scholarly journals Functional Identification of the Dextransucrase Gene of Leuconostoc mesenteroides DRP105

2020 ◽  
Vol 21 (18) ◽  
pp. 6596
Author(s):  
Renpeng Du ◽  
Zhijiang Zhou ◽  
Ye Han
Keyword(s):  
Structure Prediction ◽  
Leuconostoc Mesenteroides ◽  
Secondary Structure Prediction ◽  
Amino Acid Content ◽  
Sugar Metabolism ◽  
Functional Identification ◽  
Synthesis Mechanism ◽  
Random Coils ◽  
Metabolism Pathway ◽  
Recombination System

Leuconostoc mesenteroides DRP105 isolated from Chinese sauerkraut juice is an intensive producer of dextran. We report the complete genome sequence of Leu. mesenteroides DRP105. This strain contains a dextransucrase gene (dsr) involved in the production of dextran, possibly composed of glucose monomers. To explore the dextran synthesis mechanism of Leu. mesenteroides DRP105, we constructed a dsr-deficient strain derived from Leu. mesenteroides DRP105 using the Cre-loxP recombination system. The secondary structure prediction results showed that Leu. mesenteroides DRP105 dextransucrase (Dsr) was coded by dsr and contained 17.07% α-helices, 29.55% β-sheets, 10.18% β-turns, and 43.20% random coils. We also analyzed the dextran yield, monosaccharide change, organic acid, and amino-acid content of Leu. mesenteroides DRP105 and Leu. mesenteroides DRP105−Δdsr. The result showed that the lack of dsr changed the Leu. mesenteroides DRP105 sugar metabolism pathway, which in turn affected the production of metabolites.

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 Comparative Sequence Analysis and Patterns of Covariation in RNA Secondary Structures

Genetics ◽  
2000 ◽  
Vol 154 (2) ◽  
pp. 909-921 ◽  
Author(s):  
John Parsch ◽  
John M Braverman ◽  
Wolfgang Stephan
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Rnase P ◽  
Secondary Structures ◽  
Comparative Sequence Analysis ◽  
Small Subunit ◽  
Physical Parameters ◽  
Small Subunit Rrna ◽  
Base Pairing ◽  
Compensatory Evolution

Abstract A novel method of RNA secondary structure prediction based on a comparison of nucleotide sequences is described. This method correctly predicts nearly all evolutionarily conserved secondary structures of five different RNAs: tRNA, 5S rRNA, bacterial ribonuclease P (RNase P) RNA, eukaryotic small subunit rRNA, and the 3′ untranslated region (UTR) of the Drosophila bicoid (bcd) mRNA. Furthermore, covariations occurring in the helices of these conserved RNA structures are analyzed. Two physical parameters are found to be important determinants of the evolution of compensatory mutations: the length of a helix and the distance between base-pairing nucleotides. For the helices of bcd 3′ UTR mRNA and RNase P RNA, a positive correlation between the rate of compensatory evolution and helix length is found. The analysis of Drosophila bcd 3′ UTR mRNA further revealed that the rate of compensatory evolution decreases with the physical distance between base-pairing residues. This result is in qualitative agreement with Kimura's model of compensatory fitness interactions, which assumes that mutations occurring in RNA helices are individually deleterious but become neutral in appropriate combinations.

scholarly journals RNA Secondary Structures with Limited Base Pair Span: Exact Backtracking and an Application

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Ronny Lorenz ◽  
Peter F. Stadler
Keyword(s):  
Base Pair ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Minimum Energy ◽  
Optimal Structure ◽  
Energy Structure ◽  
C Elegans ◽  
Z Scores ◽  
Limited Base ◽  
Optimal Span

The accuracy of RNA secondary structure prediction decreases with the span of a base pair, i.e., the number of nucleotides that it encloses. The dynamic programming algorithms for RNA folding can be easily specialized in order to consider only base pairs with a limited span L, reducing the memory requirements to O(nL), and further to O(n) by interleaving backtracking. However, the latter is an approximation that precludes the retrieval of the globally optimal structure. So far, the ViennaRNA package therefore does not provide a tool for computing optimal, span-restricted minimum energy structure. Here, we report on an efficient backtracking algorithm that reconstructs the globally optimal structure from the locally optimal fragments that are produced by the interleaved backtracking implemented in RNALfold. An implementation is integrated into the ViennaRNA package. The forward and the backtracking recursions of RNALfold are both easily constrained to structural components with a sufficiently negative z-scores. This provides a convenient method in order to identify hyper-stable structural elements. A screen of the C. elegans genome shows that such features are more abundant in real genomic sequences when compared to a di-nucleotide shuffled background model.

scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

Sign in / Sign up

Export Citation Format

Share Document