scholarly journals Structural organization of TFL1-like genes in representatives of the tribe Phaseoleae DC.

2021 ◽  
Vol 66 (2) ◽  
Author(s):  
Ekaterina Krylova ◽  
Ksenia Strygina ◽  
Elena Khlestkina
Keyword(s):  
Structural Organization ◽  
Molecular Genetic ◽  
Protein Structures ◽  
In Silico Analysis ◽  
Stem Growth ◽  
Amino Acid Sequences ◽  
Stem Length ◽  
Adzuki Bean ◽  
Genome Duplication Event ◽  
Flowering Duration

The type of stem growth is one of the key features in determining plant architectonics. Stem growth type is an economically important trait. It interconnects with stem length, flowering duration, yield, resistance to lodging, and suitability of mechanized cultivation. Mutations in the TFL1 gene and its homologs have been demonstrated to change meristem indeterminacy across genera. The aim of this work was to characterize and compare the structural organization of TFL1-like genes in representatives of the tribe Phaseoleae (pigeonpea Cajanus cajan, soybean Glycine max, common bean Phaseolus vulgaris, adzuki bean Vigna angularis, mung bean V. radiata, and cowpea V. unguiculata) based on in silico analysis, including analysis of nucleotide sequences, predicted elements in promoter regions, predicted amino acid sequences, putative functional domains and 3D protein structures. We investigated TFL1 (one gene for adzuki bean, four copies for soybean, two copies for other studied species), ATC (two copies for soybean, one gene for other investigated species), and BFT (two copies for soybean, one gene for other studied species) gene family members found in whole-genome sequences databases available for representatives of the tribe Phaseoleae. The presence of duplicated copies for all genes in soybean may be a result of the last genome duplication event during the evolution of this species. Duplication of TFL1 gene to two copies in most of studied species of the tribe Phaseoleae is probably accompanied by the maintenance of the functional state of these genes. The exception is VrTFL1.2 of V. radiata, which likely had lost its functionality. This work broadens the existing data about the number of gene copies, their structural divergence and evolution, and the expected functional differences. This information will be important for understanding the molecular genetic mechanisms underlying the maintenance of indeterminacy in the growth of the shoot apical meristem, as well as in the control of the transition to the reproductive phase of plant development.

scholarly journals The Bioactivity Prediction of Peptides from Tuna Skin Collagen Using Integrated Method Combining In Vitro and In Silico

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2739
Author(s):  
Liza Devita ◽  
Hanifah Nuryani Lioe ◽  
Mala Nurilmala ◽  
Maggy T. Suhartono
Keyword(s):  
Antioxidant Activity ◽  
In Silico ◽  
Antioxidant Activities ◽  
In Silico Analysis ◽  
Weight Fraction ◽  
Amino Acid Sequences ◽  
Skin Collagen ◽  
Dpph Method ◽  
Peptide Fractions

The hydrolysates and peptide fractions of bigeye tuna (Thunnus obesus) skin collagen have been successfully studied. The hydrolysates (HPA, HPN, HPS, HBA, HBN, HBS) were the result of the hydrolysis of collagen using alcalase, neutrase, and savinase. The peptide fractions (PPA, PPN, PPS, PBA, PBN, PBS) were the fractions obtained following ultrafiltration of the hydrolysates. The antioxidant activities of the hydrolysates and peptide fractions were studied using the DPPH method. The effects of collagen types, enzymes, and molecular sizes on the antioxidant activities were analyzed using profile plots analysis. The amino acid sequences of the peptides in the fraction with the highest antioxidant activity were analyzed using LC-MS/MS. Finally, their bioactivity and characteristics were studied using in silico analysis. The hydrolysates and peptide fractions provided antioxidant activity (6.17–135.40 µmol AAE/g protein). The lower molecular weight fraction had higher antioxidant activity. Collagen from pepsin treatment produced higher activity than that of bromelain treatment. The fraction from collagen hydrolysates by savinase treatment had the highest activity compared to neutrase and alcalase treatments. The peptides in the PBN and PPS fractions of <3 kDa had antidiabetic, antihypertensive and antioxidant activities. In conclusion, they have the potential to be used in food and health applications.

scholarly journals In-silicoprediction and modeling of theEntamoeba histolyticaproteins: Serine-richEntamoeba histolyticaprotein and 29 kDa Cysteine-rich protease

