Check this box to disallow GU at the end of helices. Predicted structures can contain isolated base pairs helices of length 1 , which may be undesirable. Check this box to avoid such lonely pairs. Advanced folding options Dangling end options. Dangling end energies are stabilizing energies assigned to unpaired bases adjacent to a helix in multi-loops and free ends. Change the energy parameter file used to predict secondary structures. Please enter non-default parameters for selected method if necessary: slope m : intercept b :.
They described how this occurs with the cooperation of folding intermediates of an RNA enzyme, ribozyme. The native structure of ribozyme was important for this coupling reaction, with small alterations in its architecture determining the entire folding pattern. This interaction took place early in the folding process, with the formation of structural themes, or motifs, being linked in near-native folding intermediates. Cooperativity was also found to require the orientation of the native helix.
Tertiary interactions had little effect on the stability of the native state of the ribozyme. Understanding the results of this research will be important in guiding future studies to further evaluate the importance of RNA tertiary structure in biological systems. RNA is one of the 2 types of nucleic acids found in all cells. Its main role is to carry out instructions for protein synthesis from DNA , the 2nd type of nucleic acid which stores the genetic information in cells.
This confirms the difficulty of direct learning with a small training dataset of TR1 and the need for using a large dataset bpRNA that can effectively utilize capabilities of deep-learning networks. Supplementary Table 4 further compared the performance of individual models with the ensemble by direct learning on TR1.
The results are from a reduced TS1 62 RNAs rather than 67 because some other methods shown in the same figure do not predict secondary structure for sequences with missing or invalid bases. Nevertheless, the transfer learning achieves a respectable This indicates that the fraction of potential false positives in bpRNA is small.
Precision and sensitivity results from ten currently used predictors are also shown as labeled with open symbols for the methods accounting for pseudoknots and filled symbols for the methods not accounting for pseudoknots. On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The results presented in Fig. Figure 2 b shows the distribution of the F1 score among individual RNAs in terms of median, 25th, and 75th percentiles.
This highlights the highly stable performance of SPOT-RNA, relative to all other folding-based techniques, including mxfold, which mixes thermodynamic and machine-learning models. The ensemble defect metric describes the deviation of probabilistic structural ensembles from their corresponding native RNA secondary structure, where 0 represents a perfect prediction.
Our method was trained for RNAs with a maximum length of nucleotides, due to hardware limitations. It is of interest to determine how our method performs in terms of size dependence.
Supplementary Fig. There is a trend of lower performance for a longer RNA chain for both methods as expected. This failure is caused by the limited long RNA data in training.
By comparison, the thermodynamic algorithm in mxfold can locate the global minimum regardless of the distance between sequence positions of the base pairs. The above comparison may be biased toward our method because almost all other methods compared can only predict canonical base pairs, which include Watson—Crick A—U and G—C pairs and Wobble pairs G—U.
Indeed, all methods have a performance boost when noncanonical pairs are excluded from performance measurement. Base pairs associated with pseudoknots are challenging for both folding-based and machine-learning-based approaches because they are often associated with tertiary interactions that are difficult to predict. To make a direct comparison in the capability of predicting base pairs in pseudoknots, we define pseudoknot pairs as the minimum number of base pairs that can be removed to result in a pseudoknot-free secondary structure.
As none of the other methods predict multiplets, we ignore the base pairs associated with the multiplets in the analysis. Noncanonical pairs, triplets, and lone base pairs are also associated with tertiary interactions other than pseudoknots. Here, lone base pairs refer to a single base pair without neighboring base pairs i. Triplets refer to the rare occasion of one base forming base pairs with two other bases.
Secondary structure of RNAs is characterized by structural motifs in their layout. For each native or predicted secondary structure, the secondary-structure motif was classified by program bpRNA The performance in predicting bases in different secondary structural motifs by different methods is shown in Table 4.
