Team:Heidelberg/Model test2
Modeling
Lagoon contamination
Improved Part Introduction
-Lactamase Mutants under Different Carbenicillin Concentrations)
As a result, we decided to perform a second test for these candidates with higher antibiotic concentrations. The three highest carbenicillin concentrations of the first data set were included and the range was extended to a maximum concentration of 19.2 mg/ml. While several candidates turned out to have a weaker activity than the wildtype protein, five of them showed improved properties. The two best candidates could even grow at 19.2 mg/ml (Fig.: 2). The variants were contained one point mutation each, E102F and M67G. These most benefitial mutations, appear in many of our better candidates as well, which underlines there functionality.
The outstanding candidate was the E102F mutant, which could even grow at 19.2 mg/ml without affection by the antibiotic. This results in a improvement of at least 100 % in comparison to the wildtype ß-lactamase. This remarkable gain of activity proves, that our software is not only able to recognize protein classes, but also to generate new function.
ß-Lactamase_E102F
https://static.igem.org/mediawiki/2017/8/81/T--Heidelberg--2017_Validation_HEATMAP_LOW.png
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Figure 2: Gowth Behaviour of \(\beta\)-Lactamase Mutants under Elevated Carbenicillin Concentrations
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Figure:
The ß-lactamase_E102F was the outstanding candidate of our deep learning software based mutation screen, which could even grow at 19.2 mg/ml without affection by the antibiotic. This results in a improvement of at least 100 % in comparison to the wildtype ß-lactamase. This remarkable gain of activity proves, that our software is not only able to recognize protein classes, but also to generate new function.
