Energy and conformation determine the enantioselectivity of enzyme

Accepted Manuscript Title: Energy and Conformation Determine the Enantioselectivity of Enzyme Authors: Bei Gao, Shuiqin Jiang, Liuzhu Wang, Lujia Zhang, Dongzhi Wei PII: DOI: Reference:

S1369-703X(17)30301-7 https://doi.org/10.1016/j.bej.2017.10.017 BEJ 6811

To appear in:

Biochemical Engineering Journal

Received date: Revised date: Accepted date:

1-7-2017 27-10-2017 30-10-2017

Please cite this article as: Bei Gao, Shuiqin Jiang, Liuzhu Wang, Lujia Zhang, Dongzhi Wei, Energy and Conformation Determine the Enantioselectivity of Enzyme, Biochemical Engineering Journal https://doi.org/10.1016/j.bej.2017.10.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Energy and Conformation Determine the Enantioselectivity of Enzyme Bei Gao a, Shuiqin Jiang a, Liuzhu Wang a, Lujia Zhang b, c*, Dongzhi Wei a,*

a

State Key Laboratory of Bioreactor Engineering, New World Institute of Biotechnology, East China University of Science and Technology, Shanghai 200237, China

b

School of Chemistry and Molecular Engineering, East China Normal University,

Shanghai 200062, China c

NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai,

200062, China * Corresponding authors: Lujia Zhang, East China Normal University, 500 Dongchuan Road, Shanghai 200062, China. Fax: +8621 33503228, E-mail address: [email protected] (L. Zhang). Dongzhi Wei, East China University of Science and Technology, 130 Meilong Road,

Shanghai

200237,

China.

[email protected] (D. Wei)

Fax:

+8621

64250068,

E-mail

address:

Graphical abstract

Highlights Binding

free

energy

and

conformation

collectively

effect

on

enzyme

enantioselectivity A well-studied lipase and its mutants validated our proposal Such mechanism is benefit to rational enzyme engineer on large-scale in silico design 

Abstract Enantioselectivity of biocatalysts is a crucial property utilized for the synthesis of stereo-specific products. Owing to its complex mechanism, the development of precise and simple way to understand and alter enantioselectivity has been a significant pursuit and challenge for decades. In this study, an accurate calculation by MM/PBSA method was applied to investigate the binding free energy of R- and S-type substrates with enzyme. Meanwhile, a simple distance standard was developed to evaluate the binding conformation. Furthermore, we proposed that the binding free

energy and binding conformation had significant effect on enzyme enantioselectivity simultaneously, and played pivotal role in Km and kcat, respectively. A well-studied, important lipase LipK107, and both positive and negative mutants were explored to validate our finding. Compared with WT, the mutations indeed resulted in the alteration of both binding energy and conformation, which collectively led to the changes of enzyme enantioselectivity. Our work indicated that rational enzyme engineer should attach great importance on both the binding free energy and the binding conformation of substrate and enzyme. Besides, since the binding energy and conformation could be calculated precisely, a promising large-scale in silico design strategy could be applied to gain various enzymes with outstanding characteristics.

Key words: Chiral selectivity; Free energy; Structural conformation; Rational design; Lipase

