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Revision as of 17:48, 20 October 2020
Introduction
In synthetic biology, modeling is a powerful tool that uses theoretical models and computational approaches to predict, improve and further understand experiments. This year, modeling turned out to be one of the crucial cornerstones of our project due to the cancelled lab time.
Over the course of the project, it became increasingly clear that we wouldn’t be able to test out our project in the laboratory , which meant for us to adapt to the current situation and focus on different aspects of our project such as the modeling part. In this context, we built a total of three distinct models to gain further insights into the underlying biochemistry of our wastewater treatment approach:
We used the Rosetta Commons Software [1] developed by Baker Lab as well as the computational power of the Lichtenberg high-performance-computer at the TU Darmstadt to predict enzyme structures , test their stability, predict enzyme-ligand-interactions and enhance those by modifying the enzyme’s active sites . We wrote a python program based on the work of B. Qin et al [2] to simulate the growth mechanics and -conditions of our biofilm in cooperation with the modeling team from iGEM Hannover as well as an ODE-based MATLAB-model [3] to conceptually represent the functionality of our kill switch .
Since we weren’t able to include any self-generated data into our models this year, we hope that future iGEM teams can be inspired to use and fill them with life by expanding on them and implementing their own parameters. Our biofilm model can be used to describe biofilms built by other bacteria than B. subtilis simply by utilizing the values that describe those best. This also holds true for the kill switch model, which can be adapted to different biofilm-related systems if given the right data.
The described models can be further examined on the following pages.
Why Rosetta?
Rosetta is a software suite capable of solving a multitude of computational macromolecular problems such as de novo protein design, enzyme design,
ligand docking and structure prediction of biological macromolecules or macromolecular complexes. The Rosetta energy functions enables relatively precise
solution of a broad range of applications by considering many different energy terms relevant for protein folding such as solvation, electrostatic effects and hydrogen bonding.
It can be used to perform simulations starting from designing macromolecular structures, interactions and RNA or fibril structures up to the de novo design of a fully functioning enzyme!
It was originally developed by the David Baker Lab. [1] Rosetta is free for academic users and a very powerful tool for multiple problems that come along with elaborating a synthetic biology problem.
Although there are lot of information available in the Rosetta Documentation, it is very hard for people that want to get started with Rosetta to improve their project, especially for people without experience with console-based applications.
Nevertheless, Rosetta displays an amazing and multifaceted tool for synthetic biology. To counter the starting issues with the program we provide a guide for Rosetta on our Wiki.
We used multiple Rosetta applications to model the properties of our enzymes. The collected data allows us to predict enzyme functionality when immobilized in our biofilm with TasA, binding affinity towards different pharmaceuticals or pollutants and many more.
RosettaCM was used to generate structure predictions of the azithromycin transforming enzyme EreB and fusion proteins consisting of matrix protein TasA and our enzymes CueO, CotA and EreB [2].
Rosetta Ligand Docking was used to study the binding affinity of various substrates towards the enzymes active site[3].
Protein Design was used to enhance the binding affinity of our target molecules to the corresponding enzymes’ active site by introduction of mutations [4].
To find out more about the different aspects of the modelling using Rosetta you can read the articles thematizing these applications. If you want to use Rosetta on your own you can use our Rosetta Guide to get started with the program.
We used multiple Rosetta applications to model the properties of our enzymes. The collected data allows us to predict enzyme functionality when immobilized in our biofilm with TasA, binding affinity towards different pharmaceuticals or pollutants and many more.
RosettaCM was used to generate structure predictions of the azithromycin transforming enzyme EreB and fusion proteins consisting of matrix protein TasA and our enzymes CueO, CotA and EreB [2].
Rosetta Ligand Docking was used to study the binding affinity of various substrates towards the enzymes active site[3].
Protein Design was used to enhance the binding affinity of our target molecules to the corresponding enzymes’ active site by introduction of mutations [4].
To find out more about the different aspects of the modelling using Rosetta you can read the articles thematizing these applications. If you want to use Rosetta on your own you can use our Rosetta Guide to get started with the program.
EXO Test Angie
EXO Test Angie
EXO Test Angie
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Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat.
Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat. Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi.
Nam liber tempor cum soluta nobis eleifend option congue nihil imperdiet doming id quod mazim placerat facer possim assum. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo
References
1. CA. Roth et al. Protein Structure Prediction Using Rosetta. Methods in Enzymology 2004, 382 66-93, https://doi.org/10.1016/S0076-6879(04)83004-0
3. Song Y et al. High-Resolution Comparative Modeling with RosettaCM. Structure 2013, 21 1735-1742, https://doi.org/10.1016/j.str.2013.08.005
1. SA. Combs et al. Small-molecule ligand docking into comparative models with Rosetta, Nature Protocol 2013, 8(7) 1277-98, doi: 10.1038/nprot.2013.074
7. Moretti R et al. Rosetta and the Design of Ligand Binding Sites. Methods Mol Biol. 2016; 1414: 47–62. doi: 10.1007/978-1-4939-3569-7_4
References
1. CA. Roth et al. Protein Structure Prediction Using Rosetta. Methods in Enzymology 2004, 382 66-93, https://doi.org/10.1016/S0076-6879(04)83004-0 3. Song Y et al. High-Resolution Comparative Modeling with RosettaCM. Structure 2013, 21 1735-1742, https://doi.org/10.1016/j.str.2013.08.005 1. SA. Combs et al. Small-molecule ligand docking into comparative models with Rosetta, Nature Protocol 2013, 8(7) 1277-98, doi: 10.1038/nprot.2013.074 7. Moretti R et al. Rosetta and the Design of Ligand Binding Sites. Methods Mol Biol. 2016; 1414: 47–62. doi: 10.1007/978-1-4939-3569-7_4