Novel pet hydrolytic enzyme based on deep learning algorithm and preparation method thereof

Abstract

The application discloses a kind of PET hydrolytic enzyme of deep learning algorithm design and preparation method thereof, it is related to biotechnology field, preparation method includes selecting polyester hydrolytic enzyme PHL7, learns protein crystal structure with deep learning algorithm InstructPLM, utilizes Esmfold to carry out three-dimensional structure modeling, carries out molecular docking with candidate protein and PET, carries out molecular dynamics simulation, and good protein is screened out with substrate binding;Express, purify;Obtain hydrolytic enzyme HN and / or hydrolytic enzyme JW, amino acid sequence is as shown in SEQ ID NO:1 and / or SEQ ID NO:2;And application of novel PET hydrolytic enzyme in hydrolysis PET.The application can simply, quickly and efficiently screen out PET hydrolytic enzyme from large quantities of generated candidate sequences by screening process, and expands the source of PET hydrolytic enzyme.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a novel PET hydrolase designed based on a deep learning algorithm and its preparation method. Background Technology

[0002] Polyethylene terephthalate (PET) is a key polymer material widely used in the manufacture of beverage bottles, packaging films, and textiles. Due to its excellent durability, PET plays a vital role in many aspects of human society. However, the chemical stability of PET has also led to its accumulation in the natural environment, posing a serious threat to ecosystems. To address this issue, biodegradation technology based on PET hydrolytic enzymes has been extensively studied and has shown great potential. Compared with traditional treatment methods, biodegradation can reduce adverse environmental impacts while avoiding the use of harmful chemical reagents and demanding reaction vessels. Furthermore, the reaction conditions for biodegradation are relatively mild, allowing PET to be degraded into smaller molecules for recycling, making it both environmentally friendly and economically viable.

[0003] To date, approximately 94 enzymes capable of degrading PET have been discovered and identified. However, the application of PET hydrolases still faces several challenges, such as insufficient stability across pH and temperature ranges, slow reaction rates, and the inability to directly utilize untreated post-consumer plastics. Therefore, further enzyme engineering research is needed to improve the degradation efficiency of these enzymes or to identify new PET hydrolases.

[0004] Currently, almost all PET hydrolases are isolated from environmental microbial communities, and the efficiency of finding functional PET hydrolases in nature is very low. Enzyme engineering modification of existing PET hydrolases is also nearing its limit. With the rapid development of computer technology and artificial intelligence, the exploration of enzyme sequence space is constantly expanding. Computational methods can be used to design proteins de novo, starting from the structural and functional requirements, and then to design novel PET hydrolases. This greatly broadens the sources and diversity of PET hydrolases and holds promise for providing a more reliable and sustainable technological solution for the industrial-scale biodegradation of PET waste.

[0005] Therefore, those skilled in the art are dedicated to developing a rapid, efficient, and convenient method for designing novel PET hydrolases and screening for novel PET hydrolases, which may not only improve the efficiency of PET recycling but also help promote the development of protein design. Summary of the Invention

[0006] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is how to develop a rapid, efficient and convenient method for designing novel PET hydrolases and to screen out novel PET hydrolases.

[0007] To achieve the above objectives, this invention provides a method for designing novel PET hydrolases based on deep learning algorithms, comprising the following steps:

[0008] Step 1: Through literature review and analysis of the characteristics of enzymes with PET hydrolysis activity, the polyester hydrolase PHL7 was selected, and the protein crystal structure and corresponding amino acid sequence of the hydrolase PHL7 were obtained through the PDB database.

[0009] Step 2: Use the deep learning algorithm InstructPLM to learn the protein crystal structure of the hydrolase PHL7 obtained in Step 1 and generate a large number of sequences to screen out candidate sequences;

[0010] Step 3: Use Esmfold to perform three-dimensional structural modeling on the candidate sequences obtained in Step 2, and retain the structures with higher confidence as candidate proteins;

[0011] Step 4: Perform molecular docking of the candidate protein obtained in Step 3 with PET to obtain the enzyme-ligand complex structure;

[0012] Step 5: Perform molecular dynamics simulations on the composite structure obtained in Step 4 to screen out proteins that bind well to the substrate;

[0013] Step 6: Express and purify the proteins screened out in Step 5.

