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Computer-assisted engineering of the catalytic activity of a carboxylic acid reductase
Journal of Biotechnology 306 (2019) 97–104
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Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec
Computer-assisted engineering of the catalytic activity of a carboxylic acid reductase
T
Ge Qua,1, Beibei Liua,1, Kun Zhanga, Yingying Jianga, Jinggong Guob, Ran Wangc, Yuchen Miaob, ⁎ Chao Zhaid, Zhoutong Suna, a
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin, 300308, China State Key Laboratory of Cotton Biology, Department of Biology, Institute of Plant Stress Biology, Henan University, 85 Minglun Street, Kaifeng, 475001, China c Zhengzhou Tabacco Research Institute of CNTC, No. 2 Fengyang Street, Zhengzhou, 450001, Henan, China d State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, 368 Youyi Road, Wuchang Wuhan, 430062, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Rational design Biocatalysis Enzyme activity Carboxylic acid reductase Saturation mutagenesis
Carboxylic acid reductases (CARs) play crucial roles in the biosynthesis of optically pure aldehydes with no side products. It has inspired synthetic organic chemists and biotechnologists to exploit them as catalysts in practical applications. However, levels of activity and substrate specificity are not routinely sufficient. Recent developments in protein engineering have produced numerous biocatalysts with new catalytic properties, whereas such efforts in CARs are limited. In this study, we show that the exploitation of information derived from catalytic mechanism analysis and molecular dynamics simulations assisted the semi-rational engineering of a CAR from Segniliparus rugosus (SrCAR) with the aim of increasing activity. Guided by protein-ligand interaction fingerprinting analysis, 17 residues at the substrate binding pockets were first identified. We then performed single site saturation mutagenesis and successfully obtained variants that gave high activities using benzoic acid as the model substrate. As a result, the best mutant K524W enabled 99% conversion and 17.28 s−1 mM−1 kcat/Km, with 7- and 2-fold improvement compared to the wild-type, respectively. The engineered catalyst K524W as well as a second variant K524Q proved to be effective in the reduction of other benzoic acid derivatives. Insight into the source of enhanced activity was gained by molecular dynamics simulations.
1. Introduction As essential building blocks, aldehydes are widely used in the fragrances, pharmaceuticals, food industries and other fields (Kunjapur and Prather, 2015). Chemical synthesis of aldehydes directly from carboxylic acids are very difficult, especially when aiming for a catalytic process. It requires transition-metals catalysts and chemical reducing agents in stoichiometric amounts, and needs extreme reaction conditions. Even then, unwanted side reactions occur due to over-reduction to the alcohol products (Seyden-Penne, 1997; Ikariya and Blacker, 2007; Dub and Ikariya, 2012; Tojo and Fernández, 2006). As a green alternative to conventional methods, biocatalysis using carboxylic acid reductase (CAR) represents an attractive option with the formation of the corresponding aldehyde moieties under mild reaction conditions (e.g. ambient temperature and pressure as well as neutral pH) in an environmentally sustainable manner (Napora-Wijata et al.,
2014; Winkler, 2018). In addition, CARs also display an exceptionally broad substrate scope, varying from aromatic/heteroaromatic acids to aliphatic acids (Qu et al., 2018). Due to the above advantages, CARs are gaining broad interest both in the laboratory and industrial fields. It has been well documented that CARs are ATP- and NADPH-dependent enzymes, which consist of a three-domain architechure: an adenylation (A) domain, a thiolation (T) domain and a reductase (R) domain. Posttranslational modificatioon happens in the T domain, thereby attaching a phosphopantetheine (PPT) group to a conserved serine (Venkitasubramanian et al., 2007). The reaction mechanism has also been elucidated. The catalytic cycle comprises adenylation, thioesterfication and reduction (Venkitasubramanian et al., 2007). The first two reactions occur sequencially in the same substrate binding pocket situated in the A domain (dubbed “A domain pocket”). After thioestification, a large-scale structural reorientation within the A and T domains repositions the acyl-thioester intermediate toward the
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Corresponding author. E-mail address: [email protected] (Z. Sun). 1 Authors contribute equally to this work. https://doi.org/10.1016/j.jbiotec.2019.09.006 Received 20 May 2019; Received in revised form 5 September 2019; Accepted 10 September 2019 Available online 21 September 2019 0168-1656/ © 2019 Elsevier B.V. All rights reserved.
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Scheme 1. The general catalytic cycle of CAR reaction. Hydride and proton are colored in red and blue, respectively. R’-SH represnets the thiol group of the phosphopantetheinyl (PPT) arm.
substrate binding pocket in the R domain (dubbed “R domain pocket”), thereafter the thioester intermediate undergoes reductive cleavage by NADPH to generate the aldehyde product. Subsequently, the PPT arm swings back to the A domain pocket, setting up the next catalytic cycle (Scheme 1). Moreover, the details of the catalytic mechanism of each step conducted by the corresponding domain were also elucidated. Based on the X-ray structures (Gahloth et al., 2017), our group exploited the conformational dynamics and the catalytic mechanism of a CAR from Segniliparus rugosus (SrCAR), including investigation of the key residues in the A domain pocket and exploration of the nature of the transition states of hydride/proton transfer in the reduction step by means of molecular dynamics (MD) simulations along with density functional theory (DFT) calculations (Qu et al., 2019). Recent studies have acknowledged that CARs can be employed in many different kinds of cascade reactions to synthesize a variety of valuable products in green chemical routes (Akhtar et al., 2013; Kallio et al., 2014; Xu et al., 2016; Bai et al., 2016; Ramsden et al., 2019). However, the unlocking of the catalytic potential of these enzymes is hampered by their low activities, which unfortunately make CARs to be the limiting step in such cascade catalysis (Weber et al., 2017; Kramer et al., 2018). Engineering a highly acitive CAR is a logical way towards the potential industrial applications. As such, we sought to improve CAR-activity by protein engineering. Directed evolution of enzymes has been demonstrated to be a routinely applied algorithm for tailoring numerous biocatalysts with novel catalytic properties (Reetz, 2011, 2013; Bommarius, 2015; Sheldon and Pereira, 2017; Arnold, 2018; Denard et al., 2015; Sun et al., 2019; Cheng et al., 2015; Zeymer and Hilvert, 2018). During the past decades, plenty of protein engineering techniques including rational design have emerged (Zeymer and Hilvert, 2018). In particular, when aiming for laboratory evolution of selectivity and/or activity, combinatorial active-site saturation test (CAST) in combined with in silico methods constitute viable protein engineering techniques (Reetz et al., 2005; Sun et al., 2016a, 2016b; Wang et al., 2017; Sun et al., 2019). With respect to protein engineering of CARs, only few studies have been reported so far. For instance, Winkler and coworkers recently applied random mutagenesis on a CAR and archived 9-fold improvement of activity by a single site mutant (Schwendenwein et al., 2019); the same group also performed consensus analysis and identified highly conserved residues which impact CAR activity (Stolterfoht et al., 2017, 2018). Nevertheless, computationally assisted enzyme design of CARs is still scarce. Herein, we report a rational design approach for improving the catalytic activity of SrCAR, using benzoic acid (1) as the model substrate (Scheme 2). The aldehyde moiety is further reduced to alcohol
product by the endogenous alcohol dehydrogenases. We also carried out MD computations with the engineered mutants, which shed light on the origin of activity. 2. Results 2.1. Molecular dynamics (MD) simulations reveal key residues lining the substrate binding pockets The crystal structures of SrCAR corresponding to adenylation, thiolation and reduction steps have been reported as PDB codes 5MSW, 5MSS and 5MSV, respectively (Gahloth et al., 2017). On the basis of these X-ray structures, three models corresponding to each reaction stage were constructed. By performing all-atom MD simulations for each model, protein-ligand interaction fingerprinting analysis was carried out. The key residues that frequently contact substrate 1 in the adenylation and thiolation stages (Fig. 1A and B) were elaborated in our previous study (Qu et al., 2019). In continuation, we applied the same analysis to detect the key sites that prominently participate in the reduction step (Fig. 1C). All of the contacts that may influence substrate recognition and product release are registered, including hydrogen bonds, hydrophobic interactions, π-stacking and salt bridges. As a result, a total of 17 individual residues at the A domain and R domain pockets were collected (Table S1). Notably, residue K528 is excluded, because its pivotal role in the thiolation reaction was recently uncovered (Qu et al., 2019). Residue T935 is also discarded, because it is part of the catalytic triad in the reduction step and therefore essential for maintaining natural activity. 2.2. Site-specific saturation mutagenesis of selected residues and transformation assays Among various gene mutagenesis methodologies, focusing saturation mutagenesis (SM) at sites lining the catalytic pocket has emerged as a widely recognized technique. In the quest to investigate the catalytic properties of all variants at the 17 rationally chosen residues, SM was applied for constructing focused libraries, whereby each site is substituted by the other 19 amino acids. In detail, the 17 residues were subjected to SM individually using the Tang technique (Tang et al., 2012) that encodes all 20 canonical amino acids, presumably without introducing redundancy. Accordingly, 17 independent libraries were built, requiring in each case the screening of 60 transformants for 95% library coverage, totally 1020 variants were tested for 17 libraries (Patrick and Firth, 2005; Nov, 2012; Reetz et al., 2008). In subsequent activity assays, dozens of mutants with improved activity toward 1 were indeed obtained from 10 out of 17 libraries Scheme 2. Schematic representation of the model reaction employed in this study, which is constructed in E. coli. ADHs: the host endogenous alcohol dehydrogenases.
