In Silico Analysis of Occurrence of Tricorn Protease and Its Homologs
Florence Ng’ong’a,
Steven Nyanjom,
Fred Wamunyokoli
Issue:
Volume 5, Issue 3, June 2017
Pages:
27-35
Received:
30 May 2017
Accepted:
24 July 2017
Published:
15 August 2017
Abstract: Tricorn protease is an archaeal protease acting downstream of the proteasome and together with its interacting aminopeptidases, degrades oligopeptides to free amino acids thus playing an important role in protein turnover. This study reports a wide distribution of tricorn protease and its homologs in archaea and bacteria. The homologs were identified through a combination of PSI-BLAST, orthology clustering and domain predictions. Functionally important sites were identified through multiple sequence alignment conducted by MAFFT v. 7. The aligned sequences were used to predict the phylogenetic relationship of tricorn protease and its homologs using MEGA v. 7. The functional associations of tricorn protease were predicted through STRING network v.10.0. This study identified several tricorn protease homologs in archaea and in all the bacterial phyla complete with β-propeller, PDZ and catalytic domains. However, in eukaryotes, tricorn protease-like homologs seemed limited to viridiplantae, stramenopile and in a basal metazoa and were classified as non-peptidase homologs with unknown functions. Conserved domain architecture retrieval revealed detectable homology of tricorn protease C-terminal half with the carboxyl-terminal proteases with similar PDZ domains. Therefore, this study predicts functional conservation of tricorn core catalytic domain in prokaryotes and given its role in cellular functions, targeting this protein or its functional homologs in prokaryotic pathogens could lead to development of alternative therapeutic agents.
Abstract: Tricorn protease is an archaeal protease acting downstream of the proteasome and together with its interacting aminopeptidases, degrades oligopeptides to free amino acids thus playing an important role in protein turnover. This study reports a wide distribution of tricorn protease and its homologs in archaea and bacteria. The homologs were identifi...
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In-Silico Screening of Biomarker Genes of Hepatocellular Carcinoma Using R/Bioconductor
Afza Akbar,
Mohd Murshad Ahmed,
Safia Tazyeen,
Aftab Alam,
Anam Farooqui,
Shahnawaz Ali,
Md. Zubbair Malik,
Romana Ishrat
Issue:
Volume 5, Issue 3, June 2017
Pages:
36-42
Received:
26 July 2017
Accepted:
7 August 2017
Published:
25 August 2017
Abstract: Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigorous computational analyses. This was achieved by collecting gene expression microarray data from GEO (Gene Expression Omnibus) database as GSE series (GSE38941, GSE26495, GSE51489, GSE41804, GSE49954, GSE16593) and pre-processing it using Bioconductor repository for R. Following a robust mechanism including the use of statistical testing techniques and tools, the data was screened for DEGs (Differentially Expressed Genes). 3354 down regulated genes and 785 up regulated genes for HBV and 3462 down regulated and 251 up regulated genes for HCV were obtained. For a comparative study of DEGs from HBV and HCV, they were merged to look for potential biomarkers whose differential expression may result in carcinoma. A total of 17 biomarkers (1 up-regulated and 16 downregulated), was obtained which were further subjected to Cytoscape to generate a GRN using STRING app. Furthermore, module level analysis was performed as it offers robustness and a better understanding of complex GRNs. The work also focuses on the topological properties of the network. The results point out to the presence of a hierarchical framework in the network. They also shed a light on the interactions of biomarkers whose down regulation may result in HCC. These results can be used for future research and in exploring drug targets for this disease.
Abstract: Hepatocellular Carcinoma is a primary malignancy of the liver. It is the fifth most common cancer around the world and is a leading cause of cancer related deaths. For about 40 years HCC has been predominantly linked with Hepatitis B and Hepatitis C infection. This work aims to find out potential biomarkers for HBV and HCV infected HCC through rigo...
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