A Dynamic Programming Algorithm for Circular Single-stranded DNA Tiles Secondary Structure Prediction

Since the quantity and quality of DNA sequence directly affect the accuracy and efficiency of computation, the design of DNA sequence is critical to DNA computing. In order to improve the reliability of DNA computing, there is a rich literature targeting at making DNA sequences specifically hybridize at a lower melting temperature, no non-complementary bases pairs or mismatch hybridization in the reformed double helix. However, most of them are not good enough to control the melting temperature, because DNA sequence design problem under the constraints of hamming distance, secondary structure, molecular thermodynamic is known to be NP-hard. For the sake of achieving the lower and similar melting temperature for each DNA sequence, we proposed a DNA sequence coding method based on Bacterial Foraging Algorithm (BFA). An evaluation criterion is particularly proposed to assess the quality of DNA sequence in the optimization process. With BFA, high-quality DNA strands are replicated to avoid the participation of inferior strands in the operation. Experiments show our proposed approach significantly outperforms existing methods in terms of continuity and melting temperature.

DNA computing is a new method that uses biological molecule DNA as computing medium and biochemical reaction as computing tools. The design of DNA sequence plays an important role in improving the reliability of DNA computation. The sequence design is an approach of the control, which aims to design of DNA sequences satisfying some constraints to avoid such unexpected molecular reactions .To deal these constraints ,we convert the design of DNA sequences into a multi-objective optimize problems, and then an example is illustrated to show the efficiency of our method given here.

19] Computer modeling and display of RNA secondary and tertiary structures

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.

This work builds on previous research from an EA used to predict secondary structure of RNA molecules. The EA has the goal of predicting which canonical base pairs will form hydrogen bonds and helices. The addition of stacking energies, through INN and INN-HB, to our thermodynamic model has enhanced our predictions. We test three RNA sequences of lengths 118, 543, and 784 nucleotides using a variety of previously successful operators and parameter settings. The accuracy of the predicted structures are compared against those generated by the Nussinov DPA and also to known structures. The EA showed high accuracy of prediction especially on short sequences. On all tested sequences, the EA outperforms the Nussinov DPA.

RNA folding is hierarchical; therefore, predicting RNA secondary structure from sequence is an intermediate step in predicting tertiary structure. Secondary structure prediction is based on a nearest neighbor model using free energy minimization. To improve secondary structure prediction, all types of naturally occurring secondary structure motifs need to be thermodynamically characterized. However, not all secondary structure motifs are well characterized. Pentaloops, the second most abundant hairpin size, is one such uncharacterized motif. In fact, the current thermodynamic model used to predict the stability of pentaloops was derived from a small data set of pentaloops and from data for other hairpins of different sizes. Here, the most commonly occurring pentaloops were identified and optically melted. New experimental data for 22 pentaloop sequences were combined with previously published data for nine pentaloop sequences. Using linear regression, a pentaloop-specific model was derived. This new model is simpler and more accurate than the current model. The new experimental data and improved model can be incorporated into software that is used to predict RNA secondary structure from sequence.

Recent years, RNA secondary structure prediction has attracted much attention of many researchers, which is an important way to grasp the biochemical function of RNA. However, it is very difficult to predict the RNA secondary structure including pseudoknot, which has been identified to be an NP-complete problem. In this paper, a novel multimodal optimization evolutionary algorithm is proposed to optimize the decision space based on the minimum free energy to predict the secondary structure of RNA. Because there exist multiple equivalent secondary structures which represent the same minimum free energy, our algorithm maintain diversity in decision space to find multiple sets of secondary structure simultaneously. The performance of our algorithm is evaluated by PseudoBase instances and compared with some good prediction algorithms. The comparison results show that our method has higher accuracy in RNA secondary structure prediction.

The structure of RNA has been the subject of intense research over the last decades due to its importance for the correct functioning of RNA molecules in biological processes. Hence, a large number of models for RNA folding and corresponding algorithms for structure prediction have been developed. However, previous models often only consider base pairs, although every base is capable of up to three edge-to-edge interactions with other bases. Recently, Höner zu Siederdissen et al. presented an extended model of RNA secondary structure, including base triples together with a folding algorithm-the first thermodynamics-based algorithm that allows the prediction of secondary structures with base triples. In this article, we investigate the search space processed by this new algorithm, that is, the combinatorics of extended RNA secondary structures with base triples. We present generalized definitions for structural motifs like hairpins, stems, bulges, or interior loops occurring in structures with base triples. Furthermore, we prove precise asymptotic results for the number of different structures (size of search space) and expectations for various parameters associated with structural motifs (typical shape of folding). Our analysis shows that the asymptotic number of secondary structures of size n increases exponentially to [Formula: see text] compared to the classic model by Stein and Waterman for which [Formula: see text] structures exist. A comparison with the classic model reveals large deviations in the expected structural appearance, too. The inclusion of base triples constitutes a significant refinement of the combinatorial model of RNA secondary structure, which, by our findings, is quantitatively characterized. Our results are of special theoretical interest, because a closer look at the numbers involved suggests that extended RNA secondary structures constitute a new combinatorial class not bijective with any other combinatorial objects studied so far.

