Modeling of Three-Dimensional RNA Structures Using SimRNA

2020 ◽  
pp. 103-125
Author(s):  
Tomasz K. Wirecki ◽  
Chandran Nithin ◽  
Sunandan Mukherjee ◽  
Janusz M. Bujnicki ◽  
Michał J. Boniecki
Keyword(s):  
Three Dimensional ◽  
Rna Structures

scholarly journals The crystal structure of a Polerovirus exoribonuclease-resistant RNA shows how diverse sequences are integrated into a conserved fold

2020 ◽  
Author(s):  
Anna-Lena Steckelberg ◽  
Quentin Vicens ◽  
David A. Costantino ◽  
Jay C. Nix ◽  
Jeffrey S. Kieft
Keyword(s):  
Crystal Structure ◽  
Three Dimensional ◽  
Rna Structures ◽  
Discrete Elements ◽  
Protein Coding ◽  
Sequence Elements ◽  
Conservation Patterns ◽  
Function Relationship ◽  
Relationship Of ◽  
Folded Rna

ABSTRACTExonuclease-resistant RNAs (xrRNAs) are discrete elements that block the progression of 5’ to 3’ exonucleases using specifically folded RNA structures. A recently discovered class of xrRNA is widespread in several genera of plant-infecting viruses, within both noncoding and protein-coding subgenomic RNAs. The structure of one such xrRNA from a dianthovirus revealed three-dimensional details of the resistant fold but did not answer all questions regarding the conservation and diversity of this xrRNA class. Here, we present the crystal structure of a representative polerovirus xrRNA that contains sequence elements that diverge from the previously solved structure. This new structure rationalizes previously unexplained sequence conservation patterns and shows interactions not present in the first structure. Together, the structures of these xrRNAs from dianthovirus and polerovirus genera support the idea that these plant virus xrRNAs fold through a defined pathway that includes a programmed intermediate conformation. This work deepens our knowledge of the structure-function relationship of xrRNAs and shows how evolution can craft similar RNA folds from divergent sequences.

scholarly journals Nanopore microscope identifies RNA isoforms with structural colors

2021 ◽  
Author(s):  
Filip N Boskovic ◽  
Ulrich Felix Keyser
Keyword(s):  
Single Molecule ◽  
Three Dimensional ◽  
Rna Structures ◽  
Transcript Isoforms ◽  
Single Molecule Level ◽  
Structural Colors ◽  
Rna Targets ◽  
Dna Strands ◽  
One Step ◽  
Simultaneous Identification

Identifying RNA transcript isoforms requires intricate protocols that suffer from various enzymatic biases. Here we design three-dimensional molecular constructs that enable identification of transcript isoforms at the single-molecule level using solid-state nanopore microscopy. We refold target RNA into RNA identifiers (IDs) with designed sets of complementary DNA strands. Each reshaped molecule carries a unique sequence of structural (pseudo)colors. Structural colors consist of DNA structures, protein labels, native RNA structures, or a combination of all three. The sequence of structural colors of RNA IDs enables simultaneous identification and relative quantification of multiple RNA targets without prior amplification. Our Amplification-free RNA TargEt Multiplex Isoform Sensing (ARTEMIS) reveals structural arrangements in native transcripts in agreement with published variants. ARTEMIS discriminates circular and linear transcript isoforms in a one step, enzyme-free reaction in a complex human transcriptome using single-molecule readout.

scholarly journals Computational Design of Asymmetric Three-dimensional RNA Structures and Machines

2017 ◽  
Author(s):  
Joseph D. Yesselman ◽  
Daniel Eiler ◽  
Erik D. Carlson ◽  
Alexandra N. Ooms ◽  
Wipapat Kladwang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Computational Design ◽  
Chemical Mapping ◽  
Rna Structures ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Nanotechnology ◽  
X Ray Scattering ◽  
Nucleotide Resolution ◽  
Large Sections

