We made five mutational modifications to PETase, including hydrophobic interactions, salt bridges, hydrogen bonds, disulfide bonds and proline
When performing combinatorial mutations, we summarized the better mutation sites of each group, and started to conduct preliminary combinatorial mutations
Use MD and Docking to verify the results
To verify the results of our transformation, we use MD and Docking to detect.
Data and results
Table 1. Total number of the parts of disulfide bonds mutants
Fig.1 Structure feature of A202C and E231C
Disulfide bond plays a significant role in the thermal stability of proteins. The substitution of cysteine for alanine in A202C mutation and the substitution of cysteine for glutamic acid in E231C conferred a new disulfide bond.
Fig.2 Structure feature of L43C and K252C
The substitution of cysteine for leucine in L43C mutation and the substitution of cysteine for lysine in K252C conferred a new disulfide bond.
Table 2. Total number of the parts of Hydrophobic interaction mutants
In addition to electrostatic effects, hydrophobic interactions have a marked influence on enzymatic performance.
Fig.3 Srtucture feature of Q127L
Q127L prompts the hydrophobic packing in the protein interior.
Fig.4 Structure feature of S54W
The indolyl of S54W formed offset parallel π-π stacking interaction with the phenolic group of the Y69.
Fig.5 Structure feature of R260F
The phenyl group of R260F formed offset parallel π-π stacking interaction with the phenyl group of F261.
Fig.6 Structure feature of N275F
The phenyl group of N275F formed “T-shaped” π-π stacking interaction with the phenyl group of F284.
Fig.7 Structure feature of Q182I
Q182I prompts the hydrophobic packing in the protein interior.
Fig.8 Structure feature of A80V
A80V prompts the hydrophobic packing in the protein interior.
Table 3. Total number of the parts of salt bridge mutations
Fig.9 Structure feature of T72E, N73R and T77E
Fig.10 Structure feature of N114R
The substitution of arginine to asparagine in N73R and the substitution of glutamate to asparagine in N72E prompted new salt bridges between the oxhydryl of T77E and the amino of R34.
The substitution of arginine to asparagine in N114R formed new salt bridge with D118.
Table 4. Total number of the parts of Hydrogen Bond mutations
Fig.11 Structure feature of Q119D
For the Q119D mutant, the substitution of asparate for glutamine in Q119D mutation conferred new hydrogen bonds with the aminogroup of Ser121 and Ser122.
For the S278D mutant, the substitution of asparate for serine in S278D mutation conferred new hydrogen bonds with the aminogroup of R280 and V281.
Fig.12 Structure feature of S278D
Table 5. Total number of the parts of Proline mutations
Fig.13 Structure feature of T266P
The substitution of proline for threonline in T266P mutation reduced the conformational entropy of the local loop region. The substitution of proline for threonline in T113P mutation reduced the conformational entropy of the local loop region.
The substitution of proline for serine in S141P mutation reduced the conformational entropy of the local loop region.
Table 6. Total number of the parts of combinational mutations
Fig.14 Structure feature of S141P
A202C / E231C mutations are showen in yellow, Q127L mutation is showen in green, S54W / N260F / N275F mutations are showen in orange, T266P mutation is showen in red, E72T / R73N / E77T / R114N mutations are showen in pink, Q119D mutation is showen in blue.
We that only a single method of modification would not be able to achieve a great improvement in the thermal stability of PETase. Inspired by the sentence “ combine putative beneficial mutations to gain further improvement of stability”, we combined the mutation sites obtained by various methods to obtain the best mutant at present. We used Fold-X and Pymol to observe the structure of the mutant. According to the structure, key aspects of the molecular mechanism of improving the protein robustness by the mutations are proposed as follows: introduction of new electrostatic interactions (N114R, Q119D, T72E, N73R, T77E), improvement of hydrophobic packing in the protein surface and interior (S54W, Q127L, R260F, N275F), reduction in the conformational entropy of the local loop region (P266T), and introduction of a new disulfide bond(A202C, E231C).
Fig.15 Structure feature of PET-CRUSHER
Many previous studies suggested that compared with the initial structure, proteins with lower RMSD values during MD simulation tend to be more thermostable. The RMSD values of PET-CRUSHER are lower than the wild-type and 6ij6, and the Tm of 6ij6 is 57.67℃, which is the most thermostable mutant in the published literatures, and its fluctuation amplitude is smaller, indicating that PET-CRUSHER is more thermostable than wild-type PETase and 6ij6 at 343K.
