Figure 3 - available from: Scientific Reports
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(A) Heatmap and (B) network plot of the amino acid composition of the virus proteome originated from different hosts. Heatmap shows, maximum of the virus protein shows a positive correlation on its amino acid composition. The highest positive correlation was found between the virus proteome of bacteria and archaea (0.978) whereas the lowest correlation was found between virus proteome originated from fungi and protozoa (0.718). The dark blue mark indicates the highest and the red mark indicates the lowest correlation. Pearson's correlation study was conducted to construct the correlation plot (p < 0.05).
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A proteome-wide study of the virus kingdom based on 1.713 million protein sequences from 19,128 virus proteomes was conducted to construct an overall proteome map of the virus kingdom. Viral proteomes encode an average of 386.214 amino acids per protein with the variation in the number of protein-coding sequences being host-specific. The proteomes...
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... correlation analysis was conducted to determine the relationship between amino acid composition in viruses that infect different hosts (Fig. 3). Results revealed that all of the amino acids were positively correlated with the viruses of different hosts. The amino acid composition of in algae with Archaea, Bacteria, Human, Invertebrate, land plant, Protozoa, and Vertebrate hosts exhibited a positive correlation coefficient of > 0.90 (Fig. 3). The correlation between Algae and ...
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... in viruses that infect different hosts (Fig. 3). Results revealed that all of the amino acids were positively correlated with the viruses of different hosts. The amino acid composition of in algae with Archaea, Bacteria, Human, Invertebrate, land plant, Protozoa, and Vertebrate hosts exhibited a positive correlation coefficient of > 0.90 (Fig. 3). The correlation between Algae and Fungi; Bacteria and humans and invertebrate, Fungi and humans and invertebrates, Protozoa and Archaea, Bacteria, fungi, land plants, and vertebrates exhibited a positive correlation (Pearson) to a lesser extent. The lowest correlation was observed between Fungi and Protozoa (0.718), while the highest ...
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... and humans and invertebrate, Fungi and humans and invertebrates, Protozoa and Archaea, Bacteria, fungi, land plants, and vertebrates exhibited a positive correlation (Pearson) to a lesser extent. The lowest correlation was observed between Fungi and Protozoa (0.718), while the highest correlation was observed between Archaea and Bacteria (0.978) (Fig. 3). When correlation analysis was Figure 1. Individual amino acid composition of the virus proteome. Ala (A) amino acid was found to be highest in viruses of bacteria and fungi and lowest in the human host. Similarly, Ile (I) amino acid was found highest in the virus proteome of the protozoan host. The letters in the X-axis represent the ...
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... average of basic pI viral proteomes was pI 8.439 and the pI range was pI 8.13-8.697. A PCA analysis of basic pI viral proteomes was conducted (Supplementary Figure 3). PCA analysis indicated that the basic pI viral proteomes invertebrates and algae (0.998) clustered together, while the basic pI viral proteomes of protozoa and vertebrate clustered in close proximity to each other (Supplementary Figure 3). ...
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... PCA analysis of basic pI viral proteomes was conducted (Supplementary Figure 3). PCA analysis indicated that the basic pI viral proteomes invertebrates and algae (0.998) clustered together, while the basic pI viral proteomes of protozoa and vertebrate clustered in close proximity to each other (Supplementary Figure 3). The basic pI viral proteomes of land plants and bacteria clustered close to each other, while those of humans and fungi were far apart (Supplementary Figure 3). ...
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... analysis indicated that the basic pI viral proteomes invertebrates and algae (0.998) clustered together, while the basic pI viral proteomes of protozoa and vertebrate clustered in close proximity to each other (Supplementary Figure 3). The basic pI viral proteomes of land plants and bacteria clustered close to each other, while those of humans and fungi were far apart (Supplementary Figure 3). The correlation plot of basic pI virus protein of different host showed positive correlation from a higher to lower extent. ...
Citations
... Protein expression can be accurately described at the whole-cell or tissue level as well as in subcellular structures: protein complexes and body fluids. Proteomics can be applied to better understand disease processes, develop new biomarkers for diagnosis and early detection, and speed up drug development (Abou--Abbass et al. 2016, Fazeli et al. 2017, Mohanta et al. 2021. ...
Lipopolysaccharide (LPS), a core part of gram-negative bacteria, is crucial for inducing an inflammatory response in living things. In the current study, we used LPS from Salmonella to stimulate chicken macrophages (HD11). Proteomics was used to investigate immune-related proteins and their roles further. Proteomics investigation revealed 31 differential expression proteins (DEPs) after 4 hours of LPS infection. 24 DEPs expressions were up-regulated, while seven were down-regulated. In this investigation, ten DEPs were mainly enriched in S. aureus infection, complement, and coagulation cascades, which were all implicated in the inflammatory response and clearance of foreign pathogens. Notably, complement C3 was shown to be up-regulated in all immune-related pathways, indicating that it is a potential protein in this study. This work contributes to a better understanding and clarification of the processes of Salmonella infection in chickens. It might bring up new possibilities for treating and breeding Salmonella-infected chickens.
... In this regard, we conducted a proteomewide analysis in the present study by downloading and analysing the annotated protein sequences of all of the available cyanobacterial proteins, covering 229 cyanobacterial species (S2 File). In its entirety, our study collected and analyzed 903149 cyanobacterial protein sequences and constructed a virtual 2D map of the cyanobacterial proteome based on the Among other results [44][45][46][47], the current analysis revealed the bimodal distribution of the molecular weight and isoelectric point of cyanobacterial proteins. The presence of higher percentage of polar amino acid at the surface of the protein and non-polar amino acids at the core of the protein result in increased isoelectric point conferring a greater thermostability [48,49]. ...
... Although the virtual 2D map of the cyanobacterial and fungal [46] proteome exhibit a bimodal distribution for pI and molecular weight, the virtual 2D map of the plant proteome exhibits a trimodal distribution [44]. The virtual 2D map of virus proteomes showed host-specific modalities, molecular weight, and isoelectric points [47]. Like the cyanobacteria, the pI of most proteins encoded in the plant and fungal proteome reside in the acidic pI range [44,46]. ...
Cyanobacteria are prokaryotic Gram-negative organisms prevalent in nearly all habitats. A detailed proteomics study of Cyanobacteria has not been conducted despite extensive study of their genome sequences. Therefore, we conducted a proteome-wide analysis of the Cyanobacteria proteome and found Calothrix desertica as the largest (680331.825 kDa) and Candidatus synechococcus spongiarum as the smallest (42726.77 kDa) proteome of the cyanobacterial kingdom. A Cyanobacterial proteome encodes 312.018 amino acids per protein, with a molecular weight of 182173.1324 kDa per proteome. The isoelectric point ( pI ) of the Cyanobacterial proteome ranges from 2.13 to 13.32. It was found that the Cyanobacterial proteome encodes a greater number of acidic- pI proteins, and their average pI is 6.437. The proteins with higher pI are likely to contain repetitive amino acids. A virtual 2D map of Cyanobacterial proteome showed a bimodal distribution of molecular weight and pI . Several proteins within the Cyanobacterial proteome were found to encode Selenocysteine (Sec) amino acid, while Pyrrolysine amino acids were not detected. The study can enable us to generate a high-resolution cell map to monitor proteomic dynamics. Through this computational analysis, we can gain a better understanding of the bias in codon usage by analyzing the amino acid composition of the Cyanobacterial proteome.
