Part:BBa_K4905006

Elastin-Like Polypeptide Triblock with Leucine Zippers

Information

This part is made up of the basic parts: Leucine zipper Z1 (BBa_K4905004), Leucine zipper Z2 (BBa_K4905005), and two times Elastin-Like Polypeptide (ELP) sequence A[60]I[60] (BBa_K4905001]). This results in the sequence Z1-I[60]-A[120]-I[60]-Z2. With A[5] the sequence (VPGAG[3]VPGGG[2]), since there are five VPGXG repeats, and I the sequence (VPGIG). The numbers indicate the number of repeats of these sequences. This construct was used by the TU Eindhoven 2023 team to form a hydrogel outside as well as inside E.coli BL21 cells. A schematic overview of this is shown in figure 1.

Figure 1 | Schematic overview of the sequence of this construct. (VPGAG[3]VPGGG[2]) is from now on referred to as A[5] and VPGIG is referred to as I.

General applications

ELPs are protein polymers derived from human tropoelastin. One of their key features is that they exhibit a phase separation that is often reversible whereby samples remain soluble below Tt but form coacervates above Tt. They have many possible applications in purification, sensing, activation, and nano assembly. Furthermore, they are non-immunogenic, substrates for proteolytic biodegradation, and can be decorated with pharmacologically active peptides, proteins, and small molecules. Recombinant synthesis additionally allows precise control over ELP architecture and molecular weight, resulting in protein polymers with uniform physicochemical properties suited to the design of multifunctional biologics. As such, ELPs have been employed for various uses including as anti-cancer agents, ocular drug delivery vehicles, and protein trafficking modulators³.

Construct design

The construct consists of ELPs and two different leucine zippers that have affinity for each other. In general, ELPs have hydrophilic and hydrophobic domains that exhibit reversible phase separation behavior that is temperature-dependent. They are made from a repeating VPGXG sequence, with X replaced by specific amino acids. This results in specific properties of the ELPs, especially related to the transition temperature Tt at which the ELPs will interact with each other on the hydrophobic sites². When the temperature is below Tt, the water molecules surrounding the hydrophobic parts will go into the bulk water phase which gains the solvent entropy. This makes it possible to form interactions with other ELP molecules³.

As shown in figure 2, this construct has a hydrophilic region in the middle (A[120]) and a hydrophobic region on each side of it (I[60]). On the ends the leucine zippers Z1 and Z2 are located for stronger interactions between the ELPs. Leucine zippers consist of a repeating unit that forms an alpha helix. Two leucine zippers together form ion pairs between the helices, which causes association¹. These stronger and reversible interactions make them useful in the formation of a hydrogel at a specific Tt. In the end, the hydrogel is formed with electrostatic and hydrophobic interactions between the ELPs.

Figure 2 | Schematic representation of the composite part, an ELP with leucine zippers on the ends

As soon as the hydrogel is made inside E.coli BL21 cells, the cells are prevented from dividing. However, the cells remain functional. So they can still be used to express therapeutic agents, like Interleukin 10 in the TU Eindhoven 2023 teams project.

Sequence and Features

Assembly Compatibility:

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Results

Protein expression and purification

The protein has an expected molecular weight of 105.1 kDa Organic solvent extraction, ITC

SDS-page

Figure 3 | SDS page from this construct

Transition temperature determination

To determine the transition temperature of the ELP constructs, different solutions of the proteins were made in PBS. Using a UV-VIS spectrometer, the absorbance of light at 350 and 600 nm was measured to find the temperature at which phase separation happens. First, the samples were heated up to find the temperature where the hydrogel forms. Later, the samples were cooled again to show their reversible behavior. Two transition temperatures can be seen, the first is where the hydrophobic parts aggregate and the hydrogel forms, the second transition temperature is where the hydrophilic blocks also collapse. Two different constructs are plotted together, Z1-I60-A120-I60-Z2 has complementary leucine zippers and Z2-I60-A120-I60-Z2 has two leucine zippers that are the same, so it acts as a control group. It can be seen that their transition temperatures differ. The transition temperature of Z2-I6-A120-I60-Z2 is about 17 °C and Z1-I60-A120-I60-Z2 has a transition temperature of around 20 °C. It can also be seen hat the Z1-I60-A120-I60-Z2 construct only exhibits one transition temperature, although two were expected, this might be because of the zippers already bringing the hydrophilic domains together which loses the second transition temperature.

