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Article

Improving the Catalytic Efficiency of an AA9 Lytic Polysaccharide Monooxygenase MtLPMO9G by Consensus Mutagenesis

by
Yao Meng
1,2,
Wa Gao
1,2,
Xiaohua Liu
1,
Tang Li
1,
Kuikui Li
1,* and
Heng Yin
1,*
1
Dalian Engineering Research Center for Carbohydrate Agricultural Preparations, Dalian Technology Innovation Center for Green Agriculture, Liaoning Provincial Key Laboratory of Carbohydrates, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Catalysts 2024, 14(9), 614; https://doi.org/10.3390/catal14090614
Submission received: 2 August 2024 / Revised: 29 August 2024 / Accepted: 7 September 2024 / Published: 12 September 2024
(This article belongs to the Section Biocatalysis)
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Abstract

:
Cellulose is one of the most abundant renewable resources in nature. However, its recalcitrant crystalline structure hinders efficient enzymatic depolymerization. Unlike cellulases, lytic polysaccharide monooxygenases (LPMOs) can oxidatively cleave glycosidic bonds in the crystalline regions of cellulose, playing a crucial role in its enzymatic depolymerization. An AA9 LPMO from Myceliophthora thermophila was previously identified and shown to exhibit a highly efficient catalytic performance. To further enhance its catalytic efficiency, consensus mutagenesis was applied. Compared with the wild-type enzyme, the oxidative activities of mutants A165S and P167N increased by 1.8-fold and 1.4-fold, respectively, and their catalytic efficiencies (kcat/Km) improved by 1.6-fold and 1.2-fold, respectively. The mutants also showed significantly enhanced activity in the synergistic degradation of cellulose with cellobiohydrolase. Additionally, the P167N mutant exhibited better H2O2 tolerance. A molecular dynamics analysis revealed that the increased activity of mutants A165S and P167N was due to the closer proximity of the active center to the substrate post-mutation. This study demonstrates that selecting appropriate mutation sites via a semi-rational design can significantly improve LPMO activity, providing valuable insights for the protein engineering of similar enzymes.

Graphical Abstract

1. Introduction

Biomass, derived from agricultural crops, forest trees, grain hulls, domestic waste, and poultry manure, is a potential alternative to fossil fuels [1]. Plant cell walls, the primary source of biomass, predominantly consist of lignocellulose, which includes cellulose, hemicellulose, and lignin [2,3]. Cellulose, which constitutes approximately 40% to 50% of lignocellulose [4], is a linear polysaccharide consisting of glucose units linked to β-1,4-glycosidic bonds. The efficient utilization of this abundant polysaccharide is crucial for biomass development. However, the crystalline structure of lignocellulose, combined with the complex cross-linking of hemicellulose and lignin, creates a natural resistance barrier, hindering its hydrolysis by cellulase [5]. This presents a major obstacle to the effective use of lignocellulose in biofuel production and biorefineries.
Lytic polysaccharide monooxygenases (LPMOs) are the copper-dependent enzymes that catalyze the redox reaction to oxidatively cleave glycosidic bonds in cellulose (or chitin) molecules [6,7,8,9,10]. This process disrupts the crystalline structure, facilitating cellulase (or chitinase) binding to substrate molecules and significantly enhancing enzymatic degradation. Thus, LPMOs hold significant potential for the enzymatic digestion of crystalline polysaccharides [11,12,13,14]. LPMOs are classified into eight auxiliary activity (AA) families—namely, AA9-AA11 and AA13-AA17—in the CAZy database [15,16,17,18,19,20,21]. Among these, members of the AA9 family derived from fungi are particularly crucial for cellulose degradation [22], making AA9 a key focus of plant biomass conversion research.
Computer-assisted rational design has become a widely employed approach to identify key amino acids and conduct directed evolution. Li et al. [23] constructed a mutation library containing 3200 strains using a combination of random mutagenesis and site-saturation mutagenesis. They found that the S277 site played an important role in the catalysis of the enzyme. After the site was mutated to other amino acids, the activity of the enzyme was greatly improved. Guo Xiao et al. [24] constructed the R17L mutant of MtC1LPMO from Myceliophthora thermophila using a rational design, increasing activity by up to 1.8-fold compared with the wild-type (WT). Kruer [25] crystallized Thermobifida fusca AA10A (TfAA10A) and studied the mutations in key surface residues near the active site, finding that Trp82 and Asn83 were associated with substrate binding, although the mutation of Trp82 affected enzyme activity. However, few studies have focused on improving the activity of AA9 family members using a semi-rational design. Currently, a consensus design is considered to be an effective method for a semi-rational design approach because it does not require structural information [26,27,28,29].
The gene encoding the target protein MtLPMO9G (GenBank accession number AEO54509.1) in the genome of Myceliophthora thermophila, a thermophilic fungus isolated from high-temperature, high-humidity soils and natural compost [30,31,32], encodes 303 amino acids, including a 19-amino-acid signal peptide, an N-terminal catalytic domain, and a C-terminal carbohydrate-binding domain. A phylogenetic analysis of MtLPMO9G in comparison to 21 other reported AA9 LPMOs indicated that MtLPMO9G is situated within the C1 oxidation clade [33]. In this study, to augment its catalytic efficiency, we rationally designed three single-point mutants of MtLPMO9G—A165S, N166G, and P167N—by consensus mutagenesis using the Consensus Finder server (http://kazlab.umn.edu/, accessed on 19 June 2023) and expressed these mutants in Pichia pastoris X-33. The mutants exhibited improved catalytic activity, enhanced synergism, and increased tolerance to hydrogen peroxide. These findings provide valuable insights for the modification of catalytic activity and enzymatic properties in similar enzymes.