[0014] Furthermore, step 3 also includes:

[0015] Step 3.1: Use the SoluProt v1.0 algorithm to predict the protein solubility of the sequences generated in Step 2, and screen out sequences with high predicted solubility;

[0016] Step 3.2: Calculate the protein charge corresponding to the high solubility sequence obtained in step 3.1, and screen out the sequences with less negative charge as candidate sequences.

[0017] Furthermore, step 4 also includes using AMDock software to globally dock the candidate protein and PET molecule, screening for candidate proteins that can dock to the correct pocket, and obtaining the enzyme-ligand complex structure of the candidate protein.

[0018] Furthermore, step 5 also includes:

[0019] Step 5.1: Using Amber software, place the enzyme-ligand complex structure into a cubic water box constructed by the TIP3P model, and add counterions to obtain the enzyme protein system in solution.

[0020] Step 5.2: Optimize the enzyme-protein system in the solution obtained in Step 5.1 by minimizing energy, remove obvious unreasonable factors in the system, and obtain the enzyme-protein system with minimized energy.

[0021] Step 5.3: Constrain the protein backbone atoms of the enzyme protein system with minimized energy in step 5.2. Specifically, gradually increase the temperature from 0℃ to 338℃ to obtain the constrained enzyme protein system.

[0022] Step 5.4: Perform a 1ns pre-equilibrium kinetic simulation on the constrained enzyme-protein system obtained in Step 5.3 to obtain the enzyme-protein system after the pre-equilibrium kinetic simulation.

[0023] Step 5.5: Perform three independent isothermal kinetic simulations of the enzyme protein system obtained in step 5.4 under the NPT ensemble for 50 ns.

[0024] Furthermore, the counter ion in step 5.1 is: Na + and Cl - .

[0025] Furthermore, step 5.5 also includes: visualizing the enzyme protein structure changes of the enzyme protein system during the kinetic simulation process, and selecting a stable structure; a stable structure is defined as one where the distance between the catalytic residue Ser and His, and the distance between the substrate and the catalytic residue Ser, are both less than [a certain value].

[0026] Further, step 6 also includes: optimizing the codons of the proteins screened in step 5 using an E. coli overexpression system, then inserting the gene and histidine tag into the NdeI / XhoI site of plasmid pET-26b+, introducing the plasmid into E. coli Bl21(DE3) for cell culture at 37°C; after the OD reaches 0.4-0.6, adding isopropyl thiogalactoside to a final concentration of 0.3 mM and culturing at 30°C for 5 hours, followed by centrifugation to obtain a cell solution; then lysing, eluting, and concentrating the cell solution at 4°C, and finally purifying it in two steps: nickel affinity chromatography and desalting column chromatography.

[0027] Furthermore, the activity of the novel PET hydrolase was identified by high-performance liquid chromatography (HPLC) to analyze the reaction products of the PET hydrolase hydrolyzing the PET membrane.

[0028] This invention also discloses a method for designing novel PET hydrolases based on deep learning algorithms, which prepares novel PET hydrolases HN and / or JW; the amino acid sequence of hydrolases HN is shown in SEQ ID NO: 1, and the amino acid sequence of hydrolases JW is shown in SEQ ID NO: 2.

[0029] This invention also discloses the application of a novel PET hydrolase in the hydrolysis of PET.

[0030] In a preferred embodiment 1 of the present invention, the design and screening process of PET hydrolase is described in detail;

[0031] In another preferred embodiment 2 of the present invention, the expression and purification process of the designed PET hydrolase is described in detail.

[0032] In another preferred embodiment 3 of the present invention, the process for identifying the activity of the designed PET hydrolase is described in detail.

