Part:BBa_K5114227:Experience
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GCM-KY 2024 usage
Labwork
In order to test the FAB_GFP protein, we put the sequence under the influence of a constitutive promoter inside of a plasmid with Kanamycin resistance. Then, we transformed this plasmid into competent DH5-alpha Escherichia coli, performing both gel electrophoresis and blue-white screening to ensure proper transformation. We then took successful colonies, grew them, and exposed them to different PFAS concentrations.
Transformation and Selection
We printed the expression device (BBa_K5114228) in the pUC57-Kan plasmid from Genscript.
Fluorescence Testing
After confirming its presence, the transformed bacteria were exposed to different PFOA concentrations. Fluorescence levels were taken at regular time intervals. One plate reading is displayed in the image below:
As seen by the heat map above (in the bottom two rows), increasing concentration exposure did have a slight impact on the fluorescence. As the concentration increased, there was a slightly increased amount of fluorescence intensity. However, when compared to the fluorescence intensities of the LB Broth, which was intended to act as a negative control, there is an indication that there may be errors in the measurement of the fluorescence itself. In order to investigate this more, a proper graph was created.
The full amount of data collected in the lab can be found in the below PDF:
The FAB-GFP mechanism was taken from previous literature (https://www.nature.com/articles/s41598-023-41953-1). According to their results, they found that the FAB-GFP complex was capable of fluorescing in E. coli after exposure to concentrations of PFAS.
The results of the fluorescence for our construct are shown below:
Graph 1 displays the fluorescence values over time of a colony taken from plate 1, containing E. coli taken from the FAB-GFP construct after subtracting the LB broth fluorescence values at the corresponding times. From this, we can see there was an increase in fluorescence when PFOA was added. This is evident by comparing the heights at different points of the 0 uM PFOA concentration line to the others. However, this trend doesn’t compare with the other values. The 50 μM concentration line begins by being lower than the 5 μM concentration. Despite this, from the 1.5-hour mark onwards, it remains higher than both the 5 μM and the 250 μM concentration levels. Additionally, at 0 hours, the 250 μM was lower than the 5 μM, not following the traditional trend of a direct relationship between PFOA concentration and fluorescence intensity. Since we know that the concentration of PFAS doesn’t directly correlate to the fluorescence intensity (exemplified by this graph’s data), we can still confirm the fact that PFOA concentration increases the fluorescence intensity compared to no PFOA at all.
The data in Graph 2 implies that all cells produce basal fluorescence over time based on the increasing fluorescence reading across all cells. The fluorescence may be affected by PFAS since the fluorescence at any given time point appears to be ordered by PFAS concentration, however, more testing is needed to determine if the ordering is statistically significant and not an artifact of any inaccuracies in the fluorimeter’s readings.
When tested at our lab to confirm, the results came out inconclusive. One major reason may be that our genetic construct had a few missense mutations, which may be the leading cause of the lack of promising results. Based on the molecular dynamics simulations (shown below), there are many possibilities for the improvement of binding affinities through mutations, which may translate to better results in a practical setting. Additionally, according to Virtual Cell results (shown below), the limiting factor in the fluorescence of FAB-GFP is the availability of PFAS in the environment (using association constants given by the original author). Thus, this opens two avenues to explore: decreasing binding affinities even further, because of the simplicity of Virtual Cell and its models, and innovating a way to increase the fold change in fluorescence of the bound FAB-GFP compared to the unbound (essentially making the FAB-GFP fluoresce more when PFAS or a fatty acid is bound to it), which requires increased structural understanding of the protein.
Molecular Dynamics Simulations
After testing hlFAB in the lab to detect PFAS, we wanted to explore whether modifications could optimize its performance and then test the new hlFAB variants in the lab. To do this, we used rotamers, a feature in ChimeraX, to swap specific residues—for example, replacing residue 351 (Threonine) with Serine. You can explore the specifics of our mutations on the results page of our website. The mutated structures were then processed through our MMPBSA pipeline, also available in our repository, to calculate the dissociation constant (Kd). If the Kd value was higher than -13 kcal/mol, the mutation was considered beneficial and could potentially enhance the lower detection limit of hlFAB. These mutations are potential points of future experimentation.
Mutation Results Table
After running the first MMPBSA, we generated a graph from the python script, in order to see the charge decomposition:
We used this graph to determine likely residues to mutate.
To track the impact of various mutations on the binding affinity, we created a table comparing the ΔG values and calculated Kd for each mutation. These results show how each mutation influences the interaction between hlFAB and PFOA, and whether the changes enhance or weaken binding strength.
| Mutation | ΔG (kcal/mol) | Kd (M) | Effect on Binding Strength | Why we chose this mutation |
|---|---|---|---|---|
| Wild type + PFOA | -11.3016 | 5.8359 x 10^-9 | N/A | Baseline |
| Wild type + Palmitic Acid | -2.4944 | 0.0152 | N/A | To compare PFOA to its natural ligand. |
| ILE 52 to ARG + PFOA | -10.0965 | 4.4065 x 10^-8 | Slightly worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged residue (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| ILE 308 to ARG + PFOA | -13.1878 | 2.4654 x 10^-10 | Great improvement. | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged residue (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| SER 349 to THR + PFOA | -3.1394 | 0.0051 | Severely worse | In the binding pocket, there is a Serine and a Threonine that interact with the oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two residues that interact with PFOA the same.
