scholarly journals Sequence- and structure-selective mRNA m5C methylation by NSUN6 in animals

2020 ◽  
Author(s):  
Jianheng Liu ◽  
Tao Huang ◽  
Yusen Zhang ◽  
Tianxuan Zhao ◽  
Xueni Zhao ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Structural Model ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Rna Modification ◽  
Type I ◽  
Translation Efficiency ◽  
Type Ii ◽  
Mrna Methylation ◽  
Hairpin Structures

AbstractmRNA m5C, which has recently been implicated in the regulation of mRNA mobility, metabolism, and translation, plays important regulatory roles in various biological events. Two types of m5C sites are found in mRNAs. Type I m5C sites, which contain a downstream G-rich triplet motif and are computationally predicted to locate in the 5’ end of putative hairpin structures, are methylated by NSUN2. Type II m5C sites contain a downstream UCCA motif and are computationally predicted to locate in the loops of putative hairpin structures. However, their biogenesis remains unknown. Here we identified NSUN6, a methyltransferase that is known to methylate C72 of tRNAThr and tRNACys, as an mRNA methyltransferase that targets Type II m5C sites. Combining the RNA secondary structure prediction, miCLIP, and results from a high-throughput mutagenesis analysis, we determined the RNA sequence and structural features governing the specificity of NSUN6-mediated mRNA methylation. Integrating these features into an NSUN6-RNA structural model, we identified an NSUN6 variant that largely loses tRNA methylation but retains mRNA methylation ability. Finally, we revealed a negative correlation between m5C methylation and translation efficiency. Our findings uncover that mRNA m5C is tightly controlled by an elaborate two-enzyme system, and the protein-RNA structure analysis strategy established may be applied to other RNA modification writers to distinguish the functions of different RNA substrates of a writer protein.

scholarly journals Sequence- and structure-selective mRNA m5C methylation by NSUN6 in animals

2020 ◽  
Author(s):  
Jianheng Liu ◽  
Tao Huang ◽  
Yusen Zhang ◽  
Tianxuan Zhao ◽  
Xueni Zhao ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Structural Model ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Rna Modification ◽  
Type I ◽  
Translation Efficiency ◽  
Type Ii ◽  
Mrna Methylation ◽  
Hairpin Structures

Abstract mRNA m5C, which has recently been implicated in the regulation of mRNA mobility, metabolism, and translation, plays important regulatory roles in various biological events. Two types of m5C sites are found in mRNAs. Type I m5C sites, which contain a downstream G-rich triplet motif and are computationally predicted to locate in the 5’ end of putative hairpin structures, are methylated by NSUN2. Type II m5C sites contain a downstream UCCA motif and are computationally predicted to locate in the loops of putative hairpin structures. However, their biogenesis remains unknown. Here we identified NSUN6, a methyltransferase that is known to methylate C72 of tRNAThr and tRNACys, as an mRNA methyltransferase that targets Type II m5C sites. Combining the RNA secondary structure prediction, miCLIP, and results from a high-throughput mutagenesis analysis, we determined the RNA sequence and structural features governing the specificity of NSUN6-mediated mRNA methylation. Integrating these features into an NSUN6-RNA structural model, we identified an NSUN6 variant that largely loses tRNA methylation but retains mRNA methylation ability. Finally, we revealed a weak negative correlation between m5C methylation and translation efficiency. Our findings uncover that mRNA m5C is tightly controlled by an elaborate two-enzyme system, and the protein-RNA structure analysis strategy established may be applied to other RNA modification writers to distinguish the functions of different RNA substrates of a writer protein.

Comparative surface and ultrastructural views of methylotrophic bacteria by TEM, STEM, SE, and freeze-etch electron microscopy.

1987 ◽  
Vol 45 ◽  
pp. 792-793
Author(s):  
T.A. Fassel ◽  
M.J. Schaller ◽  
M.E. Lidstrom ◽  
C.C. Remsen
Keyword(s):  
Cytoplasmic Membrane ◽  
Structural Features ◽  
Methylotrophic Bacteria ◽  
Type I ◽  
Type Ii ◽  
Membrane Complex ◽  
Oxidation Of Methane ◽  
Methane Oxidizing Bacteria ◽  
Wall Structures ◽  
Methylomonas Albus

