Design and application of a ketoreductase mutant

Abstract

The present disclosure provides a ketoreductase mutant having activity on both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol, and a computational design method for obtaining the mutant. The ketoreductase mutant disclosed in the present application can catalyze the reaction of racemic 1,3-butanediol to synthesize 4-hydroxy-2-butanone, which has good industrial application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the computer-aided design and virtual screening of protease mutants, and more particularly to a computational method combining the virtual screening of protease stable mutants and the virtual screening of catalytically active mutants to achieve the design of protease mutants with non-natural substrate activity. BACKGROUND

[0002] Protease catalysts play an important role in modern synthetic industry. With the continuous expansion of the application range of enzyme catalysis, the catalytic performance of natural enzymes existing in nature cannot meet the requirements of enzyme research and industrial application. Directed evolution is one of the important technical means for modifying proteases, and is a strategy of protein engineering closer to the natural evolution mode. It is a method for rapid modification of proteins with a certain purpose by simulating the natural evolution process in vitro. In the case of unknown target protein three-dimensional structure information and action mechanism, the evolution process that would take millions of years in nature can be completed in a very short time, thereby obtaining an enzyme with desired functions. In recent years, directed evolution technology has been widely used in the development of enzyme catalysts required in the fields of pharmaceuticals, food, and chemical industry, triggering another revolution in the field of biocatalysis technology, and greatly expanding the research and application range of protein engineering. The applicant has been committed to the research and application of enzyme directed evolution technology, and has successfully developed a large number of protease catalysts for the production of pharmaceuticals and fine chemicals. However, laboratory directed evolution usually requires screening of a large number of enzyme mutation libraries, and the construction of these mutation libraries and subsequent screening work are heavy tasks for researchers. Based on the research of a large number of previous directed evolution samples, the applicant combined computational biology and bioinformatics technology to design a feasible computational method that can effectively screen the enzyme mutants in the imagination, reliably predict the enzyme mutants with desired performance, and greatly reduce the range of mutation libraries required for the construction and screening of laboratory directed evolution. The computational method disclosed in the present application not only complements the laboratory directed evolution process, but also breaks through the limitations of the library range that can be constructed and screened in the laboratory, reduces the research and development cost, improves the research and development efficiency, and more effectively obtains enzyme mutants with desired performance.

[0003] Now many computational methods have been disclosed for studying proteins. For example, there are molecular docking algorithms for observing the binding mode of small molecule substrates with proteins, homology modeling and ab initio modeling algorithms for constructing three-dimensional models of proteins, and tools such as Foldx, I-mutant, Rosetta, etc. for calculating the stability of protein structures, which have been widely used in the design of protein mutations. However, since these algorithms are all empirical mathematical calculations, the calculation formula contains some physical principle energy calculation terms and statistical energy terms obtained by statistical analysis of existing databases. So far, computer-aided mutation design methods have their own limitations, and the performance of the mutants predicted by calculation is difficult to reliably match the experimental verification results. There is no computational design process for protease mutants to reliably predict enzyme mutants with desired performance. SUMMARY

[0004] The present application particularly develops an effective computer virtual screening method for enzyme mutants, and a reliable calculation method for predicting enzyme mutants with desired performance. The present application constructs a special processing method for stability calculation results, and creatively adds a reaction energy barrier calculation process, which can improve the accuracy of protease mutation virtual screening, greatly reduce the number of mutations required for experimental screening, save manpower and resources, and unexpectedly achieve the effect of enzyme engineering modification that cannot be achieved by traditional enzyme directed evolution methods.

[0005] The calculation method of the present application is shown in Figure 1 , which comprises the following four specific steps:

[0006] (1) Protein structure model acquisition: obtain a three-dimensional structure model of the protein according to the amino acid sequence of the target protease. The structure model can be an experimentally obtained structure model from the PDB (protein data bank) database, or a virtually constructed structure model according to the protein sequence using homology modeling or ab initio modeling method. Generally, homology modeling with higher homology will be more accurate than ab initio modeling. For protein structures from different sources, the catalytic conformation is required, i.e. substrate molecule (product or transition state)-protein complex.

