Microplastic pollution, alongside global climate change and biodiversity loss, has become one of the three major global crises facing all mankind. Microplastic pollution control and prevention has also been highly valued by the international community, from a technical point of view, enzymatic degradation is one of the most promising, green, and environmentally friendly plastic treatment methods that can effectively degrade PET plastics.
Our project aims to enhance the efficiency of plastic degradation by combining PETase and spider-silk protein. Through DNA fusion and expression in E-coli, we create a new species with the ability to collect microplastic and catalyze its degradation in water, represented by P-R (PETase-spider silk fusion protein) in the essay.
On this basis, we examined and compared the differences between our new product and pure PETase (characteristics such as degradation efficiency and viscosity) and discussed our results. Our research has positive significance for environmental protection and the control of microplastic pollution.
We wanted to investigate the degradation effect of a novel enzyme (composed of PETase and spider silk protein) on plastics, which ultimately boils down to an enzyme-catalyzed reaction in which the relationship between the initial concentration of the enzymatic reaction and the concentration of substrate should follow the Michaelis-Menten equation[1]:
The equation shows the relationship between the concentration of the substrate and the initial reaction speed of the enzyme catalyzing reaction. To develop a linear equation which makes the data easier to process, the traditional Michaelis-Menten equation could also be expressed in the form of:
This equation aiming to develop a linear relationship between the variables is known as the Lineweaver-Burk equation. By plotting 1/v0 versus 1/[S], a straight line would be yielded, and its gradient as well as y-intercept would possibly indicate the values of Vmax and Km. In general, the value of Km is determined only by the nature of the enzyme, independent of the concentration of the enzyme, and can therefore be used to identify different enzymes.
The Michaelis-Menten model gives a model of the equilibrium state of the enzymatic reaction as follows:
In which S, E, P stands for substrate, enzyme, and product respectively. At a relatively low concentration of substrate, [S]+Km is approximately equal to Km, which leads to a positive linear relationship between and [S] and v0. As the concentration of the substrate elevates, [S]+Km slowly approaches to be equal to the value of [S]. As [S]→∞, v0→Vmax, which is a fixed value. So as [S] increases, the reaction slowly transits from a first order reaction to a zero-order reaction for S.
Thus, the relationship between v0 of a specific enzyme-catalytic reaction the concentration of the substrate could be presented as a graph as shown in the fig.1. [1]
Figure 1: The theoretical relationship between the speed of enzymatic reactions and its substrate concentration.
Since the result of our project experiment is a new kind of biological material with the function of which haven’t yet been tested, it’s necessary for us to compare its degrading ability with relative enzymes. Our model would set 2 major compare groups.
The first set of comparisons reflects the degradation effect of the combination (i.e., our new product), because pure PETase represents the type of unprocessed enzyme, and we can see if the new product is more efficient or advantageous at degrading plastics. The second set of comparisons reflects the advantages and disadvantages of our products compared to enzymes that may have the same additional function or advantage (viscosity or the ability to collect microplastics). The comparison would be mostly taken in consideration of the factor of degrading speed, and would be realized through the help of the Michaelis-Menten equation which we’ve just mentioned.
However, due to the other uncontrollable factors, comparison with the latter group could be only realized through qualitatively instead of quantitively. The comparison would be mostly taken in consideration of the factor of degrading speed, and would be realized through the help of the Michaelis-Menten equation which we’ve just mentioned.
Since the construction of the model requires tracking the variation of the concentration of product yielded in enzymatic reaction dependent on time, the enzymatic assay would be the best apparatus available for completing this. However, this apparatus doesn’t allow the concentration measure of direct plastic degrading reaction. Instead, it used the reaction of another alternative reaction that would yield the product p-nitrophenol, the OD value of which reflecting the catalyzing ability of different enzymes.
