Novel MicroRNAs and Their Functional Targets from Phytophthora infestans and Phytophthora cinnamomi

2021 ◽  
Vol 23 ◽  
Author(s):  
Binta Varghese ◽  
Ravisankar V ◽  
Deepu Mathew
Keyword(s):  
Developmental Biology ◽  
Phytophthora Infestans ◽  
Tumor Suppressor Genes ◽  
Structure Prediction ◽  
Target Genes ◽  
Secondary Structure Prediction ◽  
Phytophthora Cinnamomi ◽  
Comparative Method ◽  
Structural Features ◽  
Crop Varieties

Background: Even though miRNAs play viral roles in developmental biology by regulating the translation of mRNAs, they are poorly studied in oomycetes, especially in plant pathogen Phytophthora. Objective: The study was aimed to predict and identify the putative miRNAs and their targets in Phytophthora infestans and Phytophthora cinnamomi. Methods: Homology based comparative method was used to identify the unique miRNA sequences in P. infestans and P. cinnamomi with 148,689 EST and TSA sequences of these species. Secondary structure prediction of sRNAs for the 76 resultant sequences has been performed with MFOLD tool and their targets were predicted using psRNAtarget. Result: Novel miRNAs, miR-8210 and miR-4968 were predicted from P. infestans and P. cinnamomi, respectively along with their structural features. The newly identified miRNAs were identified to play important roles in gene regulation, with few of their target genes predicted as transcription factors, tumor suppressor genes, stress responsive genes, DNA repairing genes etc. Conclusion: The miRNAs and their targets identified have opened new interference and editing targets for the development of Phytophthora resistant crop varieties.

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 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.

Novel Computational Approaches to Developing Potential STAT4 Silencing siRNAs for Immunomodulation of Atherosclerosis

2020 ◽  
Vol 16 (5) ◽  
pp. 599-604 ◽  
Author(s):  
Sakineh Poorhosein Fookolaee ◽  
Samad Karkhah ◽  
Mahdiye Saadi ◽  
Subho Majumdar ◽  
Ahmad Karkhah
Keyword(s):  
Gene Expression ◽  
Structure Prediction ◽  
Target Genes ◽  
Secondary Structure Prediction ◽  
Vascular Dysfunction ◽  
Gc Content ◽  
Small Interfering Rnas ◽  
Multiple Sequence ◽  
Reading Frame ◽  
Immunomodulatory Therapies

Background: Small interfering RNAs (siRNAs) are known as commonly used targeting mRNAs tools for suppressing gene expression. Since Signal Transducer and Activator of Transcription 4 (STAT4) is considered as a significant transcription factor for generation and differentiation of Th1 cells during vascular dysfunction and atherosclerosis, suppressing STAT4 could represent novel immunomodulatory therapies against atherosclerosis. Objective: Therefore, the current study was conducted to design efficient siRNAs specific for STAT4 and to evaluate different criteria affecting their functionality. Methods: In the present study, all related sequences of STAT4 gene were retrieved from Gen Bank database. Multiple sequence alignment was carried out to recognize Open Reading Frame (ORF) and conserved region. Then, siDirect 2.0 server was applied for the development of candidate siRNA molecules and confirmation of predicted molecules was performed using Dharma siRNA technology and GeneScript siRNA targetfinder. In addition, BLAST tool was used against whole Genebank databases to identify potential off-target genes. DNA/RNA GC content calculator and mfold server were used to calculate GC content and secondary structure prediction of designed siRNA, respectively. Finally, IntaRNA program was used to study the thermodynamics of interaction between predicted siRNA and target gene. Results: Based on the obtained results, three efficient siRNA molecules were designed and validated for STAT4 gene silencing using computational methods, which may result in suppressing STAT4 gene expression. Conclusion: According to our results, this study shows that siRNA targeting STAT4 can be considered as a therapeutic agent in many Th1-mediated pathologic conditions specially atherosclerosis.

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).

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.

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.

Unfolding Single RNA Molecules with Optical Tweezers

2001 ◽  
Vol 7 (S2) ◽  
pp. 26-27
Author(s):  
Carlos Bustamante ◽  
Jan Liphardt ◽  
Bibiana Onoa ◽  
Steven B. Smith ◽  
Delphine Collin ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Optical Tweezers ◽  
Single Molecule ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Features ◽  
Rna Molecules ◽  
Group I ◽  
Tertiary Folding ◽  
Tertiary Interactions

RNA molecules must fold into specific three-dimensional shapes to perform their structural and catalytic functions. Unlike proteins, RNAs secondary structural features are usually stable enough to form by themselves in solution. The reason is that in RNA, the stabilization energy gained from the formation of secondary structure is substantially larger than the energies involved in tertiary interactions. As a result, the formation of tertiary interactions is expected to alter only slightly the pre-existing secondary structural contacts. Moreover, secondary structure prediction is robust and can be made without taking into consideration tertiary folding. However, bulk studies of the energetics and kinetics of their secondary and tertiary folding are often frustrated by the presence of multiple species and multiple folding pathways in solution. These problems are circumvented in single-molecule studies in which the folding/unfolding trajectories of the individual molecules can be followed. The T. thermophila group I intron ribozyme is organized into several domains whose mechanical unfolding can be investigated independently, and whose tertiary contacts are stabilized by numerous Mg++ ions.We have begun characterization of the ribozyme by analysis of the P5abc domain because:

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.

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