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Fig. 1. Substrate binding modes and protein-ligand interaction fingerprint for adenylation (A), thiolation (B) and reduction (C), respectively. PPT: phosphopantetheine; AMP: adenosine monophosphate; Sub: substrate 1. The cyan radar plots represent the protein-ligand interactions in each reaction state. The statistical interaction frequency (%) in the plot was obtained from the MD runs. Figures shown in (A) and (B) are modified from our previous work (Qu et al., 2019).
(Fig. 2), specifically by detecting the formation of compound 3 (Scheme 2). There are two things that one needs to be aware of: 1. PCR-based SM libraries always suffer from amino acid bias (Zeymer and Hilvert, 2018; Li et al., 2018); 2. Some positive hits shown in the 96 well plates are likely be the wild-type (WT), and their increased apparent activity may be caused by a higher expression level of SrCAR in E. coli. Thus, all the positive hits collected from 17 libraries should be checked by sequencing. After removal of redundant transformants as well as WT (Fig. 2), 39 function-gained mutants were harvested from 10 libraries, involving seven residues (S266, S408, G430, T505, Y519, R522 and K524) at the A domain pocket and three other sites (V936, A937 and Q1015) at the R domain pocket. We found that the conversion toward 1 are improved from 14% of WT to as high as 99% of engineered mutants (Fig. 3 and Table S2). Specifically, the two mutants K524W (with > 99% conversion) and A937 V (with 85% conversion) display the best conversion with regard to the sites situated in the A- and R-domain pocket,
respectively, thereafter the combinatorial mutant K524W/A937 V was constructed for further analysis. In comparison to K524W, the double mutant K524W/A937 V show even better performance toward 1 (Fig. S1). On the other hand, seven libraries, including those at T265, G267, Y431, G432, T434, D507 and M999, fail to harbor improved mutants (Fig. 2), and the consensus analysis shows that the residues T265, G267, Y431, G432, T434, D507 are highly conserved with other crystallized ANL family members (Fig. S2), indicating that the original amino acids at these sites may play a crucial role for activity. 2.3. Characterization of the best mutants by kinetic parameters and biotransformation In all the single-site mutants, K524W and K524Q are characterized by highest conversion (> 99%, Fig. S3), showing 7-fold increases in the conversion relative to WT. Thereafter, the two best SrCAR mutants were
Fig. 2. Histograms of the number of positive hits in each library. The numbers of non-redundant variants are indicated by grey. 99
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Fig. 3. Screening results of single site saturation mutagenesis toward the reduction of 1 to 3 by E. coli cells containing SrCAR and its variants. The 17 mutant sites are listed on the left, while 20 canonical amino acids used as building blocks are shown horizontally. Table 1 Kinetic parameters of SrCAR and its mutants in the position 524 towards 1. Michaelis–Menten curve fits are shown in Supplementary Fig. S4. Enzymes
Km (mM)
kcat (s−1)
kcat/Km (s−1 mM−1)
WT K524Q K524W K524A K524C K524L K524M K524V
0.60 0.37 0.39 3.05 4.69 2.44 3.96 4.72
5.30 5.28 6.74 8.67 2.96 3.74 3.63 7.18
8.83 14.27 17.28 2.84 0.63 1.53 0.92 1.52
± ± ± ± ± ± ± ±
0.07 0.04 0.04 0.52 0.74 0.37 0.51 1.62
± ± ± ± ± ± ± ±
0.16 0.10 0.27 0.69 0.22 0.22 0.21 1.14
characterized by enzyme kinetics. Intriguingly, the kcat value of the WT enzyme is similar with the mutant K524Q (5.30 s−1 vs 5.28 s−1) in the reaction of substrate 1, and is lower than that of K524W (6.74 s−1). Whereas the Km values are more diverse. In the case of K524W, the Km value was estimated to be 0.39 mM, which is 2-times lower than that of WT (0.60 mM, Table 1). It suggests that K524W induces optimal substrate affinity ensuring highest efficiency. Afterwards, the biotransformation with respect to the two selected variants K524Q and K524W were performed using 10 mM of substrate 1 (∼25 mg) in 20 mL of reaction volume, using WT SrCAR as control. The mutant K524W achieves a full conversion within 4 h (Fig. 4), showing the best performance which is consistent with the activity assay result. Along with K524Q and K524W, other mutants in the position 524 were also characterized. They generally show higher Km values (diverged from 2.44 to 4.72 mM) and lower kcat/Km values (ranged from 0.63 to 2.84 s−1 mM−1) compared to WT (Table 1). Additionally, 20 mM of substrate 1 was also tested, both K524Q and K524W showed a better transformation than WT, while neither of them can reach a full conversion, only ∼50% conversion were observed (Fig. S5).
Fig. 4. Comparison of biotransformation 1 with formation of 3 employing WT, K524Q and K524W with 10 mM substrate loadings.
shows a significant oscillation toward AMP at the beginning of the MD runs (Fig. 5A). But in the cases of Q524 and W524, both of the side chains keep exploring the solvent and stay away from AMP (Fig. 5B and C). As a consequence, the ε-amino group of the K524 residue is engaged in a bifurcated hydrogen bond to the ribose hydroxyl oxygen atoms of AMP in the WT model (Fig. S7A), whereas such H-bond interactions were not detected in K524Q (Fig. S7B) or K524W models (Fig. S7C). It indicates that residue K524 may function in releasing AMP. Thus, we hypothesized that positively charged amino acids may hinder the release of AMP by forming the ionic and H-bonding interactions with the ribose hydroxyl groups of AMP. As demonstrated by the recent biosynthetic experiments (Duan et al., 2015; Kunjapur et al., 2016; Finnigan et al., 2017), by-products of the reaction, such as AMP and pyrophosphate (PPi) could act as inhibitors due to their ability to occupy the catalytic pocket. In order to double-check this conclusion, two more mutants at position 524 were constructed using the other positively charged amino acids arginine and histidine, respectively. As expected in our model, both K524R and K524H have modest activity, even lower than WT (Table S3), suggesting that positive amino acids at position 524 hamper enzyme activity by anchoring the side product AMP. In addition to computational analysis, isothermal titration calorimetry (ITC) were also
2.4. Shedding light on the origin of enhanced activity As shown in Fig. S6, residue 524 is located at the surface of SrCAR, observed in both X-ray structures corresponding to the adenylation and thiolation conformations. It is of interest to learn the effect of mutation on activity at position 524. In order to probe the molecular mechanism, 300 ns MD simulations were performed on the WT, K524Q as well as K524W. Strikingly, the MD runs revealed that the side-chain of K524 100
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Fig. 5. Overlay of 10 representative snapshots derived from the MD simulations of the WT (A), K524Q (B) and K524W (C) models. The initial side-chain conformation (colored in grey, PDB code 5MSS) oscillates rapidly toward AMP during simulations, and the motion is indicated by a dashed arrow. PPT_Sub: the complex of the thioester; AMP: adenosine monophosphate.