Methylotrophic bacteria play an Important role in the environment in the oxidation of methane and methanol. Extensive intracytoplasmic membranes (ICM) have been associated with the oxidation processes in methylotrophs and chemolithotrophic bacteria. Classification on the basis of ICM arrangement distinguishes 2 types of methylotrophs. Bundles or vesicular stacks of ICM located away from the cytoplasmic membrane and extending into the cytoplasm are present in Type I methylotrophs. In Type II methylotrophs, the ICM form pairs of peripheral membranes located parallel to the cytoplasmic membrane. Complex cell wall structures of tightly packed cup-shaped subunits have been described in strains of marine and freshwater phototrophic sulfur bacteria and several strains of methane oxidizing bacteria. We examined the ultrastructure of the methylotrophs with particular view of the ICM and surface structural features, between representatives of the Type I Methylomonas albus (BG8), and Type II Methylosinus trichosporium (OB-36).

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

scholarly journals Coupled Translocation Events Generate Topological Heterogeneity at the Endoplasmic Reticulum Membrane

1998 ◽  
Vol 9 (9) ◽  
pp. 2681-2697 ◽  
Author(s):  
Kenneth Moss ◽  
Andrew Helm ◽  
Yun Lu ◽  
Alvina Bragin ◽  
William R. Skach
Keyword(s):  
Endoplasmic Reticulum ◽  
Structural Features ◽  
Endoplasmic Reticulum Membrane ◽  
Type I ◽  
Type Ii ◽  
Hydrophobic Residues ◽  
P Glycoprotein ◽  
C Terminus ◽  
Signal Anchor ◽  
Hydrophilic Residues

Topogenic determinants that direct protein topology at the endoplasmic reticulum membrane usually function with high fidelity to establish a uniform topological orientation for any given polypeptide. Here we show, however, that through the coupling of sequential translocation events, native topogenic determinants are capable of generating two alternate transmembrane structures at the endoplasmic reticulum membrane. Using defined chimeric and epitope-tagged full-length proteins, we found that topogenic activities of two C-trans (type II) signal anchor sequences, encoded within the seventh and eighth transmembrane (TM) segments of human P-glycoprotein were directly coupled by an inefficient stop transfer (ST) sequence (TM7b) contained within the C-terminus half of TM7. Remarkably, these activities enabled TM7 to achieve both a single- and a double-spanning TM topology with nearly equal efficiency. In addition, ST and C-trans signal anchor activities encoded by TM8 were tightly linked to the weak ST activity, and hence topological fate, of TM7b. This interaction enabled TM8 to span the membrane in either a type I or a type II orientation. Pleiotropic structural features contributing to this unusual topogenic behavior included 1) a short, flexible peptide loop connecting TM7a and TM7b, 2) hydrophobic residues within TM7b, and 3) hydrophilic residues between TM7b and TM8.

Combined HRTEM and EFTEM Study of Precipitates in Tungsten and Chromium-Containing TiB2

1999 ◽  
Vol 589 ◽  
Author(s):  
W. Mader ◽  
B. Freitag ◽  
K. Kelm ◽  
R. Telle ◽  
C. Schmalzried
Keyword(s):  
Chemical Composition ◽  
Structural Model ◽  
Chemical Constituents ◽  
Type I ◽  
Type Ii ◽  
Resolution Limit ◽  
Energy Filtering ◽  
The Matrix ◽  
Tungsten Concentration ◽  
Elemental Maps

AbstractThe structure and chemical composition of two types of precipitates in the system TiB2-WB2-CrB2 were studied by means of high-resolution TEM and energy filtering TEM. Type I particles (W2B5 structure) are precipitated at the basal plane of the hexagonal matrix whereas type II precipitates are thin platelets lying parallel to the {1100} prism planes. Lattice imaging yields displacements of the metal positions with respect to the matrix. Information on the chemical composition at high lateral resolution is obtained from elemental maps of all chemical constituents using electron spectroscopic imaging (ESI). The type II precipitates show a decrease in the B and Ti concentration, whereas the tungsten concentration increases and the Cr is homogeneously distributed. The HRTEM results combined with the results of the elemental maps allow to develop a structural model based on the intergrowth of the β-WB structure in the TiB2-rich matrix. The two deficient boron layers in W0.5Ti0.5B with a spacing of 0.38 nm can be used to examine the resolution limit of ESI.

