RNA secondary sturcture prediction using a combined method of thermodynamics and kinetics

Nowadays, RNA is extensively acknowledged an important role in the functions of information transfer, structural components, gene regulation and etc. The secondary structure of RNA becomes a key to understand structure-function relationship. Computational prediction of RNA secondary structure does not only provide possible structures, but also elucidates the mechanism of RNA folding. Conventional prediction programs are either derived from evolutionary perspective, or aimed to achieve minimum free energy. In vivo, RNA folds during transcription, which indicates that native RNA structure is a result from both thermodynamics and kinetics. In this thesis, I first reviewed the current leading kinetic folding programs and demonstrate that these programs are not able to predict secondary structure accurately. Upon that, I proposed a new sequential folding program called GTkinetics. Given an RNA sequence, GTkinetics predicts a secondary structure and a series of RNA folding trajectories. It treats the RNA as a growing chain, and adds stable local structures sequentially. It is featured with a Z-score to evaluate stability of local structures, which is able to locate native local structures with high confidence. Since all stable local structures are captured in GTkinetics, it results in some false positives, which prevents the native structure to form as the chain grows. This suggests a refolding model to melt the false positive hairpins, probable intermediate structures, and to fold the RNA into a new structure with reliable long-range helices. By analyzing suboptimal ensemble along the folding pathway, I suggested a refolding mechanism, with which refolding can be evaluated whether or not to take place. Another way to favor local structures over long-distance structures, we introduced a distance penalty function into the free energy calculation. I used a sigmoidal function to compute the energy penalty according to the distance in the primary sequence between two nucleotides of a base pair. For both the training dataset and the test dataset, the distance function improves the prediction to some extent. In order to characterize the differences between local and long-range helices, I carried out analysis of standardized local nucleotide composition and base pair composition according to the two groups. The results show that adenine accumulates on the 5' side of local structure, but not on that of long-range helices. GU base pairs occur significantly more frequent in the local helices than that in the long-range helices. These indicate that the mechanisms to form local and long range helices are different, which is encoded in the sequence itself. Based on all the results, I will draw conclusions and suggest future directions to enhance the current sequential folding program.MSCommittee Chair: Stephen Harvey; Committee Member: Heitsch, Christine; Committee Member: Hud, Nick; Committee Member: Wartell, Roger; Committee Member: Weitz, Joshu

Geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences

Questions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric analysis is to elucidate such dependencies, especially as they pertain to the accuracy and robustness of the optimal solutions. Techniques from geometric combinatorics, including polytopes and their normal fans, have been used previously to give parametric analyses of simple models for DNA sequence alignment and RNA branching configurations. Here, we present a new computational framework, and proof-of-principle results, which give the first complete parametric analysis of the branching portion of the nearest neighbor thermodynamic model for secondary structure prediction for real RNA sequences.Comment: 17 pages, 8 figure

RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules

<p>Abstract</p> <p>Background</p> <p>In recent years, RNA molecules that are not translated into proteins (ncRNAs) have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure.</p> <p>Results</p> <p>We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE) predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space.</p> <p>Conclusion</p> <p>The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.</p

A complex adaptive systems approach to the kinetic folding of RNA

The kinetic folding of RNA sequences into secondary structures is modeled as a complex adaptive system, the components of which are possible RNA structural rearrangements (SRs) and their associated bases and base pairs. RNA bases and base pairs engage in local stacking interactions that determine the probabilities (or fitnesses) of possible SRs. Meanwhile, selection operates at the level of SRs; an autonomous stochastic process periodically (i.e., from one time step to another) selects a subset of possible SRs for realization based on the fitnesses of the SRs. Using examples based on selected natural and synthetic RNAs, the model is shown to qualitatively reproduce characteristic (nonlinear) RNA folding dynamics such as the attainment by RNAs of alternative stable states. Possible applications of the model to the analysis of properties of fitness landscapes, and of the RNA sequence to structure mapping are discussed.Comment: 23 pages, 4 figures, 2 tables, to be published in BioSystems (Note: updated 2 references