Figure 6 | Temperature dependent behaviour of the Z1-I60-A120-I60-Z2 and Z2-I60-A120-I60-Z2 ELP constructs at a concentration of 5 and 20 uM, respectively, measured at 350 nm. The temperature was varied with 0.5 °C per minute. The transition temperature of Z2-I60-A120-I60-Z2 is about 21 °C and Z1-A120-Z2 has a transition temperature of around 23.5 °C.

Figure 7 | Reversibility of the LCST behaviour of the Z1-A120-Z2 and Z2-A120-Z2 constructs. Solutions were cooled down at a rate of 1.0 °C per minute.

Characterization

Massspec

Hydrogel formation

To see if a hydrogel could form, 10 wt% ELP was dissolved in cold MQ and left at room temperature to warm up and form a gel. This gel could be dissolved again when put at 4 °C overnight.

Figure 5 | A ten percent hydrogel was formed inside of a mass spectrometry vial. ELP constructs were dissolved in MQ at 4 °C and left at room temperature as soon as they dissolved. Within minutes, a hydrogel started to form.

Dye release from hydrogel

Inhibition of bacterial growth

To follow the growth inhibition of the bacteria because of the hydrogel, a calorimetric assay was conducted with CCK-8. This type of assay can be used to detect the concentration of live bacteria in a sample and relies on the reaction between CCK-8 and dehydrogenase, which results in the formation of orange-yellow formazan. The concentration of live bacteria is proportional to the absorbance value of formazan measured at 450 nm. According to literature, the OD600 is proportional to the number of bacteria in the sample, and the relation between the OD600 and OD450 measured in samples containing CCK-8 has been shown to have a linear relationship. Based on this information, a standard curve was made that relates the OD600 to the OD450. This curve was used in further experiments to determine the number of live (and over-time thus dividing) bacteria in each sample.

Figure 10 | Standard curve relating the OD600 and OD450 of bacterial samples containing CCK-8. Imaging experiments.

Molecular Dynamics simulation

Introduction

Molecular dynamics (MD) simulations are commonly used to see how proteins or other molecules move over time. This can contribute to the understanding of molecular systems, which cannot be seen with the eye or even with the best microscopes. There are many different software packages available that can be used for MD simulations, each one with its own advantages and limitations. However, they are all based on the same principle: calculating the force between all atoms in the system with their known positions. At every time step, the positions and velocities are updated to form a trajectory of the atoms. This timestep is in the range of a few femtoseconds (10-15 s), which is needed to keep the system numerically stable. On molecule scale, this can show how proteins fold to reduce the energy in the system, or how proteins interact with each other ^[4] .

In our application of forming a hydrogel with Elastin-Like Polypeptides (ELPs), it can be useful to see how the ELPs will behave at different temperatures. Above a certain transition temperature (Tt), there is a disruption in the thermal energy of the water molecules around the ELPs, which causes the water molecules to move away from the ELPs. This makes room for interactions between the ELPs. It has been found that there will be conformational changes in another ELP sequence when the temperature rises above Tt, which makes them more ordered and increases the hydrophobicity ^[5] .

It is interesting to see how this applies to our own ELP. This will help us to understand the mechanism behind the hydrogel formation and to see what it actually looks like. We will analyze different properties of the proteins, like the secondary structure formation, compactness, and the surface area that is accessible for the solvent, to see how the conformation differs at specific temperatures below and above the Tt.

Methods

MD simulations are typically computationally expensive. This made it necessary to shorten our ELP sequence to make the computational time manageable. We chose to shorten the hydrophobic parts to I[10] instead of I[60] and to shorten the hydrophilic part to A[50] instead of A[120] or A[100], as shown in Figure 1. The leucine zippers stay the same. In this length reduction, the proportions are not the same as in the original ELP. However, we wanted to make sure that the hydrophobic parts were not shorter than the leucine zippers. This keeps more distance between the leucine zippers and the hydrophilic part and it prevents too unrealistic folding. The hydrophilic part is kept long, since this part gives the ELP its length and it might be interesting how this part behaves during the folding. The choices made for this might affect the outcome, but the combination of regions is still the same and the difference between the temperatures can still be compared.

Software

In the MD simulation, we used VMD 1.9.4 software for the visualization and NAMD 2.14 software for the simulation itself. This choice is based on the paper from López Barriero et al. who also used this software in a related topic. We had a meeting with one of the authors of this paper, Diego López Barreiro, who explained that they chose this combination of software because they were made by the same people and thus have a more integrated workflow. Since there are also clear tutorials available, we chose to use this software ^[6] .