1. Introduction As the efficacy of many pharmaceuticals and agrochemicals is specific to chirality, the chirality of a compound has received an increasing concern. But the synthesis of non-racemic compound is not an easy job. Enzyme-catalyzed reaction is considered to be one of the most promising and effective solution. Nowadays industrial processes increasingly resort to application of enzymes in the production of enantiomerically pure compounds and chiral intermediates [1-3], not only because the spatial configuration of enzyme is an efficient chiral selector, but also because biocatalysis produces less polluted by-products and eliminates energy-intensive process. In that sense, it serves as Green Chemistry [4-5]. However, the natural biocatalysts often cannot meet the requirement of industrial processes, so the protein engineering is used to obtain the desired enzyme. With the development of structure analysis and computational technology, rational design is supposed to be the most promising method [6-8]. Meanwhile, lots of studies has been done to understand the mechanism of the enzyme as a chiral selector, and some regulations has been concluded [9-14]. Typically, there are two kinds of opinions. Some work tried to explain the enzyme chirality from the view of energy, for example, Hermann’s  work showed that binding free energy determined the prefer substrate of enzyme [14]. But the accurate binding free energy value was difficult to obtain whether experimentally or computationally. On the other hand, Kazlauskas’ [12] and Pleiss’  [9] work revealed that the structure, especially the binding state, was an important determinant, thus a large amount of configuration models were summarized. But there was still a defect that almost all the conclusion only based on a static co-crystal or molecular docking result, which may different from the dynamic state of a real system. Therefore, replacing the static analysis by a dynamic analysis is more

than apparent. LipK107 is a well-studied lipase in our lab. It was cloned from Proteus sp., and successfully expressed in Escherichia coli with high hydrolytic activity [15,16]. The LipK107 shows promising application potential for biodiesel production, and its crystal structure has also been resolved [17,18] (PDB number: 3W9U). Besides, LipK107 displayed activity on both R and S isomers in chiral resolution. So LipK107 is an excellent model to detect the difference and similarity when it catalyzes R- and S-type substrates. In this study, the binding free energy of (RS)-β-phenylalanine butyl ester with LipK107 was calculated through the Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) method, which was a precise method for calculating the free energy for drug design in recent years [19.20]. Meanwhile, a distance-based criterion was designed to evaluate the binding conformation of the enzyme and substrates. Molecular dynamics was applied to analyze the dynamic change of this distance in a period. Furthermore, both positive and negative mutants of LipK107 have been explored, and their enantioselectivity, binding free energy and binding conformation with (RS)-β-phenylalanine butyl ester were investigated carefully, to validate our proposal that the binding free energy and binding conformation have significant effect on enzyme enantioselectivity simultaneously.

2. Materials and methods

2.1. Computational methods MD simulation and MM/PBSA calculation were carried out on “Magic cube” (Dawning 5000A) of Shanghai super computer center with the software packages of Amber12. Other computation work was performed on the local Dell T620 workstation,

using Schrödinger 2009U2 for the molecular docking, Discovery Studio V3.5 (DS3.5) for virtual mutations.

2.2. Substrate docking and mutation selection The structures of substrates were draw by ChemBioOffice2008. The geometries of substrates were optimized at the HF/6-31G** level in the gas phase using the Gaussian03 program. Partial atomic charges were derived by Antechamber module in Amber12 package using the RESP method  [21]. The molecular docking was carried out through Schrödinger [22,23].  The size of the docking box is 20 Å 20 Å 20 Å, and the center is the 79SER. Substrates were treated by the LigPrep module in the condition of pH 7.0±2.0. The molecular docking was performed using the Ligand Docking in XP (extra precision) module  [22]. Docking results of LipK107 with R-β-phenylalanine butyl ester and S-β-phenylalanine butyl ester were used as initial structures. The complex structures were read in by DS3.5 and “Build and Edit Protein” protocol was applied to do the mutation. The confirmation stage was “extended”, indicating that the direction of side chain of the replaced residue maintained the original state. Four mutations G12R+I139R, G12K+I139R, G233K, H78W were selected for the further analysis.

