Team:CityU HK/Collaborations
Partnership
Partner team: Hong_Kong-CUHK
Project: Magneto-bacter vinelandii Magnetosome-forming Azotobacter vinelandii with Downstream Applications
In our collaboration work with the Chinese University of Hong Kong iGEM team, we were given the task to assist them with their project by doing a simulation that look into the interaction between GFP-nanobody to different concentrations of GFP.
It is our honor to be able to collaborate and participate in their project. A wonderful experience we have gained through.
Project: Magneto-bacter vinelandii Magnetosome-forming Azotobacter vinelandii with Downstream Applications
In our collaboration work with the Chinese University of Hong Kong iGEM team, we were given the task to assist them with their project by doing a simulation that look into the interaction between GFP-nanobody to different concentrations of GFP.
It is our honor to be able to collaborate and participate in their project. A wonderful experience we have gained through.
Background
The project of the CUHK iGEM team is to explore the use of magnetosome together with GFP specific nanobody for immunoprecipitation. Magnetosome is an organelle with magnetic iron crystal surrounded by lipid bilayer. It is originated from magnetotactic bacteria for navigation by sensing the Earth magnetic field. Biomolecules, such as antibodies, can be immobilized on magnetosome for various applications. One of applications is to use the magnetosome together with nanobody for immunoprecipitation. To make the experiment measurable, green fluorescent protein (GFP) specific nanobody was used (Figure 1)
Figure 1: Schematic diagram of magnetosome, GFP specific nanobody and GFP
Task
Simulate the binding dynamics of a fixed concentration of magnetosome with GFP specific nanobody expressed on its surface in different initial concentrations of antigens (GFP).
Calculation
Software - SimBiology
SimBiology is a toolbox in Matlab that enable us to model and simulate the dynamics of the association and dissociation between molecules. Most of the function can be utilized through its GUI (Graphical User Interface).
Once the model is constructed (i.e. the mathematic relationships between molecules are set up, parameters such as association constant, molarity of reagent, etc. are provided), we can simulate the reaction or even scan through one or more variables. The “scan” function enables us to simulate the dynamics of the system when some parameters, such as the initial concentration of the reagent, are at different values.
Once the model is constructed (i.e. the mathematic relationships between molecules are set up, parameters such as association constant, molarity of reagent, etc. are provided), we can simulate the reaction or even scan through one or more variables. The “scan” function enables us to simulate the dynamics of the system when some parameters, such as the initial concentration of the reagent, are at different values.
Assumption
1). All nanobodies are bound on the surface of magnetosome.
2). Each magnetosome have the same number of GFP specific nanobody on the surface.
3). Magnetosome does not interact with GFP directly nor interfere the reaction between GFP specific nanobody and GFP.
4). The GFP specific nanobody expressed on magnetosome have the same association and dissociation rate constant to GFP when compared with the GFP specific nanobody alone.
5). The binding of GFP and GFP specific nanobody is one to one.
6). The reaction follows mass action, which means the reaction rate is proportional to the concentration of the reagent.
2). Each magnetosome have the same number of GFP specific nanobody on the surface.
3). Magnetosome does not interact with GFP directly nor interfere the reaction between GFP specific nanobody and GFP.
4). The GFP specific nanobody expressed on magnetosome have the same association and dissociation rate constant to GFP when compared with the GFP specific nanobody alone.
5). The binding of GFP and GFP specific nanobody is one to one.
6). The reaction follows mass action, which means the reaction rate is proportional to the concentration of the reagent.
ModelFigure 1 shows the model constructed in SimBiology. The blue rounded rectangles represent the reagents, including antigens (GFP), GFP specific nanobody and the nanobody-antigens-complex. The yellow circle represents the binding reaction. The arrow points toward the nanobody-antigens-complex indicate that the complex is the product of the forward reaction. The double arrows above the yellow circle “binding” indicate that the reaction is reversible.
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Figure 2. Interaction between antigens (GFP) and GFP specific nanobody to form the nanobody-antigen complex
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Result and discussion of the simulation
By using the scan function of SimBiology, the dynamic of the system was simulated with changing the initial concentration of the antigen (the green line) from 0 M to 1.6x10-6 M with an interval of 2x10-7 M.
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Figure 3. Simulation kinetics of the interaction between GFP-nanobody and antigen
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Two observations were made from the simulation above. First, when the concentration of the antigen (GFP) is below that of the GFP specific nanobody (7.78 x10-7 M, given in the Calculation section above), as shown in Figure 3.1 to Figure 3.4, the antigen (GFP) becomes the limiting reagent and the final molarity of the nanobody-antigen complex (represented by a red line) equals to the initial molarity of the antigen. However, when the concentration of the antigen is higher than that of the GFP specific nanobody, as shown in Figure 3.5 to Figure 3.9, GFP specific nanobody becomes the limiting reagent, so the final molarity of the nanobody-antigen complex equals to the initial molarity of the GFP specific nanobody.
Another observation is that, as the concentration of the antigen increases, the rate of the reaction (i.e. formation of the nanobody-antigen complex) reaches equilibrium faster (Figure 3.5 to Figure 3.9). This can be explained by the increased forward reaction rate which depends on the concentration of the GFP specific nanobody and antigen (GFP).
Another observation is that, as the concentration of the antigen increases, the rate of the reaction (i.e. formation of the nanobody-antigen complex) reaches equilibrium faster (Figure 3.5 to Figure 3.9). This can be explained by the increased forward reaction rate which depends on the concentration of the GFP specific nanobody and antigen (GFP).
Reference
Marta H. Kubala†Kovtun†, Kirill Alexandrov* andBrett M. Collins*Oleksiy. (2010 September). Structural and thermodynamic analysis of the GFP:GFP-nanobody complex. Protein Science, Volume 19, Issue 12, Pages 2389-2401.