Unique Structural Features That Influence Neutrophil Emigration Into the Lung

2003 ◽  
Vol 83 (2) ◽  
pp. 309-336 ◽  
Author(s):  
Alan R. Burns ◽  
C. Wayne Smith ◽  
David C. Walker
Keyword(s):  
Alveolar Epithelium ◽  
Structural Features ◽  
Alveolar Epithelial Cells ◽  
Alveolar Wall ◽  
Type I ◽  
Type Ii ◽  
Alveolar Epithelial ◽  
Capillary Bed ◽  
Vascular Beds ◽  
Alveolar Capillary

Neutrophil emigration in the lung differs substantially from that in systemic vascular beds where extravasation occurs primarily through postcapillary venules. Migration into the alveolus occurs directly from alveolar capillaries and appears to progress through a sequence of steps uniquely influenced by the cellular anatomy and organization of the alveolar wall. The cascade of adhesive and stimulatory events so critical to the extravasation of neutrophils from postcapillary venules in many tissues is not evident in this setting. Compelling evidence exists for unique cascades of biophysical, adhesive, stimulatory, and guidance factors that arrest neutrophils in the alveolar capillary bed and direct their movement through the endothelium, interstitial space, and alveolar epithelium. A prominent path accessible to the neutrophil appears to be determined by the structural interactions of endothelial cells, interstitial fibroblasts, as well as type I and type II alveolar epithelial cells.

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.

Protein Secondary Structure Determination (PSSD): A New and Simple Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 246-253
Author(s):  
Anindya Sundar Panja ◽  
Bidyut Bandopadhyay ◽  
Akash Nag ◽  
Smarajit Maiti
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Computational Algorithm ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Dynamic Algorithm ◽  
Previous Algorithm ◽  
Function Relationship

Background: Our present investigation was conducted to explore the computational algorithm for the protein secondary structure prediction as per the property of evolutionary transient and large number (each 50) of homologous mesophilic-thermophilic proteins. </P><P> Objectives: These mesophilic-thermophilic proteins were used for numerical measurement of helix-sheetcoil and turn tendency for which each amino-acid residue is screened to build up the propensity-table. Methods: In the current study, two different propensity windows have been introduced that allowed predicting the secondary structure of protein more than 80% accuracy. Results: Using this propensity matrix and dynamic algorithm-based programme, a significant and decisive outcome in the determination of protein (both thermophilic and mesophilic) secondary structure was noticed over the previous algorithm based programme. It was demonstrated after comparison with other standard methods including DSSP adopted by PDB with the help of multiple comparisons ANOVA and Dunnett’s t-test. Conclusion: The PSSD is of great importance in the prediction of structural features of any unknown, unresolved proteins. It is also useful in the studies of proteins structure-function relationship.

scholarly journals Protein Profiles: Biases and Protocols

2020 ◽  
Author(s):  
Gregor Urban ◽  
Mirko Torrisi ◽  
Christophe N. Magnan ◽  
Gianluca Pollastri ◽  
Pierre Baldi
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Profile Similarity ◽  
Redundancy Reduction ◽  
Test Sets

AbstractThe use of evolutionary profiles to predict protein secondary structure, as well as other protein structural features, has been standard practice since the 1990s. Using profiles in the input of such predictors, in place or in addition to the sequence itself, leads to significantly more accurate predictors. While profiles can enhance structural signals, their role remains somewhat surprising as proteins do not use profiles when folding in vivo. Furthermore, the same sequence-based redundancy reduction protocols initially derived to train and evaluate sequence-based predictors, have been applied to train and evaluate profile-based predictors. This can lead to unfair comparisons since profile may facilitate the bleeding of information between training and test sets. Here we use the extensively studied problem of secondary structure prediction to better evaluate the role of profiles and show that: (1) high levels of profile similarity between training and test proteins are observed when using standard sequence-based redundancy protocols; (2) the gain in accuracy for profile-based predictors, over sequence-based predictors, strongly relies on these high levels of profile similarity between training and test proteins; and (3) the overall accuracy of a profile-based predictor on a given protein dataset provides a biased measure when trying to estimate the actual accuracy of the predictor, or when comparing it to other predictors. We show, however, that this bias can be avoided by implementing a new protocol (EVALpro) which evaluates the accuracy of profile-based predictors as a function of the profile similarity between training and test proteins. Such a protocol not only allows for a fair comparison of the predictors on equally hard or easy examples, but also completely removes the need for selecting arbitrary similarity cutoffs when selecting test proteins. The EVALpro program is available for download from the SCRATCH suite (http://scratch.proteomics.ics.uci.edu).

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