these residues, mutant K524W was found to show the best performance toward substrate 1, resulting in an excellent conversion (> 99%) and a kcat/Km value of 17.28 s−1 mM−1. This amount to 7-fold and 2-fold improvements relative to WT, respectively (Fig. 2 and Table 1). Furthermore, MD simulations together with mutagenesis analysis unveiled the origin of enhanced activity. Collectively, this structural-guided mutagenesis strategy coupled with knowledge of the catalytic mechanism paves the way to engineering CARs with increased activity, which is crucial for the fundamental understanding of enzymatic reactions. Further progress may be possible by applying combinatorial and iterative saturation mutagenesis (Reetz, 2011, 2013; Sun et al., 2016b).
performed, whereas the binding affinity was too low to be measured in both cases of AMP and PPi (Fig. S8). 2.5. Evaluating the best variants as catalysts in the reduction of other substrates The two best SrCAR variants K524Q and K524W obtained from the libraries described above were then evaluated as catalysts in the reduction of other benzoic acids derivatives. As CARs exhibit a better tolerance for para-substituted due to steric properties (Duan et al., 2015; Gahloth et al., 2017), para-substituted benzoic acids 4∼8 were selected for evaluation (Fig. 6). As a result, all of the substrates can be transformed by WT and the variants, although with different efficiency. In the case of compound 4, truly notable improvements were not observed; nevertheless, K524W still ensures a better performance than WT SrCAR, leading to 42% conversion. Toward substrate 5∼8 In sharp contrast, both mutants K524Q and K524W show remarkably increased transformation in the case of substrates 5-8 (91%–99%). This finding suggests that the best variants K524Q and K524W evolved for substrate 1 are also suitable for reducing para-substituted benzoic acids with high catalytic efficiency.
4. Materials and methods 4.1. Materials PrimeSTAR DNA polymerase and restriction enzyme Dpn I were obtained from TAKARA and NEB, respectively. The oligonucleotide synthesis and DNA sequencing were conducted by GENEWIZ technology. All reagents and chemicals were purchased from commercial sources and used without further purification. The gene of wild-type (WT) SrCAR (GenBank: WP_007468889) was synthesized and inserted into Nde I and Xho I sites of vector pET24a.
3. Discussion CARs are able to catalyze the reduction of carboxylic acids with formation of the corresponding aldehydes without over oxidation under mild conditions. As such, CARs are frequently implanted into cascade processes to produce valuable chemical compounds (Qu et al., 2018). However, the productivity is hampered by the low catalytic activity of CARs (Weber et al., 2017; Kramer et al., 2018). This underlying obstacle calls for protein engineering to improve activity. In the present study, we were guided by a structure-based analysis needed for performing efficient saturation mutagenesis (SM) of SrCAR. Accordingly 17 key residues were identified by protein-ligand interaction fingerprint analysis along with MD runs. After site-specific SM exploration at
4.2. Primer design and library creation Site-specific saturation mutagenesis was performed using megaprimer approach (Tyagi et al., 2004) with the corresponding primers of selected residues (Table S4). The PCR conditions for short fragment: 95 °C for 5 min, (95 °C for 30 s, 56 °C for 30 s, 72 °C for 40 s) ×28 cycles, 72 °C for 10 min. For mega-PCR: 95 °C for 5 min, (95 °C for 30 s, 60 °C for 30 s, 72 °C for 12 min) ×28 cycles, 72 °C for 10 min. The PCR products were analyzed on agarose gel by electrophoresis and digested with Dpn I at 37 °C for 3 h. The cloned library was then transformed
Fig. 6. Results of testing the best SrCAR mutants toward para-substituted benzoic acids. 101
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was determined in a 0.2 mL volume of phosphate buffer (50 mM, pH 7.4) containing 1 mM ATP, 10 mM MgCl2, 0.1 mM NADPH, the purified enzyme, 2% DMSO (v/v)and a varying concentration of substrate 1 (0.05～5 mM). Then the two best mutants K524Q and K524W were subjected to biotransformation reactions. They were grown overnight at 37 °C in LB medium containing 50 μg/mL kanamycin. Then the preculture was inoculated into 500 mL TB medium containing 50 μg/mL kanamycin and grown at 37 °C until the OD600 reached 0.8-0.9. After protein expression induced by 0.1 mM IPTG at 20 °C for 16 h, cells were pelleted by centrifugation, washed once with phosphate buffer (50 mM, pH 7.4) and then resuspended in pH 7.4 phosphate buffer (20 mL, 50 mM). Subsequently, 1.4 mg/mL lysozyme, 6 U/mL DNase I, 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 1 mg/mL GDH and substrate 1 (10 mM) dissolved in 2% v/v DMSO were added. Bioconversion was carried out at 30 °C, 220 rpm and terminated by adjusting the pH to 1–2 with 6 M HCl. The product and remaining substrate were extracted using an equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC to monitor the completion of the reaction.
into electro-competent E. coli BAP1 (Pfeifer et al., 2001) to create the final library for Quick Quality Control (Bougioukou et al., 2009) and screening. The 17 sites were individually substituted to the other 19 canonical amino acids by the Tang method (Tang et al., 2012). 4.3. Screening procedures Single colonies from the mutant libraries were separately inoculated into 300 μL LB medium with 50 μg/mL kanamycin in 96 deep-well plates and grown at 37 °C for 12 h. Then 200 μL culture was inoculated into 800 μL TB medium containing 0.2 mM IPTG and 50 μg/mL kanamycin. After expression at 30 °C for 16 h, the cell pellets were harvested by centrifugation and lysed by adding 300 μL of 50 mM, pH 7.4 phosphate buffer containing 6 U DNase I and 1 mg/mL lysozyme at 30 °C for one hour with shaking. The crude lysate was centrifuged for 30 min 4000 rpm at 4 °C. A quantity of 200 μL of supernatant was transferred into new deep-well plates for reaction with 5 mM substrate 1, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose and 1 mg/mL GDH for 20 h at 30 °C 800 rpm, and terminated by adjusting the pH to 1–2 with 6 M HCl. The product and remaining substrate were extracted using an equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC.
4.6. Computational procedures
4.4. Expression and purification of SrCAR
The preparations of protein and ligand structures were carried out in Schrodinger Maestro software (Schrödinger Suite, 2015). PDB codes 5MSW, 5MSS and 5MSV are adopted for adenylation, thiolation and reduction modelling, respectively, while missing residues in the X-ray structures were completed by Modeller 9.19 (Webb and Sali, 2016). The initial structures of the K524Q and K524W variants were generated via the PyMol program (http://www.pymol.org) based on the WT model. The pose of benzoic acid in 5MSD and phosphopantetheine complex in 3NYQ, as well as the thioester geometry of the phosphopantetheine complex in 1W6U were adopted to assemble the complex conformations in the model generation. The protonation of apo-protein models was accomplished at pH 7.4 (to mimic the the experimental conditions) using H++ webserver (Ramu et al., 2012). The resulting system’s geometries were minimized (5000 steps for steepest conjugate and 5000 steps for conjugate gradient) to remove poor contacts and relax the system. The systems were then heated from 0 to 303 K under the constant amount of NVT ensemble for 50 ps, and were maintained for 50 ps of density equilibration using the NPT ensemble at constant temperature of 303 K and pressure of 1.0 atm (ntt = 3) with a collision frequency of 2 ps−1 and pressure relaxation time of 1 ps. The heating and density equilibrations were carried out with a weak restraint of 10 kcal mol−1 Å-2 performed only on the protein residues. After removal of all restraints applied during heating and density dynamics, the systems were equilibrated for 10 ns to get well settled pressure and temperature. After proper minimizations and equilibrations, a productive MD run of 300 ns was performed for each system. During all MD simulations, the covalent bonds containing hydrogen were constrained using SHAKE algorithm (Ryckaert et al., 1977), with a 2 fs time-step. The trajectory file was written every 100 steps. Molecular dynamics (MD) simulations were performed with the GPU version of Amber 2016 (Case et al., 2016). More details regarding the MD procedures were described in the previous study (Qu et al., 2019). Analysis of trajectories was performed by CPPTRAJ (Roe and Cheatham, 2013). The protein − ligand interaction frequency in the MD simulations was performed by using the PLIP tool (Salentin et al., 2015) with the ‘–peptides’ setting. The convergence of all the MD simulations was evaluated by root-mean-square deviation (RMSD) analysis (Fig. S9).