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3160 ◽  
Author(s):  
Kumar Manochitra ◽  
Subhash Chandra Parija
Keyword(s):  
Amino Acid ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Treatment Modalities ◽  
Bioinformatic Tools ◽  
Complex Protein ◽  
A Cell ◽  
Quaternary Structures

BackgroundAmoebiasis is the third most common parasitic cause of morbidity and mortality, particularly in countries with poor hygienic settings. There exists an ambiguity in the diagnosis of amoebiasis, and hence there arises a necessity for a better diagnostic approach. Serine-richEntamoeba histolyticaprotein (SREHP), peroxiredoxin and Gal/GalNAc lectin are pivotal inE. histolyticavirulence and are extensively studied as diagnostic and vaccine targets. For elucidating the cellular function of these proteins, details regarding their respective quaternary structures are essential. However, studies in this aspect are scant. Hence, this study was carried out to predict the structure of these target proteins and characterize them structurally as well as functionally using appropriatein-silicomethods.MethodsThe amino acid sequences of the proteins were retrieved from National Centre for Biotechnology Information database and aligned using ClustalW. Bioinformatic tools were employed in the secondary structure and tertiary structure prediction. The predicted structure was validated, and final refinement was carried out.ResultsThe protein structures predicted by i-TASSER were found to be more accurate than Phyre2 based on the validation using SAVES server. The prediction suggests SREHP to be an extracellular protein, peroxiredoxin a peripheral membrane protein while Gal/GalNAc lectin was found to be a cell-wall protein. Signal peptides were found in the amino-acid sequences of SREHP and Gal/GalNAc lectin, whereas they were not present in the peroxiredoxin sequence. Gal/GalNAc lectin showed better antigenicity than the other two proteins studied. All the three proteins exhibited similarity in their structures and were mostly composed of loops.DiscussionThe structures of SREHP and peroxiredoxin were predicted successfully, while the structure of Gal/GalNAc lectin could not be predicted as it was a complex protein composed of sub-units. Also, this protein showed less similarity with the available structural homologs. The quaternary structures of SREHP and peroxiredoxin predicted from this study would provide better structural and functional insights into these proteins and may aid in development of newer diagnostic assays or enhancement of the available treatment modalities.

scholarly journals Morphophenological characterization of ornamental ginger and selection for landscape use

2018 ◽  
Vol 24 (3) ◽  
pp. 255-260
Author(s):  
Carlos Eduardo Ferreira de Castro ◽  
Ana Cecilia Ribeiro Castro ◽  
Charleston Gonçalves ◽  
Vivian Loges
Keyword(s):  
Phenotypic Variability ◽  
Stem Growth ◽  
Stem Length ◽  
Leaf Color ◽  
Flowering Season ◽  
Flower Stem ◽  
Number Of Leaves ◽  
Selection For ◽  
Inflorescence Length

Many species of Zingiber have great ornamental potential, due to durability and exotic appearance of the inflorescences. Despite its large phenotypic variability, they are scarcely exploited or not yet exploited regarding the ornamental potential. To conserve potential ornamental genotypes, and subsidize breeding program, the Agronomic Institute (IAC) maintain a Germoplasm Collection of Ornamental Zingiberales with promising accessions, including Zingiber. The aim was the morphophenological characterization of ten Zingiber accessions and the indication for landscape purposes. A large variation was observed to the evaluated characters: Clump height (CH); Inflorescence visualization (IV); Clump area (CA); Clump density (CD); Leaf stem Firmness (LSF); Number of leaf stems per clump (NLSC); Number of leaves per stem (NLS); Leaf color (LCol); Evergreen tendency (ET); Flower stem growth (FSG); Flower stem length (FSLe); Flower stem diameter (FSD); Flower stem per clump (FSC); Color sensorial perception (CSP); Flower stem weight (FSW); Inflorescence length (IL); Inflorescence diameter (ID); Bracts aspects (BAs); and Flowering season (FSe). The accessions very suitable and with the best performance to use for landscape purpose were Z. spectabile, IAC Anchieta (Z. spectabile), Z. newmanii.

