{% extends "layout.html" %} {% block title %}Model{% endblock %} {% block lead %}Explain your model's assumptions, data, parameters, and results in a way that anyone could understand.{% endblock %} {% block page_content %}
After using hlFAB in the lab and receiving a negative result, no GFP expression, we wanted to understand more about what happened when FAB-GFP interacted with PFOA. To accomplish this, we would need to simulate PFOA binding to FAB and analyze the bind to determine its effectiveness. We used AutoDock Vina, a docking software, to speed up the process, to place PFOA in the FAB binding domain. After the docking study, the best pose was selected, and the resulting complex was brought into Amber. From there, a quick implicit simulation was run to confirm that PFOA did stay in the FAB binding domain, confirming a bind. After we confirmed that PFOA was bound to FAB, we used the Molecular Mechanics Poisson-Boltzmann Surface Area method (MMPBSA) to evaluate the strength of the bind. MMPBSA works by removing the ligand (PFOA) from the receptor (FAB) and calculates the force required to do so; a higher force (ΔG) means a stronger bond. The MMPBSA method is based on the free energy equation: ΔG_bind = ΔE_vdw + ΔE_elec + ΔG_solv - TΔS, where each term represents van der Waals energy, electrostatic energy, solvation free energy, and entropy, respectively. After running the MMPBSA, the charge contribution was analyzed with a custom Python script, allowing us to determine what residues played the most significant roles in the bind. As an added benefit, inding the ΔG also allows us to calculate accurate rate constants for Vcell. Furthermore, we wanted to see how to improve the bind of PFOA and hlFAB, potentially decreasing the lower detection limit of FAB. Using ChimeraX and Rotamers, we could accurately mutate hlFAB by swapping amino acid residues. To optimize the MMPBSA calculation, we created a pipeline that allows us to retrieve the ΔG of any hlFAB mutation and PFOA.
We used AlphaFold to fold the FAB-GFP structure using the sequence found in Dr. Berger’s paper. We then retrieved the SDF file for PFOA from its PubChem page. The charges for PFOA were generated from Antechamber, the industry standard. In AutoDock Vina, we selected the entire FAB domain to ensure we tried all the possibilities.
We then took the resulting pose, and split it into 3 files, the receptor, the ligand, and the complex. The files were then fed through the pipeline to get the MMPBSA results. The MMPBSA pipeline contains x stages: Creating topology in LEaP, Minimizing the energy, Heating the structure, Equilibrating the density, Equilibrating the structure, A production run, the MMPBSA, and finally the data curation script.
To get an idea of how our system would play out and determine the effect size of different perturbations on the system, we modeled our genetic constructs in the Virtual Cell software using the Gibson-Bruck stochastic solver. The stochastic solver is more accurate for nanoscale reactions than deterministic differential equations, and Virtual Cell provides a view-friendly user interface for designing reaction networks.
Whenever possible, we extracted rate constants from existing literature. Due to inconclusive experimental results, all our rate constants were extracted from literature or estimated. All our models are publicly available on VCell and are also saved to our team’s software gitlab. You will need to download Virtual Cell to see our models. See our Engineering page for more details on how we constructed and refined our models, and see our Contributions page for how to access our models.
Construct Name | Biomodel Name | Creator of Biomodel | Short Description |
---|---|---|---|
Prma_GFP Construct | pRMA_GFP construct 1 v1.3 | Pillow123 | This construct explored the use of the pRMA promoter in response to PFAS to produce SF_GFP |
FAB_GFP Construct | Construct 3_Cycle3_dgl | dglVcell | This construct explored the FAB protein mechanism in expressing FAB_GFP in response to PFAS |
Synthetic_Transcription_Factor Construct | FAB_GFP_Construct_v1.1_Douglas | dglVcell | In this construct we modeled the dynamics of GFP under the control of a potentially PFAS inducible synthetic transcription factor |
This simulates GFP production when under the control of the prmA promoter, which has been shown to upregulate transcription in the presence of PFAS. This is a model of our part BBa_K5114823. Because the mechanism of the prmA promoter is poorly understood, we approximated its response to PFAS as the DNA binding to PFAS molecules which increases its rate of transcription. This model also takes into account the leakiness of the promoter. Experimental evidence suggests PFAS presence only increases expression of prmA controlled proteins by about 3 fold.