2. Results and Discussion

2.1. Selection of Target Residues to Improve MtLPMO9G Activity and Site-Directed Mutagenesis

A three-dimensional structural model of MtLPMO9G was predicted using AlphaFold software, revealing a β-sandwich fold structure consisting of eight antiparallel β-sheets. The loops connecting these β-sheets constituted a flat substrate-binding surface, providing a structural basis for LPMOs to act on crystalline polysaccharides [34,35,36]. The active center comprises two conserved histidines (His1 and His70), a tyrosine (Tyr151), and a copper ion. Using Consensus Finder (http://kazlab.umn.edu/), the MtLPMO9G sequence was analyzed. The tools ran a BLASTp search against the NCBI “nr” database (http://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 12 November 2022) using the input protein sequence, aligning approximately 800 sequences and calculating the counts and frequencies of each amino acid at each position. Based on these calculations, the three most highly ranked mutation sites—A165S, N166G, and P167N—were further analyzed [37].
First, site-directed mutagenesis was performed on these three amino acids. A site-directed mutation vector was constructed by RF cloning and subsequently transformed into P. pastoris after sequencing verification. Following screening using a Zeocin-resistant plate, genomic DNA from the transformants was extracted and verified by a PCR using universal primers for pPICZαA (Figure 1A). Three strains of MtLPMO9G mutants were successfully obtained. After induced expression and purification, a target protein with a molecular weight of 43 kDa was obtained (Figure 1B).

2.2. Comparison of Activities of MtLPMO9G and Its Mutants

After successfully obtaining the MtLPMO9G mutants, their activity was evaluated using high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) (Metrohm, Herisau, Switzerland). The product spectrum revealed a significant increase in cellulose oligosaccharide oxidation products for mutants A165S and P167N (Figure 2A). To quantitatively compare the activities of MtLPMO9G and its mutants, the soluble products generated by LPMOs were further digested using cellobiohydrolase (CBH), resulting in the complete hydrolysis of the highly polymerized cellobiose oxidation products into cellobiose and its oxidation products. The activity of LPMOs was then assessed by comparing the yield of the cellobiose oxidation products. The activities of mutants A165S and P167N increased by approximately 78% and 43%, respectively, compared with MtLPMO9G, while the activity of N166G did not show a significant increase (Figure 2B). Consequently, a more in-depth study was conducted on mutants A165S and P167N due to their enhanced activity. Similar research, such as that of Zhou Xiaoli et al. [38], engineered chitin-active CjLPMO10A from Cellvibrio japonicus using a rational disulfide bond design. Compared with the WT, the specific activity of the variant M1 (N78C/H116C) increased by 50%.

