Polyethylene terephthalate (PET) has become one of the most widely used artificial synthetic plastics in the industry for its excellent durability and convenient processing performance. However, with its extensive utilization and low recovery rate, it has brought serious ecological dam age. In industrial applications, a higher degradation temperature can improve the chain mobility and accessibility of the ester bond of high ly crystalline PET. But the high temperature degradation of the physical side will cause significant energy loss, and will bring pollution to the environment. Using enzymatic hydrolysis can avoid energy loss and environmental pollution. Because of the mildness of the enzyme act ion, it is denatured and inactivated at high temperatures. Therefore, improving the thermal stability of the PET degrading enzyme (IsPETase ) is the key to improving the biodegradation efficiency. Keep enzyme activity longer at high temperature.
In order to improve the thermal stability of PETase, we have consulted many literatures and found that the thermal stability of the enzyme can be improved by the following methods: introducing disulfide bonds, increasing hydrophobic interactions, introducing salt bridges, build ing hydrogen bond networks, and proline Mutation and other methods. Based on the above methods, we rationally design a series of single poin t mutations, and combine these single points to generate multiple point mutations. Although these mutants cannot be experimentally verified this year, we screened and verified the mutants through modeling prediction, molecular docking, and molecular dynamics simulations.
Our principle is to increase its thermal stability as much as possible without affecting the enzyme activity, and increase the Tm value of PETase to the glass transition temperature of PET ℃, so that PETase can degrade PET faster and more effectively.
We have designed many single-point mutations, but not all mutants meet our requirements. After screening and verification, we selected some successful single-point mutations and combined these single points to get multi-site mutants.
We use the scientific geometric parameters derived from literature research and use the computer software DbD2 and MODIP to predict the mutation sites most likely to form disulfide bonds. When we conducted homology modeling and visual analysis of the mutants, we found that certain mutations were site-directed The candidate did not achieve the desired function. We tried to further change the geometric criteria and considered using factor B as a supplement to guide us in designing better disulfide bonds.
We use PyMOL to mark the aggregation positions of all hydrophobic residues. In the structure of IsPETase, hydrophobic residues surround Gln127 residues. To clarify that hydrophobic residues reduce the impact of polar collisions, we mutated Gln127 to other hydrophobic amino acid residues, and found that all these mutations have a positive effect on the stability of PETase.
By reading the literature, we chose the homologous enzyme sequence alignment method to introduce. We selected three PETase homologous enzymes , LCC, CUT190, and Tfcut2. By comparing their sequences, we selected some mutation sites that formed salt bridges and improved thermal stability.
First, we identified some mutation sites by comparing homologous enzymes, and then estimated thermal stability through website prediction. In addition, we also use software prediction and other methods to find potential mutation sites. With the in-depth development of subsequent projects, we are trying to use these mutation sites to construct a hydrogen bond network to achieve the purpose of improving the thermal stability of PETase.
Use the protein stability prediction website Minnesota to find a suitable proline substitution site (E246P), use the Expasy website to model the mutant, use the thermal stability prediction website Saves to evaluate the stability of the mutant, and use the software PyMOL conducts an in-depth study of the mutant structure. We find it that the proline mutation sites predicted by the website cannot meet the requirements for improving stability. Following the failure of the above mutant modification, read more proline-related literature and find some knowledge about how proline can improve thermal stability, and rationally design through structural analysis to get mutants with ideal effects.
When performing combinatorial mutations, we summarized the better mutation sites of each group, and conducted preliminary combinatorial mutations. We found that some points will make the overall DDG worse when combined. In the subsequent combined mutations, choose one Try one by one. Although this will waste time, we did screen out some useful mutations with excellent results. We screened thousands of combined mutations, from 5 mutants to 15 mutants, such as A40P/S54W/S92P/T113P/Q119D/Q127L/Q182I/S193R/K252L/R260F/T266P/N275F. It includes the introduction of disulfide bonds and salt bridges, and proline mutations and hydrophobic interactions.
We mainly used FoldX prediction, docking, MD evaluation methods to test our in combination mutation.
We used FoldX to calculate the ΔΔG of the combined mutation and test its thermal stability. We got PDB of mutant, and we carried visual screening out based on this.
After homologous modeling, we got the PDB of the mutant. Docking was used to simulate the binding of the substrate and the mutant enzyme.
We used MD to get the RMSF, RMSD, RG, Sasa and other parameters of the combined mutants, and then predicted the dynamic thermal stability of the mutants.