For the MD simulation, different input files are needed. A more detailed description of these input files can be found below. PyMol is used to make the PDB files of the ELP, with a FASTA file of the amino acid sequence as input. The PDB file is then imported in VMD, where the PSF file is made with integrated option of the automatic PSF builder. The CHARMM force field topology file for proteins is used in this step, with the corresponding parameter file for the next steps. For the different parts of the MD simulation, configuration files are made. This is the direct input in the MD simulation ^[7] .

Implicit solvent simulation

As first step, a generalized born implicit solvent is used to fold the protein. In an implicit solvent, the solvent (in this case water) molecules are not individually modeled, but with an approximation of the mean force from the solvent on the protein. Different theories can be used for this, where we will use the generalized born model. Implicit solvents are faster than an explicit solvent with simulated water molecules and more detailed than a simulation in vacuum ^[8] . The MD simulation is performed at the temperatures 276 K, 288 K, 298 K, and 310 K, since the transition temperature was found to be 293 K in the conducted UV-VIS experiments. Simulations at 288 K and 298 K are runned in triplet to reduce the chance of coincidence in the results . This is necessary, since the initial velocities are chosen by the NAMD software and thus differ every time. Different velocities can cause differences in the final stable conformations, which can affect the results of the analytical methods.

Just like in López Barreiro et al., first energy minimalization is done for 20 000 time steps. Then, Langevin dynamics is used with Generalized Born implicit solvent. The simulation step was 2 fs with a simulation time of 50 ns. The cutoff distance is 18 Å, the switch distance 16 Å^[6].

Analyzation methods

The results are analyzed with Root Mean Square Distance (RMSD), ratio of secondary structures, Radius of Gyration and Relative shape anisotropy, and Solvent Accessible Surface Area (SASA) to show the different behaviors of the ELP at different temperatures. They are calculated with the MDTraj python library. For the analysis, the analyzation methods are calculated for the 1000 frames of the last 5 ns, which has a stable conformation at all temperatures. Even when the conformation is stable, there are still some small differences. These differences are taken into account with this distribution of the results of the last 5 ns.

RMSD

RMSD can be used to measure the difference in position between the backbone of a protein and its initial structural conformation. During the folding process, the RMSD curve rises until the point where the folding stops and the conformation is stable ^[9] . So with the RMSD curve we can see if the folding is finished and from what time the further analysis can start.

Radius of Gyration and shape

The radius of gyration is a measure of how compact the protein is. It indicates the total size of the chain molecule. This can be used as a measure for the variation of the structure of the protein during the MD simulation ^[10] . The Rg will start very high at the start of the MD simulation and will drop until the protein comes into a stable conformation. Literature found that there is a drop in the average radius of gyration around the critical temperature when comparing the stable regions of the protein at different temperatures^[5].

To get more insights in the compactness of the ELP, we will also compute the relative shape anisotropy. In this method, the result will be between 0 (symmetric sphere) and 1 (linear chain)^[11].

Secondary structures

With an analysis of the secondary structure, we can assess how structured the protein is at the different temperatures. The DSSP method is used, which recognizes structures based on H-bonding patterns. In this method, residues are classified into coils, alpha-helices, or beta-sheets. For every frame, the ratio of them is calculated by dividing them with the total amount of residues. This results in three plots with the ratios of coils, alpha-helices and beta-sheets at different temperatures. It is observed in literature that ELPs form more secondary structures at higher temperatures^[5].

SASA

SASA is a way to define the peptide surface and interior. It measures the surface area of the protein that is accessible by solvent molecules^[12]. It is suggested in the literature that the behavior of ELPs related to the transition temperature abruptly decreases SASA^[5]. It is also suggested that there is less heterogeneity in the structure when the temperature is increased.

Results

In figure …, some frames are shown from one of the simulations at … °C. The left figure is from frame 3000, the middle figure from frame …, and the right figure is from frame … . These frames are a bit different for the other simulations, but the process is comparable when comparing them by eye. The frames are not included here, but can be found at the wiki of the TU Eindhoven 2023 team. When comparing the simulations at the four temperatures, they all start with folding at the ends of the ELP. This was as expected since those parts are the most hydrophobic. Some of the simulations also show the folding of the ELP in the middle at the more hydrophilic part. However, there is no clear relationship between temperature and this folding behavior. So it is probably caused by luck in the simulation.

