EP, Rivas E, Eddy SR, et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucl Acids Res. 2018;46:D335–D342. 10.1093/nar/gkx1038 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Weinberg Z. The Zasha Weinberg Database (ZWD). https://bitbucketorg/zashaw/zashaweinbergdata/ Accessed 11 February 2019. 2018;.
34. Leontis NB, Westhof E. Geometric nomenclature and classification of RNA base pairs. RNA. 2001;7:499–512. 10.1017/S1355838201002515 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Weinberg Z, Wang JX, Bogue J, Yang J, Corbino K, Moy RH, et al. Comparative genomics reveals 104 candidate structured RNAs from Bacteria, Archaea, and their metagenomes. Genome Biol. 2010;11(3):R31 10.1186/gb-2010-11-3-r31 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Landau GM, Vishkin U, Nussinov R. An efficient string matching algorithm with k differences for nucleotide and amino acid sequences. Nucl Acids Res. 1986;14(1):31–46. 10.1093/nar/14.1.31 [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Rivas E, Lang R, Eddy SR. A range of complex probabilistic models for RNA secondary structure prediction that include the nearest neighbor model and more. RNA. 2012;18:193–212. 10.1261/rna.030049.111 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Mathews DH, Turner DH. Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol. 2006;16:270–278. 10.1016/j.sbi.2006.05.010 [DOI] [PubMed] [Google Scholar]
39. Markham NR, Zuker M. UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol. 2008;453:3–31. 10.1007/978-1-60327-429-6_1 [DOI] [PubMed] [Google Scholar]
40. Knudsen B, Hein J. RNA secondary structure prediction using stochastic context-free grammars and evolutionary History. Bioinformatics. 1999;15:446–454. 10.1093/bioinformatics/15.6.446 [DOI] [PubMed] [Google Scholar]
41. Dowell RD, Eddy SR. Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics. 2004;5:71 10.1186/1471-2105-5-71 [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Weinberg Z, Breaker RR. R2R—software to speed the depiction of aesthetic consensus RNA secondary structures. BMC Bioinformatics. 2011;12:3. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Weis F, Bron P, Giudice E, Rolland JP, Thomas D, Felden B, et al. tmRNA-SmpB: a journey to the centre of the bacterial ribosome. EMBO J. 2010;29:3810–3818. 10.1038/emboj.2010.252 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Kelley ST, Harris JK, Pace NR. Evaluation and refinement of tmRNA structure using gene sequences from natural microbial communities. RNA. 2001;7:1310–1316. 10.1017/S1355838201010573 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Fu J, Yaser Hashem Y, Wower I, Lei J, Liao HY, Zwieb C, et al. Visualizing the transfer-messenger RNA as the ribosome resumes translation. EMBO J. 2010;29:3819–3825. 10.1038/emboj.2010.255 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Torres-Larios A, Swinger KK, Krasilnikov AS, Pan T, Mondragón A. Crystal structure of the RNA component of bacterial ribonuclease P. Nature. 2005;437(7058):584–587. 10.1038/nature04074 [DOI] [PubMed] [Google Scholar]
47. Montange R, Batey RT. Structure of the S-adenosylmethionine riboswitch regulatory mRNA element. Nature. 2006;441:1172–1175. 10.1038/nature04819 [DOI] [PubMed] [Google Scholar]
48. Wan R, Yan C, Bai R, Wang L, Huang M, Wong CCL, et al. The 3.8 Å structure of the U4/U6.U5 tri-snRNP: Insights into spliceosome assembly and catalysis. Science. 2016;351(6272):466–475. 10.1126/science.aad6466 [DOI] [PubMed] [Google Scholar]
49. Peselis A, Serganov AS. Structural insights into ligand binding and gene expression control by an adenosylcobalamin riboswitch. Nat Struct Mol Biol. 2012;19:1182–1184. 10.1038/nsmb.2405 [DOI] [PubMed] [Google Scholar]
50. Giegé R, Sissler M, Florentz C. Universal rules and idiosyncratic features in tRNA identity. NAR. 1998;26:5017–5035. 10.1093/nar/26.22.5017 [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Shi H, Moore PB. The crystal structure of yeast phenylalanine tRNA at 1.93 A resolution: a classic structure revisited. RNA. 2000;6:10911105. [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Ares M, Igel AH. Mutations define essential and nonessential U2 RNA structures. Mol Biol Rep. 1990;14(2–3):131–132. 10.1007/bf00360444 [DOI] [PubMed] [Google Scholar]
53. Perriman RJ, Ares M. Rearrangement of competing U2 RNA helices within the spliceosome promotes multiple steps in splicing. Genes Dev. 2007;21(7):811–820. 10.1101/gad.1524307 [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Ataide SF, Schmitz N, Shen K, Ke A, Shan SO, Doudna JA, et al. The crystal structure of the signal recognition particle in complex with its receptor. Science. 2011;331:881–886. 10.1126/science.1196473 [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Gao A, Serganov A. Structural insights into recognition of c-di-AMP by the ydaO riboswitch. Proc Natl Acad Sci USA. 2014;10:787–792. [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Dann CE, Wakeman CA, Sieling CL, C BS, Irnov I, Winkler WC. Structure and mechanism of a metal-sensing regulatory RNA. Cell. 2007;130:878–892. 10.1016/j.cell.2007.06.051 [DOI] [PubMed] [Google Scholar]
