Part:BBa_K5114227

Human liver fatty binding protein-GFP conjugation coding sequence

Sequence and Features

Assembly Compatibility:

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The FAB_GFP conjugation sequence was originally characterized by Mann and Berger in 2023 and used to test for fluorescence upon titration of PFOA. FAB is a human liver fatty acid binding protein found within the cytoplasm of human liver cells and mediates the intercellular transport of various fatty acids .

This is the sequence that was reconstructed from Mann and Berger's original publication and codon optimized by Genscript before synthesizing. rsremover.com was then used to ensure RFC10 compliance.

Usage and Biology

The FAB_GFP protein is oriented in a way that creates a beta-barrel in the folding of the GFP. This beta barrel allows for water inflow, causing the chromatophore in GFP to be disrupted and therefore halting fluorescence. However, when FAB binds to its ligand (natively to fatty acid-like molecules and, in our case, PFOA), the confirmation will change, greatly reducing the inflow of water and increasing fluorescence to a theoretically detectable level.

Below are images of the FAB_GFP conjugated protein and its structure:

GFP

Human liver fatty acid binding protein

Combined hlFAB-GFP

We, Team GCM-KY 2024, decided to use a FAB_GFP complex for PFAS detection for a multitude of reasons. Primarily, PFAS has a structure quite similar to a fatty acid, thus warranting the possibility that PFAS may bind with a FAB molecule. We ran a reverse screening search on PFOA’s smile string to confirm this, resulting in high binding probabilities with FAB. We also conducted an exhaustive docking study via Autodock Vina and Amber. The docking study revealed a high likelihood that PFOA can bind in the FAB domain; to test how strong the bind was, the best pose predicted by Autodock Vina was used and a simulation under explicit water solvent conditions was used. After the simulations were completed, a Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method was used to calculate the Gibbs free energy of binding. The result was -13 kcal/mol, meaning 13 kcal of energy would be required to dissociate PFOA from FAB. Which indicates strong, drug-like binding to the FAB domain. This study supported our hypothesis that Fatty Acid Binding Proteins can be used as a PFAS detector.

Molecular Dynamics Testing

The initial docking of hlFAB with PFOA was successful, providing a solid foundation for further analysis. Using Autodock Vina, the docking procedure predicted an initial binding free energy (ΔG) of -11.3016 kcal/mol for the wild-type hlFAB bound to PFOA. This value reflects the strength of the interaction between the protein and the ligand, where a more negative ΔG indicates a stronger, more favorable binding.

To better understand the significance of this result, we calculated the dissociation constant (Kd) from the ΔG using the following equation:

ΔG = RT * ln(Kd)

Where:

1. R, ideal gas constant, (1.987 * 10^-3 cal/mol·K)
2. Temperature (300K)
3. ΔG is the binding free energy (-11.3016 Kcal/mol)

By rearranging the equation to solve for ΔG:

e^(ΔG / RT) = Kd

Substituting in the values, we get:

5.836 nano Molar

This calculation gives a dissociation constant (Kd) of approximately 5.836 nM, which indicates strong binding. Since Kd means the concentration required of the ligand to bind to half of all the receptors, a low Kd, especially in the nanomolar range, signifies that the ligand (PFOA) binds tightly to the protein (hlFAB), making it an effective candidate for detection in our experimental work.

Thus, we did have success in our basic part via Molecular Dynamics Simulations (modeling).

Characterization

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.

Plasmid map

These were transformed into DH5-alpha E. coli and cultured on Kanamycin plates containing X-gal. The presence of colonies and positive blue/white screening results indicate successful transformation.

Blue white screening

We performed a restriction digest with SapI and ran the results on a gel. The bands that were produced were as expected. Construct 2 is the hlFAB-GFP expression device. The ladder is 1 kb per band.

Restriction digest

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: Open full 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. [ ]

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.

The binding affinity for PFOA to FAB-GFP was taken from the original authors' experimentally determined Kd of 19.4 uM.

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.

As shown here, in the long term there is essentially no unbound PFAS, meaning all of it is bound to FAB-GFP and therefore contributing to fluorescence. Furthermore, 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.