Comprehensive Analysis of Non Redundant Protein Database

Bagheri, Hamid; Dyer, Robert; Severin, Andrew J.; Rajan, Hridesh

doi:10.21203/rs.3.rs-54568/v1

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…To remove all the redundant proteins, the CD-Hit-h analysis approach was utilized. Furthermore, the non-redundant proteins were considered for further processing [46].…”

Section: Cd-hit Analysis (Cluster Data With High Identity and Tolerance)mentioning

confidence: 99%

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

et al. 2022

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Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

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“…To remove all the redundant proteins, the CD-Hit-h analysis approach was utilized. Furthermore, the non-redundant proteins were considered for further processing [46].…”

Section: Cd-hit Analysis (Cluster Data With High Identity and Tolerance)mentioning

confidence: 99%

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

et al. 2022

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“…GeneMarkS software was used to predict the protein-coding genes of the bacterial genome [34]. Comparing the protein sequence of the predicted gene with the NR (Non-Redundant Protein Database) [35], COG (Cluster of Orthologous Groups of proteins), KEGG (Kyoto Encyclopedia of Genes and Genomes) [36], eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) [37], Swiss-Prot [38] and GO (Gene Ontology) [39] databases was performed by Diamond blast p. For this analysis, the comparison result with the highest score was selected for annotation and the following cutoff values were applied (e-value <1e-6 and amino acid sequence identity of at least 40 %). Orthologous average nucleotide identity (ANI) [40] and digital DNA–DNA hybridization (dDDH) [41] values were calculated using the ANI calculator tool from the EzBioCloud (www.ezbiocloud.net/tools/ani) [42] and the Genome-to-Genome Distance Calculator (http://ggdc.dsmz.de/ggdc) [43], respectively.…”

Section: Genome Featuresmentioning

confidence: 99%

Lysobacter antarcticus sp. nov., an SUF-system-containing bacterium from Antarctic coastal sediment

Liu

Jiang

Niu

et al. 2022

International Journal of Systematic and Evolutionary Microbiology

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A Gram-stain-negative, heterotrophic, aerobic, non-motile, rod-shaped bacterial strain (GW1-59T) belonging to the genus Lysobacter was isolated from coastal sediment collected from the Chinese Great Wall Station, Antarctica. The strain was identified using a polyphasic taxonomic approach. The strain grew well on Reasoner's 2A media and could grow in the presence of 0–4 % (w/v) NaCl (optimum, 1 %), at pH 9.0–11.0 and at 15–37 °C (optimum, 30 °C). Strain GW1-59T possessed ubiquinone-8 as the sole respiratory quinone. The major phospholipids were diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine. The major fatty acids were summed feature 9 (10-methyl C16 : 0 and/or iso-C17 : 1 ω9c), iso-C15 : 0, iso-C16 : 0, iso-C17 : 0, C16 : 0 and iso-C11 : 0 3-OH. DNA–DNA relatedness with Lysobacter concretionis Ko07T, the nearest phylogenetic relative (98.5 % 16S rRNA gene sequence similarity) was 23.4 % (21.1–25.9 %). The average nucleotide identity value between strain GW1-59T and L. concretionis Ko07T was 80.1 %. The physiological and biochemical results and low level of DNA–DNA relatedness suggested the phenotypic and genotypic differentiation of strain GW1-59T from other Lysobacter species. On the basis of phenotypic, phylogenetic and genotypic data, a novel species, Lysobacter antarcticus sp. nov., is proposed. The type strain is GW1-59T (=CCTCC AB 2019390T=KCTC 72831T).

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“…Protein sequence information is easily deduced from DNA using the genetic code. For most discovered proteins this type of information is known: the most comprehensive non-redundant protein sequence database currently contains 174 million sequences [ 16 ] and is doubling in size every 28 months [ 17 ]. However, determining protein structure is a complex process and thus there are considerably fewer structures available—approximately 175,000 structures in the Protein Data Bank [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction

Czibula

Albu

Bocicor

et al. 2021

Entropy

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Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled AutoPPI, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. The results indicate good performances of our proposed model, with accuracy and AUC values of over 0.97 in all cases. The best performing model relies on a Siamese architecture in both the encoder and the decoder, which advantageously captures common features in protein pairs. Comparisons with other machine learning techniques applied for the same problem prove that AutoPPI outperforms most of its contenders, for the considered data sets.

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Comprehensive Analysis of Non Redundant Protein Database

Cited by 7 publications

References 22 publications

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

Lysobacter antarcticus sp. nov., an SUF-system-containing bacterium from Antarctic coastal sediment

AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction

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