E.coli BAP1 harboring the recombinant plasmids of SrCAR and its mutants were grown overnight at 37 °C in LB medium containing 50 μg/ mL kanamycin. The preculture was inoculated into 100 mL TB medium containing 50 μg/mL kanamycin and allowed to grow at 37 °C until the OD600 reached 0.8-0.9. The temperature was then decreased to 20 °C and protein expression was induced by 0.1 mM IPTG for 16 h. Cells were harvested by centrifugation and lysed by sonication. And the cell debris was removed by centrifugation. A part of the supernatant were lyophilized under vacuum to form the crude enzyme powder. The others were then loaded onto a His-trap HP column (5 mL, GE Healthcare) pre-equilibrated with equilibrium buffer (20 mM phosphate buffer, 0.5 M NaCl, 20 mM imidazole, pH 7.4), and the proteins were eluted with 20–500 mM imidazole solution containing 500 mM NaCl and 20 mM phosphate buffer (pH 7.4) at a flow rate of 3 mL/min. The fractions containing the target protein were collected and dialyzed against 20 mM sodium phosphate buffer (pH 7.4) for desalting using centrifugal filter units with 30 KDa cut-off (Millipore, USA). Finally, the enzyme solution was concentrated for kinetic parameters and total turnover numbers determination. Protein concentration was measured using the Bradford method. 4.5. Activity assay, kinetic parameters and biotransformation Activity assay was carried out as follows: 2 mM substrate 1, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 1 mg/mL GDH and 10 mg/mL crude enzyme powder of the SrCAR variant were mixed in 0.5 mL potassium phosphate (50 mM, pH 7.4). The reaction was carried out at 30 °C for 24 h, and terminated by adjusting the pH to 1～2 with 6 M HCl solution. The product and remaining substrate were extracted using equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC equipped with an SH-Rtx-1 column (60 × 0.32 mm ID, 0.25 μm film thickness). The temperature program of GC was set as: oven 140 °C (initial, hold time 3 min), 30 °C/min to 280 °C (hold time 2 min), injector 300 °C, detector 320 °C. The conversion was determined based on authentic standards. The kinetic parameters were obtained by measuring the initial velocities of the enzyme reaction, the protein and substrate concentration were adjust to monitor the linear reaction period during 4 min, and curve-fitting according to the Michaelis-Menten equation. Enzyme activity was measured using a spectrophotometric assay at 340 nm, corresponding to the cofactor NADPH (ε340 = 6220 M−1 cm-1). The assay
4.7. Substrate scope investigation Reactions were conducted in 2 mL Eppendorf Tubes by mixing phosphate buffer solution (50 mM, pH 7.4), 2 mM substrate, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 102
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1 mg/mL GDH and 10 mg/mL crude enzyme powder of the SrCAR variant. The reaction mixture was stirred at 30 °C, 1000 rpm for 24 h, and terminated by adjusting the pH to 1–2 with 6 M HCl solution for GC or HPLC analysis. For 4-methoxybenzoic acid, the product and remaining substrate were extracted using an equal volume of ethyl acetate and dried with anhydrous Na2SO4 for GC analysis performed with an SH-Rtx-1 column (60 × 0.32 mm ID, 0.25 μm film thickness). The temperature program of GC was set as: oven 140 °C (initial, hold time 3 min), 30 °C/min to 280 °C (hold time 2 min), injector 300 °C, detector 320 °C. For the other acid substrates, including 4-hydroxybenzoic acid, 4-fluorobenzoic acid, 4-chlorobenzoic acid and 4-bromobenzoic acid, the aqueous phase was centrifuged at 12,000 rpm for 10 min and analyzed by HPLC equipped with an Agilent SB C18 column (4.6 mm × 150 mm × 5 μm) with a flow rate of 1 mL/min (the ratio of acetonitrile to 0.1% TFA is 4:6) at 220 nm. The conversion was determined based on authentic standards for GC and HPLC analysis. All reactions were carried out in triplicate.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was financially supported by CAS Pioneer Hundred Talents Program (No. 2016-053), the Key Research Program of the Chinese Academy of Sciences (No. KFZD-SW-212 and KFZD-SW-215), the Natural Science Foundation of Tianjin (No.18JCYBJC24600), the Biological Resources Programme, Chinese Academy of Sciences (No.KFJ-BRP-009), and the State Key Laboratory of Cotton Biology Open Fund (No. CB2019A20). G.Q thanks the National Natural Science Foundation of China (No.31900909), the state key laboratory of biocatalysis and enzyme engineering (No. SKLBEE2018001), and Science & Technology Foundation for Selected overseas Chinese scholar of Tianjin (2017) for generous support. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jbiotec.2019.09.006. References Akhtar, M.K., Turner, N.J., Jones, P.R., 2013. Carboxylic acid reductase is a versatile enzyme for the conversion of fatty acids into fuels and chemical commodities. Proc. Natl. Acad. Sci. U. S. A. 110, 87–92. https://doi.org/10.1073/pnas.1216516110. Arnold, F.H., 2018. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. 57, 4143–4148. https://doi.org/10.1002/anie.201708408. Bai, Y., Yin, H., Bi, H., Zhuang, Y., Liu, T., Ma, Y., 2016. De novo biosynthesis of gastrodin in Escherichia coli. Metab. Eng. 35, 138–147. https://doi.org/10.1016/j.ymben.2016. 01.002. Bommarius, A.S., 2015. Biocatalysis: a status report. Annu. Rev. Chem. Biomol. Eng. 6, 319–345. https://doi.org/10.1146/annurev-chembioeng-061114-123415. Bougioukou, D.J., Kille, S., Taglieber, A., Reetz, M.T., 2009. Directed evolution of an enantioselective enoate-reductase: testing the utility of iterative saturation mutagenesis. Adv. Synth. Catal. 351, 3287–3305. https://doi.org/10.1002/adsc. 200900644. Case, D.A., Betz, R.M., Cerutti, D.S., Cheatham III, T.E., Darden, T.A., Duke, R.E., Giese, T.J., Gohlke, H., Goetz, A.W., Homeyer, N., Izadi, S., Janowski, P., Kaus, J., Ko-valenko, A., Lee, T.S., LeGrand, S., Li, P., Lin, C., Luchko, T., Luo, R., Madej, B., Mermelstein, D., Merz, K.M., Monard, G., Nguyen, H.T., Omelyan, I., Onufriev, A., Roe, D.R., Roitberg, A., Sagui, C., Simmer-ling, C.L., Botello-Smith, W.M., Swails, J., Walker, R.C., Wang, J., Wolf, R.M., Wu, X., Xiao, L., Kollman, P.A., 2016. AMBER 2016. University of California, San Francisco. Cheng, F., Zhu, L., Schwaneberg, U., 2015. Directed evolution 2.0: improving and deciphering enzyme properties. Chem. Commun. 51, 9760–9772. https://doi.org/10. 1039/c5cc01594d. Denard, C.A., Ren, H., Zhao, H., 2015. Improving and repurposing biocatalysts via directed evolution. Curr. Opin. Chem. Biol. 25, 55–64. https://doi.org/10.1016/j.cbpa. 2014.12.036.