Functional nucleic acids as substrate for information processing

Information processing applications driven by self-assembly and conformation dynamics of nucleic acids are possible. These underlying paradigms (self-assembly and conformation dynamics) are essential for natural information processors as illustrated by proteins. A key advantage in utilising nucleic acids as information processors is the availability of computational tools to support the design process. This provides us with a platform to develop an integrated environment in which an orchestration of molecular building blocks can be realised. Strict arbitrary control over the design of these computational nucleic acids is not feasible. The microphysical behaviour of these molecular materials must be taken into consideration during the design phase. This thesis investigated, to what extent the construction of molecular building blocks for a particular purpose is possible with the support of a software environment. In this work we developed a computational protocol that functions on a multi-molecular level, which enable us to directly incorporate the dynamic characteristics of nucleic acids molecules. To allow the implementation of this computational protocol, we developed a designer that able to solve the nucleic acids inverse prediction problem, not only in the multi-stable states level, but also include the interactions among molecules that occur in each meta-stable state. The realisation of our computational protocol are evaluated by generating computational nucleic acids units that resembles synthetic RNA devices that have been successfully implemented in the laboratory. Furthermore, we demonstrated the feasibility of the protocol to design various types of computational units. The accuracy and diversity of the generated candidates are significantly better than the best candidates produced by conventional designers. With the computational protocol, the design of nucleic acid information processor using a network of interconnecting nucleic acids is now feasible

ViennaRNA Package 2.0

<p>Abstract</p> <p>Background</p> <p>Secondary structure forms an important intermediate level of description of nucleic acids that encapsulates the dominating part of the folding energy, is often well conserved in evolution, and is routinely used as a basis to explain experimental findings. Based on carefully measured thermodynamic parameters, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties.</p> <p>Results</p> <p>The <monospace>ViennaRNA</monospace> Package has been a widely used compilation of RNA secondary structure related computer programs for nearly two decades. Major changes in the structure of the standard energy model, the <it>Turner 2004 </it>parameters, the pervasive use of multi-core CPUs, and an increasing number of algorithmic variants prompted a major technical overhaul of both the underlying <monospace>RNAlib</monospace> and the interactive user programs. New features include an expanded repertoire of tools to assess RNA-RNA interactions and restricted ensembles of structures, additional output information such as <it>centroid </it>structures and <it>maximum expected accuracy </it>structures derived from base pairing probabilities, or <it>z</it>-<it>scores </it>for locally stable secondary structures, and support for input in <monospace>fasta</monospace> format. Updates were implemented without compromising the computational efficiency of the core algorithms and ensuring compatibility with earlier versions.</p> <p>Conclusions</p> <p>The <monospace>ViennaRNA Package 2.0</monospace>, supporting concurrent computations <monospace>via OpenMP</monospace>, can be downloaded from <url>http://www.tbi.univie.ac.at/RNA</url>.</p

An Iterative Loop Matching Approach to the Prediction of RNA Secondary Structures with Pseudoknots

Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to the difficulty in modeling and complexity in computing. Although several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching (Tabaska et. al, Bioinformatics, 14:691-9, 1998), an algorithm for pseudoknot prediction with comparative analysis, suffers from low prediction accuracy in many cases. Here we present an algorithm, iterative loop matching, for predict-ing RNA secondary structures including pseudoknots reliably and efficiently. The method can utilize either thermodynamic or comparative information or both, thus is able to predict for both aligned sequences and individual sequences. Results: We have tested the algorithm on a number of RNA families, including both structures with and without pseudoknots. Using 8–12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots. Furthermore, it produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high prediction accuracy on individual sequences, comparable to the PKNOTS algorithm (Rivas & Eddy, J Mol Biol, 285:2053-68, 1999), while using much less computational resources. Availability: The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/˜zhang/projects/rna/ilm/

An Improved Algorithm for RNA Secondary Structure Prediction

Though not as abundant in known biological processes as proteins,RNA molecules serve as more than mere intermediaries betweenDNA and proteins, e.g. as catalytic molecules. Furthermore,RNA secondary structure prediction based on free energyrules for stacking and loop formation remains one of the few majorbreakthroughs in the field of structure prediction. We present anew method to evaluate all possible internal loops of size at mostk in an RNA sequence, s, in time O(k|s|^2); this is an improvementfrom the previously used method that uses time O(k^2|s|^2).For unlimited loop size this improves the overall complexity ofevaluating RNA secondary structures from O(|s|^4) to O(|s|^3) andthe method applies equally well to finding the optimal structureand calculating the equilibrium partition function. We use ourmethod to examine the soundness of setting k = 30, a commonlyused heuristic