57. Ban N, Nissen P, Hansen J, Moore PB, Steitz TA. The complete atomic structure of the large ribosomal subunit at 2.4 Å Resolution. Science. 2000;289:905–920. 10.1126/science.289.5481.905 [DOI] [PubMed] [Google Scholar]
58. Serganov A, Huang L, Patel DJ. Coenzyme recognition and gene regulation by a flavin mononucleotide riboswitch. Nature. 2009;458:233–237. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Jones CP, Ferré-D’Amaré AR. Recognition of the bacterial alarmone ZMP through long-distance association of two RNA subdomains. Nat Struct Mol Biol. 2015;22(9):679–685. 10.1038/nsmb.3073 [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Ren A, Rajashankar KR, Patel DJ. Fluoride ion encapsulation by Mg2+ ions and phosphates in a fluoride riboswitch. Nature. 2012;486:85–89. 10.1038/nature11152 [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Ren A, Xue Y, Peselis A, Serganov A, AHH M, Patel DJ. Structural and dynamic basis for low-affinity, high-selectivity binding of L-Glutamine by the Glutamine riboswitch. Cell Rep. 2015;13:1800–1813. 10.1016/j.celrep.2015.10.062 [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Zwieb C, Nues RWV, Rosenblad MA, Brown JD, Samuelsson T. A nomenclature for all signal recognition particle RNAs. RNA. 2005;11(1):7–13. 10.1261/rna.7203605 [DOI] [PMC free article] [PubMed] [Google Scholar]
63. Kazantsev AV, Krivenko AA, Harrington DJ, Holbrook SR, Adams PD, Pace NR. Crystal structure of a bacterial ribonuclease P RNA. PNAS. 2005;102:1339213397. [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Toor N, Keating KS, Taylor SD, Pyle AM. Crystal structure of a self-spliced Group II intron. Science. 2008;320(5872):77–82. 10.1126/science.1153803 [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Yan C, Hang J, Wan R, Huang M, Wong CC, Shi Y. Structure of a yeast spliceosome at 3.6-angstrom resolution. Science. 2015;349:1182–1191. 10.1126/science.aac7629 [DOI] [PubMed] [Google Scholar]
66. Sun Q, Zhu X, Qi J, An W, Lan P, Tan D, et al. Molecular architecture of the 90S small subunit pre-ribosome. eLife. 2017;6:e22086 10.7554/elife.22086 [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Ramrath D, Yamamoto H, Rother K, Wittek D, Pech M, Mielke T, et al. The complex of tmRNASmpB and EF-G on translocating ribosomes. Nature. 2012;485:526–529. 10.1038/nature11006 [DOI] [PubMed] [Google Scholar]
68. Massire C, Jaeger L, Westhof E. Derivation of the three-dimensional architecture of bacterial ribonuclease P RNAs from comparative sequence analysis. 1998;279:773–793. [DOI] [PubMed] [Google Scholar]
69. Yang H, Jossinet F, Leontis N, Chen L, Westbrook J, Berman HM, et al. Tools for the automatic identification and classification of RNA base pairs. Nucl Acids Res. 2003;3113:3450–3460. 10.1093/nar/gkg529 [DOI] [PMC free article] [PubMed] [Google Scholar]
70. Brennan T, Sundaralingam M. Structure of transfer RNA molecules containing the long variable loop. Nucl Acids Res. 1976;3:3235–3251. 10.1093/nar/3.11.3235 [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Auffinger P, Westhof E. Singly and bifurcated hydrogen-bonded base-pairs in tRNA anticodon hairpins and ribozymes. J Mol Biol. 1999;292:467–483. 10.1006/jmbi.1999.3080 [DOI] [PubMed] [Google Scholar]
72. Zaug AJ, Cech TR. Analysis of the structure of tetrahymena nuclear RNAs in vivo: telomerase RNA, the self-splicing rRNA intron, and U2 snRNA. RNA. 1995;1:363–374. [PMC free article] [PubMed] [Google Scholar]
73. Williams GD, Chang RY, Brian DA. A phylogenetically conserved Hairpin-Type 3’ untranslated region pseudoknot functions in coronavirus RNA replication. Journal of Virology. 1999;73:8349–8355. 10.1128/JVI.73.10.8349-8355.1999 [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Leontis NB, Westhof E. Analysis of RNA motifs. Curr Opin Struct Biol. 2003;13:300–308. [DOI] [PubMed] [Google Scholar]
75. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Research. 2000;28:235–242. 10.1093/nar/28.1.235 [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics. 2013;29:2487–2489. 10.1093/bioinformatics/btt403 [DOI] [PMC free article] [PubMed] [Google Scholar]
77. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–2935. 10.1093/bioinformatics/btt509 [DOI] [PMC free article] [PubMed] [Google Scholar]
78. Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLOS ONE. 2010;5:e9490 10.1371/journal.pone.0009490 [DOI] [PMC free article] [PubMed] [Google Scholar]
79. Eddy SR. Accelerated profile HMM searches. PLOS Comp Biol. 2011;7:e1002195 10.1371/journal.pcbi.1002195 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008387.r001