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Computer-assisted engineering of the catalytic activity of a carboxylic acid reductase
T
Ge Qua,1, Beibei Liua,1, Kun Zhanga, Yingying Jianga, Jinggong Guob, Ran Wangc, Yuchen Miaob, ⁎ Chao Zhaid, Zhoutong Suna, a
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, 32 West 7th Avenue, Tianjin Airport Economic Area, Tianjin, 300308, China State Key Laboratory of Cotton Biology, Department of Biology, Institute of Plant Stress Biology, Henan University, 85 Minglun Street, Kaifeng, 475001, China c Zhengzhou Tabacco Research Institute of CNTC, No. 2 Fengyang Street, Zhengzhou, 450001, Henan, China d State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, 368 Youyi Road, Wuchang Wuhan, 430062, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Rational design Biocatalysis Enzyme activity Carboxylic acid reductase Saturation mutagenesis
Carboxylic acid reductases (CARs) play crucial roles in the biosynthesis of optically pure aldehydes with no side products. It has inspired synthetic organic chemists and biotechnologists to exploit them as catalysts in practical applications. However, levels of activity and substrate specificity are not routinely sufficient. Recent developments in protein engineering have produced numerous biocatalysts with new catalytic properties, whereas such efforts in CARs are limited. In this study, we show that the exploitation of information derived from catalytic mechanism analysis and molecular dynamics simulations assisted the semi-rational engineering of a CAR from Segniliparus rugosus (SrCAR) with the aim of increasing activity. Guided by protein-ligand interaction fingerprinting analysis, 17 residues at the substrate binding pockets were first identified. We then performed single site saturation mutagenesis and successfully obtained variants that gave high activities using benzoic acid as the model substrate. As a result, the best mutant K524W enabled 99% conversion and 17.28 s−1 mM−1 kcat/Km, with 7- and 2-fold improvement compared to the wild-type, respectively. The engineered catalyst K524W as well as a second variant K524Q proved to be effective in the reduction of other benzoic acid derivatives. Insight into the source of enhanced activity was gained by molecular dynamics simulations.
1. Introduction As essential building blocks, aldehydes are widely used in the fragrances, pharmaceuticals, food industries and other fields (Kunjapur and Prather, 2015). Chemical synthesis of aldehydes directly from carboxylic acids are very difficult, especially when aiming for a catalytic process. It requires transition-metals catalysts and chemical reducing agents in stoichiometric amounts, and needs extreme reaction conditions. Even then, unwanted side reactions occur due to over-reduction to the alcohol products (Seyden-Penne, 1997; Ikariya and Blacker, 2007; Dub and Ikariya, 2012; Tojo and Fernández, 2006). As a green alternative to conventional methods, biocatalysis using carboxylic acid reductase (CAR) represents an attractive option with the formation of the corresponding aldehyde moieties under mild reaction conditions (e.g. ambient temperature and pressure as well as neutral pH) in an environmentally sustainable manner (Napora-Wijata et al.,
2014; Winkler, 2018). In addition, CARs also display an exceptionally broad substrate scope, varying from aromatic/heteroaromatic acids to aliphatic acids (Qu et al., 2018). Due to the above advantages, CARs are gaining broad interest both in the laboratory and industrial fields. It has been well documented that CARs are ATP- and NADPH-dependent enzymes, which consist of a three-domain architechure: an adenylation (A) domain, a thiolation (T) domain and a reductase (R) domain. Posttranslational modificatioon happens in the T domain, thereby attaching a phosphopantetheine (PPT) group to a conserved serine (Venkitasubramanian et al., 2007). The reaction mechanism has also been elucidated. The catalytic cycle comprises adenylation, thioesterfication and reduction (Venkitasubramanian et al., 2007). The first two reactions occur sequencially in the same substrate binding pocket situated in the A domain (dubbed “A domain pocket”). After thioestification, a large-scale structural reorientation within the A and T domains repositions the acyl-thioester intermediate toward the
⁎
Corresponding author. E-mail address: [email protected] (Z. Sun). 1 Authors contribute equally to this work. https://doi.org/10.1016/j.jbiotec.2019.09.006 Received 20 May 2019; Received in revised form 5 September 2019; Accepted 10 September 2019 Available online 21 September 2019 0168-1656/ © 2019 Elsevier B.V. All rights reserved.
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Scheme 1. The general catalytic cycle of CAR reaction. Hydride and proton are colored in red and blue, respectively. R’-SH represnets the thiol group of the phosphopantetheinyl (PPT) arm.
substrate binding pocket in the R domain (dubbed “R domain pocket”), thereafter the thioester intermediate undergoes reductive cleavage by NADPH to generate the aldehyde product. Subsequently, the PPT arm swings back to the A domain pocket, setting up the next catalytic cycle (Scheme 1). Moreover, the details of the catalytic mechanism of each step conducted by the corresponding domain were also elucidated. Based on the X-ray structures (Gahloth et al., 2017), our group exploited the conformational dynamics and the catalytic mechanism of a CAR from Segniliparus rugosus (SrCAR), including investigation of the key residues in the A domain pocket and exploration of the nature of the transition states of hydride/proton transfer in the reduction step by means of molecular dynamics (MD) simulations along with density functional theory (DFT) calculations (Qu et al., 2019). Recent studies have acknowledged that CARs can be employed in many different kinds of cascade reactions to synthesize a variety of valuable products in green chemical routes (Akhtar et al., 2013; Kallio et al., 2014; Xu et al., 2016; Bai et al., 2016; Ramsden et al., 2019). However, the unlocking of the catalytic potential of these enzymes is hampered by their low activities, which unfortunately make CARs to be the limiting step in such cascade catalysis (Weber et al., 2017; Kramer et al., 2018). Engineering a highly acitive CAR is a logical way towards the potential industrial applications. As such, we sought to improve CAR-activity by protein engineering. Directed evolution of enzymes has been demonstrated to be a routinely applied algorithm for tailoring numerous biocatalysts with novel catalytic properties (Reetz, 2011, 2013; Bommarius, 2015; Sheldon and Pereira, 2017; Arnold, 2018; Denard et al., 2015; Sun et al., 2019; Cheng et al., 2015; Zeymer and Hilvert, 2018). During the past decades, plenty of protein engineering techniques including rational design have emerged (Zeymer and Hilvert, 2018). In particular, when aiming for laboratory evolution of selectivity and/or activity, combinatorial active-site saturation test (CAST) in combined with in silico methods constitute viable protein engineering techniques (Reetz et al., 2005; Sun et al., 2016a, 2016b; Wang et al., 2017; Sun et al., 2019). With respect to protein engineering of CARs, only few studies have been reported so far. For instance, Winkler and coworkers recently applied random mutagenesis on a CAR and archived 9-fold improvement of activity by a single site mutant (Schwendenwein et al., 2019); the same group also performed consensus analysis and identified highly conserved residues which impact CAR activity (Stolterfoht et al., 2017, 2018). Nevertheless, computationally assisted enzyme design of CARs is still scarce. Herein, we report a rational design approach for improving the catalytic activity of SrCAR, using benzoic acid (1) as the model substrate (Scheme 2). The aldehyde moiety is further reduced to alcohol
product by the endogenous alcohol dehydrogenases. We also carried out MD computations with the engineered mutants, which shed light on the origin of activity. 2. Results 2.1. Molecular dynamics (MD) simulations reveal key residues lining the substrate binding pockets The crystal structures of SrCAR corresponding to adenylation, thiolation and reduction steps have been reported as PDB codes 5MSW, 5MSS and 5MSV, respectively (Gahloth et al., 2017). On the basis of these X-ray structures, three models corresponding to each reaction stage were constructed. By performing all-atom MD simulations for each model, protein-ligand interaction fingerprinting analysis was carried out. The key residues that frequently contact substrate 1 in the adenylation and thiolation stages (Fig. 1A and B) were elaborated in our previous study (Qu et al., 2019). In continuation, we applied the same analysis to detect the key sites that prominently participate in the reduction step (Fig. 1C). All of the contacts that may influence substrate recognition and product release are registered, including hydrogen bonds, hydrophobic interactions, π-stacking and salt bridges. As a result, a total of 17 individual residues at the A domain and R domain pockets were collected (Table S1). Notably, residue K528 is excluded, because its pivotal role in the thiolation reaction was recently uncovered (Qu et al., 2019). Residue T935 is also discarded, because it is part of the catalytic triad in the reduction step and therefore essential for maintaining natural activity. 2.2. Site-specific saturation mutagenesis of selected residues and transformation assays Among various gene mutagenesis methodologies, focusing saturation mutagenesis (SM) at sites lining the catalytic pocket has emerged as a widely recognized technique. In the quest to investigate the catalytic properties of all variants at the 17 rationally chosen residues, SM was applied for constructing focused libraries, whereby each site is substituted by the other 19 amino acids. In detail, the 17 residues were subjected to SM individually using the Tang technique (Tang et al., 2012) that encodes all 20 canonical amino acids, presumably without introducing redundancy. Accordingly, 17 independent libraries were built, requiring in each case the screening of 60 transformants for 95% library coverage, totally 1020 variants were tested for 17 libraries (Patrick and Firth, 2005; Nov, 2012; Reetz et al., 2008). In subsequent activity assays, dozens of mutants with improved activity toward 1 were indeed obtained from 10 out of 17 libraries Scheme 2. Schematic representation of the model reaction employed in this study, which is constructed in E. coli. ADHs: the host endogenous alcohol dehydrogenases.