2.3. Molecular dynamics simulation Antechamber program in Amber12 was used to parameterize the molecules and created topologies. Proteins were dealt with Amber ff99SB force field, which proves to be a useful molecular mechanical tool for rational design [24]. Substrates were treated by Generalized Amber Force Field (GAFF), which has parameters for almost all the organic molecules and totally compatible with the AMBER macromolecular

force fields [25]. The complex was applied in TIP3P explicit solvent simulations, under

periodic

boundary

conditions,

with

neutralizing

counterions

where

electrostatics was treated via PME. MD simulation was carefully performed by Amber12 [26] with 7 stages. At the first two stages, this system was minimized using a position restraint of 50.0 kcal/mol/Å2 and 20.0 kcal/mol/Å2, respectively. At the third stage, no restraint was applied. The minimization of each stage had 20000 steps in order to reach the convergence. Subsequently, using the Andersen temperature coupling scheme, the system was heated from 0 K to 300 K in 0.05 ns at a restraint of 10.0 kcal/mol/Å2, and the density equilibrium was kept for 0.05 ns at a restraint of 2.0 kcal/mol/Å2. Finally, the full system equilibrium was maintained with no restraints for 0.5 ns, and the MD simulation was carried out at 300 K for 20 ns. MD can be capable of sampling a representative region of the total phase space of the system because the initial velocities are assigned random directions and they are also adjusted to ensure that the center of mass of the system is at rest. Therefore, a trajectory of 20 ns was used in this work. The SHAKE [27] algorithm was applied to regulate all bonds including hydrogen atoms, and non-bonded interactions were calculated by a cutoff of 12 Å. Langevin [28] dynamics was employed to control the temperature through a collision frequency of 2.0 ps-1, meanwhile, the pressure was regulated by isotropic position scaling protocol. Eight systems were simulated here, G12K+I139R as the receptor and S/R-β-phenylalanine butyl ester as the ligand, respectively; G12R+I139R as the receptor and S/R-β-phenylalanine butyl ester as the ligand, respectively; G233K as the receptor and S/R-β-phenylalanine butyl ester as the ligand, respectively; H78W as the receptor and S/R-β-phenylalanine butyl ester as the ligand, respectively.

2.4. Free energy calculation

The binging free energy was calculated by Molecular Mechanics Poisson– Boltzmann Surface Area (MM-PBSA) approach, which is an effective method for the calculation of biomolecular complexes [19,20]. As presented in Eq. 1, binding free energy was calculated by subtracting the free energy of the unbound receptor and ligand from the bound complex. Eq.2 and Eq.3 illustrated that the calculation combined

molecular

mechanical

(MM)

energy,

a

continuum

solvent

Poisson-Boltzmann (PB) model for polar solvation, and a solvent accessible surface area (SA) dependent nonpolar solvation term. The total free energy of a biomolecular system was expressed as a sum of these energy contributions plus an additional solute entropy term (-TSsolute), as shown in Eq.2. Gbind  Gcomplex  (G protein  G ligand )

G  EMM  GPB  GSA  TS solute

EMM =Ees  EvdW  Eint

Eq. 1 Eq.2 Eq.3

The energies described in the equations above are single point energies of the system. However, here end-state calculations estimate these energies according to the averages from an ensemble of representative structures, shown as Eq.4 where i is the index of a particular frame and N is the total number of frames analyzed.

Eq. 4 In order to accurately analyze the correspondence between energy and conformations, trajectories were based on molecular dynamics simulations, and so were the following conformation evaluations. The binding free energies of ten systems were calculated, eight of which related to the mutants and the other two

related to the wild type of LipK107. The mm_pbas.pl software package [29] in amber12 program was used to do the calculation. NMA approach was employed for deriving entropic values. The solvent-accessible-surface area was used to correlate the repulsive (cavity) term only, and a surface-integration approach was applied to compute the attractive (dispersion) term. All simulations were proceeded for 20 ns, and each of them exported 800 steps of trajectories. The enthalpy part including EMM, GPB and GSA was calculated as the average of 100 snapshots taken from 800 steps uniformly, while the entropy part took 50 snapshots average from 800 steps.

2.5. Conformation evaluation The measurement of DH-O2 was done by cpptraj package in amber12. Trajectories used to do the distance analysis were the same to the MM/PBSA calculation for each system. All the distances of 800 frames output in 20 ns simulation were calculated and drawn out.

