Rna secondary structures with pseudoknots are often predicted by minimizing free energy, which is nphard. Rna pseudoknot prediction in energybased models journal of. We have introduced a heuristic modeling approach to solve rna structure including pseudoknots employing structural mapping, sequential folding, and a new entropy model including globular effects that are capable of predicting a number of important pseudoknots with the minimum free energy. Hairpin, pseudoknot, melting temperature, ionic buffer 2. While predicting the secondary structure of rna is vital for researching its function, determining rna secondary structure is challenging, especially for that with pseudoknots. Predicting model and algorithm in rna folding structure. Although several computational models have been developed to predict 3d structures for rna. Real rna secondary structures often have local instead of global optimization because of kinetic reasons. These maps generate exponentially many structures and each of these has a neutral network of exponential size. Allowing all possible configurations of pseudoknots is not compatible. Predicting rna pseudoknot folding thermodynamics europe pmc. Based on the idea of iteratively forming stable stems, and the character that the stems in rna molecules are relatively stable, an algorithm is presented to predict rna secondary structure including pseudoknots, it is an improvement from the previously used algorithm,the algorithm takes on3 time and on2 sapce, in predicting accuracy. The predicting accuracy, the time complexity and space complexity outperform existing.
Singlemolecule detection of rna folding in a protein nanopore. A heuristic method for computational rna pseudoknot. In an rna pseudoknot, a minimum of two loops l 1 and l 2 span the corresponding helical stem s 2 and s 1, respectively. This type of compact folding is presumed to play a key role in ribosomal function, catalysis of rnase p ribozyme, rna splicing, andor recognition of trnalike. Solution structure and thermodynamics of a divalent metal ion binding site in an rna pseudoknot rubenl. Predicting rna pseudoknot folding thermodynamics citeseerx. This example of a naturally occurring pseudoknot is found in the rna component of human telomerase. Since then, the model has been extended to predict the structures and folding thermodynamics of htype pseudoknots and rnarna complexes. Predicting rna folding thermodynamics with a reduced chain representation model. Because the secondary structure is related to the function of the rna, we would like to be able to predict the secondary structure. New algorithm for predicting rna secondary structure with. Also a large number of rna structures lie within 510% of the predicted global energy minimum1.
Charge density of cation determines inner versus outer. Nesbitta,b,1 ajila, university of colorado and national institute of standards and technology, boulder, co 80309. Statistical thermodynamics for rna structures with simple tertiary contacts and pseudoknots a dissertation presented to the faculty of the graduate school university of missouricolumbia. The method relies on approximations of the sequence dependence of. The theory presented in chapter 4 provides key information on folding maps into rna pseudoknot structures. Solution structure and thermodynamics of a divalent metal ion. However, the sequencedependent details ofthe folding pathways and the link between collapse and folding are poorly understood.
In this paper we consider the problem of predicting rna secondary structures with pseudoknots based on free energy minimization. G r m a t i dnarna structure ibivu vrije universiteit. The reduced backbone charge is independent of the ionic strength and is 60% of the rna bare charge at 37 c. Over the past two years, advances have been made in the estimation of folding free energy change, the mapping of secondary structure and the implementation of computer programs for structure prediction. In the present study, we develop a polymer statistical mechanical model to compute the conformational entropy from first principle. The pseudoknot was first recognized in the turnip yellow mosaic virus in 1982. Predicting rna pseudoknot folding thermodynamics core. The model is validated through extensive experimental tests both for the native structures and for the folding. The model can treat ion correlation effects explicitly by considering an ensemble of discrete ion distributions. Nov, 2017 singlemolecule detection of rna folding in a protein nanopore. Finding the correct secondary structure can be compared to the. Thirumalai department of chemistry and biochemistry and biophysics program, institute for physical science and technology, university of maryland, college park, maryland 20742, united states abstract. For example, folding may not be determined only by thermodynamics, the sequence dependence of free energy changes is far from completely known, and the folding space for rna is enormous.
Through thermodynamics experiments, it has been possible to estimate the free energy of some of the common types of loops that arise. To gain insight into macromolecule function one must investigate the structure. Solution structure and thermodynamics of a divalent metal. Pseudoknots fold into knotshaped threedimensional conformations but are not true topological knots. Predicting the folding thermodynamics for rna pseudoknots has been greatly hampered by the lacking of a physical model that can give accurate thermodynamic parameters especially loop entropy parameters.
