RNA secondary structure: physical and computational aspects

2000 ◽  
Vol 33 (3) ◽  
pp. 199-253 ◽  
Author(s):  
Paul G. Higgs
Keyword(s):  
Molecular Evolution ◽  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Protein Structures ◽  
Energy Structure ◽  
Folding Kinetics ◽  
Phosphate Groups

1. Background to RNA structure 2001.1 Types of RNA 2001.1.1 Transfer RNA (tRNA) 2001.1.2 Messenger RNA (mRNA) 2011.1.3 Ribosomal RNA (rRNA) 2011.1.4 Other ribonucleoprotein particles 2021.1.5 Viruses and viroids 2021.1.6 Ribozymes 2021.2 Elements of RNA secondary structure 2031.3 Secondary structure versus tertiary structure 2052. Theoretical and computational methods for RNA secondary structure determination 2082.1 Dynamic programming algorithms 2082.2 Kinetic folding algorithms 2102.3 Genetic algorithms 2122.4 Comparative methods 2133. RNA thermodynamics and folding mechanisms 2163.1 The reliability of minimum free energy structure prediction 2163.2 The relevance of RNA folding kinetics 2183.3 Examples of RNA folding kinetics simulations 2213.4 RNA as a disordered system 2274. Aspects of RNA evolution 2334.1 The relevance of RNA for studies of molecular evolution 2334.1.1 Molecular phylogenetics 2344.1.2 tRNAs and the genetic code 2344.1.3 Viruses and quasispecies 2354.1.4 Fitness landscapes 2354.2 The interaction between thermodynamics and sequence evolution 2364.3 Theory of compensatory substitutions in RNA helices 2384.4 Rates of compensatory substitutions obtained from sequence analysis 2405. Conclusions 2466. Acknowledgements 2467. References 246This article takes an inter-disciplinary approach to the study of RNA secondary structure, linking together aspects of structural biology, thermodynamics and statistical physics, bioinformatics, and molecular evolution. Since the intended audience for this review is diverse, this section gives a brief elementary level discussion of the chemistry and structure of RNA, and a rapid overview of the many types of RNA molecule known. It is intended primarily for those not already familiar with molecular biology and biochemistry.Ribonucleic acid consists of a linear polymer with a backbone of ribose sugar rings linked by phosphate groups. Each sugar has one of the four ‘bases’ adenine, cytosine, guanine and uracil (A, C, G, and U) linked to it as a side group. The structure and function of an RNA molecule is specific to the sequence of bases. The phosphate groups link the 5′ carbon of one ribose to the 3′ carbon of the next. This imposes a directionality on the backbone. The two ends are referred to as 5′ and 3′ ends, since one end has an unlinked 5′ carbon and one has an unlinked 3′ carbon. The chemical differences between RNA and DNA (deoxyribonucleic acid) are fairly small: one of the OH groups in ribose is replaced by an H in deoxyribose, and DNA contains thymine (T) bases instead of U. However, RNA structure is very different from DNA structure. In the familiar double helical structure of DNA the two strands are perfectly complementary in sequence. RNA usually occurs as single strands, and base pairs are formed intra-molecularly, leading to a complex arrangement of short helices which is the basis of the secondary structure. Some RNA molecules have well-defined tertiary structures. In this sense, RNA structures are more akin to globular protein structures than to DNA.The role of proteins as biochemical catalysts and the role of DNA in storage of genetic information have long been recognised. RNA has sometimes been considered as merely an intermediary between DNA and proteins. However, an increasing number of functions of RNA are now becoming apparent, and RNA is coming to be seen as an important and versatile molecule in its own right.

Statistical Mechanical Prediction of Ligand Perturbation to RNA Secondary Structure and Application to the SAM-I Riboswitch

2018 ◽  
Author(s):  
Osama Alaidi ◽  
Fareed Aboul-ela
Keyword(s):  
Secondary Structure ◽  
Ligand Binding ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Key Factor ◽  
Statistical Mechanical

ABSTRACTThe realization that non protein-coding RNA (ncRNA) is implicated in an increasing number of cellular processes, many related to human disease, makes it imperative to understand and predict RNA folding. RNA secondary structure prediction is more tractable than tertiary structure or protein structure. Yet insights into RNA structure-function relationships are complicated by coupling between RNA folding and ligand binding. Here, we introduce a simple statistical mechanical formalism to calculate perturbations to equilibrium secondary structure conformational distributions for RNA, in the presence of bound cognate ligands. For the first time, this formalism incorporates a key factor in coupling ligand binding to RNA conformation: the differential affinity of the ligand for a range of RNA-folding intermediates. We apply the approach to the SAM-I riboswitch, for which binding data is available for analogs of intermediate secondary structure conformers. Calculations of equilibrium secondary structure distributions during the transcriptional “decision window” predict subtle shifts due to the ligand, rather than an on/off switch. The results suggest how ligand perturbation can release a kinetic block to the formation of a terminator hairpin in the full-length riboswitch. Such predictions identify aspects of folding that are most affected by ligand binding, and can readily be compared with experiment.