This graph demonstrates the dynamics of the simple genetic circuit. The presence of GFP in the absence of PFAS is due to the basal production rate of the promoter. As shown, the amount of GFP by the end of the simulation increases as the amount of PFAS added increases. There appears to be a significant increase in the amount of GFP produced compared to basal production in the presence of as little as 0.01 uM (micromolar) of PFAS. As the amount of initial PFAS increases, the increase in GFP production slows down, likely due to saturation of DNA with PFAS. Thus, the highest curves represent maximal GFP production rate.
This model served as our first experience with modeling genetic circuits to detect PFAS, however we were skeptical that prmA would work in our chassis of E. coli because the transcriptional machinery that regulate the prmA promoter appears to be unique to Rhodococcus, as we found in a BLAST search. Additionally, chemicals very rarely bind directly to DNA to modulate transcriptional activity, so this model should not be taken as entirely true.
hlFAB-GFP is a protein made of circularly permuted GFP and human liver fatty acid binding protein, created by Mann and Berger in 2023. It acts as a PFAS sensor by fluorescence upon binding with PFAS. When no PFAS is bound, water can infiltrate the hydrophobic barrel of GFP that shields its chromophore and quench the fluorescence. When PFAS is bound to the hlFAB domain, less water can infiltrate and fluorescence is stronger. This models our part BBa_K5114228.
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.
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 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.
From 1E-6 to 1E-4, 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.
A synthetic estradiol transcription factor (synTrans or STF for short) was originally created by fusing the hormone binding domain of human estrogen receptor alpha with the DNA binding domain of the Lex transcription factor and the Herpes-Simplex Virus protein VP16 to recruit ribosomes. The STF has been successfully used in yeast as a gratuitous transcription factor. Since PFAS has been shown to be an agonist for human estrogen receptor, we attempted to express it in E. coli to see if we could use the STF as a transcription factor inducible by PFAS. This is a model for our parts TBD and TBD.
Cell and environment volumes were kept at 1 and 1000 um^3, respectively. Initial PFAS concentrations were also kept the same. Simulations were carried out in ideal settings that were not at steady state and had no promoter leakage (expression of GFP without the STF binding to the DNA), as well as in more realistic conditions where there was promoter leakage and the concentrations of the STF and its mRNA were constant.
Compared to hlFAB-GFP, this construct appears to be much more sensitive to small quantities of PFAS. Each initial PFAS concentration resulted in much more GFP produced compared to the hlFAB-GFP. The increase in GFP production as initial PFAS concentration increases does not appear to increase logarithmically as did the hlFAB-GFP construct. However, the graphs imply the amount of GFP produced has yet to reach a steady state, indicating that longer exposure times to PFAS may produce more differentiated levels of GFP. When considering GFP production in the presence of leaking promoters, it appears that it is more difficult to distinguish concentrations below 1E-4, as the shapes of the graph are very similar. It is possible that longer incubation time will make for more differentiated graphs, although more testing is required. The differentiability of the graphs will also depend on the sensitivity of the fluorimeter used. Steady state conditions appear to slightly increase the rate of GFP at any given time, likely because there is more free STF available to bind to PFAS as soon as PFAS diffuses into the cell. In summary, a synthetic transcription factor inducible by PFAS will likely produce much more fluorescence than something similar to hlFAB-GFP, with the fluorescence more limited by time than by the amount of PFAS available. However, promoter leakage must be kept to a minimum to better separate very low concentrations of PFAS, or be incubated for longer periods of time. Our modeling demonstrates that steady state conditions do not significantly affect the fluorescence time or maximal fluorescence for hlFAB-GFP, and affects the STF by simply increasing the rate at which PFAS can be bound and GFP can be produced.