Decision Letter 0

Tamar Schlick, Nir Ben-Tal

11 Aug 2020

Dear Dr Rivas,

Thank you very much for submitting your manuscript "RNA structure prediction using positive and negative evolutionary information" 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. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We apologize for the delay in handling your manuscript but one reviewer was extremely late and we have decided to proceed without that review.

From the two reviews of your manuscript, your contribution is clearly considered important and innovative. However, both reviewers pose questions and issues on the technical aspects that should be clarified in the paper and algorithm.

Please address them in full in your revision.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Tamar Schlick

Associate Editor

PLOS Computational Biology

Nir Ben-Tal

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: This is an important paper in the difficult pursuit of finding structural elements in sequence alignments. Sequence alignments are extremely powerful for establishing 2D helical regions maintained by complementary Watson-Crick pairs with GU wobbles (and the 2D structures for all “ancillary” RNAs were deduced in this way). This is based on covariations or exchanges, during evolution, between the structurally similar Watson-Crick pairs. RNAs with sequences highly conserved throughout phylogeny form therefore a difficult case. Also, the extraction of information about bases that form 3D pairs is delicate because they generally are highly conserved and do not covary strictly speaking. This has been achieved with good alignments displaying enough sequence variation. For large structures, this can be reached especially when coupled with real space molecular assembly and experimental data (mutations, chemical probing,…) in order to restrict the search range for covariations (as in JMB 216,585(90), Science 254, 853 (91), JMB 279, 773 (98)). Here, the author exploits astutely sophisticated statistical analysis to obtain reliable 2D structures with potential 3D information.

There is here original thinking and new programming yielding new data. An immense effort has been made to compare with many available RNA structures. An amazing amount of work and the results are all available in a variety of files with the codes. The paper is a real step forward, but it is hard to read and follow, not only because of the many cases discussed (too often maybe too cursorily). Some sentences state an observation but without a figure or enough precision to locate the observation in the structure. A thorough re-reading by the author would certainly help. I will give some examples below. Because of the importance of the work, I feel it is worth this additional effort to clarify some points. Some may sound trivial, but the recent controversy with long ncRNAs revealed the deep misunderstandings even among (computer) scientists working on RNA.

At the onset, the author should state clearly what nucleotide exchanges are considered to indicate covariations. Only those pertaining to standard WC pairs and GU wobble? Some GA pairs do form pairs through their Watson-Crick edges. But AG can also form “sheared “pairs (trans Hoogsteen-Sugar edge) in which case AG is not structurally the same as GA, but such AG pairs do exchange with AA pairs interacting through the same edges. Or is any variation accepted? I do not think so from reading the text. This would be useful for understanding, for example lines 38-39.

I do not understand line 146.

Line 148. I understand the definition is “operational” but a one base pair helix is not meaningful since a helix is characterized by a twist angle between steps and the rise between two steps along the helical axis.

Lines 167-168. I find the use of “triplets” as confusing. Sometimes they are used to compare with triples in 3D and sometimes with alternative structures. It is written that “nc” is used for 3D structures, but in Fig.5 for the tRNA Cacofold, one finds “nc”. That figure would need more explanations. For some nucleotides, there are several “tr” (e.g. R9) and one “nc”. See also the legend of Fig.5 (where I do not understand the second sentence): “Four nc pairs and one pk pair with covariation support are found by CaCoFold and confirmed by the 1EHZ structure. Four base triplets (tr) and two pseudoknots (pk) have covariation support but have not been assigned to any basepair type by RNAView. » See also line 331, what does this mean? Only pairs with two H-bonds are reported by RNAview and many other similar programs (for example, as discussed below 32…38 form a single H-bond contact, at most).

Fig. 5b. Among the possible positive pairs, those that fit the 3D structure are selected, but without knowing it, all those would be equivalent. I guess, this is the same for the pairs between the invariant G18G19 and invariant U55C56 where all possibilities are open and cannot be distinguished (pk1, pk2). I do not remember seeing a tRNA with U56 by the way. What is very interesting (and this is why clarity and explanations are required) is that variations between regions are observed without those variations reflecting a pair or a contact but instead a concerted variation due to tRNA/protein interactions (like anticodon triplet and N73, see Lines 327…) or global constraints on the highly functional anticodon loop (tr_4 between 36 and 38). Nucleotides 32 and 38 form a single H-bond contact and the most frequent ones are C32_A38 or Y32_Y38 (and it is one or the other depending on the anticodon triplet). For the “triplets”, nc_2 and nc_5, is it really meant as a triple or as an alternative fold? With a change in the definition of a helix this may be clarified. All tRNAs were selected, but the type I and type II tRNAs do not have the same triples. Re-doing the calculations separately for each class might enhance the values (at least for type I).

Lines 172-173. I am not sure that the first part of that sentence is correct (the second yes).

Overall, the comparisons with experimental data are not easy to follow. For the tmRNAs, there are more recent structures (one you cite somewhere but do not compare with Ramrath et al. Nature 2012; and a more recent one, Scient Rep (2018) 8:13587).

Line 217. Difficult to see that.

Line 294. Difficult to see. You may want to look at the results from comparative analysis on RNAseP, A and B (JMB 279, 773 (98)).

Lines 302, 303. What is the meaning of “??”?

Lines 314-315. State which pairs with numbers and show them clearly on the Figures.

Line 317. “Y-Y covarying pair”, which covariations are observed?

Line 339. ??

Lines 381. What is precisely meant here? Is it in part due to the huge amount of tRNA sequences and the expected changes occurring in the helices? The ‘almost’ is interesting: which ones are not involved in “covariations”?

On Fig. S7 I did not see Type 12.

Reviewer #2: The Rivas manuscript describes a new software tool for predicting

consensus RNA secondary from aligned nucleotide sequence. The tool has

a number of novel aspects that make this a useful addition to the

literature on the subject.

