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Optimization of rPDT fusion protein expression by Escherichia coli in pilot scale fermentation: a statistical experimental design approach

Overview of attention for article published in AMB Express, August 2018
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Title
Optimization of rPDT fusion protein expression by Escherichia coli in pilot scale fermentation: a statistical experimental design approach
Published in
AMB Express, August 2018
DOI 10.1186/s13568-018-0667-3
Pubmed ID
Authors

Nasser Nassiri Koopaei, Parissa Khadiv-Parsi, Mohammad Reza Khoshayand, Mohammad Ali Mazlomi, Abbas Kebriaeezadeh, Hamid Moloudian, Roya Solhi, Mahdi Aminian

Abstract

High yield recombinant protein production is highly desirable for biotechnological purposes. In the design of recombinant expression conditions, a number of essential central elements such as expression strain, type of medium, bioprocess optimization, and mathematical modeling should be considered. Well-designed industrial scale production of one recombinant protein with optimized influential parameters and yield can address the cost and production reproducibility issues. In the present study, statistical experimental design methodology was used to investigate the effect of fermentation conditions (dissolved oxygen, IPTG, and temperature) on rPDT production by Escherichia coli. rPDT is a recombinant fusion protein consisting of three different protein domains including the N-terminal 179 amino acid fragment of the S1 subunit of pertussis toxin, the full-length genetically detoxified diphtheria toxin (CRM197), and the 50 kDa tetanus toxin fragment C. A 15 Box-Behnken design augmented with center points revealed that IPTG and DO at the center point and low temperature will result in high yield. The optimal condition for rPDT production were found to be 100 µM IPTG, DO 30% and temperature 20 °C.

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Mendeley readers

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The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Ph. D. Student 7 18%
Student > Master 4 10%
Researcher 3 8%
Professor 2 5%
Other 4 10%
Unknown 13 33%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 28%
Agricultural and Biological Sciences 7 18%
Chemical Engineering 3 8%
Engineering 3 8%
Mathematics 1 3%
Other 2 5%
Unknown 13 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2018.
All research outputs
#20,530,891
of 23,100,534 outputs
Outputs from AMB Express
#976
of 1,245 outputs
Outputs of similar age
#291,145
of 334,082 outputs
Outputs of similar age from AMB Express
#30
of 51 outputs
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So far Altmetric has tracked 1,245 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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