2.3. Comparison of Thermal Stability of MtLPMO9G and Its Mutants

The melting temperature (Tm) of MtLPMO9G and its mutants was measured using a Prometheus Panta instrument (NanoTemper, Munich, Germany) to evaluate the thermal stability. The temperature range of the thermal denaturation curve was from 25 °C to 95 °C. The Tm value was determined by the first derivative of the thermal denaturation curve. The Tm of the WT was 61.11 ± 0.01 °C, while the Tm values for mutants A165S and P167N were 60.74 ± 1.86 °C and 53.92 ± 0.73 °C, respectively, indicating reductions of 0.37 °C and 7.19 °C compared with the WT (Figure 3). Hydrophobic interactions are known to play a critical role in the thermal stability of proteins [39]. Generally, enzyme thermal stability is positively correlated with hydrophobicity and thermophilic proteins typically contain a higher number of hydrophobic amino acids than mesophilic proteins as these residues enhance protein hydrophobicity and, consequently, thermal stability [40]. Pace et al. reported that hydrophobic interactions contribute to protein stability by approximately 64% [41]. In this study, the substitution of the hydrophobic amino acids Ala and Pro resulted in decreased thermal stability.

2.4. Synergistic Effect of MtLPMO9G and Its Mutants with Cellobiohydrolase

AA9-family LPMOs enhance cellulose degradation by glycoside hydrolase via the disruption of cellulose’s crystalline regions, thereby exposing cellulose chain ends to the solvent. This interaction creates a synergistic effect in the reaction system. To further investigate, we compared the synergistic effects of the WT MtLPMO9G and its mutants with cellobiohydrolase (CBH). We examined the differences by varying the enzyme concentration ratios of LPMO to CBH (1:10, 1:1, and 10:1). The results indicated that mutants A165S and P167N improved the hydrolysis efficiency of PASC by CBH II compared with the WT. Specifically, with an LPMO to CBH II ratio of 1:10, the addition of A165S increased cellobiose release by 14.3% compared with the WT (Figure 4). At a 1:1 ratio, no significant difference in cellobiose release was observed between the mutants and the WT. However, at a 10:1 ratio, cellobiose release increased by 8.1% and 6.2% in the A165S and P167N mutants, respectively, compared with the WT. These findings suggest that the mutants exhibited a stronger synergistic effect than the WT at higher LPMO concentrations. Other LPMO and CBH synergistic studies, such as Nguyen Huyen et al. [42], simultaneously treated microcrystalline cellulose with CsLPMO9 and a commercial cellulase cocktail, leading to a higher reduction in the sugar yield over the sum of the yields of individual treatments with CsLPMO9 and the cellulase cocktail. Similarly, a simultaneous treatment of PASC resulted in an increased yield.

2.5. Comparison of the Substrate-Binding Ability of MtLPMO9G and Its Mutants

Cellulose degradation by LPMOs is a dynamic process that encompasses substrate binding, enzyme catalysis, and product dissociation. The conformation of the enzyme–substrate complex significantly influences enzyme–substrate interactions and plays a critical role in the catalytic process. Therefore, we investigated the substrate-binding ability of MtLPMO9G and its mutants. Binding constants were determined using the saturation binding assay method described by Hansson et al. [43]. By varying the substrate concentration, we measured different levels of saturation-bound protein. The obtained data were fitted to the Langmuir adsorption isotherm (Figure 5) to calculate the equilibrium dissociation constant (Kd), maximum binding capacity (Bmax), and distribution coefficient (Kr) for each protein (Table 1). The results indicated that the substrate-binding ability of mutants A165S and P167N decreased compared with the WT.

2.6. Comparison of the H2O2 Tolerance Ability of MtLPMO9G and Its Mutants

H2O2 is produced during the LPMO reaction and its accumulation inhibits LPMO activity. Therefore, the H2O2 tolerance of MtLPMO9G and its mutants was investigated via the exogenous addition of H2O2. At 5 h and 12 h after the reaction began, there was no significant change in the H2O2 tolerance of mutants A165S and P167N compared with the WT when exposed to 50 μM and 100 μM H2O2. However, 24 h into the reaction, the H2O2 tolerance of P167N significantly increased compared with the WT under the addition of 100 μM H2O2 (Figure 6). Therefore, the H2O2 tolerance of P167N was enhanced.
LPMOs are important industrial enzymes that can significantly reduce the cost of lignocellulosic biomass degradation when used in conjunction with commercial enzymatic cocktails [44]. The synergistic effect of MtLPMO9G with cellobiohydrolases was evaluated using the model cellulose substrate PASC. As previously reported, substituting Ala165 and Pro167 decreased substrate binding and increased synergy. We hypothesized that a higher proportion of unbound LPMOs could supply H2O2 to LPMOs bound to the substrate, assuming that unbound LPMOs produced H2O2. Mutants A165S and P167N could be more resistant to H2O2-induced self-oxidation, thus effectively oxidizing cellulose and enhancing synergy with cellobiohydrolases during cellulose degradation. On the other hand, Eibinger et al. recently demonstrated that the adsorption time of LPMOs on a substrate is much longer than CBH, allowing up to six catalytic events per retention time [7]. Given the overlapping specificity of the two enzymes for the crystalline region, the enhancing effect of LPMOs could potentially be improved by carefully balancing the oxidative and binding capacities to optimize the adsorption and desorption frequencies.