The approach evaluates the significance of potential covariation

signals, using a null model that accounts for phylogenetic structure

of aligned sequences. This pairs are used as constraints for iterative

rounds of Nussinov-style style structure prediction that maximises the

number of (covarying) basepairs. The iterative rounds of prediciton

mean that pseudoknots, and even alternative structures and/or triples

are captured. Further rounds of basepair prediction extend stems that

are conserved, yet don't have strong signatures of covariation.

This is a significant improvement of the state-of-the-art methods that

generally ignore pseudoknots and alternative structures -- and uses an

arguably more reasonable set of metrics for optimisation than

combining conservation, covariation and thermodynamic models.

I do think some improvements in the manuscript could be made.

1. It is probably worth pointing out that the majority (97–98%) of the

rRNA secondary structure had been inferred before the crystal

structure was published thanks to covariation analysis [1].

2. It was unclear to me what was meant by "negative information". The

MS defines this as basepairs that "show variation but not significant

covariation". So, does this include column pairs that include "U:A &

U:G" type of variation, that admittedly is not strictly covarying, but

may be structurally consistent in the right context. Some careful

definitions of positive and negative information would help the reader

immensly.

3. The use of a -log(E-value) scores concerns me slightly. With BLAST

E-values this doesn't have a very good history (e.g. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0721-2),

as directly using bit-scores can be a more accurate measure. I wonder if the

author considered experimenting with directly using the G-score for the first layer?

4. I'm curious about the types of Rfam families that could be improved

by CaCoFold. Were these predominantly riboswitches and ribozymes, or

other types? It's disappointing the the 5S rRNA has not been updated

in Rfam. A number of papers have now pointed out flaws in this

e.g. Rivas, Clements & Eddy (2017) and Gardner & Eldai (2015). Have

the curators of Rfam been alerted to this issue?

5. Do the improved consensus structures improve the performance of the

corresponding covariance models for homology search? Or are these

robust to errors in alignments and structures?

6. Regarding the use of PDB structures. Presumably the consensus

structure is generally a subset of basepairs parsed from 3D

structures. I did see this explicitly mentioned in the MS.

REFERENCES:

[1] Gutell, R. R., Lee, J. C., & Cannone, J. J. (2002). The accuracy

of ribosomal RNA comparative structure models. Current opinion in

structural biology, 12(3), 301-310.

Minor corrections:

A "Y-Y" covariation is mention on pg 15. Presumambly this is a

non-canonical interaction?

The "??" throughout the manuscript suggest missing references?

Figure 3: "Transfer-messenger RNA" spelled incorrectly

Installing the supplement:

# tar zxvf rscape_v1.5.2.tar.gz

gzip: stdin: unexpected end of file

rscape_v1.5.2/lib/hmmer/easel/documentation/figures/gev_density.pdf

rscape_v1.5.2/lib/hmmer/easel/documentation/figures/rnaseP-ecoli.pdf

tar: Unexpected EOF in archive

tar: Error is not recoverable: exiting now

# cd rscape_v1.5.2/

# ./configure

config.status: creating src/Makefile

config.status: creating lib/FastTree/src/Makefile

config.status: creating lib/R2R/R2R-current/src/Makefile

config.status: error: cannot find input file: `lib/RNAVIEW/src/Makefile.in'

v1.5.6 seems to install fine though.</lots></lots>

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methods

PLoS Comput Biol. 2020 Oct 30;16(10):e1008387. doi: 10.1371/journal.pcbi.1008387.r002

Author response to Decision Letter 0

24 Aug 2020

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(119.4KB, pdf)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008387.r003

Decision Letter 1

Tamar Schlick, Nir Ben-Tal

10 Sep 2020

Dear Dr Rivas,

Thank you very much for submitting your manuscript "RNA structure prediction using positive and negative evolutionary information" 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.

The reviewers have appreciated the thorough comments and changes made to the original manuscript,

but some technical issues remain, and one reviewer clarifies his prior comments.

Please address these points in full in a revision.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Tamar Schlick

Associate Editor

PLOS Computational Biology

Nir Ben-Tal

Deputy Editor

PLOS Computational Biology

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

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

The reviewers have appreciated the thorough comments and changes made to the original manuscript,

but some technical issues remain, and one reviewer clarifies his prior comments.

Please address these points in full in a revision.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: See attachment

Reviewer #2: My comments have been thoroughly addressed.

Reviewer #3: The author did not understand my first comment. SHAPE produces flexibility signals which are translated in secondary structure prediction programs by propensities of nucleotides to participate or not in base pairs; therefore equivalent to pairs and non-pairs. These programs always find acceptable solutions, and therefore they do not represent the bottleneck of RNA structure prediction. Besides, some of these programs also predict tertiary interactions (not referred to by the author). However, I agree with the author that a structure thus obtained rather corresponds to the structure under experimental observation than a structure supported by covariations.

In my initial comment on X-ray crystal structures, I said “denatured”, but what I meant is more “not under physiological conditions”. I am sorry for this lack of precision. Therefore, I do not agree with the author that "crystallographers carefully reproduce native conditions". I would rather say that the crystallographers, although they try to reproduce the native conditions, aim at obtaining crystals, whether the physiological conditions are respected or not and they seldom are. The case of U2 spliceosomal RNA is interesting because the aligned homologs lead to interactions that differ from the crystal. Q1. Does the covariation data substantiate both alternatives, or one of them only?

Now, about using RNAStructure rather than RNAalifold: I understand that it is not necessary to show both if indeed it leads to the same conclusions. Q2. Is that the case ?