2.6. Site-directed mutagenesis In our previous work, lipase gene lipK107 (GenBank accession no. EU600201) was cloned from Proteus sp., and ligated into pET28a  [16]. The resulting plasmid pET28a-lipK107 was used as template for amplifying plasmids that containing mutant lipases. Primers carrying the specific mutations (Table 1) were synthetized, and the site-directed mutagenesis PCR were done using KOD-Plus Mutagenesis Kit (Toyobo, Japan). The resulted plasmids were then transformed into Escherichia coli BL21, and mutant lipases G12K+I139R, G12R+I139R, G233K and H78W were expressed at 20 °C with 0.5 mM isopropylthiogalactoside (IPTG). The LipK107 and its mutant proteins were purified through Ni-NTA sepharose columns, and lyophilized in the

lyophilizer (Christ, Germany). The dry powders were used as catalysts.

2.7. Enantioselectivity assay The compound (RS)-β-phenylalanine butyl ester was synthesized as described previously [30]. Specifically, (RS)-β-phenylalanine (3 g, 18 mmol) was added with gentle stirring at 0 °C in portions to a solution of butanol (100 ml) and SOCl2 (8 ml, 22 mmol). The reaction was completed by stirring at 20 °C overnight and then concentrated. The solid residue was suspended in CH2Cl2 and treated with acetic anhydride and pyridine successively. The solvent was removed and the residue was crystallized from hexane/ethyl acetate. (RS)-β-phenylalanine butyl ester (0.1 mmol) in 5 ml Tris-HCl buffer (pH 7.5) was mixed with 10 mg purified LipK107 or its mutant proteins. The reaction was performed at 20 °C for 12 h, and the progress was monitored through withdrawing aliquots of the reaction mixture for HPLC analysis on a Chirobiotic T column (4.6 mm i.d. × 250 mm) (Advanced Separation Technologies Inc.). Conversion (c), enantiomeric excesse (ee), and enantiomeric ratio (E) were calculated with the corresponding peak positions using Eq.5 to Eq.7 [31-33], respectively. In addition, s and p represent the substrate and product, respectively. All values were average values obtained from three independent experiments. c = ees/(ees + eep)

Eq. 5

ee = 100×(A−B)/(A + B)

Eq. 6

E = ln[1−c(1 + eep)]/ln[1−c(1−eep)] = ln[(1−c)(1−ees)]/ln[(1−c)(1 + ees)]

Eq. 7

3. Results and discussion

3.1. Understanding the enantiomer selectivity Generally, enzymatic processes are multistep transformations, which involve substrate-binding, conversion to the enzyme-product complex, and product release (Eq. 8). The preceding reaction is related to two crucial parameters (Eq. 9): kcat and Km. Km reflects the free energy difference between free enzyme plus substrate in solution and the ground-state complex, while kcat corresponds to the free energy difference between the ground-state and transition-state of the enzyme-substrate complex [34-36]. The concentrations of enzyme ([E]) and substrate ([S]) have the same value independent of the substrate chirality, i.e., whether the substrates are of the R- or S-type, so Km and kcat control the selectivity directly, as shown in Eq. 9. In biocomputation, Km can be calculated from the binding free energies of the substrate and enzyme according to the MM/PBSA method. However, measurement of kcat remains a challenge. QM/MM [37,38] is regarded as the only method to accurately measure the kcat of enzyme. In QM/MM calculation, different windows, which only differ in bond length, simulate different transition states; however, every window begins with the same condition, consisting of the same conformation of the ground-state complex. Accordingly, the kcat is closely related to the conformation of the ground-state of the protein-substrate complex. Besides, as for a typical chemical reaction, the position and conformation of substrate shift freely, indicating that substrate can touch catalyst easily, and adjust its situation to form the lowest-energy critical complex. Therefore, the situation of substrate may have little effect on the reaction. Different from it, enzyme-substrate complex is tightly fixed, owing to its large structure and the strong interaction between the enzyme and the ligand. Therefore, to a certain extent, the ground-state form of the protein-substrate complex determines the fastest reaction route and subsequent transition states, which mainly

determines the kcat (Eq. 10). Above all, the conformation of the ground-state complex could be a good criterion for simply evaluating kcat. Eq. 8 Eq. 9 Eq. 10