2.7. Enzyme Kinetics of MtLPMO9G and Its Mutants

To evaluate the catalytic efficiency of MtLPMO9G and its mutants, enzyme activity was measured using varying concentrations of 2,6-DMP as a chromogenic substrate and H2O2 as a co-substrate. The maximum velocity (Vmax) and substrate affinity (Km) of the WT and mutants A165S and P167N (Table 2) were determined from the enzyme activity curves (Figure 7). The WT exhibited a Vmax of 662.2 ± 44.3 U/g, while A165S had a higher Vmax of 808.5 ± 94.4 U/g. The Vmax of P167N was 683.5 ± 82.9 U/g, slightly higher than that of the WT. The apparent Km value of the WT was 23.7 ± 3.5 mM, whereas the Km for A165S and P167N were lower, at 17.3 ± 5.1 mM and 19.9 ± 5.7 mM, respectively. Given that the turnover rate (kcat) is proportional to the Vmax under a constant protein concentration and because the protein concentration was the same for all samples, it was concluded that the kcat of A165S was 1.2 times that of the WT. The catalytic efficiencies (kcat/Km) of A165S and P167N were 1.6 times and 1.2 times higher than that of the WT, respectively.

2.8. Molecular Dynamics Analysis of MtLPMO9G and Its Mutants

To investigate how mutations at positions 165 and 167 enhanced enzymatic activity, molecular dynamic (MD) simulations were performed on the WT and the mutants at 30 °C for 20 ns (Figure 8A). During this period, the minimum distance between Cu(II) in the active center and the substrate plane was measured. The results indicated that in the A165S mutant, this distance remained below 8 Å throughout the 20 ns simulation. In contrast, in the WT, this distance increased to approximately 16 Å by the end of the simulation. Additionally, the distance between Cu(II) and the substrate plane in P167N was significantly shorter than in the WT (Figure 8B). These findings suggest that mutations at positions 165 and 167 reduced the distance between the active center and the substrate, thereby enhancing enzyme activity. Other studies such as Cheng Chao et al. [45] revealed that the substitution N526S, located in the CBM, likely stabilizes the cellulose-binding surface and enhances the binding capacity of HcLPMO10 to cellulose via MD simulations, thereby enhancing enzyme activity.

3. Materials and Methods

3.1. Materials

M. thermophila ATCC 42464 was obtained from the American Type Culture Collection (ATCC; Manassa, VA, USA). The Pichia pastoris X-33 strain and the pPICZαA expression vector were purchased from Invitrogen (Carlsbad, CA, USA). Escherichia coli Top 10 was acquired from Novagen (Madison, WI, USA). Restriction enzymes DpnI and SacI, along with the antibiotic bleomycin (Zeocin™), were sourced from Thermo Fisher Scientific (Waltham, MA, USA). Gene cloning and universal identification primers were purchased from BGI Technology Co., Ltd. (Beijing, China). Cellobiohydrolase I (CBH I) was obtained from Megazyme (Bray, Wicklow, Ireland). Microcrystalline cellulose Avicel PH-101 was purchased from Sigma-Aldrich (St. Louis, MO, USA) and was utilized for the preparation of phosphoric acid swollen cellulose (PASC) [46,47,48]. All other chemicals, which were of analytical grade, were procured from Sigma-Aldrich (St. Louis, MO, USA).

3.2. Selection of MtLPMO9G Mutation Sites

The structure of MtLPMO9G (GenBank accession number AEO54509.1) was predicted using AlphaFold 2.0 software. Consensus Finder (http://kazlab.umn.edu/) was employed for the online analysis to identify the consensus sequence and determine the mutation sites. The protein structure was visualized using PyMOL 2.5.4 software.