BackgroundCatalase (EC 1.11.1.6) is a heme-containing tetrameric enzyme that plays a critical role in signaling and hydrogen peroxide metabolism. It was the first enzyme to be crystallized and isolated. Catalase is a well-known industrial enzyme used in diagnostic and analytical methods in the form of biomarkers and biosensors, as well as in the textile, paper, food, and pharmaceutical industries. In silico analysis of CAT genes and proteins has gained increased interest, emphasizing the development of biomarkers and drug designs. The present work aims to understand the catalase evolutionary relationship of plant species and analyze its physicochemical characteristics, homology, phylogenetic tree construction, secondary structure prediction, and 3D modeling of protein sequences and its validation using a variety of conventional computational methods to assist researchers in better understanding the structure of proteins. ResultsAround 65 plant catalase sequences were computationally evaluated and subjected to bioinformatics assessment for physicochemical characterization, multiple sequence alignment, phylogenetic construction, motif and domain identification, and secondary and tertiary structure prediction. The phylogenetic tree revealed six unique clusters where diversity of plant catalases was found to be the largest for Oryza sativa. The thermostability and hydrophilic nature of these proteins were primarily observed, as evidenced by a relatively high aliphatic index and negative GRAVY value. The distribution of 5 sequence motifs was uniformly distributed with a width length of 50 with the best possible amino residue sequences that resemble the plant catalase PLN02609 superfamily. Using SOPMA, the predicted secondary structure of the protein sequences revealed the predominance of the random coil. The predicted 3D CAT model from Arabidopsis thaliana was a homotetramer, thermostable protein with 59-KDa weight, and its structural validation was confirmed by PROCHECK, ERRAT, Verify3D, and Ramachandran plot. The functional relationships of our query sequence revealed the glutathione reductase as the closest interacting protein of query protein. ConclusionsThis theoretical plant catalases in silico analysis provide insight into its physiochemical characteristics and functional and structural understanding and its evolutionary behavior and exploring protein structure-function relationships when crystal structures are unavailable.

G-quadruplexes are abundant locally stable structural elements in nucleic acids. The combinatorial theory of RNA structures and the dynamic programming algorithms for RNA secondary structure prediction are extended here to incorporate G-quadruplexes using a simple but plausible energy model. With preliminary energy parameters, we find that the overwhelming majority of putative quadruplex-forming sequences in the human genome are likely to fold into canonical secondary structures instead. Stable G-quadruplexes are strongly enriched, however, in the 5'UTR of protein coding mRNAs.

A Bayesian statistical algorithm for RNA secondary structure prediction

Ribonucleic acid is vital in numerous stages of protein synthesis; it also possesses important functional and structural roles within the cell. The function of an RNA molecule within a particular organic system is principally determined by its structure. The current physical methods available for structure determination are time-consuming and expensive. Hence, computational methods for structure prediction are sought after. The energies involved by the formation of secondary structure elements are significantly greater than those of tertiary elements. Therefore, RNA structure prediction focuses on secondary structure. We present P-RnaPredict, a parallel evolutionary algorithm for RNA secondary structure prediction. The speedup provided by parallelization is investigated with five sequences, and a dramatic improvement in speedup is demonstrated, especially with longer sequences. An evaluation of the performance of P-RnaPredict in terms of prediction accuracy is made through comparison with 10 individual known structures from 3 RNA classes (5S rRNA, Group I intron 16S rRNA and 16S rRNA) and the mfold dynamic programming algorithm. P-RnaPredict is able to predict structures with higher true positive base pair counts and lower false positives than mfold on certain sequences. P-RnaPredict is available for non-commercial usage. Interested parties should contact Kay C. Wiese (wiese@cs.sfu.ca).

Fourier transform coupled to tryptophan-scanning mutagenesis: Lessons from its application to the prediction of secondary structure in the acetylcholine receptor lipid-exposed transmembrane domains

DNA computing relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. Therefore, much work has focused on designing the DNA sequences to make the molecular computation more reliable. Sequence design involves with a number of heterogeneous and conflicting design criteria and traditional optimization methods may face difficulties. In this paper, we formulate the DNA sequence design as a multiobjective optimization problem and solve it using a constrained multiobjective evolutionary algorithm (EA). The method is implemented into the DNA sequence design system, NACST/Seq, with a suite of sequence-analysis tools to help choose the best solutions among many alternatives. The performance of NACST/Seq is compared with other sequence design methods, and analyzed on a traveling salesman problem solved by bio-lab experiments. Our experimental results show that the evolutionary sequence design by NACST/Seq outperforms in its reliability the existing sequence design techniques such as conventional EAs, simulated annealing, and specialized heuristic methods.

Combining Prediction of Protein Aggregation Propensities with Prediction of Other One-Dimensional Properties