[0007] (2) Substrate docking analysis: Determine the amino acid sites on the three-dimensional structure of the target protease that bind to the substrate molecule, then dock the enzyme's natural substrate or target substrate (including non-natural substrates) with the protein. According to the different conformational choices of the natural substrate or target substrate docking results, select the sites suitable for mutation in the active site of the protein structure. There are many computational software available for molecular docking, such as Discovery studio, Schrodinger, Yasara (including Autodock and Autodockvina plug-ins), etc. By comparing the docking conformation, the amino acid sites that need to be mutated are selected.

[0008] (3) Mutant stability calculation: Calculate the stability of single-point mutant and / or multi-point combination mutant at the sites selected according to the docking results. First, use python script to generate the mutant set required for virtual screening, then use Yasara or Rosetta software to generate the structure model of each mutant, and use ddg_monomer, Cartesian_ddg, FoldX, Provean, ELASPIC or Amber TI algorithms to calculate the structure stability of each mutant, finally use python analysis script to calculate the free energy difference (ΔΔG) between the structure of each mutant and the wild-type enzyme structure.

[0009] There are two processing strategies for the results of the above stability calculation: simple sorting method 【3a】 and statistical method 【3b】.

[0010] 【3a】 Simple sorting method: Simply sort the ΔΔG results of the mutants from low to high, and select the top-ranked mutants as the stable mutants predicted by computer virtual screening.

[0011] 【3b】 Statistical method: Sort the ΔΔG results of the mutants from low to high, and select the top-ranked mutants and the bottom-ranked mutants for frequency analysis of mutated amino acid residues. For a specific amino acid site, subtract the frequency of amino acid residues that appear more frequently in unstable mutants from the frequency of amino acid residues that appear more frequently in stable mutants to obtain a set of theoretical mutable amino acid residues at that site. Finally, arrange and combine the mutable amino acid residues at each site as the stable mutants predicted by computer virtual screening.

[0012] Evaluation criteria for mutant stability calculation results: ΔΔG≤-1 kcal / mol is a stable mutant; ΔΔG≥1 kcal / mol is an unstable mutant; -1 kcal / mol < ΔΔG < 1 kcal / mol is an ineffective mutant. This standard is also applicable to the evaluation of stability results obtained by other calculation methods.

[0013] There are many methods for calculating protein structure stability, so the Rosetta program is not the only method used in this process. The creative contribution of the present application in the stability calculation process is the use of statistical methods to process the calculation results. The statistical analysis method is used to screen the amino acid residues at each site, and then the screened mutations are combined to obtain stable mutants predicted by computer virtual screening.

[0014] Current computer virtual screening methods generally stop here. After obtaining stable mutants predicted by virtual screening using a simple sorting method, the mutants are verified in the laboratory using specific experimental protocols, and no further calculations are used to evaluate the catalytic activity of these stable mutants. In addition, due to the limitations of stability calculations, stable mutants determined by the simple sorting method will miss some mutants that actually have high stability and activity in experimental verification. The present application uses statistical methods to screen stable mutants, which can obtain stable mutants that cannot be predicted by current algorithms. Moreover, although the current common calculation method excludes a large number of unstable mutants through virtual screening, reducing the number of mutants that need to be verified in the laboratory, it cannot further judge the catalytic activity of the predicted stable mutants, nor can it evaluate whether the predicted stable mutants have activity on non-natural substrates.

[0015] The proteinase mutant design method disclosed in the present application further increases the calculation method and process of the reaction energy barrier of each mutant in the catalytic chemical reaction process (natural substrate or non-natural substrate), realizes the judgment of the catalytic activity of the predicted stable mutants, and can evaluate whether the predicted stable mutants have activity on non-natural substrates.

