RNA Secondary Structure Alteration Caused by Single Nucleotide Variants
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Nucleic acid structure
Dynamic programming algorithms that predict RNA secondary structure by minimizing the free energy have had one important limitation. They were able to predict only one optimal structure. Given the uncertainties of the thermodynamic data and the effects of proteins and other environmental factors on structure, the optimal structure predicted by these methods may not have biological significance. We present a dynamic programming algorithm that can determine optimal and suboptimal secondary structures for an RNA. The power and utility of the method is demonstrated in the folding of the intervening sequence of the rRNA of Tetrahymena . By first identifying the major secondary structures corresponding to the lowest free energy minima, a secondary structure of possible biological significance is derived.
Maxima and minima
Sequence (biology)
Folding (DSP implementation)
Nucleic acid structure
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Nucleic acid structure
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Prediction of RNA secondary structure is one of the pivotal tasks in bioinformatics. Several computational methods based on dynamic programming, statistical models, have been proposed with considerable success. A typical substructure that occurs in several classes of RNAs, called pseudoknot, plays vital role in many biological processes. Prediction of the pseudoknots in RNA secondary structure is still an open research problem. In this paper, we employ matched filtering approach to determine the secondary structure of a target RNA. The central idea is to match the stem patterns in the base-pairing matrix of RNA with unknown secondary structure. The proposed approach predicts number of stems, loops and also the presence of pseudoknot in the secondary structure of RNA. Illustrative examples on real RNA sequences illustrate the effectiveness and accuracy of our proposed method.
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The crystal structure based model of the catalytic center of Ago2 revealed that the siRNA and the mRNA must be able to form an A-helix for correct positing of the scissile phosphate bond for cleavage in RNAi. This suggests that base pairing of the target mRNA with itself, i.e. secondary structure, must be removed before cleavage. Early on in the siRNA design, GC-rich target sites were avoided because of their potential to be involved in strong secondary structure. It is still unclear how important a factor mRNA secondary structure is in RNAi. However, it has been established that a difference in the thermostability of the ends of an siRNA duplex dictate which strand is loaded into the RNA-induced silencing complex. Here, we use a novel secondary structure prediction method and duplex-end differential calculations to investigate the importance of a secondary structure in the siRNA design. We found that the differential duplex-end stabilities alone account for functional prediction of 60% of the 80 siRNA sites examined, and that secondary structure predictions improve the prediction of site efficacy. A total of 80% of the non-functional sites can be eliminated using secondary structure predictions and duplex-end differential.
Duplex (building)
Cleavage (geology)
Thermostability
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We report a method for predicting the most stable secondary structure of RNA from its primary sequence of nucleotides. The technique consists of a series of three computer programs interfaced to take the nucleotide sequence of any RNA and (a) list all possible helical regions, using modified Watson-Crick base-pairing rules; (b) create all possible secondary structures by forming permutations of compatible helical regions; and (c)evaluate each structure for total free energy of formation from a completely extended chain. A free energy distribution and the base-by-base bonding interactions of each possible structure are catalogued by the system and are readily available for examination. The method has been applied to 62 tRNA sequences. The total free-energy of the predicted most stable structures ranged from -19 to -41 kcal/mole (-22 to -49 kJ/mole). The number of structures created was also highly sequence-dependent and ranged from 200 to 13,000. In nearly all cases the cloverleaf is predicted to be the structure with the lowest free energy of formation.
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The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD.
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Background Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy. Method In this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure. Results and Conclusions Computationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at http://mostafa.ut.ac.ir/ESD-Fold/.
Thermodynamic free energy
Nucleic acid structure
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Nucleic acid structure
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Determining the structure of ribosomal RNAs (rRNAs) is one of the crucial steps in understanding the process of protein synthesis, for which rRNAs are one of the basic components. Nevertheless, due to extreme technical difficulties, spatial (3D) structures have been resolved experimentally for only 14 organisms. Also, computational prediction of 3D rRNA structure is almost impossible, and prediction of secondary structure (the list of base pairs in the folded RNA), an important intermediate step between sequence and 3D structure that is used broadly in modeling of RNA structures, is in the case of rRNAs hindered by both extreme sequence length and high structure complexity. Here we present a proof-of-concept for an rRNA secondary structure prediction method that utilizes known structures as structural templates. Our template-based prediction algorithm determines those regions of the sequence for which structure is being predicted that are conserved well enough so that their secondary structure can be copied over from the template. The structure of the remaining, unconserved regions is predicted using a thermodynamic folding model. Applying a baseline implementation of our algorithm to the E. coli 16S rRNA, we have achieved state-of-the-art recall and precision using the structure of T. thermophilus 16S rRNA as a template.
Sequence (biology)
Nucleic acid structure
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While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, 'DMfold', a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database.
Maximization
Base (topology)
Nucleic acid structure
Pseudoknot
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