scholarly journals Cloning and molecular characterization of two novel LMW-m type glutenin genes from Triticum spelta L.

Genetika ◽  
2021 ◽  
Vol 53 (1) ◽  
pp. 141-155
Author(s):  
Ruomei Wang ◽  
Junwei Zhang ◽  
Fei Luo ◽  
Nannan Liu ◽  
Slaven Prodanovic ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Open Reading Frames ◽  
Wheat Quality ◽  
Amino Acid Residues ◽  
Triticum Spelta ◽  
Spelt Wheat ◽  
A Genome ◽  
Food And Feed

Spelt wheat (Triticum spelta L., 2n=6x=42, AABBDD), as a hexaploid wheat species, is important sources of food and feed in Europe. It also serves as an important genetic resource for improvement of wheat quality and resistance. In this study, two novel m-type low molecularglutenin subunit (LMW-GS) genes, named as TsLMW-m1 and TsLMW-m2 were cloned by allelic specific polymerase chain reaction (AS-PCR)from German spelt wheat cultivars Rochbergers fruher Dinke and Schwabenkorn, respectively. The complete open reading frames (ORFs) of both genes contained 873 bp encoding 290 amino acid residues, and had typical LMW-GS structural features. Two same deletions with 24 bp at the position of 707-730 bp were present in both genes, while TsLMW-m1 had two nonsynonymous single-nucleotide polymorphism (SNP) variations at the positions of 434 bp (C-A transversion) and 857 bp (G-A transition). Phylogenic analysis revealed that both LMW-m genes were closely related to those from wheat A genome, suggesting that both subunits are encoded by the Glu-A3 locus. Secondary structure prediction showed that TsLMW-m1 and TsLMW-m2 subunits had more ?-helices than other wheat LMW-GS including superior quality subunit EU369717, which would benefit to form superior gluten structures and dough properties. The authenticity and expression activity of TsLMW-m1 and TsLMW-m2 genes were verified by prokaryotic expression in E. coli. Our results indicated that two newly cloned TsLMW-m genes could have potential values for wheat quality improvement.

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 RRE-Finder: a Genome-Mining Tool for Class-Independent RiPP Discovery

mSystems ◽  
2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Alexander M. Kloosterman ◽  
Kyle E. Shelton ◽  
Gilles P. van Wezel ◽  
Marnix H. Medema ◽  
Douglas A. Mitchell
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Clusters ◽  
High Confidence ◽  
Genetic Feature ◽  
Uniprotkb Database ◽  
A Genome ◽  
Mining Tool

Bioinformatics-powered discovery of novel ribosomal natural products (RiPPs) has historically been hindered by the lack of a common genetic feature across RiPP classes. Herein, we introduce RRE-Finder, a method for identifying RRE domains, which are present in a majority of prokaryotic RiPP biosynthetic gene clusters (BGCs). RRE-Finder identifies RRE domains 3,000 times faster than current methods, which rely on time-consuming secondary structure prediction. Depending on user goals, RRE-Finder can operate in precision mode to accurately identify RREs present in known RiPP classes or in exploratory mode to assist with novel RiPP discovery. Employing RRE-Finder on the UniProtKB database revealed several high-confidence RREs in novel RiPP-like clusters, suggesting that many new RiPP classes remain to be discovered.

scholarly journals Polypeptide p41 of a Norwalk-Like Virus Is a Nucleic Acid-Independent Nucleoside Triphosphatase

2001 ◽  
Vol 75 (4) ◽  
pp. 1611-1619 ◽  
Author(s):  
Thomas Pfister ◽  
Eckard Wimmer
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Open Reading Frames ◽  
Nucleoside Triphosphate ◽  
Sequence Information ◽  
Sequence Motifs ◽  
Alpha Helices ◽  
Consensus Sequences ◽  
First Time

ABSTRACT Southampton virus (SHV) is a member of the Norwalk-like viruses (NLVs), one of four genera of the family Caliciviridae. The genome of SHV contains three open reading frames (ORFs). ORF 1 encodes a polyprotein that is autocatalytically processed into six proteins, one of which is p41. p41 shares sequence motifs with protein 2C of picornaviruses and superfamily 3 helicases. We have expressed p41 of SHV in bacteria. Purified p41 exhibited nucleoside triphosphate (NTP)-binding and NTP hydrolysis activities. The NTPase activity was not stimulated by single-stranded nucleic acids. SHV p41 had no detectable helicase activity. Protein sequence comparison between the consensus sequences of NLV p41 and enterovirus protein 2C revealed regions of high similarity. According to secondary structure prediction, the conserved regions were located within a putative central domain of alpha helices and beta strands. This study reveals for the first time an NTPase activity associated with a calicivirus-encoded protein. Based on enzymatic properties and sequence information, a functional relationship between NLV p41 and enterovirus 2C is discussed in regard to the role of 2C-like proteins in virus replication.

