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{{Heidelberg/partspanelleft|ß-Lactamase_E102F|The aim of our project was to generate protein functionality, <i>in vivo</i> through PACE and PREDCEL and in <i>in vitro</i> with help of our software suite AiGEM. To prove that our circuit works, we mutated ß-lactamases based on the scores of our software. Many different mutants were cloned and screened for their minimal inhibitory concentration (MIC). Visit our software validation site <a href="https://2017.igem.org/Team:Heidelberg/Validation">here</a>. Typical methods to determine MICs are broth or agar dilutions. The principle is as simple as effective. The microorganism is cultured in liquid medium or on plates with different concentrations of the inhibiting agent <x-ref>JM10291</x-ref>. With this method, concentrations which are tolerated by the organism and those who inhibit growth can be determined. However, such an assay gives no information about the kind of toxicity e.g. whether the chemical is cytotoxic or cytostatic.<br> | {{Heidelberg/partspanelleft|ß-Lactamase_E102F|The aim of our project was to generate protein functionality, <i>in vivo</i> through PACE and PREDCEL and in <i>in vitro</i> with help of our software suite AiGEM. To prove that our circuit works, we mutated ß-lactamases based on the scores of our software. Many different mutants were cloned and screened for their minimal inhibitory concentration (MIC). Visit our software validation site <a href="https://2017.igem.org/Team:Heidelberg/Validation">here</a>. Typical methods to determine MICs are broth or agar dilutions. The principle is as simple as effective. The microorganism is cultured in liquid medium or on plates with different concentrations of the inhibiting agent <x-ref>JM10291</x-ref>. With this method, concentrations which are tolerated by the organism and those who inhibit growth can be determined. However, such an assay gives no information about the kind of toxicity e.g. whether the chemical is cytotoxic or cytostatic.<br> | ||
In our case, we want to evaluate differences in the activity of a well characterized protein. Consequently, such an dilution method is ideal. It is easy to setup, very robust and gives exactly the information that is needed for the characterization.<br> | In our case, we want to evaluate differences in the activity of a well characterized protein. Consequently, such an dilution method is ideal. It is easy to setup, very robust and gives exactly the information that is needed for the characterization.<br> | ||
Revision as of 18:13, 1 November 2017
ß-Lactamase_E102F
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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.
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