It's a loop world – single strands in RNA as structural and functional elements

2011 ◽  
Vol 2 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Christian Schudoma
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Three Dimensional ◽  
Rna Structures ◽  
Rna Molecules ◽  
Processing Pathways ◽  
Ligand Interactions ◽  
3D Architecture ◽  
Microrna Processing ◽  
Rna Structural Motifs

AbstractUnpaired regions in RNA molecules – loops – are centrally involved in defining the characteristic three-dimensional (3D) architecture of RNAs and are of high interest in RNA engineering and design. Loops adopt diverse, but specific conformations stabilised by complex tertiary structural interactions that provide structural flexibility to RNA structures that would otherwise not be possible if they only consisted of the rigid A-helical shapes usually formed by canonical base pairing. By participating in sequence-non-local contacts, they furthermore contribute to stabilising the overall fold of RNA molecules. Interactions between RNAs and other nucleic acids, proteins, or small molecules are also generally mediated by RNA loop structures. Therefore, the function of an RNA molecule is generally dependent on its loops. Examples include intermolecular interactions between RNAs as part of the microRNA processing pathways, ribozymatic activity, or riboswitch-ligand interactions. Bioinformatics approaches have been successfully applied to the identification of novel RNA structural motifs including loops, local and global RNA 3D structure prediction, and structural and conformational analysis of RNAs and have contributed to a better understanding of the sequence-structure-function relationships in RNA loops.

scholarly journals Computational modeling of RNA 3D structure based on experimental data

2019 ◽  
Vol 39 (2) ◽  
Author(s):  
Almudena Ponce-Salvatierra ◽  
Astha ◽  
Katarzyna Merdas ◽  
Chandran Nithin ◽  
Pritha Ghosh ◽  
...  
Keyword(s):  
Experimental Data ◽  
Computational Methods ◽  
Rna Structure ◽  
Structure Prediction ◽  
3D Structure ◽  
Rna Structures ◽  
Data Types ◽  
Rna Sequences ◽  
Rna Molecules

Abstract RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

scholarly journals RNA 3D Structure Prediction Using Coarse-Grained Models

2021 ◽  
Vol 8 ◽  
Author(s):  
Jun Li ◽  
Shi-Jie Chen
Keyword(s):  
Structure Prediction ◽  
3D Structure ◽  
Three Dimensional ◽  
Coarse Grained ◽  
Biological Functions ◽  
3D Structures ◽  
Rna Molecules ◽  
Atomic Structures ◽  
Long Time

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

scholarly journals Molecular Cloning, Modeling, and Characterization of Type 2 Metallothionein from Plantago ovata Forsk

Sequencing ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Amitava Moulick ◽  
Debashis Mukhopadhyay ◽  
Shonima Talapatra ◽  
Nirmalya Ghoshal ◽  
Sarmistha Sen Raychaudhuri
Keyword(s):  
Molecular Cloning ◽  
Structure Prediction ◽  
3D Structure ◽  
Three Dimensional ◽  
Transcript Level ◽  
Copper Stress ◽  
Dimensional Structure ◽  
Functional Study ◽  
Plantago Ovata

Plantago ovata Forsk is a medicinally important plant. Metallothioneins are cysteine rich proteins involved in the detoxification of heavy metals. Molecular cloning and modeling of MT from P. ovata is not reported yet. The present investigation will describe the isolation, structure prediction, characterization, and expression under copper stress of type 2 metallothionein (MT2) from this species. The gene of the protein comprises three exons and two introns. The deduced protein sequence contains 81 amino acids with a calculated molecular weight of about 8.1 kDa and a theoretical pI value of 4.77. The transcript level of this protein was increased in response to copper stress. Homology modeling was used to construct a three-dimensional structure of P. ovata MT2. The 3D structure model of P. ovata MT2 will provide a significant clue for further structural and functional study of this protein.

scholarly journals Data mining and machine learning methods for chromosome conformation data analysis

2019 ◽  
Author(s):  
◽  
Oluwatosin Oluwadare
Keyword(s):  
Data Analysis ◽  
Human Genome ◽  
Structure Prediction ◽  
Learning Algorithm ◽  
3D Structure ◽  
Three Dimensional ◽  
Gene Clusters ◽  
Research Field ◽  
Chromosome Conformation ◽  
Graphical Tool

Sixteen years after the sequencing of the human genome, the Human Genome Project (HGP), and 17 years after the introduction of Chromosome Conformation Capture (3C) technologies, three-dimensional (3-D) inference and big data remains problematic in the field of genomics, and specifically, in the field of 3C data analysis. Three-dimensional inference involves the reconstruction of a genome's 3D structure or, in some cases, ensemble of structures from contact interaction frequencies extracted from a variant of the 3C technology called the Hi-C technology. Further questions remain about chromosome topology and structure; enhancer-promoter interactions; location of genes, gene clusters, and transcription factors; the relationship between gene expression and epigenetics; and chromosome visualization at a higher scale, among others. In this dissertation, four major contributions are described, first, 3DMax, a tool for chromosome and genome 3-D structure prediction from H-C data using optimization algorithm, second, GSDB, a comprehensive and common repository that contains 3D structures for Hi-C datasets from novel 3D structure reconstruction tools developed over the years, third, ClusterTAD, a method for topological associated domains (TAD) extraction from Hi-C data using unsupervised learning algorithm. Finally, we introduce a tool called, GenomeFlow, a comprehensive graphical tool to facilitate the entire process of modeling and analysis of 3D genome organization. It is worth noting that GenomeFlow and GSDB are the first of their kind in the 3D chromosome and genome research field. All the methods are available as software tools that are freely available to the scientific community.