3.2. Conformation evaluation There is no universal criterion to define the conformation of an enzyme-substrate complex. Since the enzyme-substrate complex is tightly fixed, and a close relationship exists between kcat and the ground-state complex, a basic principle for stipulating the conformation is an articulate description of the distance between key atoms in the catalytic center and substrate scissile bond. In our model, S79 was in the active site of LipK107 and was directly involved in the catalytic reaction. An O2 atom, which was part of the ester bond of (RS)-β-phenylalaninebutyl ester, was disrupted during the catalytic reaction (Fig. 1) [9,15,17]. Therefore, the distance between HG atom in S79 of LipK107 and the O2 atom of the substrate (designated as DHG-O2) was adopted as the standard. This process highlighted S79 as the promoter of ester bond breakage and established the proximity of HG to O2 as a crucial microenvironment. Namely, a smaller DHG-O2 value signified a higher probability of catalysis. The time-course distribution of the DHG-O2 value is shown in Fig. 2 and 3. Fig. 4 and 5 show that in all of these 10 systems, RMSD values were smooth in the 20-ns simulation process, so these systems were stable and suitable for further study.

3.3. LipK107 enantioselectivity measurement The binding free energy of the enzymes and their substrates is shown in Table 2.

The enthalpy of R-β-phenylalanine butyl ester with LipK107 was -28.97 KJ/mol, the entropy(-TΔS) was -16.76 KJ/mol, and the total binding free energy was -45.73 KJ/mol. The enthalpy of S-β-phenylalanine butyl ester with LipK107 was -24.78 KJ/mol, the entropy(-TΔS) was -14.49 KJ/mol, and the total binding free energy was -39.27 KJ/mol. These values indicated that R-type substrate interacted with the LipK107 in lower energy. The first and second laws of thermodynamics intuitively illustrate that lower binding free energy promotes the simpler and shorter path for product formation. In this case, the R-isomer was expected to enter the catalytic center and settle in the lower energy state. Nevertheless, an intriguing paradox was observed wherein the enantioselectivity of S-β-phenylalanine butyl ester was higher than that of the R-type (Table 3). We ascribed this phenomenon to the significantly shorter DHG-O2 of the S-isomer (2 Å) compared to that of the R-isomer (5 Å) (Fig. 2B); that is, a shorter DHG-O2 value signified a greater possibility of catalysis, and this agrees well with Pleiss’s conclusion on other lipases [9]. In this study, the DHG-O2 value obtained from a series of molecular dynamics simulation trajectories of 20 ns represented the natural state of the enzyme and substrate in a period. Compared with the previous static analysis, our result could better illustrate the true condition. The binding free energy calculated by the MM/PBSA method was obtained based on the same trajectories, so the relationship between binding free energy and binding conformation values could be easily discovered. Here, we propose that the binding free energy and binding conformation simultaneously have a significant effect on enzyme enantioselectivity.

