The GPI-anchored protein used in this study comes from the genome of Candida tropicalis, but there is currently no systematic study to predict and characterize the anchor protein in the genome. Therefore, we have constructed an anchor protein prediction model that can accurately locate the anchor protein sequences and help us reduce experimental burden for screening and characterization.
For surface display systems, each polypeptide follows the same structure with a signal peptide on the N terminus to lead the protein out of the cell, a serine/threonine to support the structure, and a GPI-anchored protein on the C terminus to bind to the inner cell membrane.
The principle of the GPI-anchored protein prediction model is as follows.
First input the Candida tropicalis genome to obtain the protein-encoding sequence. Then, use the GPI anchor sequence prediction website to make predictions, and set the screening threshold according to the prediction results. Those that do not meet the characteristics of the GPI-anchored sequence will be eliminated. The second round of prediction is continued for the matching sequences. Because the N-terminal of anchor protein contains the characteristics of signal peptide, the sequence containing the signal peptide with higher confidence in the prediction results will be subjected to the third round of prediction.
Fig.1 Prediction model of GPI-anchored Protein.
Through prediction, we obtained 129 anchor protein candidates. Then we constructed plasmids one by one and observed them by fluorescence. It was found that there were 25 anchor proteins that could express GFP to the periplasm as expected (Figure 2). Furthermore, through the immunofluorescence verification, there were 9 anchor proteins that could successfully display the target protein on the cell membrane (Figure 3). Finally, we performed sequence analysis on these 9 verified anchor proteins to correct the thresholds of the three prediction conditions.
Fig.2 Representative images of strains that can successfully display anchor proteins.
Fig.3 Representative images of the strain 4609.
Feedbacks
The model is universal and instructive. For strains from other sources, more stringent screening thresholds can be set to reduce the workload and obtain anchor proteins that can effectively display the target protein.
Michaelis-Menten equation
In biochemistry, Michaelis–Menten kinetics is one of the best-known models of enzyme kinetics. The model takes the form of an equation describing the rate of enzymatic reactions, by relating reaction rate v (rate of formation of product, [P]) to [S], the concentration of a substrate S. Its formula is given by
Here, Vmax represents the maximum rate achieved by the system, happening at saturating substrate concentration for a given enzyme concentration. The value of the Michaelis constant KM is numerically equal to the substrate concentration at which the reaction rate is half of Vmax.
In our research, in order to quickly determine the Michaelis constant of PETase, we chose p-nitrophenyl acetate as the substrate. At the same time, we explored whether the addition of MHETase would affect the kinetic parameters of PETase.
Kinetic parameter determination methods：
Take p-nitrophenyl acetate (0.2, 0.5, 1, 2, 3, 4 mM) as the substrate. The bacterial population OD600 is 1. The buffer is composed of 45 mM Na2HPO4-HCl (pH 7.0), 90 mM NaCl and 10% (v/v) DMSO. And then react for 90s at 30℃ with 900 rpm. The reaction volume is 1 mL. Subsequently, centrifuge at 15000 g for 1 min to take the supernatant and then stop the reaction. Finally, measure the absorbance at 415nm with a microplate reader at room temperature.
Michaelis-menten equation fitting was performed using GraphPad Prism. The result was shown in Figure 4. The Km of PETase was 763.4 μM and Vmax was14.67 μM /s. However, when MHETase was added, the Km was 1042 μM and Vmax was 16.5 μM /s. The results showed that the addition of MHETase did not significantly promote the activity of PETase. In contract, it significantly reduced the affinity of the substrate.
Fig.4 Determination of kinetic parameters. Michaelis-menten equation fitting curve of PETase (Left). Michaelis-menten equation fitting curve of PETase+MHETase (Right).
Feedbacks
The results indicated that the affinity of the substrate might decrease if the two enzymes were displayed on the surface of a cell at the same time. Therefore, we chose to display PETase and MHETase respectively. Afterwards, we can mix the two kinds of cells with different proportions to find the most efficient way to degrade the plastic.
References
Eisenhaber, Birgit, et al. "A sensitive predictor for potential GPI lipid modification sites in fungal protein sequences and its application to genome-wide studies for Aspergillus nidulans, Candida albicans Neurospora crassa, Saccharomyces cerevisiae and Schizosaccharomyces pombe." Journal of molecular biology 337.2 (2004): 243-253.
Möller, Steffen, Michael DR Croning, and Rolf Apweiler. "Evaluation of methods for the prediction of membrane spanning regions." Bioinformatics 17.7 (2001): 646-653.