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.

HATODAS II – heavy-atom database system with potentiality scoring

2009 ◽  
Vol 42 (3) ◽  
pp. 540-544 ◽  
Author(s):  
Michihiro Sugahara ◽  
Yukuhiko Asada ◽  
Hiroki Shimada ◽  
Hideyuki Taka ◽  
Naoki Kunishima
Keyword(s):  
Amino Acid ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Solvent Accessibility ◽  
Heavy Atom ◽  
Database System ◽  
Protein Crystals ◽  
Amino Acid Residues ◽  
Sequence Alignments ◽  
Homologous Proteins

HATODAS II is the second version of HATODAS (the Heavy-Atom Database System), which suggests potential heavy-atom reagents for the derivatization of protein crystals. The present expanded database contains 3103 heavy-atom binding sites, which is four times more than the previous version. HATODAS II has three new criteria to evaluate the feasibility of the search results: (1) potentiality scoring for the predicted heavy-atom reagents, (2) exclusion of the disordered amino acid residues based on the secondary structure prediction and (3) consideration of the solvent accessibility of amino acid residues from a homology model. In the point mutation option, HATODAS II suggests possible mutation sites into reactive amino acid residues such as Met, Cys and His, on the basis of multiple sequence alignments of homologous proteins. These new features allow the user to make a well informed decision as to the possible heavy-atom derivatization experiments of protein crystals.

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).

Cloning and Characterization of Three Genes Encoding LMW-GSs from Triticum aestivum, Cultival Cheyenne

2012 ◽  
Vol 554-556 ◽  
pp. 1116-1120 ◽  
Author(s):  
Mei Rong Chen ◽  
Xing Shen ◽  
Lin Li ◽  
Song Qing Hu
Keyword(s):  
Amino Acid ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Amino Acid Residues ◽  
Repetitive Domain ◽  
Genebank Accession ◽  
Genes Encoding ◽  
Α Helix ◽  
Β Sheet

Three low molecular weight subunit genes, named LMW-CND1 (GeneBank accession JQ780048), LMW-CND2 (GeneBank accession JQ779840), LMW-CND3 (GeneBank accession JQ779841), with a ORF of 1053 bp, 903 bp, 969 bp, respectively, were isolated from cv. Cheyenne and characterized detailed in molecular level. The proteins encoded by the genes, with 350, 300, 322 amino acid residues respectively, differ only in repetitive domain of sequences due to insertion or deletion of repeats in this domain. Highly similarity in amino-acid sequence between these three subunits and other published LMW-GSs was also observed, showing that all three genes published here are typical LMW-GS genes and closely related to the genes on chromosome 1D. Besides, secondary structure prediction of proteins indicated that, in the three LMW-GSs, random loop accounts for no less than 70 %, α-helix amounts to 26 %, average, and only 1.4 %~1.7 % is β-sheet.

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.

scholarly journals Inhibitory Effect of an Acidic Peptide on the Activity of an Antimicrobial Peptide from the Scorpion Mesobuthus martensii Karsch

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3314 ◽  
Author(s):  
Wanxia Shi ◽  
Pengchen He ◽  
Xian-Chun Zeng ◽  
Weiwei Wu ◽  
Xiaoming Chen
Keyword(s):  
Staphylococcus Aureus ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Coiled Coil ◽  
Inhibitory Effect ◽  
Scorpion Venom ◽  
Amino Acid Residues ◽  
Acidic Peptide ◽  
Venom Glands ◽  
Acidic Peptides

Highly acidic peptides with no disulfide bridges are widely present in the scorpion venoms; however, none of them has been functionally characterized so far. Here, we cloned the full-length cDNA of a short-chain highly acidic peptide (referred to as HAP-1) from a cDNA library made from the venom glands of the Chinese scorpion Mesobuthus martensii Karsch. HAP-1 contains 19 amino acid residues with a predicted IP value of 4.25. Acidic amino residues account for 33.3% of the total residues in the molecule of HAP-1. HAP-1 shows 76–98% identities to some scorpion venom peptides that have not yet been functionally characterized. Secondary structure prediction showed that HAP-1 contains a beta-sheet region (residues 9–17), and two coiled coil regions (residues 1–8 and 18–19) located at the N-terminal and C-terminal regions of the peptide, respectively. Antimicrobial assay showed that HAP-1 does not have any effect on the growth of the bacterium Staphylococcus aureus AB94004. However, it potently inhibits the antimicrobial activity of a 13-mer peptide from M. martensii Karsch against Staphylococcus aureus AB94004. This finding is the first characterization of the function of such highly acidic peptides from scorpions.

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