Based on the entropy parameters, we develop a folding thermodynamics model that enables us to compute the sequencespecific rna pseudoknot folding free energy landscape and thermodynamics. A pseudoknot is a nucleic acid secondary structure containing at least two stemloop structures in. Allowing pseudoknots introduces modeling and computational problems. The accurate prediction of rna secondary structure from primary sequence has had enormous impact on research from the past 40 years. Cao s, chen sj 2006 predicting rna pseudoknot folding thermodynamics. Gonzalezjrandignaciotinocojr department of chemistry university of california berkeley and structural biology department, physical biosciences division lawrence berkeley national laboratory, berkeley, ca 947201460 usa.
Despite the extensive experimental studies on rna pseudoknot folding thermodynamics 2129, our ability to quantitatively predict rna pseudoknot structure and folding. Algorithms based on thermodynamics predict only 5070% of the base pairs correctly. 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. We first give a brief comparison of energybased methods for predicting rna secondary structures with pseudoknots. Based on the idea of iteratively forming stable stems, and the character that the stems in rna molecules are relatively stable, an algorithm is presented to predict rna secondary structure including pseudoknots, it is an improvement from the previously used algorithm,the algorithm takes on3 time and on2. Thermodynamics and nucleotide cyclic motifs for rna structure prediction algorithm. In this study, we develop an allatom model to predict the ion electrostatics in rna folding. For example, mirnas regulate protein coding gene expression by binding to 3 utrs, small nucleolar rnas guide posttranscriptional modifications by binding to rrna, u4 spliceosomal rna and u6 spliceosomal rna bind to each other forming part of the spliceosome and many small. Predicting ion binding properties for rna tertiary structures. As rna structure formation is of hierarchical nature, secondary structure is the basis for the tertiary. In an rna pseudoknot, the stability and folding thermodynamics are largely determined by the interplay between the loops and the helical stems.
The dna folding problem similar to the protein and rna folding problems, there is a corresponding dna. The stacking interactions are parametrized using a set of nucleotidespecific parameters, which. The second computes common foldings for a family of. Predicting rna secondary structures with pseudoknots by mcmc. Despite the extensive experimental studies on rna pseudoknot folding thermodynamics 21 29, our ability to quantitatively predict rna pseudoknot structure and folding stability is very limited 30 34. Most rnas fold during transcription from dna into rna through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Three dimensional structure of almost the same pseudoknot from telomerase rna.
Rivas e, eddy sr 1999 a dynamic programming algorithm for rna structure prediction including pseudoknots. Below we describe several unique features of the vfold model. Predicting 3d structure and stability of rna pseudoknots in. Molecularcrowding effects on singlemolecule rna folding. Our model can be used to predict the folding thermodynamics for any rna molecule in the presence of monovalent ions. A heuristic method for computational rna pseudoknot prediction. The first attempts structure prediction of single sequences based on minimizing the free energy of folding.
Rna pseudoknot prediction in energybased models journal. We present a thermodynamically robust coarsegrained model to simulate folding of rna in monovalent salt solutions. Thermodynamics of dna motifs 417 of biotechnology techniques that exploit the threedimensional folding potential of dna have also been demonstrated including dna nanotechnology 75 and dna computing 21. Formation of a pseudoknot pk in the conserved rna core domain in the ribonucleoprotein human telomerase is required for function. Nanopore electric snapshots of an rna tertiary folding pathway. Predicting rna pseudoknot folding thermodynamics article pdf available in nucleic acids research 349. Coarsegrained modeling of large rna molecules with knowledgebased potentials and structural filters.
In this study, we introduce the pseudoknot local motif model and dynamic partner sequence. This is mainly due to lacking of the thermodynamic parameters, especially the chain entropy parameters. Prediction of rna secondary structure by free energy. Although several computational models have been developed to predict 3d structures for rna pseudoknots to. The model includes stacking, hydrogen bond, and electrostatic interactions as fundamental components in describing the stability of rna structures. A pseudoknot is a nucleic acid secondary structure containing at least two stemloop structures in which half of one stem is intercalated between the two halves of another stem. The structures of some rna molecules contain socalled pseudoknots. Statistical thermodynamics for rna pseudoknots a dissertation. A polymer physics framework for the entropy of arbitrary. With the entropy parameters predicted from the vfold model and the energy parameters for the tertiary contacts as inserted parameters, we can then predict the rna folding thermodynamics, from which we can extract the tertiary contact thermodynamic parameters from theory experimental comparisons.