[0016] (4) Reaction Barrier Calculation: This method is based on the force field description of different substrate reaction states, and also provides a quantum mechanical description of chemical reactions within the framework of valence bond theory. This allows the reaction barrier calculation to utilize the computational speed of classical force field-based methods while carrying a large amount of chemical and thermodynamic information, thus providing a meaningful physical description of bonding and breaking processes. Preparations before the reaction barrier calculation include selecting the simulation force field, determining the rate-limiting step of the target enzyme reaction, the reaction transition state form of the substrate small molecule compound binding, etc. After determining the calculation parameters, the reaction barrier calculation will be performed in the cadee flow. This invention uses Qtools to analyze the calculation results. In the cadee calculation flow, for example, the default simulation calculation time is set to 12.6 ns. First, the system is gradually heated from 0.01 K to 300 K within a 90 ps simulation time. For example, 200 kcal mol is applied to all protein atoms in the simulation. -1 Harmonic suppression, applying 20 kcal mol to all water atoms -1 Harmonic suppression was applied initially, then gradually decreased as the temperature increased. A Berendsen thermostat was used to regulate the temperature, with a time step of 1 fs. For all simulations, the reaction coordinates were set to λ = 0.5 to begin subsequent reaction barrier calculations for reaction steps approaching the transition state. Molecular dynamics (MD) simulations were performed for each of the four parallel calculations at 8 ns intervals, and the results were used as a starting point for empirical valence bond simulations. In the 8ns MD simulation, a snapshot was taken every 1ns to obtain the structure close to the transition state. Based on this structure, an empirical valence bond simulation was performed at 520ps, distributed in 26 FEP / US windows at 20ps each (λ = 0, 0.05, 0.075, 0.1, 0.125, 0.15, 0.2, 0.25, 0.30, 0.35, 0.40, 0.425, 0.45, 0.55, 0.575, 0.6, 0.65, 0.70, 0.75, 0.80, 0.85, 0.875, 0.90, 0.925, 0.95, 1). Using the default Cadee computation flow would be a very lengthy process. Therefore, this invention reduces the MD simulation time from 8 ns to 4 ns, and the number of repeated computation cycles in each parallel computation copy is reduced to 4. This reduces the computation time to less than 24 hours, without significantly altering the results. Cadee can perform multi-task parallel computation on multi-core computers; therefore, on high-performance computers, Cadee can screen the catalytic activity of large-scale mutants.

[0017] The reaction energy barrier is the minimum energy required for the reactant molecules to reach the activated molecule, and the size of the energy barrier can reflect the difficulty of the reaction. As shown in Figure 2 The difference between the potential energy of the enzyme and the substrate in the free state and the potential energy of the activated molecule formed by the combination of the two is the reaction energy barrier, that is, the difference from the energy minimum point (optimal enzyme-substrate combination conformation) to the energy maximum point (optimal enzyme-substrate transition state combination conformation). In our calculation process, it is the difference between the energy minimum point of the initial state and the energy maximum point of the activated state.

[0018] Experimental verification of the catalytic activity of the mutant: the Figure 1 The calculated catalytic activity mutant is constructed and expressed in the laboratory, and then the catalytic activity of the target substrate is detected to verify whether the mutant predicted by the calculation method has the desired catalytic performance.

