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ISSN (Print): 1872-2083
ISSN (Online): 2212-4012

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Research Article

In silico Analysis of Different Signal Peptides for Secretory Production of Arginine Deiminase in Escherichia coli

Author(s): Mahboubeh Zarei, Navid Nezafat, Mohammad Hossein Morowvat, Mohsen Ektefaie and Younes Ghasemi*

Volume 13, Issue 3, 2019

Page: [217 - 227] Pages: 11

DOI: 10.2174/1872208313666190101114602

Price: $65

Abstract

Background: Secretory production of recombinant protein in bacterial hosts fulfills several advantages. Selecting an appropriate secretory signal peptide is a critical step in secretory production of different protein. Several patents report the usage of signal peptides for secretory production of recombinant proteins in E. coli. In silico identification of suitable signal peptides is a reliable and cost-effective alternative to experimental approaches.

Objective: This study was aimed to predict best signal peptides for the secretory production of recombinant arginine deiminase in E. coli.

Methods: In this study, 30 different signal peptide sequences were retrieved from database. The signal peptide probability, location of cleavage sites, and n, h and c regions were predicted by SignalP 4.1 and Phobius servers. After purging the 30 predicted secretory signal peptides, TorT, bla, NrfA, TolB, PapC, PldA, Lpp were removed. Several physicochemical properties of the remaining potential SPs were determined by ProtParam, PROSO II, and SOLpro servers for theoretically selecting the best candidates.

Results and Conclusion: Based on physicochemical properties, the signal peptides of OmpC, OmpF, and DsbA were identified respectively as the promising candidates for efficient secretory production of arginine deiminase in E. coli. Although the computational approach has established itself as a basis of modern biotechnology, the experimental study is necessary to validate its results. The criteria used in this study could be applied to other targets for recombination processes.

Keywords: Arginine deiminase, secretory production, signal peptide, In silico, E. Coli, protein.

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