[0033] The beneficial technical effects of this invention are as follows:

[0034] This invention utilizes deep learning algorithms to design novel PET hydrolases, overcoming the problem of low efficiency in finding PET hydrolases with good functions in nature. Furthermore, the deep learning algorithm expands the sequence space, broadening the sources of PET hydrolases.

[0035] The screening process provided by this invention can quickly and efficiently screen a large number of generated sequences, and rapidly screen the sequences based on the protein's net charge, predicted solubility, and affinity for the substrate, ultimately obtaining two PET hydrolases that exhibit good hydrolytic activity on PET membranes.

[0036] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0037] Figure 1 This is a comparison diagram of the tertiary structures of PHL7 and the designed PET hydrolase (HN, JW) in a preferred embodiment of the present invention.

[0038] Figure 2 This is a gel image of a PET hydrolase (HN, JW) designed according to a preferred embodiment 2 of the present invention.

[0039] Figure 3 This is a product diagram of HN and JW hydrolyzed PET film after 48 hours, which is a preferred embodiment of the present invention. Detailed Implementation

[0040] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0041] Example 1: Design and screening of PET hydrolases

[0042] Polyester hydrolase PHL7 exhibits excellent hydrolytic ability against amorphous PET membranes at 70°C. This invention uses the crystal structure of polyester hydrolase PHL7 as the initial structure and employs the published InstructPLM reverse folding algorithm to learn the protein structure and generate a large number of candidate sequences. The three-dimensional structure of hydrolase PHL7 is shown below. Figure 1 As shown, part A is the structure 7NEI of PHL 7 in the PDB database; part B is the structural model predicted by HN; and part C is the structural model predicted by JW.

[0043] Protein solubility prediction for PHL7 and candidate sequences was performed using the published SoluProt v1.0 software. The predicted solubility score for PHL7 was 0.636; sequences with a predicted solubility score below 0.64 were removed. The net charge of PHL7 and candidate sequences was calculated; PHL7 carries 5 negative charges, so candidate sequences with positive charges, no charge, and negative charges less than or equal to 5 were retained. Three-dimensional structural modeling of the candidate sequences was performed using Esmfold, retaining candidate sequences with a predicted local distance difference test (pLDDT) greater than or equal to 85 and their corresponding structures. Global docking of candidate proteins and 4PET molecules was performed using AMDock software, screening for candidate proteins whose substrates could dock into the correct pocket and obtaining their complex structures. The enzyme-substrate complex structure was placed in a cubic water cell constructed using the TIP3P model using Amber software, with the addition of an anti-counterfeit ion (Na₂O₃). + Cl - This approach helps maintain the overall electroneutrality of the system, simulating the real enzyme protein solution environment. Then, the enzyme protein system in solution is optimized for energy minimization to remove obvious unreasonable factors. Next, the protein backbone atoms are constrained, and the temperature is gradually increased from 0℃ to a high-temperature environment (338℃) to maintain the rationality of the overall structure. Afterwards, a 1ns pre-equilibrium kinetic simulation is performed to facilitate early relaxation of the structure. Finally, each system undergoes three independent 50ns isothermal kinetic simulations under the NPT ensemble to facilitate full relaxation and optimization of the protein's three-dimensional structure. Finally, the enzyme protein structure change information (RMSD) during the kinetic simulation is visualized, and a relatively stable structure is selected. The distances between the catalytic residues Ser and His, and between the substrate and the catalytic residue Ser, are calculated, and the simulation is performed with both distances being less than [value missing]. Two candidate proteins were identified. Using Gromacs software, molecular dynamics simulations were performed on the candidate proteins obtained in the previous step with an amorphous PET membrane simulant for 100 ns to calculate the energy and area of ​​the interaction between the two candidate proteins and the PET membrane.