Otherwise, I find that the new version is improved from the original version.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Francois Major

Figure Files:

Data Requirements:

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Attachment

Submitted filename: PLOS_Comp_Rivas.docx

Click here for additional data file.^{(13.8KB, docx)}

PLoS Comput Biol. 2020 Oct 30;16(10):e1008387. doi: 10.1371/journal.pcbi.1008387.r004

Author response to Decision Letter 1

16 Sep 2020

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(47.7KB, pdf)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008387.r005

Decision Letter 2

Tamar Schlick, Nir Ben-Tal

24 Sep 2020

Dear Dr Rivas,

We are pleased to inform you that your manuscript 'RNA structure prediction using positive and negative evolutionary information' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Tamar Schlick

Associate Editor

PLOS Computational Biology

Nir Ben-Tal

Deputy Editor

PLOS Computational Biology

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

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008387.r006

Acceptance letter

Tamar Schlick, Nir Ben-Tal

19 Oct 2020

PCOMPBIOL-D-20-00868R2

RNA structure prediction using positive and negative evolutionary information

Dear Dr Rivas,

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.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Laura Mallard

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. tmRNA structure predicted by RNAalifold and covariation analysis.

(PDF)

Click here for additional data file.^{(372.6KB, pdf)}

S2 Fig. CaCoFold structures confirmed by known 3D structures (part 3/7).

(PDF)

Click here for additional data file.^{(2.8MB, pdf)}

S3 Fig. CaCoFold structures confirmed by known 3D structures (part 4/7).

(PDF)

Click here for additional data file.^{(2MB, pdf)}

S4 Fig. CaCoFold structures confirmed by known 3D structures (part 5/7).

(PDF)

Click here for additional data file.^{(662.4KB, pdf)}

S5 Fig. CaCoFold structures confirmed by known 3D structures (part 6/7).

(PDF)

Click here for additional data file.^{(830.3KB, pdf)}

S6 Fig. CaCoFold structures confirmed by known 3D structures (part 7/7).

(PDF)

Click here for additional data file.^{(2MB, pdf)}

S7 Fig. Examples of RNAs without a 3D structure for which the CaCoFold structure has more positive basepairs (green shading) than the structure given by the corresponding database.

We provide examples of differences corresponding to Types 1 to 11. A description of all different types is given in Table 1.

(PDF)

Click here for additional data file.^{(919.4KB, pdf)}

S1 File. Supplemental material.

Includes alignments, and source code.

(GZ)

Click here for additional data file.^{(88.8MB, gz)}

S1 Table

(PDF)

Click here for additional data file.^{(109.1KB, pdf)}

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(119.4KB, pdf)}

Attachment

Submitted filename: PLOS_Comp_Rivas.docx

Click here for additional data file.^{(13.8KB, docx)}

Attachment

Submitted filename: response.pdf

Click here for additional data file.^{(47.7KB, pdf)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.