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Fig. 1. Substrate binding modes and protein-ligand interaction fingerprint for adenylation (A), thiolation (B) and reduction (C), respectively. PPT: phosphopantetheine; AMP: adenosine monophosphate; Sub: substrate 1. The cyan radar plots represent the protein-ligand interactions in each reaction state. The statistical interaction frequency (%) in the plot was obtained from the MD runs. Figures shown in (A) and (B) are modified from our previous work (Qu et al., 2019).
(Fig. 2), specifically by detecting the formation of compound 3 (Scheme 2). There are two things that one needs to be aware of: 1. PCR-based SM libraries always suffer from amino acid bias (Zeymer and Hilvert, 2018; Li et al., 2018); 2. Some positive hits shown in the 96 well plates are likely be the wild-type (WT), and their increased apparent activity may be caused by a higher expression level of SrCAR in E. coli. Thus, all the positive hits collected from 17 libraries should be checked by sequencing. After removal of redundant transformants as well as WT (Fig. 2), 39 function-gained mutants were harvested from 10 libraries, involving seven residues (S266, S408, G430, T505, Y519, R522 and K524) at the A domain pocket and three other sites (V936, A937 and Q1015) at the R domain pocket. We found that the conversion toward 1 are improved from 14% of WT to as high as 99% of engineered mutants (Fig. 3 and Table S2). Specifically, the two mutants K524W (with > 99% conversion) and A937 V (with 85% conversion) display the best conversion with regard to the sites situated in the A- and R-domain pocket,
respectively, thereafter the combinatorial mutant K524W/A937 V was constructed for further analysis. In comparison to K524W, the double mutant K524W/A937 V show even better performance toward 1 (Fig. S1). On the other hand, seven libraries, including those at T265, G267, Y431, G432, T434, D507 and M999, fail to harbor improved mutants (Fig. 2), and the consensus analysis shows that the residues T265, G267, Y431, G432, T434, D507 are highly conserved with other crystallized ANL family members (Fig. S2), indicating that the original amino acids at these sites may play a crucial role for activity. 2.3. Characterization of the best mutants by kinetic parameters and biotransformation In all the single-site mutants, K524W and K524Q are characterized by highest conversion (> 99%, Fig. S3), showing 7-fold increases in the conversion relative to WT. Thereafter, the two best SrCAR mutants were
Fig. 2. Histograms of the number of positive hits in each library. The numbers of non-redundant variants are indicated by grey. 99
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Fig. 3. Screening results of single site saturation mutagenesis toward the reduction of 1 to 3 by E. coli cells containing SrCAR and its variants. The 17 mutant sites are listed on the left, while 20 canonical amino acids used as building blocks are shown horizontally. Table 1 Kinetic parameters of SrCAR and its mutants in the position 524 towards 1. Michaelis–Menten curve fits are shown in Supplementary Fig. S4. Enzymes
Km (mM)
kcat (s−1)
kcat/Km (s−1 mM−1)
WT K524Q K524W K524A K524C K524L K524M K524V
0.60 0.37 0.39 3.05 4.69 2.44 3.96 4.72
5.30 5.28 6.74 8.67 2.96 3.74 3.63 7.18
8.83 14.27 17.28 2.84 0.63 1.53 0.92 1.52
± ± ± ± ± ± ± ±
0.07 0.04 0.04 0.52 0.74 0.37 0.51 1.62
± ± ± ± ± ± ± ±
0.16 0.10 0.27 0.69 0.22 0.22 0.21 1.14
characterized by enzyme kinetics. Intriguingly, the kcat value of the WT enzyme is similar with the mutant K524Q (5.30 s−1 vs 5.28 s−1) in the reaction of substrate 1, and is lower than that of K524W (6.74 s−1). Whereas the Km values are more diverse. In the case of K524W, the Km value was estimated to be 0.39 mM, which is 2-times lower than that of WT (0.60 mM, Table 1). It suggests that K524W induces optimal substrate affinity ensuring highest efficiency. Afterwards, the biotransformation with respect to the two selected variants K524Q and K524W were performed using 10 mM of substrate 1 (∼25 mg) in 20 mL of reaction volume, using WT SrCAR as control. The mutant K524W achieves a full conversion within 4 h (Fig. 4), showing the best performance which is consistent with the activity assay result. Along with K524Q and K524W, other mutants in the position 524 were also characterized. They generally show higher Km values (diverged from 2.44 to 4.72 mM) and lower kcat/Km values (ranged from 0.63 to 2.84 s−1 mM−1) compared to WT (Table 1). Additionally, 20 mM of substrate 1 was also tested, both K524Q and K524W showed a better transformation than WT, while neither of them can reach a full conversion, only ∼50% conversion were observed (Fig. S5).
Fig. 4. Comparison of biotransformation 1 with formation of 3 employing WT, K524Q and K524W with 10 mM substrate loadings.
shows a significant oscillation toward AMP at the beginning of the MD runs (Fig. 5A). But in the cases of Q524 and W524, both of the side chains keep exploring the solvent and stay away from AMP (Fig. 5B and C). As a consequence, the ε-amino group of the K524 residue is engaged in a bifurcated hydrogen bond to the ribose hydroxyl oxygen atoms of AMP in the WT model (Fig. S7A), whereas such H-bond interactions were not detected in K524Q (Fig. S7B) or K524W models (Fig. S7C). It indicates that residue K524 may function in releasing AMP. Thus, we hypothesized that positively charged amino acids may hinder the release of AMP by forming the ionic and H-bonding interactions with the ribose hydroxyl groups of AMP. As demonstrated by the recent biosynthetic experiments (Duan et al., 2015; Kunjapur et al., 2016; Finnigan et al., 2017), by-products of the reaction, such as AMP and pyrophosphate (PPi) could act as inhibitors due to their ability to occupy the catalytic pocket. In order to double-check this conclusion, two more mutants at position 524 were constructed using the other positively charged amino acids arginine and histidine, respectively. As expected in our model, both K524R and K524H have modest activity, even lower than WT (Table S3), suggesting that positive amino acids at position 524 hamper enzyme activity by anchoring the side product AMP. In addition to computational analysis, isothermal titration calorimetry (ITC) were also
2.4. Shedding light on the origin of enhanced activity As shown in Fig. S6, residue 524 is located at the surface of SrCAR, observed in both X-ray structures corresponding to the adenylation and thiolation conformations. It is of interest to learn the effect of mutation on activity at position 524. In order to probe the molecular mechanism, 300 ns MD simulations were performed on the WT, K524Q as well as K524W. Strikingly, the MD runs revealed that the side-chain of K524 100
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Fig. 5. Overlay of 10 representative snapshots derived from the MD simulations of the WT (A), K524Q (B) and K524W (C) models. The initial side-chain conformation (colored in grey, PDB code 5MSS) oscillates rapidly toward AMP during simulations, and the motion is indicated by a dashed arrow. PPT_Sub: the complex of the thioester; AMP: adenosine monophosphate.
