Title:Evaluation of Different Signal Peptides for Secretory Production of
Recombinant Human Interferon-gamma: Bioinformatics Approach
Volume: 20
Issue: 2
Author(s): Niloofar Ghoshoon, Younes Ghasemi, Hoda Jahandar, Mohammad Bagher Ghoshoon*Navid Nezafat*
Affiliation:
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
- Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences,
Shiraz, Iran
Keywords:
Signal peptide, human interferon-gamma, bioinformatics, E. coli, OmpC, PhoE, AnsB.
Abstract:
Background: The fusion of the secretory signal peptide to the N-terminal of a polypeptide’s
amino acid sequence is an attractive technique for the secretory production of heterologous proteins. On
the other hand, applying computational analysis may be beneficial in overcoming the barriers of trial-anderror
approaches in detecting proper signal sequences.
As the scope of this study, the most probable effective properties of 30 signal sequences for the extracellular
production of recombinant human interferon-gamma (rhIFN-γ) were analysed.
Methods: Online available web server, SignalP5.0, was used to predict signal peptides’ probability, most
likely translocation pathways, and cleavage site location. The physicochemical features of signal peptides
and rhIFN-γ were assessed by the ProtParam tool, and the solubility of protein was predicted by SOLpro.
Results: Finally, 12 high probable signal peptides, including OmpC, PhoE, AnsB, and OmpA, were theoretically
detected with ideal solubility probabilities and almost balanced physicochemical properties;
hopes to be helpful in future experimental studies for the secretion of rhIFN-γ.
Conclusion: The experimental analysis is required to validate the in silico results and focus on in-lab
affecting factors such as cultivation methods and conditions.