[0019] In the traditional directed evolution process, there is usually no clear mutation site for the evolved protease, and it is impossible to predict which amino acid residues are beneficial mutations. Therefore, a large number of diversity libraries are often constructed, and the screening of these libraries will occupy a large amount of time and resources. After the implementation of the calculation design process of the present application, that is, on the basis of the docking of the protease and the substrate molecule, the stability of the enzyme structure is calculated, and the stability result processing method disclosed in the present application is used for virtual screening, which can filter out a large number of unstable mutations and invalid mutations, and the mutant library required for laboratory screening will be greatly reduced. Regarding the current stability calculation of enzymes, on the one hand, the calculation itself is an empirical algorithm, which cannot guarantee complete accuracy, and in order to make the calculation feasible, the flexibility changes of the amino acid skeleton and many other factors are not considered as variables. In addition, small molecules in the substrate docking are often treated as rigid bodies for docking, and the surrounding environmental factors are also simplified. Therefore, in the actual application of the calculation method, the success rate of the mutant verified in the laboratory is not high, and many good mutants are often missed. In addition, in the current calculation method, the energy is usually directly sorted to find the mutant with the lowest energy, and it is believed that this is the mutant sequence that is most likely to have activity for the target reaction. In the present application, the applicant optimizes the stability result processing method based on years of practical experience, and particularly proposes a mutation frequency analysis in the present application, that is, the theoretically mutable amino acid residue types at the site are obtained by subtracting the amino acid residue types with higher frequency in unstable mutations from the amino acid residue types with higher frequency in stable mutations, and finally the mutable amino acid residues at each site are combined to obtain stable mutants, which to some extent ensures that effective mutations are not easily excluded, and such mutants are often ignored in the virtual screening process of the current known algorithm.

[0020] According to the analysis of the parameters of the energy function in the current algorithm, the calculation in the design of the current algorithm actually calculates the stability of the enzyme mutant protein structure, and does not involve the activity judgment of the enzyme mutant in the specific reaction process. The stability of the overall structure of the enzyme mutant is one aspect of enzyme engineering modification, and is also a prerequisite for catalytic activity. However, the discovery (especially the pioneering of non-natural substrate activity) and improvement of enzyme catalytic activity are more concerned by the industry, because the use of high-activity enzyme mutants to improve the catalytic efficiency of the substrate can realize the practicability in industry, but the existing calculation method cannot realize the judgment of the catalytic activity after the stability screening is completed. Therefore, on the basis of optimizing the stability calculation method, the reaction energy barrier calculation is further applied to judge the activity of the enzyme mutant in the specific reaction process, and the substrate molecules, intermediates and reaction transition states and other factors are considered, so that the catalytic activity of the predicted stable mutant is realized. The calculation and design method disclosed by the application can obtain a more concise mutant library for experimental verification, greatly improving the accuracy and research and development efficiency of proteinase mutation virtual screening; the calculation of the reaction energy barrier effectively screens the active enzyme mutant, and improves the efficiency and effect of enzyme engineering modification. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The calculation and design process of the proteinase mutant

[0022] Figure 2 The reaction energy barrier schematic diagram

[0023] Figure 3 The reaction of ketoreductase catalyzing 1,3-butanediol to generate 4-hydroxy-2-butanone

[0024] Figure 4 The reaction energy barrier calculation process

[0025] Figure 5 The atomic number of 1,3-butanediol

[0026] Figure 6 The reaction of ketoreductase catalyzing 4-hydroxy-2-butanone to generate 1,3-butanediol

[0027] Figure 7 The reaction of ketoreductase catalyzing 1,3-butanediol to generate 4-hydroxy-2-butanone

[0028] Figure 8 The GC spectrum of 4-hydroxy-2-butanone and 1,3-butanediol

[0029] Figure 9 The GC spectrum of (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol DETAILED DESCRIPTION

[0030] The present application is further illustrated by the following examples, which are provided to describe the subject matter in a clear and complete manner, but are not limiting of the present application. Obviously, the described examples are only a part of the embodiments of the present application, rather than all the embodiments. Based on the examples in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application. In addition, the equipment and reagents involved in the following examples are commercially available unless otherwise specified.