these residues, mutant K524W was found to show the best performance toward substrate 1, resulting in an excellent conversion (> 99%) and a kcat/Km value of 17.28 s−1 mM−1. This amount to 7-fold and 2-fold improvements relative to WT, respectively (Fig. 2 and Table 1). Furthermore, MD simulations together with mutagenesis analysis unveiled the origin of enhanced activity. Collectively, this structural-guided mutagenesis strategy coupled with knowledge of the catalytic mechanism paves the way to engineering CARs with increased activity, which is crucial for the fundamental understanding of enzymatic reactions. Further progress may be possible by applying combinatorial and iterative saturation mutagenesis (Reetz, 2011, 2013; Sun et al., 2016b).
performed, whereas the binding affinity was too low to be measured in both cases of AMP and PPi (Fig. S8). 2.5. Evaluating the best variants as catalysts in the reduction of other substrates The two best SrCAR variants K524Q and K524W obtained from the libraries described above were then evaluated as catalysts in the reduction of other benzoic acids derivatives. As CARs exhibit a better tolerance for para-substituted due to steric properties (Duan et al., 2015; Gahloth et al., 2017), para-substituted benzoic acids 4∼8 were selected for evaluation (Fig. 6). As a result, all of the substrates can be transformed by WT and the variants, although with different efficiency. In the case of compound 4, truly notable improvements were not observed; nevertheless, K524W still ensures a better performance than WT SrCAR, leading to 42% conversion. Toward substrate 5∼8 In sharp contrast, both mutants K524Q and K524W show remarkably increased transformation in the case of substrates 5-8 (91%–99%). This finding suggests that the best variants K524Q and K524W evolved for substrate 1 are also suitable for reducing para-substituted benzoic acids with high catalytic efficiency.
4. Materials and methods 4.1. Materials PrimeSTAR DNA polymerase and restriction enzyme Dpn I were obtained from TAKARA and NEB, respectively. The oligonucleotide synthesis and DNA sequencing were conducted by GENEWIZ technology. All reagents and chemicals were purchased from commercial sources and used without further purification. The gene of wild-type (WT) SrCAR (GenBank: WP_007468889) was synthesized and inserted into Nde I and Xho I sites of vector pET24a.
3. Discussion CARs are able to catalyze the reduction of carboxylic acids with formation of the corresponding aldehydes without over oxidation under mild conditions. As such, CARs are frequently implanted into cascade processes to produce valuable chemical compounds (Qu et al., 2018). However, the productivity is hampered by the low catalytic activity of CARs (Weber et al., 2017; Kramer et al., 2018). This underlying obstacle calls for protein engineering to improve activity. In the present study, we were guided by a structure-based analysis needed for performing efficient saturation mutagenesis (SM) of SrCAR. Accordingly 17 key residues were identified by protein-ligand interaction fingerprint analysis along with MD runs. After site-specific SM exploration at
4.2. Primer design and library creation Site-specific saturation mutagenesis was performed using megaprimer approach (Tyagi et al., 2004) with the corresponding primers of selected residues (Table S4). The PCR conditions for short fragment: 95 °C for 5 min, (95 °C for 30 s, 56 °C for 30 s, 72 °C for 40 s) ×28 cycles, 72 °C for 10 min. For mega-PCR: 95 °C for 5 min, (95 °C for 30 s, 60 °C for 30 s, 72 °C for 12 min) ×28 cycles, 72 °C for 10 min. The PCR products were analyzed on agarose gel by electrophoresis and digested with Dpn I at 37 °C for 3 h. The cloned library was then transformed
Fig. 6. Results of testing the best SrCAR mutants toward para-substituted benzoic acids. 101
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was determined in a 0.2 mL volume of phosphate buffer (50 mM, pH 7.4) containing 1 mM ATP, 10 mM MgCl2, 0.1 mM NADPH, the purified enzyme, 2% DMSO (v/v)and a varying concentration of substrate 1 (0.05～5 mM). Then the two best mutants K524Q and K524W were subjected to biotransformation reactions. They were grown overnight at 37 °C in LB medium containing 50 μg/mL kanamycin. Then the preculture was inoculated into 500 mL TB medium containing 50 μg/mL kanamycin and grown at 37 °C until the OD600 reached 0.8-0.9. After protein expression induced by 0.1 mM IPTG at 20 °C for 16 h, cells were pelleted by centrifugation, washed once with phosphate buffer (50 mM, pH 7.4) and then resuspended in pH 7.4 phosphate buffer (20 mL, 50 mM). Subsequently, 1.4 mg/mL lysozyme, 6 U/mL DNase I, 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 1 mg/mL GDH and substrate 1 (10 mM) dissolved in 2% v/v DMSO were added. Bioconversion was carried out at 30 °C, 220 rpm and terminated by adjusting the pH to 1–2 with 6 M HCl. The product and remaining substrate were extracted using an equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC to monitor the completion of the reaction.
into electro-competent E. coli BAP1 (Pfeifer et al., 2001) to create the final library for Quick Quality Control (Bougioukou et al., 2009) and screening. The 17 sites were individually substituted to the other 19 canonical amino acids by the Tang method (Tang et al., 2012). 4.3. Screening procedures Single colonies from the mutant libraries were separately inoculated into 300 μL LB medium with 50 μg/mL kanamycin in 96 deep-well plates and grown at 37 °C for 12 h. Then 200 μL culture was inoculated into 800 μL TB medium containing 0.2 mM IPTG and 50 μg/mL kanamycin. After expression at 30 °C for 16 h, the cell pellets were harvested by centrifugation and lysed by adding 300 μL of 50 mM, pH 7.4 phosphate buffer containing 6 U DNase I and 1 mg/mL lysozyme at 30 °C for one hour with shaking. The crude lysate was centrifuged for 30 min 4000 rpm at 4 °C. A quantity of 200 μL of supernatant was transferred into new deep-well plates for reaction with 5 mM substrate 1, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose and 1 mg/mL GDH for 20 h at 30 °C 800 rpm, and terminated by adjusting the pH to 1–2 with 6 M HCl. The product and remaining substrate were extracted using an equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC.
4.6. Computational procedures
4.4. Expression and purification of SrCAR
The preparations of protein and ligand structures were carried out in Schrodinger Maestro software (Schrödinger Suite, 2015). PDB codes 5MSW, 5MSS and 5MSV are adopted for adenylation, thiolation and reduction modelling, respectively, while missing residues in the X-ray structures were completed by Modeller 9.19 (Webb and Sali, 2016). The initial structures of the K524Q and K524W variants were generated via the PyMol program (http://www.pymol.org) based on the WT model. The pose of benzoic acid in 5MSD and phosphopantetheine complex in 3NYQ, as well as the thioester geometry of the phosphopantetheine complex in 1W6U were adopted to assemble the complex conformations in the model generation. The protonation of apo-protein models was accomplished at pH 7.4 (to mimic the the experimental conditions) using H++ webserver (Ramu et al., 2012). The resulting system’s geometries were minimized (5000 steps for steepest conjugate and 5000 steps for conjugate gradient) to remove poor contacts and relax the system. The systems were then heated from 0 to 303 K under the constant amount of NVT ensemble for 50 ps, and were maintained for 50 ps of density equilibration using the NPT ensemble at constant temperature of 303 K and pressure of 1.0 atm (ntt = 3) with a collision frequency of 2 ps−1 and pressure relaxation time of 1 ps. The heating and density equilibrations were carried out with a weak restraint of 10 kcal mol−1 Å-2 performed only on the protein residues. After removal of all restraints applied during heating and density dynamics, the systems were equilibrated for 10 ns to get well settled pressure and temperature. After proper minimizations and equilibrations, a productive MD run of 300 ns was performed for each system. During all MD simulations, the covalent bonds containing hydrogen were constrained using SHAKE algorithm (Ryckaert et al., 1977), with a 2 fs time-step. The trajectory file was written every 100 steps. Molecular dynamics (MD) simulations were performed with the GPU version of Amber 2016 (Case et al., 2016). More details regarding the MD procedures were described in the previous study (Qu et al., 2019). Analysis of trajectories was performed by CPPTRAJ (Roe and Cheatham, 2013). The protein − ligand interaction frequency in the MD simulations was performed by using the PLIP tool (Salentin et al., 2015) with the ‘–peptides’ setting. The convergence of all the MD simulations was evaluated by root-mean-square deviation (RMSD) analysis (Fig. S9).