|
| PHE 50 to ARG + PFOA | -6.9105 | 9.2318 x 10^-6 | Severely worse | Due to this residue having a very small contribution (thin slice of the pie graph) to the bind, changing this residue to a positively charged residue (ARG) would increase the binding strength to PFOA, since PFOA has a net charge of -1. |
| THR 351 to SER + PFOA | -12.2177 | 1.2550 x 10^-9 | Slightly worse | In the binding pocket, there is a Serine and a Threonine that interact with the oxygens on PFOA, and they form hydrogen bonds between FAB and PFOA. So the idea was to make the two residues that interact with PFOA the same.
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This table organizes the results of each mutation, highlighting how the mutations affect the protein's ability to bind with PFOA. For example, the ILE 308 mutation shows an improvement in binding, while the SER 349 mutation significantly weakens the interaction. These findings provide clear direction on which mutations could improve the lower detection limit of hlFAB, and helps identify key residues such as SER 349 and THR 351.
Thus, we saw further success in our basic part with Molecular Dynamics Simulations (modeling).
Virtual Cell Kinetic Modeling
We used the Virtual Cell (VCell) software to model the behavior of a cell that constitutively expressed this gene and how it reacts to different concentrations of environmental PFAS. More information can be found at our team's wiki page: https://2024.igem.wiki/gcm-ky/model Rate constants were all taken from existing literature, including the binding constant (Kd) of PFOA to the FAB-GFP molecule, as experimentally characterized by the original creators.
Virtual Cell Biomodel of FAB-GFP constitutively expressed
Simulations were carried out for 100 minutes (6000) seconds at various concentrations of initial PFAS. The environment was made to be 1000 um^3 in volume because that is the expected amount of water “available” to each individual cell (max density of E. coli is around 10^9 cells/ml). Cell volume was set to 1 um^3 based on established cell sizes of E. coli at stationary phase. Simulations were carried out at non-steady-state and steady-state (concentration of FAB_GFP and FAB_GFP_mRNA are stable) conditions, found by deterministic ODE modeling. Regulations for PFAS are on the parts per trillion (ppt) level, so we used 1 ppt as our minimum concentration. Assuming 1 ppt=1 ng/L and the molar weight of PFOA, a common model PFAS, is 414 g/mol, that means 1 ppt is approximately 2E-6 uM of PFOA. Therefore, we simulated from 1E-6 uM initial PFAS in the environment up to 1E-2 uM, which would correlate to roughly 5 ppb.
VCell modeling indicates that a significant amount of bound FAB-GFP is present when there is PFAS, and that more PFAS leads to more bound FAB-GFP, in agreement with the original publication. Interestingly, steady state modeling does not yield significant differences in maximal fluorescence. This is likely because the fluorescence is actually limited by the amount of PFAS available to bind to hlFAB-GFP. This can be clearly seen in the graphs below.
Since the environment is set to be 1000 times larger in volume than the cell, the final concentration of bound hlFAB-GFP is 1000 times the initial concentration of PFAS, indicating nearly every molecule of PFAS is bound to a hlFAB-GFP.
For initial PFAS concentrations of 1E-6 uM to 1E-4 uM, the stochastic nature of the simulation is evident: each step up on the graph represents one PFAS molecule diffusing into the cell from the environment and binding to one molecule of hlFAB-GFP. Thus, our model indicates that biosensors that directly fluoresce upon binding to PFAS are limited by the amount of PFAS available to them, and not necessarily by the binding affinity of the protein. Future work should focus on increasing the effective fluorescence of the molecule.
Possible Uses for Other Teams
There are many ways that teams could use this to expand on our project. Although molecular dynamic models suggested success of this protein, when tested at our lab to confirm, the results came out inconclusive. One major reason may be that our genetic construct had a few missense mutations, which may be the leading cause of the lack of promising results. Based on the molecular dynamics simulations, there are many possibilities for the improvement of binding affinities through mutations, which may translate to better results in a practical setting. Additionally, according to Virtual Cell results, the limiting factor in the fluorescence of FAB-GFP is the availability of PFAS in the environment (using association constants given by the original author). Thus, this opens two avenues to explore: decreasing binding affinities even further, because of the simplicity of Virtual Cell and its models, and innovating a way to increase the fold change in fluorescence of the bound FAB-GFP compared to the unbound (essentially making the FAB-GFP fluoresce more when PFAS or a fatty acid is bound to it), which requires increased structural understanding of the protein.
Teams could also use this part for their own benefit. Although we didn’t see much success in the results of this protein within E. coli, it’s possible that other teams could use this part for either fatty acid binding or for binding with PFOA (and other types of PFAS). More testing of the corrected sequence is needed to properly determine the real functionality of this protein. We will also need to determine if a stronger, inducible promoter would work better than the constitutive promoter (Pconst-BBa_J23100) that we used.
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