[0044] Example 2: Expression and purification of the designed PET hydrolase

[0045] The two candidate proteins obtained in Example 1 were codon-optimized using an E. coli overexpression system. The gene and six histidine tags were then inserted into the NdeI / XhoI site of pET26b. The plasmid was introduced into E. coli Bl21(DE3) cells and cultured at 37°C. After the OD reached 0.4-0.6, isopropyl thiogalactoside (IPTG) was added to a final concentration of 0.3 mM and the cells were cultured at 30°C for 5 hours, followed by centrifugation. The cell solution was then lysed, eluted, and concentrated at 4°C, and finally purified by two steps: nickel affinity chromatography and desalting column chromatography. The gel images of the two candidate proteins (HN, JW) are shown below. Figure 2 As shown, channel 1 is the protein marker; channel 2 is the supernatant after HN disruption and centrifugation; channel 3 is the precipitate after HN disruption and centrifugation; channel 4 is the mixed protein eluted by HN; channel 5 is the target protein HN eluted by nickel column; channel 6 is the protein marker; channel 7 is the supernatant after JW disruption and centrifugation; channel 8 is the precipitate after JW disruption and centrifugation; channel 9 is the mixed protein eluted by JW; and channel 10 is the target protein JW eluted by nickel column.

[0046] Example 3: Activity identification of the designed PET hydrolase

[0047] The protein obtained in Example 2 was reacted with an amorphous PET membrane purchased from GoodFellow Company at 30°C to 60°C and pH 7 to 8. Samples were taken at 24 hours and 48 hours of incubation, diluted 10-fold with water, and then diluted 2-fold with acetonitrile. An untreated substrate solution was used as a control. The reaction products were analyzed by high-performance liquid chromatography (HPLC) using an Agilent 1260 Infinity II system equipped with an Agilent Poroshell 120EC-C18 column (150 × 4.6 mm, 4 μm) at a detection wavelength of 280 nm. The column oven was set at 30°C, using eluent A (H₂O containing 0.1% [v / v] trifluoroacetic acid) and eluent B (acetonitrile) as the mobile phase, and maintained at the following gradient for 20 min: 0-4 min, 5% B; 4-16 min, 5%-30% B; 16-18 min, 30% B; 18-20 min, 5% B, with a fixed flow rate of 0.7 ml / min and a column temperature of 30°C. The product chromatogram of PET film (purchased from Good Fellow, catalog number es30-fm-000145) hydrolyzed in 1M pH 8 phosphate buffer for 48 hours is shown below. Figure 3 As shown, Part A represents the yield of hydrolysates formed after co-incubating JW (protein concentration 300 nM) with PET film at different pH and temperatures for 48 hours; Part B represents the yield of hydrolysates formed after incubating HN and PET film at different concentrations at 55°C for 48 hours; the vertical axis represents the concentrations of MHET and TPA in the reaction solution after the reaction; error bars represent the standard errors of three independent experiments. Figure 3 It can be seen that the hydrolytic capacity of JW is significantly weaker than that of HN, and the optimal temperature for JW is 45℃. Its hydrolytic capacity at pH 8 is greater than that at pH 7 and pH 7.5. The hydrolytic capacity of HN changes significantly with the increase of its protein concentration from 300 nM to 900 nM. When its concentration increases from 900 nM to 1500 nM, the total amount of hydrolysis products changes very little, but the content of MHET in the hydrolysis products decreases.

[0048] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A PET hydrolytic enzyme HN, characterized in that, The amino acid sequence of the hydrolase HN is shown in SEQ ID NO:

1.

2. The application of the PET hydrolase HN as described in claim 1 in the hydrolysis of PET.

3. Use according to claim 2, wherein the compound is ###0002### The steps of hydrolyzing PET are as follows: reacting the hydrolytic enzyme HN with the PET membrane; using an untreated PET membrane substrate solution as a control, analyzing the reaction products using high performance liquid chromatography.

4. The use according to claim 3, wherein the compound is ###0002### The reaction is carried out at a temperature of 30°C to 60°C, and the pH value of the reaction is 7 or 8.

5. The use according to claim 3, wherein the compound is ###0002### The concentration of the hydrolase HN is 300 nM to 1500 nM.

6. The use according to claim 3, wherein the compound is ###0002### The reaction time is 24 to 48 hours.

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