scholarly journals Identification of Immune Related LRR-Containing Genes in Maize (Zea maysL.) by Genome-Wide Sequence Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Song ◽  
Baoqiang Wang ◽  
Xinghua Li ◽  
Jianfen Wei ◽  
Ling Chen ◽  
...  
Keyword(s):  
Zea Mays ◽  
Plant Disease Resistance ◽  
Interleukin 1 ◽  
Protein Structures ◽  
Gene Clusters ◽  
Amino Acid Sequences ◽  
Leucine Rich Repeat ◽  
Immune Receptors ◽  
Genome Wide ◽  
First Time

A large number of immune receptors consist of nucleotide binding site-leucine rich repeat (NBS-LRR) proteins and leucine rich repeat-receptor-like kinases (LRR-RLK) that play a crucial role in plant disease resistance. Although many NBS-LRR genes have been previously identified inZea mays, there are no reports on identifying NBS-LRR genes encoded in the N-terminal Toll/interleukin-1 receptor (TIR) motif and identifying genome-wide LRR-RLK genes. In the present study, 151 NBS-LRR genes and 226 LRR-RLK genes were identified after performing bioinformatics analysis of the entire maize genome. Of these identified genes, 64 NBS-LRR genes and four TIR-NBS-LRR genes were identified for the first time. The NBS-LRR genes are unevenly distributed on each chromosome with gene clusters located at the distal end of each chromosome, while LRR-RLK genes have a random chromosomal distribution with more paired genes. Additionally, six LRR-RLK/RLPs including FLS2, PSY1R, PSKR1, BIR1, SERK3, and Cf5 were characterized inZea maysfor the first time. Their predicted amino acid sequences have similar protein structures with their respective homologues in other plants, indicating that these maize LRR-RLK/RLPs have the same functions as their homologues act as immune receptors. The identified gene sequences would assist in the study of their functions in maize.

scholarly journals In Silico Analysis of Genetic VapC Profiles from the Toxin-Antitoxin Type II VapBC Modules among Pathogenic, Intermediate, and Non-Pathogenic Leptospira

2019 ◽  
Vol 7 (2) ◽  
pp. 56
Author(s):  
Alexandre Lopes ◽  
Bruna Azevedo ◽  
Rebeca Emídio ◽  
Deborah Damiano ◽  
Ana Nascimento ◽  
...  
Keyword(s):  
Pathogenic Bacteria ◽  
In Silico Analysis ◽  
Amino Acid Sequences ◽  
Protein Alignment ◽  
Pathogenic Leptospira ◽  
Leptospira Spp ◽  
Unique Species ◽  
Silico Analysis ◽  
Leptospira Species ◽  
Complex Manner

Pathogenic Leptospira spp. is the etiological agent of leptospirosis. The high diversity among Leptospira species provides an array to look for important mediators involved in pathogenesis. Toxin-antitoxin (TA) systems represent an important survival mechanism on stress conditions. vapBC modules have been found in nearly one thousand genomes corresponding to about 40% of known TAs. In the present study, we investigated TA profiles of some strains of Leptospira using a TA database and compared them through protein alignment of VapC toxin sequences among Leptospira spp. genomes. Our analysis identified significant differences in the number of putative vapBC modules distributed in pathogenic, saprophytic, and intermediate strains: four in L. interrogans, three in L. borgpetersenii, eight in L. biflexa, and 15 in L. licerasiae. The VapC toxins show low identity among amino acid sequences within the species. Some VapC toxins appear to be exclusively conserved in unique species, others appear to be conserved among pathogenic or saprophytic strains, and some appear to be distributed randomly. The data shown here indicate that these modules evolved in a very complex manner, which highlights the strong need to identify and characterize new TAs as well as to understand their regulation networks and the possible roles of TA systems in pathogenic bacteria.

scholarly journals In silico analysis of virulence associated genes in genomes of Escherichia coli strains causing colibacillosis in poultry

2017 ◽  
Vol 61 (4) ◽  
pp. 421-426 ◽  
Author(s):  
Joanna Kołsut ◽  
Paulina Borówka ◽  
Błażej Marciniak ◽  
Ewelina Wójcik ◽  
Arkadiusz Wojtasik ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Amino Acid ◽  
Virulence Factors ◽  
De Novo ◽  
Protein Sequences ◽  
In Silico Analysis ◽  
Amino Acid Sequences ◽  
Common Disease ◽  
Bacterial Genomes ◽  
E Coli