3.3. Construction of MtLPMO9G Mutants

The site-directed mutagenesis of the pPICZαA vector containing the MtLPMO9G gene was performed using RF cloning [49], with the primers synthesized as listed in Table 3. The PCR product was digested with 1 μL DpnI for 16 h. The resulting products were then transformed into E. coli Top10 competent cells and positive transformants were verified by a colony PCR. The correct plasmids were extracted and sent to BGI for sequencing and the sequencing results were analyzed using MEGA 11.0 software. The correctly sequenced plasmid was linearized with SacI for 12 h and transformed into P. pastoris X-33 competent cells. The yeast genome was extracted and verified by a colony PCR to confirm positive yeast transformants.

3.4. Expression and Purification of MtLPMO9G Mutants

P. pastoris transformants expressing MtLPMO9G mutants were initially cultured in a BMGY medium for 24 h at 28 °C with shaking at 200 rpm. The cells were then moved to a BMMY medium and cultured with shaking for four days, with 0.5% methanol added every 24 h. To remove the precipitation, the cultures were centrifuged for 30 min at 8000× g and 4 °C. A 10 kDa ultrafiltration membrane was used to concentrate the supernatant and it was desalted three times with 20 mM Tris-HCl buffer (pH 7.3) to achieve a final volume of 10 mL. After further centrifugation at 13,000× g and 4 °C for 20 min, the sample was purified using the anion-exchange resin HiTrapTM DEAE FF (80 mL) with buffer A (20 mM Tris-HCl; pH 7.4) and buffer B (20 mM Tris-HCl, 1 M NaCl, and pH 7.4). The purified samples were analyzed using SDS-PAGE. For the activity assays, the purified proteins were first incubated with 1 mM Cu2+ for 2 h at 4 °C to saturate the copper catalytic center and then diluted with 20 mM Tris-HCl buffer (pH 7.3) to remove the extra Cu2+.

3.5. Assay of the Oxidative Activity of MtLPMO9G Mutants

We used 500 μL solutions containing 5 mg/mL substrate, 1 μM LPMO, and 1 mM ascorbic acid in 50 mM NH4Ac (pH 5.0) for the LPMO reactions. In 2 mL tubes, the reactions were incubated for 24 h at 40 °C and with 1000 rpm of shaking. After this incubation, CBH I was added and the reactions were kept at 40 °C for a further 12 h. Using a mobile phase of 0.1 M NaOH (A) and 0.1 M NaOH with 500 mM sodium acetate (B) at a flow rate of 1 mL/min, the resulting products were examined using HPAEC-PAD. Using a pulsed amperometric detector, the detection was carried out in accordance with the separation protocol of Westereng et al. [50,51] for oligosaccharides and oligosaccharide acids.

3.6. Thermal Stability Assay of MtLPMO9G Mutants

The melting temperature (Tm) was measured to evaluate the thermal stability of MtLPMO9G and its mutants. Protein was diluted in 20 mM Tris–HCl buffer (pH 7.3) to a final concentration of approximately 1 mg/mL with less than 20% glycerol. The Tm values were measured using a Prometheus Panta instrument.

3.7. Assay of the Synergy between MtLPMO9G Mutants and Cellobiohydrolase

The substrate was treated with LPMOs following the LPMO oxidation activity assay method. The reaction system contained 50 mM NH4Ac (pH 5.0), 4 mg/mL PASC, 1 mM ascorbic acid, LPMOs, and CBH II in ratios of 1:10, 1:1, and 10: 1. The reactions were conducted for 24 h at 40 °C. The cellobiose content was determined using HPAEC-PAD. All experiments were performed in triplicate.

3.8. Assay of the Substrate-Binding Affinity of MtLPMO9G Mutants

The affinity of MtLPMO9G and its mutants to PASC substrates was measured as described by Hansson et al. [43]. The reactions were performed in 50 mM ammonium acetate buffer (pH 5.0) with 1 μM LPMO and varying concentrations of PASC (0, 1, 2, 3, 4, 5, 6, and 7 g/L). After 2 h of incubation, the samples were centrifuged for 5 min at 13,000× g. Quick StartTM Bradford 1 × Dye Reagent (Bio-Rad, Hercules, CA, USA) was used to measure the enzyme concentration in the supernatant. The data were then fitted to binding isotherms to determine the Kd and Bmax.