RMSD

RMSD plots are made for each of the three simulations at the four temperatures. They are made for the complete 50 ns, but also for the last 5 ns to show how stable the conformation is related to frame 9000. This is important since there can still be a lot of changes in the structure while the moved distance of the atoms stays the same. Since there are a lot of plots made, they are not visualized here. However, they can be found on the wiki of the TU Eindhoven 2023 team.

For all of the measurements, the equilibrium seems to be reached. When looking at the plots of the last 5 ns, most RMSD plots have a variability of a maximum of 1 nm. Only the third simulation at 37 °C has a slightly larger difference. An RMSD of 0 describes a stable conformation, but there are always some small deviations. So it can be said that an RMSD of 0.25 nm has a high stability, but also 0.2-0.5 nm will give a high stability^[10]. With this information and when looking at the course of the curves, it can still be concluded that the conformations are relatively stable since the RMSD is still very small.

Secondary structure

In Figure … a, b, and c, the boxplots for the secondary structure analysis of the individual simulations are shown. This shows some variability between the simulations of a single temperature, but also overlapping deviations. This happens especially for the α-helix and β-sheet plots. The simulation results are taken together for each temperature to make them easier to analyze, which is shown in Figure … d, e, and f.

Overall, almost no secondary structures seem to be formed. The rate of the coils is much higher than the rates for alpha-helices and beta-sheets. It is possible that more secondary structures make the ELP more stable, but that it is stuck in a local minimum. This can be proven with more simulations and some other simulation techniques.

When comparing the boxplots of the different temperatures, there does not seem to be a convincing difference in the secondary structure formation. There are some small differences in the medians and distributions, but there is no clear relation between those differences. We can thus conclude that there is no difference in the secondary structure between the temperatures.

Radius of gyration

The radius of gyration results of the individual simulations are shown in Figure … a. The individual simulation results of 3 and 37°C show large differences in the results. This can be caused by the different velocities that are chosen for the single simulations. Since the individual simulations of 3 and 37°C differ a lot, the total boxplot in Figure … b shows a large distribution of the results.

There is a clear difference between the radius of gyration at 15 °C and 25 °C, which are just below and just above the transition temperature. When looking at the median at 37 °C, also these results show a lower radius of gyration than 15 °C. At 3 °C, the radius of gyration is for two of the simulations very low compared to the other temperatures. When looking at the final conformations of the simulations, this can be caused by the shape of the stable configurations. When the shape is more cylindrical instead of spherical, which seems especially the case for the simulations at 15°C, the radius of gyration will increase. As mentioned in the methods section, we also computed the relative shape anisotropy.

In Figure … a, the relative shape anisotropy is shown for the individual simulations. There is clearly a difference in the shape between the different simulations, also in those with the same temperature. For example 25°C, which has a clear variability between the simulations. Some of the differences were already expected after the analysis of the radius of gyration. The second simulation at 3°C is clearly less spherical than the other two simulations at that temperature. Overall, when looking at figure … b, the shape of the ELP at 15°C is less spherical than at 25°C. Because of the large variabilities between the measurements at 3 and 37°C, it cannot be concluded how this is affected at lower and higher temperatures.

This analysis suggests that there is a difference in the compactness of the ELP below and above the transition temperature. Below the transition temperature, the ELP is less compact. At a temperature above the transition temperature, the ELP will become more hydrophobic with fewer solvent molecules around it. This makes the folded ELP more compact, but also more likely to induce interactions with other ELPs. However, for harder conclusions with the role of solvent molecules included, explicit simulations are needed.

SASA

In Figure … a, the individual results show some deviations between the simulations at specific temperatures, especially at 3 and 37 °C. When taking the individual simulations together for each temperature in Figure … b, there are some small deviations shown. SASA at 25 °C seems to be lower than SASA at 15 °C, while the median SASA at 3 °C is also in this case lower than the rest of the temperatures, but with a large deviation. The results for 37 °C also have a large deviation, with a median higher than the median of 15 °C. When comparing the results with the relative shape anisotropy, there is a relationship between more spherical simulation results and a lower SASA. There seems to be a smaller difference than the difference found in the radius of gyration and the anisotropy, which might be because ends that stick out probably have more effect on SASA than the relative shape anisotropy.

The conclusion will be that there is a small but significant difference in SASA at 15 and 25 °C, but that SASA at 37°C becomes higher again. So there is a small effect of the transition temperature on SASA, but not as clear as the radius of gyration. It was expected that SASA would become much lower when passing the transition temperature^[2], but in our ELPs it seems more subtle. This might thus be caused by the ends of the ELP that stick out of the spherical shape of the folded ELP.

