Amino Acids Sequence-based Analysis of Arginine Deiminase from Different Prokaryotic Organisms: An In Silico Approach

摘要

Background: Arginine deiminase is a bacterial enzyme, which degrades L-arginine.Some human cancers such as hepatocellular carcinoma (HCC) and melanoma are auxotrophic forarginine. Therefore, PEGylated arginine deiminase (ADI-PEG20) is a good anticancer candidatewith antitumor effects. It causes local depletion of L-arginine and growth inhibition in arginineauxotrophictumor cells. The FDA and EMA have granted orphan status to this drug. Some recentlypublished patents have dealt with this enzyme or its PEGylated form.Objective: Due to increasing attention to it, we aimed to evaluate and compare 30 arginine deiminaseproteins from different bacterial species through in silico analysis.Methods: The exploited analyses included the investigation of physicochemical properties, multiplesequence alignment (MSA), motif, superfamily, phylogenetic and 3D comparative analyses ofarginine deiminase proteins thorough various bioinformatics tools.Results: The most abundant amino acid in the arginine deiminase proteins is leucine (10.13%)while the least amino acid ratio is cysteine (0.98%). Multiple sequence alignment showed 47 conservedpatterns between 30 arginine deiminase amino acid sequences. The results of sequencehomology among 30 different groups of arginine deiminase enzymes revealed that all the studiedsequences located in amidinotransferase superfamily. Based on the phylogenetic analysis, two majorclusters were identified. Considering the results of various in silico studies; we selected thefive best candidates for further investigations. The 3D structures of the best five arginine deiminaseproteins were generated by the I-TASSER server and PyMOL. The RAMPAGE analysis revealedthat 81.4%-91.4%, of the selected sequences, were located in the favored region of argininedeiminase proteins.Conclusion: The results of this study shed light on the basic physicochemical properties of thirtymajor arginine deiminase sequences. The obtained data could be employed for further in vivo andclinical studies and also for developing the related therapeutic enzymes.