Predicting RNA Secondary Structure Using a Improved BPSO Based on Stem Combination

2013 ◽  
Vol 325-326 ◽  
pp. 1551-1554
Author(s):  
Yi Qi
Keyword(s):  
Secondary Structure ◽  
Sensitivity And Specificity ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Experimental Results ◽  
New Method ◽  
Rna Structure Prediction ◽  
New Strategies

In this paper, we present an improved BPSO to predict RNA secondary structure to improve the performance with two new strategies. First one is to reduce the searching space of PSO through super stem set construction. Second is to modify the general BPSO updating process to settle stem permutation and combination problems. The experimental results show that the new method is effective for RNA structure prediction in terms of sensitivity and specificity by different sequence datasets including simple pseudoknot.

incaRNAfbinv 2.0: a webserver and software with motif control for fragment-based design of RNAs

2020 ◽  
Vol 36 (9) ◽  
pp. 2920-2922
Author(s):  
Matan Drory Retwitzer ◽  
Vladimir Reinharz ◽  
Alexander Churkin ◽  
Yann Ponty ◽  
Jérôme Waldispühl ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
Practical Interest ◽  
Supplementary Information ◽  
Command Line ◽  
Design Tools ◽  
Additional Information ◽  
Non Coding Rna ◽  
Rna Design

Abstract Summary RNA design has conceptually evolved from the inverse RNA folding problem. In the classical inverse RNA problem, the user inputs an RNA secondary structure and receives an output RNA sequence that folds into it. Although modern RNA design methods are based on the same principle, a finer control over the resulting sequences is sought. As an important example, a substantial number of non-coding RNA families show high preservation in specific regions, while being more flexible in others and this information should be utilized in the design. By using the additional information, RNA design tools can help solve problems of practical interest in the growing fields of synthetic biology and nanotechnology. incaRNAfbinv 2.0 utilizes a fragment-based approach, enabling a control of specific RNA secondary structure motifs. The new version allows significantly more control over the general RNA shape, and also allows to express specific restrictions over each motif separately, in addition to other advanced features. Availability and implementation incaRNAfbinv 2.0 is available through a standalone package and a web-server at https://www.cs.bgu.ac.il/incaRNAfbinv. Source code, command-line and GUI wrappers can be found at https://github.com/matandro/RNAsfbinv. Supplementary information Supplementary data are available at Bioinformatics online.

A New Method to Predict RNA Secondary Structure Based on RNA Folding Simulation

2016 ◽  
Vol 13 (5) ◽  
pp. 990-995 ◽  
Author(s):  
Yuanning Liu ◽  
Qi Zhao ◽  
Hao Zhang ◽  
Rui Xu ◽  
Yang Li ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Rna Secondary Structure ◽  
Rna Folding ◽  
New Method ◽  
Folding Simulation

scholarly journals Bioinformatic and Physical Characterizations of Genome-Scale Ordered RNA Structure in Mammalian RNA Viruses

2008 ◽  
Vol 82 (23) ◽  
pp. 11824-11836 ◽  
Author(s):  
Matthew Davis ◽  
Selena M. Sagan ◽  
John P. Pezacki ◽  
David J. Evans ◽  
Peter Simmonds
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Large Scale ◽  
Rna Viruses ◽  
Plant Viruses ◽  
Large Set ◽  
Conserved Sequence ◽  
Evolutionarily Conserved ◽  
Genome Scale

ABSTRACT By the analysis of thermodynamic RNA secondary structure predictions, we previously obtained evidence for evolutionarily conserved large-scale ordering of RNA virus genomes (P. Simmonds, A. Tuplin, and D. J. Evans, RNA 10:1337-1351, 2004). Genome-scale ordered RNA structure (GORS) was widely distributed in many animal and plant viruses, much greater in extent than RNA structures required for viral translation or replication, but in mammalian viruses was associated with host persistence. To substantiate the existence of large-scale RNA structure differences between viruses, a large set of alignments of mammalian RNA viruses and rRNA sequences as controls were examined by thermodynamic methods (to calculate minimum free energy differences) and by algorithmically independent RNAz and Pfold methods. These methods produced generally concordant results and identified substantial differences in the degrees of evolutionarily conserved, sequence order-dependent RNA secondary structure between virus genera and groups. A probe hybridization accessibility assay was used to investigate the physical nature of GORS. Transcripts of hepatitis C virus (HCV), hepatitis G virus/GB virus-C (HGV/GBV-C), and murine norovirus, which are predicted to be structured, were largely inaccessible to hybridization in solution, in contrast to the almost universal binding of probes to a range of unstructured virus transcripts irrespective of G+C content. Using atomic force microscopy, HCV and HGV/GBV-C RNA was visualized as tightly compacted prolate spheroids, while under the same experimental conditions the predicted unstructured poliovirus and rubella virus RNA were pleomorphic and had extensively single-stranded RNA on deposition. Bioinformatic and physical characterization methods both identified fundamental differences in the configurations of viral genomic RNA that may modify their interactions with host cell defenses and their ability to persist.