E.coli BAP1 harboring the recombinant plasmids of SrCAR and its mutants were grown overnight at 37 °C in LB medium containing 50 μg/ mL kanamycin. The preculture was inoculated into 100 mL TB medium containing 50 μg/mL kanamycin and allowed to grow at 37 °C until the OD600 reached 0.8-0.9. The temperature was then decreased to 20 °C and protein expression was induced by 0.1 mM IPTG for 16 h. Cells were harvested by centrifugation and lysed by sonication. And the cell debris was removed by centrifugation. A part of the supernatant were lyophilized under vacuum to form the crude enzyme powder. The others were then loaded onto a His-trap HP column (5 mL, GE Healthcare) pre-equilibrated with equilibrium buffer (20 mM phosphate buffer, 0.5 M NaCl, 20 mM imidazole, pH 7.4), and the proteins were eluted with 20–500 mM imidazole solution containing 500 mM NaCl and 20 mM phosphate buffer (pH 7.4) at a flow rate of 3 mL/min. The fractions containing the target protein were collected and dialyzed against 20 mM sodium phosphate buffer (pH 7.4) for desalting using centrifugal filter units with 30 KDa cut-off (Millipore, USA). Finally, the enzyme solution was concentrated for kinetic parameters and total turnover numbers determination. Protein concentration was measured using the Bradford method. 4.5. Activity assay, kinetic parameters and biotransformation Activity assay was carried out as follows: 2 mM substrate 1, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 1 mg/mL GDH and 10 mg/mL crude enzyme powder of the SrCAR variant were mixed in 0.5 mL potassium phosphate (50 mM, pH 7.4). The reaction was carried out at 30 °C for 24 h, and terminated by adjusting the pH to 1～2 with 6 M HCl solution. The product and remaining substrate were extracted using equal volume of ethyl acetate, dried with anhydrous Na2SO4 and analyzed by GC equipped with an SH-Rtx-1 column (60 × 0.32 mm ID, 0.25 μm film thickness). The temperature program of GC was set as: oven 140 °C (initial, hold time 3 min), 30 °C/min to 280 °C (hold time 2 min), injector 300 °C, detector 320 °C. The conversion was determined based on authentic standards. The kinetic parameters were obtained by measuring the initial velocities of the enzyme reaction, the protein and substrate concentration were adjust to monitor the linear reaction period during 4 min, and curve-fitting according to the Michaelis-Menten equation. Enzyme activity was measured using a spectrophotometric assay at 340 nm, corresponding to the cofactor NADPH (ε340 = 6220 M−1 cm-1). The assay
4.7. Substrate scope investigation Reactions were conducted in 2 mL Eppendorf Tubes by mixing phosphate buffer solution (50 mM, pH 7.4), 2 mM substrate, 2% DMSO (v/v), 1 mM ATP, 10 mM MgCl2, 0.1 mM NADP+, 100 mM glucose, 102
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1 mg/mL GDH and 10 mg/mL crude enzyme powder of the SrCAR variant. The reaction mixture was stirred at 30 °C, 1000 rpm for 24 h, and terminated by adjusting the pH to 1–2 with 6 M HCl solution for GC or HPLC analysis. For 4-methoxybenzoic acid, the product and remaining substrate were extracted using an equal volume of ethyl acetate and dried with anhydrous Na2SO4 for GC analysis performed with an SH-Rtx-1 column (60 × 0.32 mm ID, 0.25 μm film thickness). The temperature program of GC was set as: oven 140 °C (initial, hold time 3 min), 30 °C/min to 280 °C (hold time 2 min), injector 300 °C, detector 320 °C. For the other acid substrates, including 4-hydroxybenzoic acid, 4-fluorobenzoic acid, 4-chlorobenzoic acid and 4-bromobenzoic acid, the aqueous phase was centrifuged at 12,000 rpm for 10 min and analyzed by HPLC equipped with an Agilent SB C18 column (4.6 mm × 150 mm × 5 μm) with a flow rate of 1 mL/min (the ratio of acetonitrile to 0.1% TFA is 4:6) at 220 nm. The conversion was determined based on authentic standards for GC and HPLC analysis. All reactions were carried out in triplicate.
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work was financially supported by CAS Pioneer Hundred Talents Program (No. 2016-053), the Key Research Program of the Chinese Academy of Sciences (No. KFZD-SW-212 and KFZD-SW-215), the Natural Science Foundation of Tianjin (No.18JCYBJC24600), the Biological Resources Programme, Chinese Academy of Sciences (No.KFJ-BRP-009), and the State Key Laboratory of Cotton Biology Open Fund (No. CB2019A20). G.Q thanks the National Natural Science Foundation of China (No.31900909), the state key laboratory of biocatalysis and enzyme engineering (No. SKLBEE2018001), and Science & Technology Foundation for Selected overseas Chinese scholar of Tianjin (2017) for generous support. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jbiotec.2019.09.006. References Akhtar, M.K., Turner, N.J., Jones, P.R., 2013. Carboxylic acid reductase is a versatile enzyme for the conversion of fatty acids into fuels and chemical commodities. Proc. Natl. Acad. Sci. U. S. A. 110, 87–92. https://doi.org/10.1073/pnas.1216516110. Arnold, F.H., 2018. Directed evolution: bringing new chemistry to life. Angew. Chem. Int. Ed. 57, 4143–4148. https://doi.org/10.1002/anie.201708408. Bai, Y., Yin, H., Bi, H., Zhuang, Y., Liu, T., Ma, Y., 2016. De novo biosynthesis of gastrodin in Escherichia coli. Metab. Eng. 35, 138–147. https://doi.org/10.1016/j.ymben.2016. 01.002. Bommarius, A.S., 2015. Biocatalysis: a status report. Annu. Rev. Chem. Biomol. Eng. 6, 319–345. https://doi.org/10.1146/annurev-chembioeng-061114-123415. Bougioukou, D.J., Kille, S., Taglieber, A., Reetz, M.T., 2009. Directed evolution of an enantioselective enoate-reductase: testing the utility of iterative saturation mutagenesis. Adv. Synth. Catal. 351, 3287–3305. https://doi.org/10.1002/adsc. 200900644. Case, D.A., Betz, R.M., Cerutti, D.S., Cheatham III, T.E., Darden, T.A., Duke, R.E., Giese, T.J., Gohlke, H., Goetz, A.W., Homeyer, N., Izadi, S., Janowski, P., Kaus, J., Ko-valenko, A., Lee, T.S., LeGrand, S., Li, P., Lin, C., Luchko, T., Luo, R., Madej, B., Mermelstein, D., Merz, K.M., Monard, G., Nguyen, H.T., Omelyan, I., Onufriev, A., Roe, D.R., Roitberg, A., Sagui, C., Simmer-ling, C.L., Botello-Smith, W.M., Swails, J., Walker, R.C., Wang, J., Wolf, R.M., Wu, X., Xiao, L., Kollman, P.A., 2016. AMBER 2016. University of California, San Francisco. Cheng, F., Zhu, L., Schwaneberg, U., 2015. Directed evolution 2.0: improving and deciphering enzyme properties. Chem. Commun. 51, 9760–9772. https://doi.org/10. 1039/c5cc01594d. Denard, C.A., Ren, H., Zhao, H., 2015. Improving and repurposing biocatalysts via directed evolution. Curr. Opin. Chem. Biol. 25, 55–64. https://doi.org/10.1016/j.cbpa. 2014.12.036.
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