3.4. Comparison of mutants Four mutations were introduced to change the selectivity of LipK107 (WT),

resulting in two positive mutants and two negative mutants. The structures of the all mutants were rebuilt and the binding was simulated in silico. Computational derivation of the conformational changes and the binding free energies of the mutants were detected. The enantioselectivity of the mutants was monitored as described in Section 2.7, and carefully compared to results of dry experiments. For one of the negative mutants, G12K+I139R, the binding free energy with S-type substrate was -44.76 KJ/mol, which was almost equal to the value of R-type substrate (-44.14 KJ/mol) (Table 2). On the other hand, the DHG-O2 value for S-isomer was much smaller than that of R-isomer (Fig. 2A), indicating that the mutant enzyme has an obvious preference for S-isomer. Experimental results showed that the enantioselectivity of G12K+I139R of the S-β-phenylalanine butyl ester was indeed higher than that of the R-type, and the eep value was 38.1%, as shown in Table 3. This result demonstrated that when the binding free energy is similar, the binding conformation determines the enantioselectivity. Furthermore, compared with WT, the G12K+I139R mutant shifted the energy of S-type from -39.27 KJ/mol to -44.76 KJ/mol, while the change in the energy of the R-type was negligible, from -45.73 KJ/mol to -44.14 KJ/mol (Table 2), suggested that the S-type substrate was slightly preferred by G12K+I139R compared to the WT. In contrast, the DHG-O2 of S-isomer increased significantly (from 2 Å to 4 Å), but the DHG-O2 of R-isomer changed marginally, as shown in Fig. 2, illustrating that the binding conformation of the S-type changed dramatically and distinctly destroyed the S-type selectivity. Therefore, the corresponding eep value decreased from 52.97% to 38.10%, and the enantioselectivity E was reduced from 5.60 to 3.01 (Table 3), as expected. It can be concluded that although both the energy and conformation changed in the G12K+I139R mutant, the enantioselectivity was altered in accordance with the remarkably changed factors.

Similarly, an interesting result was observed with the other negative mutant, G12R+I139R. Firstly, the DHG-O2 of both the R-type and the S-type substrates vacillated wildly, but both shared roughly the same position in the catalytic center (Fig. 2), suggesting that the distance constraint was not the determining factor for enantioselectivity of the G12R+I139R mutant. Secondly, the binding free energy with S-isomer was -47.97 KJ/mol, which was much lower than that with R-isomer (-30.77 KJ/mol) (Table 2), indicating that the binding free energy could be the protagonist in this case. As expected, the G12R+I139R mutant showed a preference for the S-type substrate, with an eep value of 20.76% (Table 3). This result demonstrated that when the binding conformation is similar, the binding free energy determines the enantioselectivity. Furthermore, comparing the G12R+I139R mutant with WT, the binding free energy of S-isomer decreased from -39.27 KJ/mol to -47.97 KJ/mol, and the energy of R-isomer increased from -45.73 KJ/mol to -30.77 KJ/mol (Table 2), indicating a remarkable benefit for the S-isomer. However, the DHG-O2 of S-type increased to 300%, from 2 Å to 6 Å (Fig. 2), which was the dominating factor for enantioselectivity. Thus, the eep of S-β-phenylalanine butyl ester was reduced from 38.10% to 20.76%, and the enantioselectivity E decreased from 3.01 to 1.80, as shown in Table 3. For the positive mutants, the outputs were also consistent with our theory. For both mutants, G233K and H78W, the binding free energy of S-type (-48.62 KJ/mol, -43.49 KJ/mol) was significantly lower than that of the R-type (-39.50 KJ/mol, -34.18 KJ/mol) (Table 2). Meanwhile, the DHG-O2 of S-type (3 Å, 2 Å) was dramatically smaller than that of the R-type (6 Å, 6 Å) (Fig. 3). Therefore, mutants G233K and H78W displayed an obvious preference for the S-type substrate (Table 3). Subsequently, the G233K mutant was compared with the WT. G233K shifted the