Predicting rna secondary structures with pseudoknots by. Rna viruses employ a plethora of structure elements to invade the host cell. A 3d model of a pseudoknot the 2 helices in the structure preceding slide are stacked coaxially. Jun 23, 2007 the most probable secondary structure of an rna molecule, given the nucleotide sequence, can be computed efficiently if a stochastic contextfree grammar scfg is used as the prior distribution of the secondary structure. A loop and a helix can become correlated through, for example, the excluded volume interactions. Our paper presents an efficient algorithm for predicting rna structure with pseudoknots, and the algorithm takes on 3 time and on 2 space, the experimental tests in rfam10.
Author summary rna pseudoknotted structures and their stability can play important roles in rna cellular functions such as transcription, splicing and translation. We showed that both the inner and outer shell bindings are. Most ab initio pseudoknot predicting methods provide very few folding scenarios for a given rna sequence and have low sensitivities. To validate the helixbased strategy for rna folding landscape partition, we randomly generate rna sequences i. Coarsegrained model for predicting rna folding thermodynamics. Prediction of rna pseudoknots using heuristic modeling with. The most probable secondary structure of an rna molecule, given the nucleotide sequence, can be computed efficiently if a stochastic contextfree grammar scfg is used as the prior distribution of the secondary structure. Predicting rna pseudoknot folding thermodynamics europe. We use molecular simulations of a coarsegrained model, which reproduces most of the salient features of the experimental melting profiles of pk and. Allowing all possible configurations of pseudoknots is not compatible with contextfree grammar models. Pseudoknots are complicated and stable rna structure. Coarsegrained model for predicting rna folding thermodynamics natalia a. Salt effects on the thermodynamics of a frameshifting rna pseudoknot under tension.
Predicting 3d structure and stability of rna pseudoknots. Molecularcrowding effects on singlemolecule rna foldingunfolding thermodynamics and kinetics nicholas f. List of rna structure prediction software wikipedia. A large percentage of rna in the cell is composed of rna that folds up into complex structures, that are often described in terms of their base pairing configurations known as secondary structure see text s1 for an introduction to this topic. Given an rna sequence, the rna folding problem is to predict the. Prediction of rna pseudoknots using heuristic modeling. Although many algorithms are available to make these predictions, the inclusion of nonnested loops, termed pseudoknots, still poses challenges arising from two main factors. Sosnick tr, pan t j 2004 reduced contact order and rna folding rates. The model is validated through extensive experimental tests both for the native structures and for the folding thermodynamics.
The rst step in rna folding is stable base pairing which leads to a secondary structure. Folding of human telomerase rna pseudoknot using ion. Rna secondary structure is often predicted from sequence by free energy minimization. Despite the extensive experimental studies on rna pseudoknot folding thermodynamics 2129, our ability to quantitatively predict rna pseudoknot structure and folding stability is very limited 3034. For example, pseudoknot folding can be cooperative or noncooperative, which involves several inter mediates in the folding process. Pdf predicting rna pseudoknot folding thermodynamics.
Crowding promotes the switch from hairpin to pseudoknot. Rna pseudoknots are a class of base pairing structures that appear in many viruses and may comprise as much as 10% of. The pseudoknot is a potentially important tertiary structural motif of rna and it has been identified in 16s rrna, u2 snrna, and some plant viral rnas with trnalike structures 1. Predicting rna pseudoknot folding thermodynamics nucleic. Free energy minimization is the most common method for predicting secondary structure when only a single sequence is known for a given function 11, 12. Song cao and shijie chen predicting rna pseudoknot folding thermodynamics a loops l1 and l2 span the deep narrow major and the shallow wide minor grooves, respectively. In vitro experiments show that the pk is in equilibrium with an extended hairpin hp structure. To obtain a comprehensive picture of the thermodynamics and folding kinetics we used molecular simulations of coarsegrained model of a pseudoknot found in the conserved core domain of the human telomerase. However, the existed models seldom consider the conditions departing from the roombody temperature and high salt 1m nacl, and thus generally hardly predict the thermodynamics and salt effect.
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