For both the examples, SPOT-RNA is able to predict noncanonical base pairs in green , pseudoknot base pairs, and lone pair in blue , while mxfold and IPknot remain unsuccessful to predict noncanonical and pseudoknot base pairs. Furthermore, Fig. Here, we did not compare the performance in pseudoknots because the number of base pairs in pseudoknots a total of 21 in this dataset is too small to make statistically meaningful comparison. For these two RNAs, experimental evidence suggests strand swapping in dimerization 44 , Thus, their monomeric native structures are obtained by replacing the swapped stand by its original stand.
For this level of performance, it is more illustrative to show a one-dimensional representation of RNA secondary structure Fig. The figures show that the relatively poor performance of Pistol Ribozyme and Mango Aptamer RNAs is in part due to the uncommon existence of a large number of noncanonical base pairs in Green.
It contains three false-positive stems with falsely predicted pseudoknots Fig. Performance comparison on these 6 RNAs with 12 other secondary-structure predictors is shown in Fig. It is the co-first same as mxfold in Mango Aptamer Fig.
However, it did not do well on adenovirus virus-associated RNA Fig. This poor prediction compared with other methods is likely because this densely contacted, base-pairing network without pseudoknots except those due to noncanonical base pairs is most suitable for folding-based algorithms that maximize the number of stacked canonical base pairs. Performance comparison of all predictors on 6 recently released after March 9, crystal structures. This work developed RNA secondary-structure prediction method purely based on deep neural network learning from a single RNA sequence.
Because only a small number of high-resolution RNA structures are available, deep-learning models have to be first trained by using a large database of RNA secondary structures bpRNA annotated according to comparative analysis, followed by transfer learning to the precise secondary structures derived from 3D structures. Without the need for folding-based optimization, the transfer-learning model yields a method that can predict not only canonical base pairs but also those base pairs often associated with tertiary interactions, including pseudoknots, lone, and noncanonical base pairs.
One advantage of a pure machine-learning approach is that all base pairs can be trained and predicted, regardless if it is associated with local or nonlocal tertiary interactions.
By comparison, a folding-based method has to have accurate energetic parameters to capture noncanonical base pairs and sophisticated algorithms for a global minimum search to account for pseudoknots. SPOT-RNA can also achieve the best prediction of base pairs in pseudoknots although the performance of all methods remains low with an F1 score of 0.
This is mainly because the number of base pairs in pseudoknots is low in the structural datasets an average of 3—4 base pairs per pseudoknot RNA in TS1, see Supplementary Table 7. Moreover, a long stem of many stacked base pairs is easier to learn and predict than a few nonlocal base pairs in pseudoknot. As a reference for future method development, we also examined the ability of SPOT-RNA to capture triple interactions: one base paired with two other bases.
This is mainly because there is a lack of data on base triples in bpRNA for pretraining and the number of both triplets and quartets is only in the structural training set TR1. Unlike X-ray structures, structures determined by NMRs resulted from minimization of experimental distance-based constraints. These 39 NMR structures, smaller with average length of 51 nucleotides, have only a total of 21 base pairs in pseudoknots.
The lack of training for long RNAs is the main reason. In addition to prediction accuracy, high computational efficiency is necessary for RNA secondary-structure prediction because genome-scale studies are often needed. This work has used a single RNA sequence as the only input. It is quite remarkable that relying on a single sequence alone can obtain a more accurate method than existing folding methods in secondary-structure prediction.
For protein contact map prediction, evolution profiles generated from PSIBLAST 40 and HHblits 49 as well as direct coupling analysis among homologous sequences 50 are the key input vectors responsible for the recent improvement in highly accurate prediction.
Indeed, recently, we have shown that using evolution-derived sequence profiles significantly improves the accuracy of predicting RNA solvent accessibility and flexibility 38 , For example, the correlation coefficient between predicted and actual solvent accessibility increases from 0.
However, the generation of sequence profiles and evolution coupling is computationally time consuming. The resulting improvement or lack of improvement is strongly depending on the number of homologous sequences available in current RNA sequence databases.