[0031] Example: Designing a mutant of ketoreductase active on a non-natural substrate (an enantiomer of a natural substrate)

[0032] The ketoreductase disclosed in patent CN111321129A can asymmetrically convert 4-hydroxy-2-butanone into the relatively expensive (R)-(-)-1,3-butanediol, which is very meaningful and industrially valuable; at the same time, the enzyme can also catalyze the reaction of converting alcohol into ketone, but due to the specificity of the enzyme to the substrate, the above-mentioned enzyme can only catalyze the substrate of (R)-(-)-1,3-butanediol. If racemic 1,3-butanediol is used as the substrate (i.e. a 1:1 mixture of (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol), the (R)-(-)-1,3-butanediol is converted into 4-hydroxy-2-butanone, and the (S)-(-)-1,3-butanediol remains unchanged, resulting in a very low overall conversion rate. In actual industrial production, racemic 1,3-butanediol is very easy to obtain, and its price is much lower than that of chiral pure (R)-(-)-1,3-butanediol, so developing a ketoreductase that can convert all racemic 1,3-butanediol into 4-hydroxy-2-butanone will have good industrial application value. This example takes the ketoreductase disclosed in CN111321129A as the starting point, which has a selectivity of >99% for (R)-(-)-1,3-butanediol, and its amino acid sequence is shown as SEQ ID NO: 2, and its DNA sequence is shown as SEQ ID NO: 1; SEQ ID NO: 2 has no activity to (S)-(-)-1,3-butanediol, i.e. (S)-(-)-1,3-butanediol is a non-natural substrate for SEQ ID NO: 2. Using the computational design algorithm disclosed in the present application, 10 ketoreductase mutants with activity to both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol are predicted by virtual screening, and after experimental verification of the 10 predicted mutants, a ketoreductase mutant with high catalytic activity that can convert both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol into 4-hydroxy-2-butanone is successfully found. The specific sequence is shown in Table 1. Figure 3 ​

[0033] Computational design process:

[0034] (1) Structure acquisition: homology modeling of SEQ ID NO: 2 was performed using YASARA software package to obtain the structure model of the target protein.

[0035] (2) Substrate docking analysis: (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol were docked with the target protein, respectively. The docking process was realized by Yasara software package. The docking results were analyzed visually in Yasara. The docking results of (S)-(-)-1,3-butanediol with the target protein showed that the steric hindrance of the side chains of amino acid residues at sites I144, H145, Q150, Y188, etc. was too large. Therefore, these sites were selected as the next step of virtual screening.

[0036] (3) Mutation stability calculation: according to the selection of mutant amino acids, the appropriate amino acid category was selected, as shown in Table 1. Then a python script was used to generate the mutation combination input file required by Rosetta, and there were 400 mutant combinations. The Cartesian_ddg algorithm was used to calculate the free energy difference (ΔΔ G) between the structure of each mutant and the wild type enzyme structure.

[0037] Table 1 Amino acid mutation category

[0038]

[0039] Then the calculation results were analyzed by the statistical analysis method of 【3b】 above, and the results are shown in Table 2.

[0040] Table 2 Calculation and analysis results

[0041]

[0042] (4) Reaction energy barrier calculation: the mutations obtained in the previous step were combined to obtain 36 mutant combinations, and the reaction energy barrier calculation was performed using the cadee calculation process, as shown in Figure 4 , and the atomic number of 1,3-butanediol is shown in Figure 5 .

[0043] Simulation settings: first, the simulation system is dissolved in a spherical water droplet with (S)-(-)-1,3-butanediol as the substrate and C3 atom as the center, and the radius of the TIP3P model water molecule is , where all atoms within are completely movable, and the simulation center is completely movable using 10 kcal mol -1 Harmonic restrainter restrains the simulation center located at 17 and All atoms between, and constrained using a harmonic force constant of 200 kcal. Atoms other than those in the solvent. The SHAKE algorithm is used to confine H atoms within the solvent. The cutoff value is used to calculate the nonbonding interactions between all atoms except those in the empirical valence bond region. For these atoms, all interactions are explicitly calculated up to the cutoff value. All long-distance static electricity exceeding this critical value is handled using the Local Reactive Field (LRF) method.