DHG-O2 of S-isomer slightly from 2 Å to 3 Å, while the change in the DHG-O2 of R-isomer was negligible (Fig. 3), suggesting a little effect on the S-type selectivity of G233K. However, the alteration of binding free energy exhibited prominent preference for the S-isomer: the binding free energy of S-isomer decreased greatly from -39.27 KJ/mol to -48.62 KJ/mol, and that of the R-isomer increased significantly from -45.73 KJ/mol to -34.18 KJ/mol (Table 2). Therefore, the G233K mutant showed higher eep (62.91%) and higher E value (8.0) than the WT (eep 52.97, E 5.6) (Table 3). Moreover, comparing the H78W mutant with WT, both the binding free energy and binding conformation displayed obvious shifts to the S-type substrate. The binding free energy of S-type reduced from -39.27 KJ/mol to -43.49 KJ/mol, and that of the R-type increased from -45.73 KJ/mol to -34.18 KJ/mol, indicating a great benefit for the S-type. Meanwhile, the DHG-O2 of S-isomer changed marginally, but the DHG-O2 of R-isomer increased from 5 Å to 6 Å, indicating slight damage to the R-type selectivity. As a result, the H78W mutant exhibited improved preference for S-β-phenylalanine butyl ester, with an eep value of 75.69% and an E value of 12.7.

4. Conclusion All of the above results indicate that the chiral selectivity of the enzyme was determined by both the binding free energy and the distance between the active center and the substrate (represented here as DHG-O2). Binding free energy is related to the Km and defines which form of a chiral molecule can easily enter the binding site, while the DHG-O2 shows which form of the chiral molecule can react most quickly. As mutations can change the structure of an enzyme in unpredictable ways, both the binding free energy and binding conformation of the R- and S-isomers can change accordingly. The balance of enantiomer selectivity lies in the shifting of these two

aspects collectively. Consequently, understanding such a mechanism is beneficial to the engineer. Furthermore, since the binding free energy and the binding conformation of a substrate with a mutated enzyme can be accurately calculated with virtual mutations, one can generate in silico mutations (as we applied in this paper) to create a favorable clone with lower binding energy, shorter distance, or both, before implementation in vitro. Compared to blind screening and directed evolution, this method will significantly improve the work efficiency. 

Ethics approval and consent to participate: Not applicable. Competing interests: The authors declare that they have no competing interests. Authors' contributions Bei Gao performed substrates docking, mutation selection, molecular dynamics simulation and drafted the manuscript. Shuiqin Jiang and Lujia Zhang participated in the free energy calculation and conformation evaluation. Liuzhu Wang carried out site-directed mutagenesis and enantioselectivity assay. Lujia Zhang and Dongzhi Wei participated in the design of the study and coordination and helped to draft the manuscript. All authors read and approved the final manuscript. Author agreement: All the authors have approved the manuscript and agreed with submission to your esteemed journal. Acknowledgements This work was funded by the National Natural Foundation of China (No. 31772007, and No. 31571786), Natural Science Foundation of Shanghai (No. 16ZR1449500), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No. U1501501, and

Open Funding Project of the State Key Laboratory of Bioreactor Engineering.

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Figure Legends Figure 1. View of (RS)-β-phenylalanine butyl ester located in LipK107. left: Overview and S-β-phenylalanine butyl ester in the active center where DHG-O2 is 3.462Å; Right: the atomic details of R-β-phenylalanine butyl ester in the active center, where DHG-O2 is 5.104Å.

Figure 2. The distribution of DHG-O2 distances after 20ns MD simulation for (RS)-β-phenylalanine butyl ester in G12K+I139R (A), LipK107 (B) and G12R+I139R (C) 1: DHG-O2 is the distance between the HG atom in SER79 of enzyme and O2 atom in (RS)-β-phenylalanine butyl ester. 2: Blue line is the DHG-O2 between enzyme and R-β-phenylalanine butyl ester, red line is the DHG-O2 between enzyme and S-β-phenylalanine butyl ester.

Figure 3. The distribution of DHG-O2 distances after 20ns MD simulation for (RS)-β-phenylalanine butyl ester in G233K (A), and H78W (B) 1: DHG-O2 is the distance between the HG atom in SER79 of enzyme and O2 atom in (RS)-β-phenylalanine butyl ester. 2: Blue line is the DHG-O2 between enzyme and R-β-phenylalanine butyl ester, red line is the DHG-O2 between enzyme and S-β-phenylalanine butyl ester.