Figure 2: Chemical Representation of P-nitrophenyl Butyrate Hydrolysis
The reaction performed in the assay is simply a hydrolysis of an ester. Normally, the hydrolysis would be comparably slow without a catalyst due to the stability of ester due to effects such as delocalization. Thus, utilizing esterase enzyme as a catalyst would significantly improve the reaction speed. Notably, the intermediate with negative charge is stabilized by the delocalization effect produced by benzene and nitryl, allowing the reaction to be not that unfavorable. Thus, the reaction itself would be a decent strategy to test the function of esterase, the enzymes aiming to catalyze the decomposition of ester.
Thus, it’s noteworthy that one substantial premise of the model is that the enzymes’ performance in the reaction producing p-nitrophenol is a typical representation of its plastic degrading ability, which is the target of our measurement. Considering that PETase is a kind of esterase itself [2], making this assumption won’t influence the countability of the model too much.
4.1 Primary data processing
Table 1 is obtained through simple processes of averaging the raw data outputted by the enzymatic assay. According to the Lambert-beer law [3], the OD value obtained has a positive linear relationship with the concentration of the product of the reaction, p-nitrophenol, the ratio of which is constant as the following table1 shows.
Table 1: OD Values of P-nitrophenol (Reaction Product)
According to the Lambert-beer law, the OD value obtained has a positive linear relationship with the concentration of the product of the reaction, p-nitrophenol, the ratio of which is constant as the fig.2 shows. This linear relationship can be expressed by the equation y=0.2183x+0.2542(in which x and y represents p-nitrophenol concentration and its OD value respectively, and in actual calculations, the inverse function of the equation was used),and the correlation coefficient between OD and concentration of p-nitrophenol is 0.955. [4]
Figure 3: The Relationship Between OD and Concentration of P-nitrophenol
Based on the linear relationship in Figure 2, we can easily convert OD values into the concentration of the reaction product. We show the concentration of the reaction product p-nitrophenol as a function of substrate concentration and time in Table 2.
Table 2: Concentration of P-nitrophenol (Reaction Product)
In Table 2, there are some negative values for concentration, which seems to be counterintuitive, and we believe that this is an error caused by some unavoidable errors in existence. Excluding these outliers, the Concentration of p-nitrophenol trends fit the basic shape shown in Figure 1. We believe that most of the reasonable data exists and corresponds to our theoretical results, and some errors for outliers can be accepted. We plot versus time for sample P-R at 0.1 mM substrate concentration in Figure 4.
Figure 4: [Product] Versus Time for Sample P-R at 0.1 mM Substrate Concentration
As the Figure 4 shows, In the initial phase, the gradient of the pattern increases steadily over time, i.e., there is sufficient substrate waiting to bind to the enzyme to produce the product, which would probably produce a nearly linear relationship between [product] and time. We use v0 to represent the gradient that changes over time, as shown below
In which Cn and tn (n=1,2) means the product concentration and time at time point 1 and 2, which is yet undefined.
However, the period in which the graph of [product] versus time is still a straight line is yet undetermined. Since then, we could first take exact data sets (concentration & time) to plot the exact concentration versus time graph and determine the general time in which concentration and time has an approximately linear relationship, which is the value of ∆t(t1-t2).
Figure 5: Catalytic Product Concentration-time Correlation at 0.1 mM Substrate Concentration
After we acquired the data experimentally, we plotted various concentration-time relationships in the Figure 5. We found that the first three data points presents a nearly linear relationship. Thus, the initial speed of the reaction, represented theoretically by the gradient of the curve at t=0, could be approximated by the following equation:
In which C(n) represents the product concentration at n minutes.
However, since the data points selected mostly deviates from the curve fit results at a certain value, errors may still exist. Graph-6 presents two graphs plotters from randomly selected data sets, in which the average error percentage is around 10%
Thus, a data set representing the relationship between substrate concentration and the initial reaction speed could be drawn, based on which further calculation and analysis could be made. The data in Table 3 is the relationship between substrate concentration and initial reaction rate that we experimentally calculate.