[0044] From the calculated reaction barrier results, the 10 mutants with the lowest reaction barrier were selected, as shown in Table 3.

[0045] Table 3. The 10 ketoreductase mutants with the lowest catalytic energy barrier for (S)-(-)-1,3-butanediol.

[0046]

[0047] (5) Experimental verification:

[0048] (5.1) with Figure 6 The reaction was performed to verify the altered chiral selectivity of the 10 mutants for 1,3-butanediol shown in Table 3. 0.1 g of 4-hydroxy-2-butanone, 0.5 mL of isopropanol, 0.1 g of wet bacterial cells expressing ketoreductase (recombinant expression process according to the method disclosed in patent CN111321129A), and 0.005 g of cofactor NAD+ were added to the reaction flask. The final reaction volume was brought up to 5 mL with 0.1 M PBS (pH 7). The reaction was carried out in a water bath at 40°C with a stirring speed of 400 rpm. After 1 hour, samples were taken and analyzed by HPLC. The chiral values ​​(ee%) of the product 1,3-butanediol were calculated, as shown in Table 4. The formula for calculating ee% is ee%=([R]-[S]) / ([R]+[S]), where [R] represents the concentration of (R)-(-)-1,3-butanediol in the sample, and [S] represents the concentration of (S)-(-)-1,3-butanediol in the sample.

[0049] Table 4

[0050]

[0051] Note 1: (S)-(-)-1,3-butanediol was not detected.

[0052] The ee% results in Table 4 indicate that the 10 ketoreductase mutants (i.e., SEQ ID NO: 4, 6, 8, 10, 12, 14, 16, 18, 20 and 22) catalyze Figure 6During the reaction, not only (R)-(-)-1,3-butanediol was generated, but also (S)-(-)-1,3-butanediol was generated, indicating that the 10 ketone reductase mutants predicted by calculation possessed activity for (S)-(-)-1,3-butanediol.

[0053] (5.2) For the racemic substrates containing both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol, which are readily available industrially, the applicant experimentally verified the catalytic performance of the above 10 ketone reductase mutants. The reaction is as follows: Figure 7 As shown.

[0054] Add 0.1 g of racemic 1,3-butanediol (i.e., a 1:1 mixture of (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol), 0.5 mL of acetone, 0.1 g of wet bacterial cells expressing ketoreductase (the recombinant expression process is based on the method disclosed in patent CN111321129A), and 0.005 g of cofactor NAD+ to the reaction flask, and then make up the final reaction volume to 5 mL with 0.1 M PBS (pH 7). Maintain the reaction at 40°C using a water bath and a stirring speed of 400 rpm. After 1 h, sample was taken for HPLC analysis, and the calculated molar conversion rates are shown in Table 5.

[0055] Table 5

[0056]

[0057]

[0058] Since SEQ ID NO: 2 is only active for (R)-(-)-1,3-butanediol and not for (S)-(-)-1,3-butanediol, its catalytic activity... Figure 7 The theoretically highest conversion rate achievable for the substrate (i.e., racemic 1,3-butanediol) in the reaction shown is 50%. The conversion data in Table 5 indicate that the 10 designed ketoreductase mutants (i.e., SEQ ID NO: 4, 6, 8, 10, 12, 14, 16, 18, 20 and 22) can achieve a conversion rate >50%, demonstrating that these mutants can simultaneously convert (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol to 4-hydroxy-2-butanone.

[0059] Analytical detection methods for compounds

[0060] GC conversion analysis method: the chromatographic column was DB-WAX 15 m*0.25 mm*0.25 μm, the carrier gas was N2, the detector was FID, the inlet temperature was 250 °C, the split ratio was 28:1, the detector temperature was 300 °C, the injection volume was 1 μL, the column temperature was 130 °C, and the temperature was increased to 150 °C at 10 °C / min and then to 160 °C at 20 °C / min, wherein the retention time of 4-hydroxy-2-butanone was 1.5 min and the retention time of 1,3-butanediol was 2.3 min, as shown in FIG. 1. Figure 8 .