Figure 4. RMSD of backbone atoms as a function of the simulation time in 20 ns for complex. A: R-β-phenylalanine butyl ester in LipK107, B: R-β-phenylalanine butyl ester in G12R+I139R, C: R-β-phenylalanine butyl ester in G12K+I139R, D: S-β-phenylalanine butyl ester in LipK107, E: S-β-phenylalanine butyl ester in G12R+I139R, F: R-β-phenylalanine butyl ester in G12K+I139R.

Figure 5. RMSD of backbone atoms as a function of the simulation time in 20 ns for complex. A: R-β-phenylalanine butyl ester in G233K, B: R-β-phenylalanine butyl ester in H78W, C: S-β-phenylalanine butyl ester in G233K, D: S-β-phenylalanine butyl ester in H78W.

Table 1 Primers used for PCR Primers

Function

Sequence (5’-3’) ataagttagctggttttaatgaaattgttgg

Gif1 Primers for G12K+I139R

gaactaaaacgataggatatttcgttgacat

Gir1

ctcgtatctcaacattttctggccatagag

Gif2 Primers for G12K+I139R

taccaaatgcatttaatactttttcgacaa

Gir2

atcgtttagctggttttaatgaaattgttgg

Gif3 Primers for G12R+I139R

The same as Gir3

Gir3

The same as Gif4

Gif4 Primers for G12R+I139R

The same as Gir4

Gir4 Gif5

caagttagttggtcgctcgagtatgcgatt Primers for G233K tcattttgtttttcagtaaagaatgtattt

Gir5

ttggagccaagggccattagcttgtcgtta

Gif6 Primers for H78W Gir6

cctataaaattcacttttttcgcttgtgtc

Table 2 The binding free energy of LipK107 and various mutants. Catalyst

Enthalpy

Entropy

Binding free energy

(KJ/mol)

(KJ/mol)

(KJ/mol)

Substrate

LipK107

SPEα

-24.78±2.65

-14.49±3.18

-39.27±5.83

LipK107

RPEβ

-28.97±3.09

-16.76±2.67

-45.73±5.76

G12K+ I139R

SPE

-27.73±3.41

-17.03±2.37

-44.76±5.78

G12K+ I139R

RPE

-27.17±3.75

-16.97±2.30

-44.14±6.05

G12R+ I139R

SPE

-32.38±3.52

-15.59±2.91

-47.97±6.43

G12R+ I139R

RPE

-16.8±3.25

-13.97±2.70

-30.77±5.95

G233K

SPE

-25.96±2.96

-22.66±1.82

-48.62±4.78

G233K

RPE

-16.13±3.07

-18.05±3.54

-34.18±6.61

H78W

SPE

-21.97±2.89

-21.52±2.02

-43.49±4.91

H78W

RPE

-21.11±2.34

-18.39±4.47

-39.50±6.81

SPEα is the substrate of S-β-phenylalanine butyl ester; RPEβ is the substrate of R-β-phenylalanine butyl ester.

Table 3 The comparison of the resolution of (RS)-β-phenylalanine butyl ester catalyzed by LipK107 and its derivatives

α

Catalyst

eep (%)α

Conversion (%)β

Eγ

WT

52.97±3.41

51.56±2.42

5.60±1.31

G12K+ I139R

38.10±2.83

46.10±3.68

3.01±0.46

G12R+ I139R

20.76±2.15

46.54±2.79

1.80±0.15

G233K

62.91±2.89

49.1±1.41

8.0±1.6

H78W

75.69±1.80

42.71±2.79

12.7±2.6

The value calculated based on S-β-phenylalanine butyl ester.

β

The reaction conversion

includes (RS)-β-phenylalanine butyl ester. γ E = ln[1 – c(1 + eep)]/ln[1 – c(1 - eep)].