[pcbi.1008387.ref001] 1. Mironov AS, Gusarov I, Rafikov R, Lopez LE, Shatalin K, Kreneva RA, et al. Sensing small molecules by nascent RNA: a mechanism to control transcription in bacteria Cell. Cell. 2002;5:747–756. 10.1016/S0092-8674(02)01134-0 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref002] 2. Winkler WC, Nahvi A, Breaker RR. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature. 2002;419:952–956. 10.1038/nature01145 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref003] 3. Babitzke P, Romeo T. CsrB sRNA family: sequestration of RNA-binding regulatory proteins. Current Opinion in Microbiology. 2007;10(2):156–163. 10.1016/j.mib.2007.03.007 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref004] 4. Chen J, Wassarman KM, Feng S, Leon K, Feklistov A, Winkelman JT, et al. 6S RNA mimics B-form DNA to regulate Escherichia coli RNA polymerase. Mol Cell. 2017;68(2):388–397.e6. 10.1016/j.molcel.2017.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref005] 5. Holley RW, Apgar J, Everett GA, Madison JT, Marquisee M, Merrill SH, et al. Structure of a ribonucleic acid. Science. 1965;14:1462–1465. 10.1126/science.147.3664.1462 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref006] 6. Noller HF, Kop JA, Wheaton V, Brosius J, Gutell RR, Kopylov AM, et al. Secondary structure model for 23S ribosomal RNA. Nucl Acids Res. 1981;9:6167–6189. 10.1093/nar/9.22.6167 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref007] 7. Gutell RR, Weiser B, Woese CR, Noller HF. Comparative anatomy of 16S-like ribosomal RNA. Prog Nucl Acids Res Mol Biol. 1985;32:155–216. 10.1016/S0079-6603(08)60348-7 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref008] 8. Pace NR, Smith DK, Olsen GJ, James BD. Phylogenetic comparative analysis and the secondary structure of Ribonuclease P RNA—A review. Gene. 1989;82:65–75. 10.1016/0378-1119(89)90031-0 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref009] 9. Michel F, Westhof E. Modelling of the three-dimensional architecture of group I catalytic introns based on comparative sequence analysis. J Mol Biol. 1990;216:585–610. 10.1016/0022-2836(90)90386-Z [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref010] 10. Brown JW, Haas ES, James BD, Hunt DA, Pace NR. Phylogenetic analysis and evolution of RNase P RNA in Proteobacteria. J Bacteriol. 1991;173:3855–3863. 10.1128/JB.173.12.3855-3863.1991 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref011] 11. Haas E, Morse D, Brown J, Schmidt F, Pace N. Long-range structure in ribonuclease P RNA. Science. 1991;254(5033):853–856. 10.1126/science.1719634 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref012] 12. Williams KP, Bartel DP. Phylogenetic analysis of tmRNA secondary structure. RNA. 1996;2:1306–1310. [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref013] 13. Michel F, Costa M, Massire C, Westhof E. Modeling RNA tertiary structure from patterns of sequence variation. Meth Enzymol. 2000;317:491–510. 10.1016/S0076-6879(00)17031-4 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref014] 14. Gutell RR, Power A, Hertz GZ, Putz EJ, Stormo GD. Identifying constraints on the higher-order structure of RNA: continued development and application of comparative sequence analysis methods. Nucl Acids Res. 1992;20:5785–5795. 10.1093/nar/20.21.5785 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref015] 15. Akmaev VR, Kelley ST, Stormo GD. Phylogenetically enhanced statistical tools for RNA structure srediction. Bioinformatics. 2000;16:501–512. 10.1093/bioinformatics/16.6.501 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref016] 16. Knudsen B, Hein J. Pfold: RNA secondary structure prediction using stochastic context-free grammars. Nucl Acids Res. 2003;31:3423–3428. 10.1093/nar/gkg614 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref017] 17. Bindewald E, Hayes R, Yingling YG, Kasprzak W, Shapiro BA. RNAJunction: a database of RNA junctions and kissing loops for three-dimensional structural analysis and nanodesign. Nucl Acids Res. 2008;36:D392–D397. 10.1093/nar/gkm842 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref018] 18. Kiryu H, Kin T, Asai K. Rfold: an exact algorithm for computing local base pairing probabilities. Bioinformatics. 2008;24:367–373. 10.1093/bioinformatics/btm591 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref019] 19. Bernhart SH, Hofacker IL, Will S, Gruber AR, Stadler PF. RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics. 2008;9:474 10.1186/1471-2105-9-474 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref020] 20. Gutell RR, Lee JC, Cannone JJ. The accuracy of ribosomal RNA comparative structure models. Curr Opin Struct Biol. 2002;12:301–310. 10.1016/S0959-440X(02)00339-1 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref021] 21. Deigan KE, Li TW, DH DHM, Weeks KM. Accurate SHAPE-directed RNA structure determination. Proc Natl Acad Sci U S A. 2009;6:97–102. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref022] 22. Zarringhalam K, Meyer MM, Dotu I, Chuang JH, Clote P. Integrating Chemical Footprinting Data Into RNA Secondary Structure Prediction. PLOS ONE. 2012;7:e45160 10.1371/journal.pone.0045160 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref023] 23. Washietl S, Hofacker IL, Stadler PF, Kellis M. RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction. Nucl Acids Res. 2012;40:4261–4272. 10.1093/nar/gks009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref024] 24. Sükösd Z, Swenson MS, Kjems J, Heitsch CE. Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions. Nucleic acids research. 2013;41(5):2807–2816. 10.1093/nar/gks1283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref025] 25. Eddy SR. Computational Analysis of Conserved RNA Secondary Structure in Transcriptomes and Genomes. Annu Rev Biophys. 2014;43:433–456. 10.1146/annurev-biophys-051013-022950 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref026] 26. Lorenz R, Bernhart SH, Siederdissen CHZ, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:1748–7188. 10.1186/1748-7188-6-26 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref027] 27. Xu ZZ, Mathews DH. Experiment-assisted secondary structure prediction with RNAstructure: methods and protocols. Methods in Molecular Biology. 2016;1490:163–176. 10.1007/978-1-4939-6433-8_10 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref028] 28. Swenson MS, Anderson J, Ash A, Gaurav P, Sükösd Z, Bader DA, et al. GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops. BMC Research Notes. 2012;5::341 10.1186/1756-0500-5-341 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref029] 29. Parisien M, Major F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature. 2008;452:51–55. 10.1038/nature06684 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref030] 30. Rivas E, Clements J, Eddy SR. A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs. Nature Methods. 2017;14:45–48. 10.1038/nmeth.4066 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref031] 31. Rivas E, Clements J, Eddy SR. Estimating the power of sequence covariation for detecting conserved RNA structure. Bioinformatics. 2020; 10.1093/bioinformatics/btaa080 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref032] 32. Kalvari I, Argasinska J, Quinones-Olvera N, Nawrocki EP, Rivas E, Eddy SR, et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucl Acids Res. 2018;46:D335–D342. 10.1093/nar/gkx1038 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref033] 33.Weinberg Z. The Zasha Weinberg Database (ZWD). https://bitbucketorg/zashaw/zashaweinbergdata/ Accessed 11 February 2019. 2018;.