3.9. Assay of the H2O2 Tolerance Ability of MtLPMO9G Mutants

The reaction mixtures contained 4 mg/mL PASC, 1 μM LPMOs, 1 mM ascorbic acid, and varying concentrations of H2O2 (0, 50, and 100 μM) in 50 mM NH4Ac buffer (pH 5.0). Each group was performed in triplicate. The reactions were conducted in 2 mL tubes at 40 °C with shaking at 1000 rpm. Samples were collected at 5 h, 12 h, and 24 h, digested with CBH, and quantitatively analyzed using HPAEC-PAD.

3.10. Assay of the Kinetics of MtLPMO9G Mutants

2,6-Dimethoxyphenol (2,6-DMP) was used as the substrate to measure the peroxidase activity of MtLPMO9G and its mutants [52]. In total, 5 mM H2O2, 1 μM LPMO, 50 mM NH4Ac buffer (pH 5.0), and different concentrations of 2,6-DMP (1, 1.25, 2.5, 5, 10, 20, 30, 40, and 50 mM) made up the reaction system. In the NH4Ac buffer, substrate 2,6-DMP and H2O2 were added and incubated for 10 min at 30 °C, LPMOs were then added for the reaction, and the increase in absorbance at 469 nm was measured after 5 min. Using the molar absorbance coefficient of coerulignone (ε469 = 53,200 M−1 cm−1), the peroxidase activity was measured. One unit (U) of peroxidase activity is defined as the oxidation of 1 μmol of coerulignone from 2 μmol of 2,6-DMP per minute. All experiments were performed in triplicate.

3.11. Molecular Dynamics Simulations

The 3D structure of MtLPMO9G was predicted using AlphaFold software and an Iβ crystal cellulose model was generated using Cellulose-Builder software (https://code.google.com/archive/p/cellulose-builder/, accessed on 28 April 2024.). The protein molecule was docked onto the surface of the cellulose model using AutoDockTool 1.5.6 and the resulting complex was submitted to the Solution Builder module of the CHARMM-GUI website (http://www.charmm-gui.org, accessed on 28 April 2024). The structural model was parameterized using a CHARMM36 all-atom force-field and the transferable intermolecular potential 3P (TIP3P) water molecule model. In this process, the glycosylation site, glycoform, mutation site, and target amino acid were entered according to the prompts and the system generated an input file for GROMACS v2022.5 software based on the information provided. MD simulations were performed using GROMACS v2022.5 software and the system went through the energy minimization, temperature equilibrium (NVT), and pressure equilibrium (NPT) and then performed a 20 ns simulation calculation. During this process, the system temperature was controlled at 30 °C, the pressure was controlled at 1 atmosphere, and the electrostatic interaction between particles was calculated using the Particle Mesh Ewald (PME) method.

4. Conclusions

In this study, consensus mutagenesis was introduced into the multi-structural domain of MtLPMO9G derived from M. thermophila to enhance its activity. The results demonstrated that the activities of mutants A165S and P167N increased by approximately 78% and 43%, respectively, compared with the WT. Kinetic assays revealed that the catalytic efficiencies (kcat/Km) of A165S and P167N were 1.6-fold and 1.2-fold higher than the WT, respectively. Furthermore, experiments on the synergistic interaction between LPMOs and cellobiohydrolase showed that the hydrolysis efficiency of PASC by mutants A165S and P167N in combination with cellobiohydrolase was significantly higher than that of the WT. The H2O2 tolerance assay indicated that, after 24 h with 100 μM H2O2, P167N exhibited increased tolerance to H2O2 compared with the WT. The MD simulations suggested that the A165S and P167N mutations brought the Cu(II) of the active center closer to the substrate, thereby enhancing the catalytic activity of LPMOs. In summary, this study successfully improved the activity of LPMOs using consensus mutagenesis. This approach provides a valuable theoretical basis to enhance the catalytic activity of these enzymes via protein engineering.