[0061] GC chiral analysis method: the sample pretreatment method was to mix 200 μL of inactivated liquid, 50 μL of MSTFA and 30 μL of anhydrous pyridine in a 1.5 mL centrifuge tube, and shake for 30 min. The chromatographic column was CP-Chirasil Dex CB (CP7502) 25 m*0.25 mm*0.25 μm, the carrier gas was N2, the detector was FID, the inlet temperature was 250 °C, the split ratio was 28:1, the detector temperature was 300 °C, the injection volume was 1 μL, the column temperature was 105 °C, and the stop time was 9 min, wherein the retention time of (R)-(-)-1,3-butanediol was 6.4 min and the retention time of (S)-(-)-1,3-butanediol was 6.6 min, as shown in FIG. 2. Figure 9 .

[0062] It should be understood that, after reading the above description of the present application, those skilled in the art can make various modifications or changes to the present application, and these equivalent forms also fall within the scope defined by the claims attached hereto.

Claims

1. A method for designing a ketoreductase mutant with activity to (S)-(-)-1,3-butanediol, the design process is: firstly, obtaining a three-dimensional structure model of ketoreductase; then, performing docking and analysis of protein structure and substrate molecule to determine the amino acid sites to be mutated; for each amino acid site to be mutated, specifying alternative amino acid residue mutations, enumerating all mutation combinations of all sites to be mutated and calculating the stability of each mutant using a protein stability calculation method, processing the stability calculation results and selecting stable amino acid residue mutation combinations to obtain predicted stable mutants; finally, performing reaction energy barrier calculation of the obtained stable mutants for the target reaction, selecting partial mutants with low reaction energy barrier to obtain predicted catalytically active mutants.

2. The computational method of claim 1, wherein, The software used for docking of substrate molecules and protein enzymes is Yasara, Discovery studio or Rosetta software.

3. The computational method of claim 1, wherein, The tools used for calculating the stability of the mutated protein structure are ddg_monomer, Cartesian_ddg, FoldX, Provean, ELASPIC and AmberTI.

4. The computational method of claim 1, wherein, The results of the stability calculation are processed by statistical methods to obtain predicted stable mutants: That is, the ΔΔG results of the mutants are sorted from low to high, and partial mutants ranked at the front and partial mutants ranked at the back are selected for frequency analysis of mutations, for a specific amino acid site, the amino acid residue types with high frequency in stable mutants are subtracted from the amino acid residue types with high frequency in unstable mutants to obtain a set of theoretically mutable amino acid residue types at the site, and finally, the mutable amino acid residues at each site are arranged and combined as stable mutants predicted by computer virtual screening.

5. The computational method of claim 1, wherein, In the "reaction energy barrier calculation", the "reaction energy barrier" is defined as the difference from the energy minimum point, i.e., the optimal conformation of enzyme-substrate binding, to the energy maximum point, i.e., the optimal conformation of enzyme-substrate transition state binding.

6. A ketoreductase mutant polypeptide, said polypeptide obtained from the method of claim 1, said polypeptide capable of simultaneously catalyzing the reaction of both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol to synthesize 4-hydroxy-2-butanone, wherein, The amino acid sequence of the polypeptide comprises residue difference X145C, X188G or X188A compared to the sequence of SEQ ID NO:

2.

7. The polypeptide of claim 6, wherein, The amino acid sequence of the polypeptide further comprises residue difference X144G compared to the sequence of SEQ ID NO:

2. 8.The polypeptide of claim 6 or 7, wherein the amino acid sequence of the polypeptide is SEQ ID NO: 4, 6, 8, 10, 12, 14, 16, 18, 20, 22.

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