Table 3: The Relationship Between v0 and [S]
4.2 Regression and Results
To compare the degrading speed of the two comparison groups, we would plot the graph of v0 versus [S] of both sample groups in a single graph, through which we could determine the ranges of substrate concentration at which each enzyme performs the best degrading speed between the two. By fitting the values of 1/v0 and 1/[S] obtained through experiments in a linear graph and determining the y-intercept as well as gradient. This would allow the determining of the parameters involved in the Michaelis-Menten equation (Vmax & Km), the value of which features a specific type of enzyme, according to the Lineweaver-Burk equation discussed above. The comparison results of the two groups are presented in Table 4.
Table 4: The Value of 1/V0 & 1/[S] for PETase, P-R Samples
By carefully examining of the data obtained, it’ s clear that the data point for P-R sample at 0.5 mM substrate concentration significantly deviates from the common trend. Thus, it’s considered as an outlier and excluded from the data set when plotting the linear relationship. To make sure that the sample size remains constant for the two groups, the 0.5 mM substrate data point is also excluded from the PETase group. By the help of the software Logger Pro, lines for each group representing the Lineweaver-Burk relationship are plotted in Figure 6 and Figure 7. The correlation constant obtained are reasonable showing a strong positive relationship.
Figure 6: Lineweaver-Burk Relationship for P-R Sample
Figure 7: Lineweaver-Burk Relationship for PETase Sample
By substituting the gradient and y-intercept into the Lineweaver-Burk equation, the parameters of the Michaelis-Menten equation could be obtained, by which we could construct the complete equation for both enzymes (PETase & P-R) and plot their substrate concentration versus initial reaction speed graph. The comparison results of the two groups are presented in Figure 8.
Purple for PETase only, black for P-R
Figure 8: [S]-V0 relationship of PETase and P-R
Unfortunately, the results demonstrated that the product of our project is inferior to pure PETase in consideration of reaction speed at the same substrate concentration, as the PETase [S]-V0 graph constantly stays above the P-R one. However, as the P-R sample was originally synthesized through convergence of the DNA of PETase and spider silk protein, loss of part of the original function of PETase would be within expectation. We suspect that three possible causes affect the enzymatic activity of P-R fusion proteins: 1) Steric hindrance effects caused by fused R proteins. 2) The increased molecular weight of the P-R fusion protein makes the calculation of the molar concentration of the enzyme inaccurate. 3) The catalytic activity of enzymes under different substrates also varies. PETase and P-R fusion proteins also differ in catalyzing hydrolysis of PET and p-nitrophenyl butyrate.
On the other hand, it’s notable that the result of the HPLC assay, which shows that the overall degrading ability of P-R surpasses that of PETase only and mentioned in the Engineering page of our project. This difference may be attributed to the fact that the HPLC assay, which directly evaluates the degrading ability through PET degrading reaction, takes into account of the enzyme’s ability to adsorb plastic by measuring the degradation product (TPA) concentration. This difference may also justify our hypothesis that the adsorption ability of P-R sample may compensate for its lack in catalytic ability.
Another conclusion that could be drawn from the results is the optimum substrate concentration for our product at which the enzymatic reaction would almost reach the maximum speed possible, and there won’t be excess input of substrate that causes no significant change to the reaction speed.
Figure 9: Principles for Determining the Optimum Substrate Concentration.
As mentioned before, the positive dependency of reaction speed decreases as the independent variable ([S]) increases in the graph. Thus, at the point with substrate concentration of [S]1 on the graph, the substrate would be most effective and economic.
Figure 10: The Range for Optimum Substrate Concentration
By the help of Desmos graphing software, it could be determined that at a substrate concentration between 20 mM and 40 mM, the reaction rate gradually becomes highly independent to the substrate concentration, and approaches the maximum rate possible (about 0.0079 mM/s). When put into practice, selecting a substrate concentration within this range would allow the substrate to be most effective and efficient.
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