Author Contributions

Y.M.: Methodology, Validation, Visualization, and Writing—Original Draft. W.G.: Investigation and Validation. X.L.: Formal Analysis. T.L.: Software and Visualization. K.L.: Methodology, Supervision, and Writing—Review and Editing. H.Y.: Conceptualization, Supervision, Writing—Review and Editing, Project Administration, and Funding Acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 32270210), the ANSO Collaborative Research Program (ANSO-CR-KP-2020-14), and the Dalian Institute of Chemical Physics Innovation Fund (DICP I202412).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank Ren in the Core Facilities of the School of Bioengineering, Dalian University of Technology, for his assistance with the protein thermal stability analysis.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. (A) PCR identification of the transformants of MtLPMO9G mutants. As the intact linearized plasmid destroyed the original AOX1 gene during the insertion of the gene and, at the same time, formed a new AOX1 gene, a fragment of the target gene (1500 bp) and a fragment of the AOX1 gene of Pichia pastoris (2000 bp) appeared at the same time in the electrophoretic detection. (B) SDS-PAGE identification of the purified MtLPMO9G mutants. M: protein marker. Lane 1, mutant A165S; lane 2, mutant N166G; lane 3, mutant P167N. The presence of the single band at 43 kDa corresponds with the target protein, indicating that purified proteins were obtained.
Figure 1. (A) PCR identification of the transformants of MtLPMO9G mutants. As the intact linearized plasmid destroyed the original AOX1 gene during the insertion of the gene and, at the same time, formed a new AOX1 gene, a fragment of the target gene (1500 bp) and a fragment of the AOX1 gene of Pichia pastoris (2000 bp) appeared at the same time in the electrophoretic detection. (B) SDS-PAGE identification of the purified MtLPMO9G mutants. M: protein marker. Lane 1, mutant A165S; lane 2, mutant N166G; lane 3, mutant P167N. The presence of the single band at 43 kDa corresponds with the target protein, indicating that purified proteins were obtained.
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Figure 2. The activity analysis of MtLPMO9G and its mutants. (A) HPLC analysis of the product profile of MtLPMO9G and its mutants on PASC; (B) quantification of cellobionic acid production following the further treatment of soluble LPMO products by CBH I. Compared with the WT (black), the activity of mutant A165S (blue) increased by about 78%, the activity of mutant P167N (red) increased by about 43%, and there was no significant change in mutant N166G (green). The data are presented as the mean ± standard deviation. * p < 0.05 and ** p < 0.01.
Figure 2. The activity analysis of MtLPMO9G and its mutants. (A) HPLC analysis of the product profile of MtLPMO9G and its mutants on PASC; (B) quantification of cellobionic acid production following the further treatment of soluble LPMO products by CBH I. Compared with the WT (black), the activity of mutant A165S (blue) increased by about 78%, the activity of mutant P167N (red) increased by about 43%, and there was no significant change in mutant N166G (green). The data are presented as the mean ± standard deviation. * p < 0.05 and ** p < 0.01.
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Figure 3. Comparison of the thermal stability of MtLPMO9G and its mutants. As shown in the table in the figure, the Tm value of mutant A165S (blue) was reduced by 0.37 °C and the Tm value of mutant P167N (red) was reduced by 7.19 °C compared with the WT (black).
Figure 3. Comparison of the thermal stability of MtLPMO9G and its mutants. As shown in the table in the figure, the Tm value of mutant A165S (blue) was reduced by 0.37 °C and the Tm value of mutant P167N (red) was reduced by 7.19 °C compared with the WT (black).
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Figure 4. Comparison of the synergy between MtLPMO9G and its mutants and CBH II. The synergistic effect of mutants A165S (blue) and P167N (red) with CBH was significantly increased when the ratio of LPMO to CBH II was 1:10 and 10:1 compared with the WT (black). The data are presented as the mean ± standard deviation. ** p < 0.01, and *** p < 0.001.