AbstractIntroduction: Colibacillosis – the most common disease of poultry, is caused mainly by avian pathogenic Escherichia coli (APEC). However, thus far, no pattern to the molecular basis of the pathogenicity of these bacteria has been established beyond dispute. In this study, genomes of APEC were investigated to ascribe importance and explore the distribution of 16 genes recognised as their virulence factors.Material and Methods: A total of 14 pathogenic for poultry E. coli strains were isolated, and their DNA was sequenced, assembled de novo, and annotated. Amino acid sequences from these bacteria and an additional 16 freely available APEC amino acid sequences were analysed with the DIFFIND tool to define their virulence factors.Results: The DIFFIND tool enabled quick, reliable, and convenient assessment of the differences between compared amino acid sequences from bacterial genomes. The presence of 16 protein sequences indicated as pathogenicity factors in poultry resulted in the generation of a heatmap which categorises genomes in terms of the existence and similarity of the analysed protein sequences.Conclusion: The proposed method of detection of virulence factors using the capabilities of the DIFFIND tool may be useful in the analysis of similarities of E. coli and other sequences deriving from bacteria. Phylogenetic analysis resulted in reliable segregation of 30 APEC strains into five main clusters containing various virulence associated genes (VAGs).

scholarly journals Sequence Analysis and Structure Prediction of Malaysia SARS-CoV-2 Strain’s Structural and Accessory Proteins

2021 ◽  
Vol 12 (3) ◽  
pp. 3259-3304
Keyword(s):  
Structure Prediction ◽  
Active Sites ◽  
Homo Sapiens ◽  
Functional Characterization ◽  
Protein Structures ◽  
Three Dimensional ◽  
Experimental Models ◽  
Amino Acid Sequences ◽  
Accessory Proteins ◽  
Evolutionary Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that transmitted from animal to human became a life-threatening pandemic in 2020. Scientists are currently testing several drugs to eradicate the COVID-19 outbreak. However, there is no 100 % effective drug or vaccine against SARS-CoV-2 has been discovered so far. In this study, we explored the structure prediction and functional analysis of 75 Malaysia SARS-CoV-2 strain’s structural and accessory proteins without the presence of experimental models. Physiochemical analysis, secondary structure analysis, structure prediction, functional characterization, active site identification, and evolutionary analysis based on the amino acid sequences retrieved from National Centre for Biotechnology Information (NCBI). Three-dimensional (3-D) protein structures were built using the Swiss model. The quality of protein models was verified by ERRAT, PROCHECK, and Verify 3D tools. Active prediction analysis revealed the high potential active sites of proteins where the anti-viral drug or vaccine may bind and inhibit the viral activities. Molecular phylogenetic analysis of ORF10, ORF8, and ORF6 proteins from five different species was analyzed. The results from this analysis proved that Homo sapiens SARS-CoV-2 had high genetic similarity with the bat coronavirus. These analyses may help in designing structure-based anti-viral drugs or to develop potential vaccines for SARS-CoV-2.

scholarly journals Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

2018 ◽  
Author(s):  
Raphael R. Eguchi ◽  
Po-Ssu Huang
Keyword(s):  
Image Classification ◽  
Protein Design ◽  
Large Scale ◽  
De Novo ◽  
Protein Structures ◽  
Semantic Segmentation ◽  
Amino Acid Sequences ◽  
Structural Quality ◽  
Small Subset ◽  
Structural Prediction

AbstractRecent advancements in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation — a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structural quality assessment. We represent protein structures as 2D α-carbon distance matrices (“contact maps”), and train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model performs exceptionally well, achieving a per-residue accuracy of 90.8% on the test set (95.0% average accuracy over all classes; 87.8% average within-structure accuracy). The unique aspect of our classifier is that it encodes sequence agnostic residue environments from the PDB and can assess structural quality as quantitative probabilities. We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.SignificanceRecent computational advances have allowed researchers to predict the structure of many proteins from their amino acid sequences, as well as designing new sequences that fold into predefined structures. However, these tasks are often challenging because they require selection of a small subset of promising structural models from a large pool of stochastically generated ones. Here, we describe a novel approach to protein model selection that uses 2D image classification techniques to evaluate 3D protein models. Our method can be used to select structures based on the fold that they adopt, and can also be used to identify regions of low structural quality. These capabilities yield a powerful tool for both protein design and structure prediction.

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