Difference between revisions of "Part:BBa K3926002"
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We used machine learning methods to predict the promoter strength after mutation, and the results are shown in Figure 1.<br> | We used machine learning methods to predict the promoter strength after mutation, and the results are shown in Figure 1.<br> | ||
https://2021.igem.org/wiki/images/5/5a/T--XHD-Wuhan-A-China--Improvement3.png<br> | https://2021.igem.org/wiki/images/5/5a/T--XHD-Wuhan-A-China--Improvement3.png<br> | ||
| − | In order to verify the true strength of our redesigned promoter, we replaced the wild-type promoter with a mutant promoter. After culturing the engineered bacteria overnight at 220 rpm, it was reactivated at a ratio of 1:100 in LB liquid medium for 4 hours. And then we tested OD588 of the samples every half hour for 6 hours. The results are shown in Figure 2. The results show that mutant PyeaR has a stronger promoter strength than the wild-type. | + | In order to verify the true strength of our redesigned promoter, we replaced the wild-type promoter with a mutant promoter. After culturing the engineered bacteria overnight at 220 rpm, it was reactivated at a ratio of 1:100 in LB liquid medium for 4 hours. And then we tested OD588 of the samples every half hour for 6 hours. The results are shown in Figure 2. The results show that mutant PyeaR has a stronger promoter strength than the wild-type.<br> |
| + | https://2021.igem.org/wiki/images/5/5a/T--XHD-Wuhan-A-China--Improvement4.png<br> | ||
Revision as of 12:02, 20 October 2021
An improved PyeaR with higher expression strength
We use machine learning model to design this promoter.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000COMPATIBLE WITH RFC[1000]
Usage and Biology
Introduction
We improved part: BBa_K216005 (PyeaR promoter), which is the promoter of the Escherichia coli yeaR/yoaG operon. The most remarkable feature of this promoter is its ability to sense nitrate and nitrite. In order to better regulate the response of the promoter to nitrate, we use machine learning models to predict and design new PyeaR sequences. Compared to the original sequence, five or six bases have been changed.
Construction of improved PyeaR
Based on the original sequence, we designed and predicted three mutation sequences that can increase the intensity of the promoter by using our machine learning model. By modifying the PCR primers, we successfully obtained the three mutated PyeaR promoters. Through homologous recombination, we replaced the wild-type promoter with the improved promoter.
Characterization
We used machine learning methods to predict the promoter strength after mutation, and the results are shown in Figure 1.
In order to verify the true strength of our redesigned promoter, we replaced the wild-type promoter with a mutant promoter. After culturing the engineered bacteria overnight at 220 rpm, it was reactivated at a ratio of 1:100 in LB liquid medium for 4 hours. And then we tested OD588 of the samples every half hour for 6 hours. The results are shown in Figure 2. The results show that mutant PyeaR has a stronger promoter strength than the wild-type.