Conclusion

In the analysis of the MD simulation of a shortened version of our ELP, different analyzation methods were applied. These bring us to the conclusion that especially the shape of the folded ELP will be altered when increasing the temperature. At a higher temperature, the results suggest a more compact ELP with a more spherical shape. When the temperature increases even more to 37°C, the difference with 15°C will slightly decrease. A temperature of 3°C generally results in more compact conformations but with a large variability between the simulations. The explanation for the more compact ELP at higher temperatures is that the residues become more hydrophobic and thus have more interactions with each other. When multiple ELPs are brought together, it is expected that this property results in increased interactions between the ELPs, especially between the hydrophobic ends and between the leucine zippers.

The radius of gyration and relative shape anisotropy give the most convincing difference between temperatures below and above the transition temperature, where the secondary structure analysis did not find a clear difference. For SASA, there seems to be a difference, but it is not as clear as the radius of gyration and relative shape anisotropy. For more convincing results, it would probably be better to use explicit solvent simulations and more simulations at more temperatures. However, due to time limitations, it was for us not possible to carry this out.

References

[1] Alber, T. (1992). Structure of the leucine zipper. Current Opinion in Genetics and Development, 2, 205–210

[2] Christensen, T., Hassouneh, W., Trabbic-Carlson, K., & Chilkoti, A. (2023). Predicting Transition Temperatures of Elastin-Like Polypeptide Fusion Proteins. https://doi.org/10.1021/bm400167h

[3] Despanie, J., Dhandhukia, J. P., Hamm-Alvarez, S. F., & MacKay, J. A. (2016). Elastin-like polypeptides: Therapeutic applications for an emerging class of nanomedicines. Journal of Controlled Release, 240, 93–108. https://doi.org/10.1016/j.jconrel.2015.11.010

[4] S. A. Hollingsworth and R. O. Dror, “Molecular dynamics simulation for all,” Neuron, vol. 99, no. 6, p. 1129, Sep. 2018, doi: 10.1016/J.NEURON.2018.08.011.

[5] N. K. Li, F. G. Quiroz, C. K. Hall, A. Chilkoti, and Y. G. Yingling, “Molecular description of the lcst behavior of an elastin-like polypeptide,” Biomacromolecules, vol. 15, no. 10, pp. 3522–3530, Oct. 2014, doi: 10.1021/BM500658W/SUPPL_FILE/BM500658W_SI_001.PDF.

[6] D. López Barreiro, A. Folch-Fortuny, I. Muntz, J. C. Thies, C. M. J. Sagt, and G. H. Koenderink, “Sequence Control of the Self-Assembly of Elastin-Like Polypeptides into Hydrogels with Bespoke Viscoelastic and Structural Properties,” Biomacromolecules, vol. 24, no. 1, pp. 489–501, Jan. 2023, doi: 10.1021/ACS.BIOMAC.2C01405/ASSET/IMAGES/LARGE/BM2C01405_0003.JPEG.

[7] T. Isgro et al., “NAMD TUTORIAL,” 2017, Accessed: Sep. 07, 2023. [Online]. Available: http://www.ks.uiuc.edu/Training/Tutorials/

[8] J. Kleinjung and F. Fraternali, “Design and application of implicit solvent models in biomolecular simulations,” Curr Opin Struct Biol, vol. 25, no. 100, p. 126, 2014, doi: 10.1016/J.SBI.2014.04.003.

[9] I. Aier, P. K. Varadwaj, and U. Raj, “Structural insights into conformational stability of both wild-type and mutant EZH2 receptor,” Scientific Reports 2016 6:1, vol. 6, no. 1, pp. 1–10, Oct. 2016, doi: 10.1038/srep34984.

[10] S. Ghahremanian, M. M. Rashidi, K. Raeisi, and D. Toghraie, “Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review,” J Mol Liq, vol. 354, p. 118901, May 2022, doi: 10.1016/J.MOLLIQ.2022.118901.

[11] J. Iwata and T. Ando, “Molecular Dynamics Study on Behavior of Resist Molecules in UV-Nanoimprint Lithography Filling Process,” Nanomaterials, vol. 12, no. 15, Aug. 2022, doi: 10.3390/NANO12152554/S1.

[12] “How to compute the Solvent Accessible Surface Areas (SASA) with GROMACS - Compchems.” Accessed: Sep. 18, 2023. [Online]. Available: https://www.compchems.com/how-to-compute-the-solvent-accessible-surface-areas-sasa-with-gromacs/#solvent-accessible-surface-area-sasa