scholarly journals Using AlphaFold to predict the impact of single mutations on protein stability and function

2021 ◽  
Author(s):  
Marina A Pak ◽  
Karina A Markhieva ◽  
Mariia S Novikova ◽  
Dmitry S Petrov ◽  
Ilya S Vorobyev ◽  
...  
Keyword(s):  
Protein Folding ◽  
Protein Stability ◽  
Structure Prediction ◽  
Large Scale ◽  
3D Structure ◽  
Three Dimensional ◽  
Single Mutation ◽  
Before And After ◽  
And Function ◽  
The Impact

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the AlphaFold predictions on the impact of a single mutation on structure with a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold cannot be immediately applied to other problems or applications in protein folding.

scholarly journals RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures

2022 ◽  
Vol 1 ◽  
Author(s):  
Zhi-Hao Guo ◽  
Li Yuan ◽  
Ya-Lan Tan ◽  
Ben-Gong Zhang ◽  
Ya-Zhou Shi
Keyword(s):  
Statistical Analysis ◽  
Rna Structure ◽  
Structure Prediction ◽  
Local Coordinate System ◽  
3D Structure ◽  
Scoring Function ◽  
Rna Structures ◽  
Cellular Functions ◽  
3D Structures ◽  
Structure Annotation

The 3D architectures of RNAs are essential for understanding their cellular functions. While an accurate scoring function based on the statistics of known RNA structures is a key component for successful RNA structure prediction or evaluation, there are few tools or web servers that can be directly used to make comprehensive statistical analysis for RNA 3D structures. In this work, we developed RNAStat, an integrated tool for making statistics on RNA 3D structures. For given RNA structures, RNAStat automatically calculates RNA structural properties such as size and shape, and shows their distributions. Based on the RNA structure annotation from DSSR, RNAStat provides statistical information of RNA secondary structure motifs including canonical/non-canonical base pairs, stems, and various loops. In particular, the geometry of base-pairing/stacking can be calculated in RNAStat by constructing a local coordinate system for each base. In addition, RNAStat also supplies the distribution of distance between any atoms to the users to help build distance-based RNA statistical potentials. To test the usability of the tool, we established a non-redundant RNA 3D structure dataset, and based on the dataset, we made a comprehensive statistical analysis on RNA structures, which could have the guiding significance for RNA structure modeling. The python code of RNAStat, the dataset used in this work, and corresponding statistical data files are freely available at GitHub (https://github.com/RNA-folding-lab/RNAStat).

scholarly journals RNA 3D modeling with FARFAR2, online

2020 ◽  
Author(s):  
Andrew Watkins ◽  
Rhiju Das
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Chemical Shifts ◽  
Three Dimensional ◽  
Structural Data ◽  
Dimensional Structure ◽  
Rna Structures ◽  
Beet Western Yellows Virus ◽  
H Nmr ◽  
Sampling Algorithms

AbstractUnderstanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2. We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the “basic interface” to the webserver, and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the “advanced interface.” We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.

scholarly journals A novel SHAPE reagent enables the analysis of RNA structure in living cells with unprecedented accuracy

2020 ◽  
Author(s):  
Tycho Marinus ◽  
Adam B. Fessler ◽  
Craig A. Ogle ◽  
Danny Incarnato
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Critical Role ◽  
Living Cells ◽  
Pathological Process ◽  
Rna Structures ◽  
Rna Molecules ◽  
Derived Data

ABSTRACTDue to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art SHAPE reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.

RNALigands: a database and web server for RNA - ligand interactions

RNA ◽  
2021 ◽  
pp. rna.078889.121
Author(s):  
Saisai Sun ◽  
Jianyi Yang ◽  
Zhaolei Zhang
Keyword(s):  
Secondary Structure ◽  
Small Molecules ◽  
Small Molecule ◽  
Neurological Diseases ◽  
Experimental Studies ◽  
Rna Structures ◽  
Structural Motifs ◽  
Rna Molecules ◽  
Small Molecule Ligands ◽  
Ligand Interactions

Motivation: RNA molecules can fold into complex and stable 3-D structures, allowing them to carry out important genetic, structural, and regulatory roles inside the cell. These complex structures often contain 3-D pockets made up of secondary structural motifs that can be potentially targeted by small molecule ligands. Indeed, many RNA structures in PDB contain bound small molecules, and high-throughput experimental studies have generated large number of interacting RNA and ligand pairs. There are considerable interests in developing small molecule lead compounds targeting viral RNAs or those RNAs implicated in neurological diseases or cancer. Results: We hypothesize that RNAs that have similar secondary structural motifs may bind to similar small molecule ligands. Towards this goal, we established a database collecting RNA secondary structural motifs and bound small molecules ligands. We further developed a computational pipeline, which takes input an RNA sequence, predicts its secondary structure, extracts structural motifs and searches the database for similar secondary structure motifs and interacting small molecules. We demonstrated the utility of the server by querying α-synuclein mRNA 5′ UTR sequence and finding potential matches which was validated as correct. Availability and Implementation: The server is publicly available at http://RNALigands.ccbr.utoronto.ca. The source code can also be downloaded at https://github.com/SaisaiSun/RNALigands.

Sign in / Sign up

Export Citation Format

Share Document