Estimating Energy Parameters for RNA Secondary Structure Predictions Using Both Experimental and Computational Data.

To better elucidate RNA structure-function relationships and to improve the design of pharmaceutical agents that target specific RNA motifs, an understanding of RNA primary, secondary, and tertiary structure is necessary. The prediction of RNA secondary structure from sequence is an intermediate step in predicting RNA three-dimensional structure. RNA secondary structure is typically predicted using a nearest neighbor model based on free energy parameters. The current free energy parameters for 2 × 3 nucleotide loops are based on a 23-member data set of 2 × 3 loops and internal loops of other sizes. A database of representative RNA secondary structures was searched to identify 2 × 3 nucleotide loops that occur in nature. Seventeen of the most frequent 2 × 3 nucleotide loops in this database were studied by optical melting experiments. Fifteen of these loops melted in a two-state manner, and the associated experimental ΔG°(37,2×3) values are, on average, 0.6 and 0.7 kcal/mol different from the values predicted for these internal loops using the predictive models proposed by Lu, Turner, and Mathews [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924] and Chen and Turner [Chen, G., and Turner, D. H. (2006) Biochemistry 45, 4025-4043], respectively. These new ΔG°(37,2×3) values can be used to update the current algorithms that predict secondary structure from sequence. To improve free energy calculations for duplexes containing 2 × 3 nucleotide loops that still do not have experimentally determined free energy contributions, an updated predictive model was derived. This new model resulted from a linear regression analysis of the data reported here combined with 31 previously studied 2 × 3 nucleotide internal loops. Most of the values for the parameters in this new predictive model are within experimental error of those of the previous models, suggesting that approximations and assumptions associated with the derivation of the previous nearest neighbor parameters were valid. The updated predictive model predicts free energies of 2 × 3 nucleotide internal loops within 0.4 kcal/mol, on average, of the experimental free energy values. Both the experimental values and the updated predictive model can be used to improve secondary structure prediction from sequence.

Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure.

In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future.

Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data. In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our CG approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state of-the-art methods. Our CG implementation is available at http://www.rnasoft.ca/CG/.

Thermodynamic determination of RNA duplex stability in magnesium solutions

An Interactive Framework for RNA Secondary Structure Prediction with a Dynamical Treatment of Constraints

RNA structure is hierarchical. Secondary structure contacts, i.e. the canonical base pair contacts, are generally stronger and form faster than the tertiary structure. Therefore, RNA secondary structures can be predicted independently of tertiary structure prediction. Furthermore, the stability of a given RNA secondary structure can be quantified using nearest neighbor free energy parameters. These parameters are the basis of a number of free energy minimization algorithms that predict RNA secondary structure for either a single sequence or multiple sequences. This article reviews the progress of RNA secondary structure prediction by free energy minimization and describes many of the algorithms that have been developed.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

Nucleotide sequences of coat protein (CP) gene from 6 isolates of Apple stem pitting virus (ASPV) were obtained. Sequence analysis of CP gene showed nucleotide identities ranged from 68.7 to 99.7%. Phylogenetic analysis showed that all ASPV isolates fell into three groups. To a certain extent, the groups were related to the host origins of virus. The hypervariable and conserved region could be found in the CP gene. The variations of nucleotide sequence and predicted RNA secondary structure were analyzed for the CP gene, and the characteristic of RNA secondary structure of ASPV 5' and 3' untranslated region (UTR) were also studied. The result indicated that predicted RNA secondary structures could play an important role in studying the molecular variation of virus. The 3' UTR of ASPV RNA was predicted to contain four stem-loop structures, and it was likely to imply that the variation of RNA secondary structure was related to groups of phylogenetic analysis.

