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Codon optimization of Saccharomyces cerevisiae mating factor alpha prepro-leader to improve recombinant protein production in Pichia pastoris

Overview of attention for article published in Biotechnology Letters, September 2016
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  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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2 tweeters

Citations

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2 Dimensions

Readers on

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27 Mendeley
Title
Codon optimization of Saccharomyces cerevisiae mating factor alpha prepro-leader to improve recombinant protein production in Pichia pastoris
Published in
Biotechnology Letters, September 2016
DOI 10.1007/s10529-016-2203-3
Pubmed ID
Authors

Jungoh Ahn, Min-Jung Jang, Kok Siong Ang, Hongweon Lee, Eui-Sung Choi, Dong-Yup Lee

Abstract

To evaluate different codon optimization parameters on the Saccharomyces cerevisiae-derived mating factor α prepro-leader sequence (MFLS) to improve Candida antarctica lipase B (CAL-B) secretory production in Pichia pastoris. Codon optimization based on the individual codon usage (ICU) and codon context (CC) design parameters enhanced secretory production of CAL-B to 7 U/ml and 12 U/ml, respectively. Only 3 U/ml was obtained with the wild type sequence while the sequence optimized using both ICU and CC objectives showed intermediate performance of 10 U/ml. These results clearly show that CC is the most relevant parameter for the codon optimization of MFLS in P. pastoris, and there is no synergistic effect achieved by considering both ICU and CC together. The CC optimized MFLS increased secretory protein production of CAL-B in P. pastoris by fourfold.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 4%
Austria 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 22%
Researcher 6 22%
Student > Ph. D. Student 5 19%
Student > Bachelor 5 19%
Unspecified 2 7%
Other 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 41%
Biochemistry, Genetics and Molecular Biology 10 37%
Chemical Engineering 2 7%
Unspecified 2 7%
Medicine and Dentistry 1 4%
Other 1 4%

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 11 September 2016.
All research outputs
#9,877,225
of 12,902,716 outputs
Outputs from Biotechnology Letters
#1,532
of 1,892 outputs
Outputs of similar age
#170,464
of 263,361 outputs
Outputs of similar age from Biotechnology Letters
#5
of 29 outputs
Altmetric has tracked 12,902,716 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,892 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,361 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.