AbstractThe emerging field of RNA nanotechnology seeks to create nanoscale 3D machines by repurposing natural RNA modules, but successes have been limited to symmetric assemblies of single repeating motifs. We present RNAMake, a suite that automates design of RNA molecules with complex 3D folds. We first challenged RNAMake with the paradigmatic problem of aligning a tetraloop and sequence-distal receptor, previously only solved via symmetry. Single-nucleotide-resolution chemical mapping, native gel electrophoresis, and solution x-ray scattering confirmed that 11 of the 16 ‘miniTTR’ designs successfully achieved clothespin-like folds. A 2.55 Å diffraction-resolution crystal structure of one design verified formation of the target asymmetric nanostructure, with large sections achieving near-atomic accuracy (< 2.0 Å). Finally, RNAMake designed asymmetric segments to tether the 16S and 23S rRNAs together into a synthetic singlestranded ribosome that remains uncleaved by ribonucleases and supports life in Escherichia coli, a challenge previously requiring several rounds of trial-and-error.

scholarly journals RNA secondary structure prediction with Convolutional Neural Networks

2021 ◽  
Author(s):  
Mehdi Saman Booy ◽  
Alexander Ilin ◽  
Pekka Orponen
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Optimal Solution ◽  
Rna Structures ◽  
Rna Sequences ◽  
Perfect Model ◽  
Wide Range ◽  
Data Driven Approach ◽  
Algorithmic Approaches

Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. In this paper, we propose a simple, yet extremely effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets. Our experiments show excellent performance of the model across a wide range of sequence lengths and RNA families. We also observe considerable improvements in predicting complex pseudoknotted RNA structures, as compared to previous approaches.

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 Building the library of RNA 3D nucleotide conformations using the clustering approach

2015 ◽  
Vol 25 (3) ◽  
pp. 689-700 ◽  
Author(s):  
Tomasz Zok ◽  
Maciej Antczak ◽  
Martin Riedel ◽  
David Nebel ◽  
Thomas Villmann ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Rna Folding ◽  
Three Dimensional ◽  
Rna Structures ◽  
Neural Gas ◽  
Levels Of Detail ◽  
Data Points ◽  
Clustering Approach ◽  
Automated Methods ◽  
Different Levels

Abstract An increasing number of known RNA 3D structures contributes to the recognition of various RNA families and identification of their features. These tasks are based on an analysis of RNA conformations conducted at different levels of detail. On the other hand, the knowledge of native nucleotide conformations is crucial for structure prediction and understanding of RNA folding. However, this knowledge is stored in structural databases in a rather distributed form. Therefore, only automated methods for sampling the space of RNA structures can reveal plausible conformational representatives useful for further analysis. Here, we present a machine learning-based approach to inspect the dataset of RNA three-dimensional structures and to create a library of nucleotide conformers. A median neural gas algorithm is applied to cluster nucleotide structures upon their trigonometric description. The clustering procedure is two-stage: (i) backbone- and (ii) ribose-driven. We show the resulting library that contains RNA nucleotide representatives over the entire data, and we evaluate its quality by computing normal distribution measures and average RMSD between data points as well as the prototype within each cluster.

scholarly journals High-throughput method for the prediction of low-resolution, three-dimensional RNA structures

2006 ◽  
Vol 50 (1) ◽  
pp. 67-68 ◽  
Author(s):  
Mariusz Popenda ◽  
Łukasz Bielecki ◽  
Ryszard W. Adamiak
Keyword(s):  
High Throughput ◽  
Three Dimensional ◽  
Rna Structures ◽  
Low Resolution ◽  
High Throughput Method

scholarly journals RNA FRABASE version 1.0: an engine with a database to search for the three-dimensional fragments within RNA structures

2007 ◽  
Vol 36 (suppl_1) ◽  
pp. D386-D391 ◽  
Author(s):  
Mariusz Popenda ◽  
Marek Błażewicz ◽  
Marta Szachniuk ◽  
Ryszard W. Adamiak
Keyword(s):  
Three Dimensional ◽  
Rna Structures

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.

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