Figure 4. Comparison of the synergy between MtLPMO9G and its mutants and CBH II. The synergistic effect of mutants A165S (blue) and P167N (red) with CBH was significantly increased when the ratio of LPMO to CBH II was 1:10 and 10:1 compared with the WT (black). The data are presented as the mean ± standard deviation. ** p < 0.01, and *** p < 0.001.
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Figure 5. Comparison of the substrate-binding ability of MtLPMO9G and its mutants. The Bmax was calculated to be increased in mutant P167N (red) compared with the WT (black).
Figure 5. Comparison of the substrate-binding ability of MtLPMO9G and its mutants. The Bmax was calculated to be increased in mutant P167N (red) compared with the WT (black).
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Figure 6. Comparison of H2O2 tolerance ability of MtLPMO9G and its mutants. (A) Reaction after 5 h; (B) reaction after 12 h; (C) reaction after 24 h. At 24 h after the reaction began, the H2O2 tolerance of mutant P167N (red) showed a significant increase compared with the WT (black) under the addition of 100 μM H2O2. The data are presented as the mean ± standard deviation. ** p < 0.01.
Figure 6. Comparison of H2O2 tolerance ability of MtLPMO9G and its mutants. (A) Reaction after 5 h; (B) reaction after 12 h; (C) reaction after 24 h. At 24 h after the reaction began, the H2O2 tolerance of mutant P167N (red) showed a significant increase compared with the WT (black) under the addition of 100 μM H2O2. The data are presented as the mean ± standard deviation. ** p < 0.01.
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Figure 7. Michaelis–Menten kinetics of MtLPMO9G and its mutants.
Figure 7. Michaelis–Menten kinetics of MtLPMO9G and its mutants.
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Figure 8. Interactions of MtLPMO9G and its mutants with cellulose probed by MD simulations. (A) Overview of MtLPMO9G (green) interacting with cellulose (pink) at t = 20 ns, where the addition of sugar chains to LPMOs was better able to measure the interaction of LPMOs with cellulose; (B) distance between Cu(II) in the active center and the substrate plane during 20 ns of the simulation.
Figure 8. Interactions of MtLPMO9G and its mutants with cellulose probed by MD simulations. (A) Overview of MtLPMO9G (green) interacting with cellulose (pink) at t = 20 ns, where the addition of sugar chains to LPMOs was better able to measure the interaction of LPMOs with cellulose; (B) distance between Cu(II) in the active center and the substrate plane during 20 ns of the simulation.
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Table 1. Substrate-binding parameters of MtLPMO9G and its mutants.
Table 1. Substrate-binding parameters of MtLPMO9G and its mutants.
Kd (μM)Bmax (μM)Kr (Bmax/Kd)
WT9.15 ± 4.941.05 ± 0.061.1 × 10−1
A165S34.37 ± 27.970.78 ± 0.172.3 × 10−2
P167N12.60 ± 10.340.89 ± 0.107.0 × 10−2
Table 2. Kinetic parameters of MtLPMO9G and its mutants.
Table 2. Kinetic parameters of MtLPMO9G and its mutants.
Vmax (U/g)Km (mM)
WT662.2 ± 44.323.7 ± 3.5
A165S808.5 ± 94.417.3 ± 5.1
P167N683.5 ± 82.919.9 ± 5.7
Table 3. The sequences and usage of the primers.
Table 3. The sequences and usage of the primers.
NameSequence (5′-3′)Usage
5′AOXGACTGGTTCCAATTGACAAGCTransformant verification
3′AOXGCAAATGGCATTCTGACATCC
pPICZαA-A165S-FCAGTTCCAACCCCGGCCCConstruction of mutant A165S
pPICZαA-A165S-RTTGGAACTGCCGCCACCAGT
pPICZαA-N166G-FTGCCGGCCCCGGCCCGACCGTCTConstruction of mutant N166G
pPICZαA-N166G-RGGGGCCGGCACTGCCGCCACCA
pPICZαA-P167N-FCAACAACGGCCCGACCGTConstruction of mutant P167N
pPICZαA-P167N-RGCCGTTGTTGGCACTGCC
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Meng, Y.; Gao, W.; Liu, X.; Li, T.; Li, K.; Yin, H. Improving the Catalytic Efficiency of an AA9 Lytic Polysaccharide Monooxygenase MtLPMO9G by Consensus Mutagenesis. Catalysts 2024, 14, 614. https://doi.org/10.3390/catal14090614

AMA Style

Meng Y, Gao W, Liu X, Li T, Li K, Yin H. Improving the Catalytic Efficiency of an AA9 Lytic Polysaccharide Monooxygenase MtLPMO9G by Consensus Mutagenesis. Catalysts. 2024; 14(9):614. https://doi.org/10.3390/catal14090614

Chicago/Turabian Style

Meng, Yao, Wa Gao, Xiaohua Liu, Tang Li, Kuikui Li, and Heng Yin. 2024. "Improving the Catalytic Efficiency of an AA9 Lytic Polysaccharide Monooxygenase MtLPMO9G by Consensus Mutagenesis" Catalysts 14, no. 9: 614. https://doi.org/10.3390/catal14090614

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