scholarly journals Quantitative high-throughput tests of ubiquitous RNA secondary structure prediction algorithms via RNA/protein binding

2019 ◽  
Author(s):  
Winston R. Becker ◽  
Inga Jarmoskaite ◽  
Kalli Kappel ◽  
Pavanapuresan P. Vaidyanathan ◽  
Sarah K. Denny ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Binding ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Nearest Neighbor ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Vast Number ◽  
Prediction Algorithms

AbstractNearest-neighbor (NN) rules provide a simple and powerful quantitative framework for RNA structure prediction that is strongly supported for canonical Watson-Crick duplexes from a plethora of thermodynamic measurements. Predictions of RNA secondary structure based on nearest-neighbor (NN) rules are routinely used to understand biological function and to engineer and control new functions in biotechnology. However, NN applications to RNA structural features such as internal and terminal loops rely on approximations and assumptions, with sparse experimental coverage of the vast number of possible sequence and structural features. To test to what extent NN rules accurately predict thermodynamic stabilities across RNAs with non-WC features, we tested their predictions using a quantitative high-throughput assay platform, RNA-MaP. Using a thermodynamic assay with coupled protein binding, we carried out equilibrium measurements for over 1000 RNAs with a range of predicted secondary structure stabilities. Our results revealed substantial scatter and systematic deviations between NN predictions and observed stabilities. Solution salt effects and incorrect or omitted loop parameters contribute to these observed deviations. Our results demonstrate the need to independently and quantitatively test NN computational algorithms to identify their capabilities and limitations. RNA-MaP and related approaches can be used to test computational predictions and can be adapted to obtain experimental data to improve RNA secondary structure and other prediction algorithms.Significance statementRNA secondary structure prediction algorithms are routinely used to understand, predict and design functional RNA structures in biology and biotechnology. Given the vast number of RNA sequence and structural features, these predictions rely on a series of approximations, and independent tests are needed to quantitatively evaluate the accuracy of predicted RNA structural stabilities. Here we measure the stabilities of over 1000 RNA constructs by using a coupled protein binding assay. Our results reveal substantial deviations from the RNA stabilities predicted by popular algorithms, and identify factors contributing to the observed deviations. We demonstrate the importance of quantitative, experimental tests of computational RNA structure predictions and present an approach that can be used to routinely test and improve the prediction accuracy.

scholarly journals RNA Secondary Structurome Revealed Distinct Thermoregulation in Plasmodium falciparum

2022 ◽  
Vol 9 ◽  
Author(s):  
Yanwei Qi ◽  
Yuhong Zhang ◽  
Quankai Mu ◽  
Guixing Zheng ◽  
Mengxin Zhang ◽  
...  
Keyword(s):  
Plasmodium Falciparum ◽  
Secondary Structure ◽  
Rna Structure ◽  
Rna Secondary Structure ◽  
Structural Changes ◽  
Environmental Changes ◽  
Parasite Development ◽  
Temperature Dependent ◽  
Trophozoite Stage ◽  
Temperature Changes

The development of Plasmodium parasites, a causative agent of malaria, requests two hosts and the completion of 11 different parasite stages during development. Therefore, an efficient and fast response of parasites to various complex environmental changes, such as ambient temperature, pH, ions, and nutrients, is essential for parasite development and survival. Among many of these environmental changes, temperature is a decisive factor for parasite development and pathogenesis, including the thermoregulation of rRNA expression, gametogenesis, and parasite sequestration in cerebral malaria. However, the exact mechanism of how Plasmodium parasites rapidly respond and adapt to temperature change remains elusive. As a fundamental and pervasive regulator of gene expression, RNA structure can be a specific mechanism for fine tuning various biological processes. For example, dynamic and temperature-dependent changes in RNA secondary structures can control the expression of different gene programs, as shown by RNA thermometers. In this study, we applied the in vitro and in vivo transcriptomic-wide secondary structurome approach icSHAPE to measure parasite RNA structure changes with temperature alteration at single-nucleotide resolution for ring and trophozoite stage parasites. Among 3,000 probed structures at different temperatures, our data showed structural changes in the global transcriptome, such as S-type rRNA, HRPII gene, and the erythrocyte membrane protein family. When the temperature drops from 37°C to 26°C, most of the genes in the trophozoite stage cause significantly more changes to the RNA structure than the genes in the ring stage. A multi-omics analysis of transcriptome data from RNA-seq and RNA structure data from icSHAPE reveals that the specific RNA secondary structure plays a significant role in the regulation of transcript expression for parasites in response to temperature changes. In addition, we identified several RNA thermometers (RNATs) that responded quickly to temperature changes. The possible thermo-responsive RNAs in Plasmodium falciparum were further mapped. To this end, we identified dynamic and temperature-dependent RNA structural changes in the P. falciparum transcriptome and performed a comprehensive characterization of RNA secondary structures over the course of temperature stress in blood stage development. These findings not only contribute to a better understanding of the function of the RNA secondary structure but may also provide novel targets for efficient vaccines or drugs.

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