The determination of RNA secondary structure (RSS) could help understand RNA's functional mechanisms, guiding the design of RNA-based therapeutics, and advancing synthetic biology applications. However, traditional methods such as NMR for determining RSS are typically time-consuming and labor-intensive. As a result, the accurate prediction of RSS remains a fundamental yet unmet need in RNA research. Various deep learning (DL)-based methods achieved improved accuracy over thermodynamic-based methods. However, the over-parameterization nature of DL makes these methods prone to overfitting and thus limits their generalizability. Meanwhile, the inconsistency of RSS predictions between these methods further aggravated the crisis of generalizability. Here, we propose TrioFold to achieve enhanced generalizability of RSS prediction by integrating base-pairing clues learned from both thermodynamic- and DL-based methods by ensemble learning and convolutional block attention mechanism. TrioFold achieves higher accuracy in intra-family predictions and enhanced generalizability in inter-family and cross-RNA-types predictions. Additionally, we have developed an online webserver equipped with widely used RSS prediction algorithms and analysis tools, providing an accessible platform for the RNA research community. This study demonstrated new opportunities to improve generalizability for RSS predictions by efficient ensemble learning of base-pairing clues learned from both thermodynamic- and DL-based algorithms.

RNA Secondary Structure Prediction (RSSP) is an optimization problem, where a stable secondary structure is acquired from an RNA primary sequence. Many exact, heuristic and metaheuristic algorithms established in recent years to solve the RSSP problem. We have resolved an accession based on metaheuristic algorithm named Fruit Fly Optimization algorithm to solve the RNA secondary structure prediction problem. FOA is a population-based metaheuristic that works better than all other related existing algorithms and has been employed in different optimization problems. We have redesigned the operators of the FOA algorithm and calculated the minimum Gibbs free energy (ΔG) of the structure to solve the RNA secondary structure problem. We have a Repair function which is known as novel operator that is used to verify and expel the repeated stem from RNA sequence, which is very time-efficient. Every quality of the solutions and spending time are calculated in designing the operators and the repair function. The raised methodology gives efficiency, robustness, and effectiveness in solving the RSSP problem.

Prediction of RNA secondary structure by free energy minimization

Many computational approaches have been developed for modelling and analysing the RNA secondary structure. These approaches are based on diverse methods such as grammars, dynamic programming, matching and evolutionary algorithms. This paper proposes a new parsing algorithm for the prediction of RNA secondary structures. The proposed algorithm is based on the shift-reduce LR parsing algorithm for programming languages. It has two main contributions: it extends the LR parsing algorithm by using a Stochastic Context-Free Grammar (SCFG) instead of Context-Free Grammar (CFG) for parsing RNA secondary structures; it extends the LR parsing algorithm by using a multithreaded approach to handle the LR parsing conflicts resulting from the use of ambiguous grammars.

Watson–Crick base pairs are crucial in the formation of double‐helical deoxyribonucleic acid (DNA) and in the storage of genetic information. They comprise two of the most common geometries involving the pairing of deoxyadenosine (dA) with deoxythymidine (dT) and deoxyguanosine (dG) with deoxycytidine (dC) through the formation of two and three hydrogen bonds, respectively. The complementarity of the antiparallel strands forming a double helix is crucial in DNA replication since the complementary strand is synthesised by adding the Watson–Crick complementary bases of the parent strand. The hybridisation of DNA or DNA–RNA ( ribonucleic acid ) strands is widely used in biotechnology and molecular biology for multiple purposes. The stability of a nucleic acid helix depends on its sequence, conformation, hydration and solution conditions, and it is measured by temperature‐dependent techniques, such as UV (ultraviolet) melting. Examples of oligonucleotide helices with chemical modifications are provided to indicate how these modifications affect the overall stability of a DNA molecule. Key Concepts: Base pairs are two nucleotide bases interacting via hydrogen bonding. Bulge is a nonpaired nucleotide base in a double‐stranded nucleic acid. Hybridisation is a process where two complementary strands interact via base pairing. Nucleic acid conformation is the three‐dimensional structural arrangement of a double‐helical nucleic acid. Nucleic acid modification is the incorporation, removal or substitution of a chemical group on the natural occurring nucleic acids. Melting curves are the optical changes of a nucleic acid or protein‐like molecules as a function of temperature. Stability of nucleic acids is the resistance of nucleic acid duplex to undergo unfolding, proportional to the magnitude of Δ G . Thermal stability is the ability of a nucleic acid duplex to resist temperature denaturation. Unfolding thermodynamic profile is the free energy (Δ G ∘ ), enthalpy (Δ H ) and entropy (Δ S ) parameters associated with the unfolding of a nucleic acid or protein molecule. Cooperativity refers to the number of base pairs that are broken simultaneously in the unfolding of a nucleic acid or protein.