[pcbi.1008387.ref034] 34. Leontis NB, Westhof E. Geometric nomenclature and classification of RNA base pairs. RNA. 2001;7:499–512. 10.1017/S1355838201002515 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref035] 35. Weinberg Z, Wang JX, Bogue J, Yang J, Corbino K, Moy RH, et al. Comparative genomics reveals 104 candidate structured RNAs from Bacteria, Archaea, and their metagenomes. Genome Biol. 2010;11(3):R31 10.1186/gb-2010-11-3-r31 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref036] 36. Landau GM, Vishkin U, Nussinov R. An efficient string matching algorithm with k differences for nucleotide and amino acid sequences. Nucl Acids Res. 1986;14(1):31–46. 10.1093/nar/14.1.31 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref037] 37. Rivas E, Lang R, Eddy SR. A range of complex probabilistic models for RNA secondary structure prediction that include the nearest neighbor model and more. RNA. 2012;18:193–212. 10.1261/rna.030049.111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref038] 38. Mathews DH, Turner DH. Prediction of RNA secondary structure by free energy minimization. Curr Opin Struct Biol. 2006;16:270–278. 10.1016/j.sbi.2006.05.010 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref039] 39. Markham NR, Zuker M. UNAFold: software for nucleic acid folding and hybridization. Methods Mol Biol. 2008;453:3–31. 10.1007/978-1-60327-429-6_1 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref040] 40. Knudsen B, Hein J. RNA secondary structure prediction using stochastic context-free grammars and evolutionary History. Bioinformatics. 1999;15:446–454. 10.1093/bioinformatics/15.6.446 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref041] 41. Dowell RD, Eddy SR. Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics. 2004;5:71 10.1186/1471-2105-5-71 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref042] 42. Weinberg Z, Breaker RR. R2R—software to speed the depiction of aesthetic consensus RNA secondary structures. BMC Bioinformatics. 2011;12:3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref043] 43. Weis F, Bron P, Giudice E, Rolland JP, Thomas D, Felden B, et al. tmRNA-SmpB: a journey to the centre of the bacterial ribosome. EMBO J. 2010;29:3810–3818. 10.1038/emboj.2010.252 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref044] 44. Kelley ST, Harris JK, Pace NR. Evaluation and refinement of tmRNA structure using gene sequences from natural microbial communities. RNA. 2001;7:1310–1316. 10.1017/S1355838201010573 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref045] 45. Fu J, Yaser Hashem Y, Wower I, Lei J, Liao HY, Zwieb C, et al. Visualizing the transfer-messenger RNA as the ribosome resumes translation. EMBO J. 2010;29:3819–3825. 10.1038/emboj.2010.255 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref046] 46. Torres-Larios A, Swinger KK, Krasilnikov AS, Pan T, Mondragón A. Crystal structure of the RNA component of bacterial ribonuclease P. Nature. 2005;437(7058):584–587. 10.1038/nature04074 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref047] 47. Montange R, Batey RT. Structure of the S-adenosylmethionine riboswitch regulatory mRNA element. Nature. 2006;441:1172–1175. 10.1038/nature04819 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref048] 48. Wan R, Yan C, Bai R, Wang L, Huang M, Wong CCL, et al. The 3.8 Å structure of the U4/U6.U5 tri-snRNP: Insights into spliceosome assembly and catalysis. Science. 2016;351(6272):466–475. 10.1126/science.aad6466 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref049] 49. Peselis A, Serganov AS. Structural insights into ligand binding and gene expression control by an adenosylcobalamin riboswitch. Nat Struct Mol Biol. 2012;19:1182–1184. 10.1038/nsmb.2405 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref050] 50. Giegé R, Sissler M, Florentz C. Universal rules and idiosyncratic features in tRNA identity. NAR. 1998;26:5017–5035. 10.1093/nar/26.22.5017 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref051] 51. Shi H, Moore PB. The crystal structure of yeast phenylalanine tRNA at 1.93 A resolution: a classic structure revisited. RNA. 2000;6:10911105. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref052] 52. Ares M, Igel AH. Mutations define essential and nonessential U2 RNA structures. Mol Biol Rep. 1990;14(2–3):131–132. 10.1007/bf00360444 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref053] 53. Perriman RJ, Ares M. Rearrangement of competing U2 RNA helices within the spliceosome promotes multiple steps in splicing. Genes Dev. 2007;21(7):811–820. 10.1101/gad.1524307 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref054] 54. Ataide SF, Schmitz N, Shen K, Ke A, Shan SO, Doudna JA, et al. The crystal structure of the signal recognition particle in complex with its receptor. Science. 2011;331:881–886. 10.1126/science.1196473 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref055] 55. Gao A, Serganov A. Structural insights into recognition of c-di-AMP by the ydaO riboswitch. Proc Natl Acad Sci USA. 2014;10:787–792. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref056] 56. Dann CE, Wakeman CA, Sieling CL, C BS, Irnov I, Winkler WC. Structure and mechanism of a metal-sensing regulatory RNA. Cell. 2007;130:878–892. 10.1016/j.cell.2007.06.051 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref057] 57. Ban N, Nissen P, Hansen J, Moore PB, Steitz TA. The complete atomic structure of the large ribosomal subunit at 2.4 Å Resolution. Science. 2000;289:905–920. 10.1126/science.289.5481.905 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref058] 58. Serganov A, Huang L, Patel DJ. Coenzyme recognition and gene regulation by a flavin mononucleotide riboswitch. Nature. 2009;458:233–237. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref059] 59. Jones CP, Ferré-D’Amaré AR. Recognition of the bacterial alarmone ZMP through long-distance association of two RNA subdomains. Nat Struct Mol Biol. 2015;22(9):679–685. 10.1038/nsmb.3073 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref060] 60. Ren A, Rajashankar KR, Patel DJ. Fluoride ion encapsulation by Mg2+ ions and phosphates in a fluoride riboswitch. Nature. 2012;486:85–89. 10.1038/nature11152 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref061] 61. Ren A, Xue Y, Peselis A, Serganov A, AHH M, Patel DJ. Structural and dynamic basis for low-affinity, high-selectivity binding of L-Glutamine by the Glutamine riboswitch. Cell Rep. 2015;13:1800–1813. 10.1016/j.celrep.2015.10.062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref062] 62. Zwieb C, Nues RWV, Rosenblad MA, Brown JD, Samuelsson T. A nomenclature for all signal recognition particle RNAs. RNA. 2005;11(1):7–13. 10.1261/rna.7203605 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref063] 63. Kazantsev AV, Krivenko AA, Harrington DJ, Holbrook SR, Adams PD, Pace NR. Crystal structure of a bacterial ribonuclease P RNA. PNAS. 2005;102:1339213397. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref064] 64. Toor N, Keating KS, Taylor SD, Pyle AM. Crystal structure of a self-spliced Group II intron. Science. 2008;320(5872):77–82. 10.1126/science.1153803 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref065] 65. Yan C, Hang J, Wan R, Huang M, Wong CC, Shi Y. Structure of a yeast spliceosome at 3.6-angstrom resolution. Science. 2015;349:1182–1191. 10.1126/science.aac7629 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref066] 66. Sun Q, Zhu X, Qi J, An W, Lan P, Tan D, et al. Molecular architecture of the 90S small subunit pre-ribosome. eLife. 2017;6:e22086 10.7554/elife.22086 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref067] 67. Ramrath D, Yamamoto H, Rother K, Wittek D, Pech M, Mielke T, et al. The complex of tmRNASmpB and EF-G on translocating ribosomes. Nature. 2012;485:526–529. 10.1038/nature11006 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref068] 68. Massire C, Jaeger L, Westhof E. Derivation of the three-dimensional architecture of bacterial ribonuclease P RNAs from comparative sequence analysis. 1998;279:773–793. [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref069] 69. Yang H, Jossinet F, Leontis N, Chen L, Westbrook J, Berman HM, et al. Tools for the automatic identification and classification of RNA base pairs. Nucl Acids Res. 2003;3113:3450–3460. 10.1093/nar/gkg529 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref070] 70. Brennan T, Sundaralingam M. Structure of transfer RNA molecules containing the long variable loop. Nucl Acids Res. 1976;3:3235–3251. 10.1093/nar/3.11.3235 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref071] 71. Auffinger P, Westhof E. Singly and bifurcated hydrogen-bonded base-pairs in tRNA anticodon hairpins and ribozymes. J Mol Biol. 1999;292:467–483. 10.1006/jmbi.1999.3080 [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref072] 72. Zaug AJ, Cech TR. Analysis of the structure of tetrahymena nuclear RNAs in vivo: telomerase RNA, the self-splicing rRNA intron, and U2 snRNA. RNA. 1995;1:363–374. [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref073] 73. Williams GD, Chang RY, Brian DA. A phylogenetically conserved Hairpin-Type 3’ untranslated region pseudoknot functions in coronavirus RNA replication. Journal of Virology. 1999;73:8349–8355. 10.1128/JVI.73.10.8349-8355.1999 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref074] 74. Leontis NB, Westhof E. Analysis of RNA motifs. Curr Opin Struct Biol. 2003;13:300–308. [DOI] [PubMed] [Google Scholar]