Mutants with lower RMSF values during MD simulation tend to be more thermostable. the RMSF value of PET-CRUSHER is significantly lower than that of wild type and 6ij6, the Tm of 6ij6 is 57.67℃, which is the most thermostable mutant in the published literature, indicating that PET-CRUSHER are more thermostable than wild-type PETase and 6ij6 at 343K.
Fig.17 Solvent accessible surface
Protein folding is driven by hydrophobic effect and is temperature dependent . Under normal conditions, the hydrophilic residues are usually on the protein surface, while hydrophobic residues are generally buried inside the protein away from the aqueous environment. If protein denatures, the hydrophobic region will be exposed to the solvent. The SASA values of PET-CRUSHER are lower than that of the wild-type and 6ij6, indicating that PET-CRUSHER is more thermostable than wild-type PETase and 6ij6 at 343K.
Fig.19 Hydrogen Bonds
Hydrogen bond is another important temperature-dependent interaction for maintaining the stability of protein.The higher number of intramolecular hydrogen bonds in mutants， the higher resistant against heat denaturation mutants have. The higher number of intramolecular hydrogen bonds present in PET-CRUSHER implies they have higher thermostability.
Fig.20 Radius of gyration
The compactness of protein is another indicator to measure the stability of protein. The effect of temperature to the overall dimension of PETaes was gleaned from the Rg analysis. Overall, the Rg of the mutants remains constant throughout the simulation which implies that mutants structures are capable to maintain their original compactness as the temperature rises, indicating that PET-CRUSHER is more stable than the wild-type protein and 6ij6 at 343K.
The left is wild type, the middle is 6ij6, and far right is PET-CRUSHER
PET-CRUSHER shows the required flexibility at appropriate temperature ranges and maintains conformational stability at high temperature. It shows a deep and rugged free-energy landscape, which indicates our mutant 8 has more possibilities to have higher thermostability.
Fig.21 Free energy topography map
To obtain the structural information of the mutants for further analysis, we used the protein structure (5xjh. PDB) of PETase with high resolution, which was analyzed by Joo, S and Kim, K.-J et al. through X-ray crystal diffraction to conduct the modeling of point mutation and multi-point mutation to generate the PDB file.
Then we used PROCHECK to evaluate the quality of the mutant model. The following figure is the evaluation result of PET-CRASHER.
Fig.22 Ramachandran Plot
The Ramachandran plot shows the phi-psi torsion angles for all residues in the structure (except those at the chain termini). The colouring/shading on the plot represents the different regions described by Morris et al. (1992): the darkest areas (here shown in red) correspond to the "core" regions representing the most favourable combinations of phi-psi values.
PET-CRASHER has over 90% of the residues in these "core" regions， which represents a good stereochemical quality of the model.
To bring our analysis closer to the reality, we need a composite model of PET and PETase. However, in the PDB database (RCSB), we did not find a ready-made model, so we needed to build a model by molecular docking.
First, the model of the substrate 2-HE (MHET) 4 was established(Figure 23).
Fig.23 2-HE（MHET）4 in AutoDockTools
Autodock Vina was used to hydrogenate the ligand and the receptor, respectively, and charge calculation was performed. Then, an irreversible bond was manually set for the ligand, and a flexible residue was set for the receptor. After arriving at the most critical step, delineating a box on the receptor range, set the docking box dimension as well as the center, requirements can satisfy the requirement of the docking space adjustment and completely cover the pocket, making software program capable of docking ligand conformation search performed within the box, and then according to the different conformation of ligand, direction, position and energy rate. (After arriving at the most critical step, delineating a box on the receptor range, set the docking box dimension as well as the center,.Requirements can satisfy the requirement of the docking space adjustment, and completely cover the pocket, making software program capable of docking ligand conformation search performed within the box. Finally, according to the different conformation of ligand, direction, position and energy rate，the computer will grade it.)
The result shows that the ligand and the pocket bind well, indicating that the docking result is accurate and our crusher remains active.
Fig.24 Setting box in Autodock vina
Fig.25 Flexible residue of PETase in Autodock vina
After running the calculation of molecular docking in the option of "Live", we finally got the corresponding docking result. A group of docking results usually contains 20 docking configurations with high scores of software. It is necessary to further filter the appropriate model based on observation and experiment.