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 Evaluating DCA-based method performances for RNA contact prediction by a well-curated dataset

2019 ◽  
Author(s):  
F. Pucci ◽  
M. Zerihun ◽  
E. Peter ◽  
A. Schug
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Data ◽  
Three Dimensional ◽  
Spatial Proximity ◽  
Dimensional Structure ◽  
Rna Sequences ◽  
Contact Prediction ◽  
Rna Molecules ◽  
Quality Of Structure

AbstractRNA molecules play many pivotal roles in the cellular functioning that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analysed by methods such as Direct Coupling Analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated dataset of about seventy RNA structures with high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor difference between the performances of the different methods. Moreover, we discuss how these predictions are robust for different contact definitions and how strongly depend on procedures used to curate and align the families of homologous RNA sequences.

scholarly journals RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

RNA ◽  
2012 ◽  
Vol 18 (4) ◽  
pp. 610-625 ◽  
Author(s):  
J. A. Cruz ◽  
M.-F. Blanchet ◽  
M. Boniecki ◽  
J. M. Bujnicki ◽  
S.-J. Chen ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure

scholarly journals Analysis of structural similarities between brain Thy-1 antigen and immunoglobulin domains. Evidence for an evolutionary relationship and a hypothesis for its functional significance

1981 ◽  
Vol 195 (1) ◽  
pp. 31-40 ◽  
Author(s):  
F E Cohen ◽  
J Novotný ◽  
M J E Sternberg ◽  
D G Campbell ◽  
A F Williams
Keyword(s):  
Functional Significance ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Evolutionary Relationship ◽  
Dimensional Structure ◽  
Sequence Homologies ◽  
Beta Structure ◽  
Beta 2 Microglobulin

The Thy-1 membrane glycoprotein from rat brain is shown to have structural and sequence homologies with immunoglobulin (Ig) domains on the basis of the following evidence. 1. The two disulphide bonds of Thy-1 are both consistent with the Ig-fold. 2. The molecule contains extensive beta-structure as shown by the c.d. spectrum. 3. Secondary structure prediction locates beta-strands along the sequence in a manner consistent with the Ig-fold. 4. On the basis of rules derived from known beta-sheet structures, a three-dimensional structure with the Ig-fold is predicted as favourable for Thy-1. 5. Sequences in the proposed beta-strands of Thy-1 and known beta-strands of Ig domains show significant sequence homology. This homology is statistically more significant than for the comparison of proposed beta-strand sequences of beta 2-microglobulin with Ig domains. An hypothesis is presented for the possible functional significance of an evolutionary relationship between Thy-1 and Ig. It is suggested that both Thy-1 and Ig evolved from primitive molecules, with an Ig fold, which mediated cell--cell interactions. The present-day role of Thy-1 may be similar to that of the primitive domain.

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

scholarly journals Protein Structure Determination in Living Cells

2019 ◽  
Vol 20 (10) ◽  
pp. 2442 ◽  
Author(s):  
Teppei Ikeya ◽  
Peter Güntert ◽  
Yutaka Ito
Keyword(s):  
Protein Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Structures ◽  
Three Dimensional ◽  
Structural Data ◽  
Sample Tube ◽  
In Cells

To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

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.

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