If the number of homologous sequences is too low which is true for most RNAs , it may introduce more noise than the signal to prediction as demonstrated in protein secondary structure and intrinsic disorder prediction 51 , Moreover, synthetic RNAs will not have any homologous sequences. Thus, we present the method with single-sequence information as input in this study.
Using sequence profiles and evolutionary coupling as input for RNA secondary-structure prediction is working in progress. Another possible method for further improving SPOT-RNA is to employ the predicted probability as a restraint for folding with an appropriate scoring function.
In mxfold, combining machine-learning and thermodynamic models leads to 0. Moreover, most thermodynamic methods simply ignore noncanonical base pairs and many do not even account for pseudoknots. Thus, balancing the performance for canonical, noncanonical, and pseudoknots will require a careful selection of appropriate scoring schemes. A simple integration may lead to high performance in one type of base pair at the expense of other types of base pairs.
We will defer this for future studies. The significantly improved performance in secondary-structure prediction should allow large improvement in modeling RNA 3D structures.
This is because the method predicts not only canonical base pairs but also provides important tertiary contacts of noncanonical and non-nested base pairs. Thus, it can serve as a more accurate, quasi-three-dimensional frame to enable correct folding into the right RNA tertiary structure.
Moreover, improvement in predicting secondary structural motifs stems, loops, and bulges, see Table 4 would allow better functional inference 54 , 55 , sequence alignment 56 , and RNA inhibitor design The datasets for initial training were obtained from bpRNA-1m Version 1.
After removing sequence similarity, 14, sequences remained. Moreover, due to hardware limitations for training on long sequences, the maximum sequence length was restricted to After preprocessing, this dataset contains 13, sequences. Here, base pairs associated with pseudoknots are defined as the minimum number of base pairs that can be removed to result in a pseudoknot-free secondary structure. After removing sequence similarity, only sequences remained.
For NMR- solved structures, model 1 structure was used as it is considered as the most reliable structure among all. The numbers of canonical, noncanonical, and pseudoknot base pairs, and base multiplets triplets and quartets for all the sets are listed in Supplementary Table 7.
For the classification of different RNA secondary-structure types, we used the same definitions as previously used by bpRNA A stem is defined as a region of uninterrupted base pairs, with no intervening loops or bulge. A hairpin loop is a sequence of unpaired nucleotides with both ends meeting at the two strands of a stem region. An internal loop is defined as two unpaired strands flanked by closing base pairs on both sides.
A bulge is a special case of the internal loop where one of the strands is of length zero. A multiloop consists of a cycle of more than two unpaired strands, connected by stems. These secondary-structure classifications were obtained by using a secondary-structure analysis program bpRNA We employed an ensemble of deep-learning neural networks for pretraining. The ensemble is made of 5 top-ranked models based on their performance on VL0 with the architecture shown in Fig.
An initial convolution layer for pre-activation was used before our ResNet blocks as proposed in He et al. The exponential linear units ELU 59 activation function and the layer normalization technique 60 were used. In some models, we used dilated convolutions that are reported to better learn longer-range dependencies Research 22 October Open Access. Early steps of large 60S ribosomal subunit biogenesis are not well understood.
Here, the authors combine biochemical experiments with protein-RNA crosslinking and mass spectrometry to show that the RNA helicase Dbp7 is key player during early 60S ribosomal assembly. The molecular events underlying the assembly and maturation of the early preS particles during eukaryotic ribosome synthesis are not well understood.
They show that the snR snoRNA acts as a RNA chaperone that assists the structuring of the 25S rRNA during the maturation of early preS particles and that Dbp7 is important for facilitating remodeling events in the peptidyl transferase center region of the 25S rRNAs during the maturation of early preS particles. Research 11 August Cryo-electron microscopy has been used to determine the structure of the Tetrahymena ribozyme a catalytic RNA at sufficiently high resolution to model side chains and metal ions.
Research 16 April Open Access. Here the authors present a catalog of conserved long-range RNA structures in the human transcriptome by defining pairs of conserved complementary regions PCCR in pre-aligned evolutionarily conserved regions. Research 02 November Open Access.
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