[pcbi.1008387.ref075] 75. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Research. 2000;28:235–242. 10.1093/nar/28.1.235 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref076] 76. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics. 2013;29:2487–2489. 10.1093/bioinformatics/btt403 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref077] 77. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–2935. 10.1093/bioinformatics/btt509 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref078] 78. Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLOS ONE. 2010;5:e9490 10.1371/journal.pone.0009490 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pcbi.1008387.ref079] 79. Eddy SR. Accelerated profile HMM searches. PLOS Comp Biol. 2011;7:e1002195 10.1371/journal.pcbi.1002195 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

RNA structure prediction using positive and negative evolutionary information

Elena Rivas

Roles

Abstract

Author summary

Introduction

Results

The CaCoFold algorithm

Fig 1. The CaCoFold algorithm.

(1) The cascade maxCov algorithm

Fig 2. RNA models used by the CaCoFold algorithm.

(2) The cascade folding algorithm

(3) Filtering of alternative helices

(4) Automatic display of the complete structure

CaCoFold finds pseudoknots, triplets and other long and short-range interactions

Fig 3. The CaCoFold algorithm applied to the transfer-messenger RNA (tmRNA).

RNAs with structures improved by positive and negative signals

Table 1. CaCoFold structures with different covariation support than the structures provided with the structural alignments.

CaCoFold structures consistent with 3D structures

Table 2. 21 RNAs with 3D structures and CaCoFold structures with different covariation support than the structures provided with the structural alignments.

Fig 4. CaCoFold structures confirmed by known 3D structures (part 1/7).

Fig 5. CaCoFold structures confirmed by known 3D structures (part 2/7).

Other CaCoFold structures with more covariation support

Discussion

Materials and methods

Implementation

Extracting the RNA structure from a PDB file

Availability

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Tamar Schlick

Nir Ben-Tal

Roles

Author response to Decision Letter 0

Decision Letter 1

Tamar Schlick

Nir Ben-Tal

Roles

Author response to Decision Letter 1

Decision Letter 2

Tamar Schlick

Nir Ben-Tal

Roles

Acceptance letter

Tamar Schlick

Nir Ben-Tal

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases