Integrating error-prone PCR and DNA shuffling as an effective molecular evolution strategy for the production of α-ketoglutaric acid by l-amino acid deaminase

Article abstract of DOI:10.1039/c6ra02940j

l-Amino acid deaminases (LAADs; EC 1.4.3.2) belong to a family of amino acid dehydrogenases that catalyze the formation of α-keto acids from l-amino acids. In a previous study, a whole cell biocatalyst with the l-amino acid deaminase (pm1) from Proteus mirabilis was developed for the one-step production of α-ketoglutarate (α-KG) from l-glutamic acid, and the α-KG titer reached 12.79 g L^-1 in a 3 L batch bioreactor. However, the product α-KG strongly inhibited pm1 activity, and the titer of α-KG was comparatively lower than expected. Therefore, in this study, multiple rounds of error-prone polymerase chain reaction (PCR) and gene shuffling were integrated for the molecular engineering of pm1 to further improve the catalytic performance and α-KG titer. A variant (pm1338g4), which contained mutations in 34 amino acid residues, was found to have enhanced catalytic efficiency. In a batch system, the α-KG titer reached 53.74 g L^-1 when 100 g of monosodium glutamate was used as a substrate. Additionally, in a fed-batch biotransformation system, the maximum α-KG titer reached 89.11 g L^-1 when monosodium glutamate was continuously fed at a constant rate of 6 g L^-1 h^-1 (from 4 to 23 h) with an initial concentration of 50 g L^-1. Analysis of the kinetics of the mutant variant showed that these improvements were achieved due to enhancement of the reaction velocity (from 56.7 μM min^-1 to 241.8 μM min^-1) and substrate affinity (the K_m for glutamate decreased from 23.58 to 6.56 mM). A possible mechanism for the enhanced substrate affinity was also evaluated by structural modeling of the mutant. Our findings showed that the integration of error-prone PCR and gene shuffling was an effective method for improvement of the catalytic performance of industrial enzymes.

Full text of DOI:10.1039/c6ra02940j

View Article Online
View Journal
RSC Advances
This article can be cited before page numbers have been issued, to do this please use: G. S. Hossain, H.
Shin, J. Li, M. Wang, G. Du, L. Liu and J. Chen, RSC Adv., 2016, DOI: 10.1039/C6RA02940J.
This is an Accepted Manuscript, which has been through the
Royal Society of Chemistry peer review process and has been
accepted for publication.
Accepted Manuscripts are published online shortly after
acceptance, before technical editing, formatting and proof reading.
Using this free service, authors can make their results available
to the community, in citable form, before we publish the edited
article. This Accepted Manuscript will be replaced by the edited,
formatted and paginated article as soon as this is available.
You can find more information about Accepted Manuscripts in the
Information for Authors.
Please note that technical editing may introduce minor changes
to the text and/or graphics, which may alter content. The journal’s
standard Terms & Conditions and the Ethical guidelines still
apply. In no event shall the Royal Society of Chemistry be held
responsible for any errors or omissions in this Accepted Manuscript
or any consequences arising from the use of any information it
contains.
www.rsc.org/advances

Page 1 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
Integrating errorꢀprone PCR and DNA shuffling as an effective molecular evolution
2
3
strategy for the production of αꢀketoglutaric acid by Lꢀamino acid deaminase
a, b
c
b
a§
b
b§§
b
4
5
Gazi Sakir Hossain , Hyunꢀdong Shin , Jianghua Li , Miao Wang , Guocheng Du , Long Liu ,Jian Chen
a
6
School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
b
7
Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University,
8
Wuxi 214122, China
c
9
School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
10
11
12
13
§
Corresponding authors: Miao Wang, Tel.: +86ꢀ510ꢀ85329079, Fax: +86ꢀ510ꢀ85329079, Eꢀmail:
§§
mwang@jiangnan.edu.cn; Long Liu, Tel.: +86ꢀ510ꢀ85918312, Fax: +86ꢀ510ꢀ85918309, Eꢀmail:
longliu@jiangnan.edu.cn
14
15
16
17
18
19
20
21
22
1

RSC Advances
Page 2 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
Abstract: LꢀAmino acid deaminases (LAADs; EC 1.4.3.2) belong to a family of amino acid dehydrogenases
that catalyze the formation of αꢀketo acids from Lꢀamino acids. In a previous study, a whole cell biocatalyst
3
with the Lꢀamino acid deaminase (pm1) from Proteus mirabilis was developed for the oneꢀstep production of αꢀ
4
ketogluterate (αꢀKG) from Lꢀglutamic acid, and the αꢀKG titer reached 12.79 g/L in a 3ꢀL batch bioreactor.
5
However, the product αꢀKG strongly inhibited pm1 activity, and the titer of αꢀKG was comparatively lower than
expected. Therefore, in this study, multiple rounds of errorꢀprone polymerase chain reaction (PCR) and gene
shuffling were integrated for the molecular engineering of pm1 to further improve the catalytic performance
and αꢀKG titer. A variant (pm1338g4), which contained mutations in 34 amino acid residues, was found to
have enhanced catalytic efficiency. In a batch system, the αꢀKG titer reached 53.74 g/L when 100 g of
monosodium glutamate was used as a substrate. Additionally, in a fedꢀbatch biotransformation system, the
maximum αꢀKG titer reached 89.11 g/L when monosodium glutamate was continuously fed at a constant rate
of 6 g/L/h (from 4 to 23 h) with an initial concentration of 50 g/L. Analysis of the kinetics of the mutant variant
showed that these improvements were achieved due to enhancement of the reaction velocity (from 56.7
ꢁM/min to 241.8 ꢁM/min) and substrate affinity (the Km for glutamate decreased from 23.58 to 6.56 mM). A
possible mechanism for the enhanced substrate affinity was also evaluated by structural modeling of the
mutant. Our findings showed that the integration of errorꢀprone PCR and gene shuffling was an effective
method for improvement of the catalytic performance of industrial enzymes.
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Keywords: LꢀAmino acid deaminase; αꢀketoglutarate; Proteus mirabilis; wholeꢀcell transformation; errorꢀprone
polymerase chain reaction; DNA shuffling
2

Page 3 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
Introduction
In directed evolution of enzymes, which mimics the Darwinian evolution process, and in classic directed
evolution research, the gene encoding an enzyme of interest is randomized and expressed in an appropriate
host. Suitable screening approaches are then used to recognize mutants that have specific properties, such as
catalyzing an anticipated chemical reaction or binding to a specific substrate. Through iterative cycles of
mutagenesis and amplification of selected mutants, useful mutations can be obtained, similar to unpretentious
Darwinian evolution, but on a greatly reduced time scale. In this manner, populations of enzymes may be
purposely evolved to obtain useful synthetic properties.1 The catalytic efficiency, solubility, and stability of an
enzyme can be elaborately established by directed evolution. Errorꢀprone polymerase chain reaction (epꢀ
PCR), cassette mutagenesis, staggered extension protocol, and DNA shuffling are the most
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
effective approaches for the evolution of certain enzymes. For example, using epꢀPCR and DNA shuffling, a
mutant variant of Dꢀ2ꢀketoꢀ3ꢀdeoxyꢀ6ꢀphosphogluconate aldolase was obtained that was capable of accepting
both Dꢀ and L
ꢀglyceraldehyde as substrates.2 In another example, using stepwise epꢀPCR and DNA shuffling,
the product specificity and pH activity range were changed for the cyclodextrin glucanotransferase of
alkaliphilic Bacillus sp. Gꢀ825ꢀ6.3 Specifically, in cases where chemical routes are challenging to duplicate, the
compatibility of enzymes with minor aqueous environments has led to their growing use as biocatalysts in
synthetic chemistry. 4, 5
Recently, many researchers have explored microbial bioconversion routes for the production of chemicals
manufactured from petroleumꢀbased feedstocks, which are not only nonrenewable but also unsustainable due
to their harmful effects on the environment and on energy security.6 Alphaꢀketoglutarate (αꢀKG) is an example
of one such chemical; αꢀKG plays crucial roles in carbon coordination, nitrogen metabolism, and energy
metabolism and is used for the chemical functionality of novel Nꢀheterocyclic compounds, which are
3

RSC Advances
Page 4 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
commonly applied as anticancer agents.7, 8
Various chemical synthesis methods are currently being used for the production of αꢀKG.8, 9 The synthesis of
αꢀKG from diethyl succinate and diethyl oxalate is a multistep process, and harsh chemicals such as cyanides
are used, resulting in toxic waste. Alternatively, glyoxylic acid is oxidized chemically with sodium glutamate
using a copper catalyst; this process also produces glycine as a main product, in addition to other
byproducts.10 However, the major drawbacks of chemical synthesis are the lack of selectivity, inclusion of
cyanides or copper catalysts, low yield, and production of toxic chemicals that generate environmental
hazards. Alternative methods for αꢀKG production include microbial fermentation and enzymatic
transformation.11 For several decades, studies have examined the fermentation process using different
microbes, including Corynebacterium glutamicum, Bacillus spp., Arthrobacter paraffineus, Candida spp.,
Pichia spp., Pseudomonas fluorescens, Serratia marcescens, and Yarrowia lipolytica,9, 12, 13 for the production
of αꢀKG. Efforts to discover more ideal substitute carbon sources are still underway with the goal of making the
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
process more economically viable. On the other hand, Lꢀglutamic acid has been used for αꢀKG production by
enzymatic biotransformation with recombinant L
ꢀglutamate oxidase.14 However, owing to the production of
large amounts of hydrogen peroxide due to oxidation, it is necessary to add catalase to the reaction, along
with oxidase. Although these fermentation and enzymatic processes generate less environmental pollution
than chemical synthesis processes, they are still limited to laboratory level research because of the formation
of large amounts of byproducts and the high manufacturing costs.
In a previous study, protein engineering of LꢀAAD as well as metabolic engineering of αꢀKG utilization pathway
in Bacillus subtilis was performed for the production of αꢀKG from Lꢀglutamic acid by whole cell biocatalysis
(Scheme 1). The highest αꢀKG titer reached 12.79 g/L by that engineered strain.15 However, this whole cell
biocatalysis process exhibited several drawbacks, including low substrate solubility of Lꢀglutamic acid and
4

Page 5 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
relatively low αꢀKG production titers. In the current study, highly soluble monosodium glutamate (MSG) was
used as a substrate and further protein engineering of P. mirabilis LꢀAAD was conducted to improve the overall
3
performance of the biocatalytic process. Eight rounds of epꢀPCR followed by four rounds of gene shuffling
4
were performed to improve the biocatalytic efficiency of LꢀAAD. Furthermore, substrate feeding was optimized
5
to further increase αꢀKG titers. The new optimized wholeꢀcell biotransformation process by the evolved
produced αꢀKG at a titer of 89.11 g/L, which was 6.97 times higher than previously reported.
LꢀAAD
6
7
8
Experimental
9
Microorganisms, vectors, chemicals and cultivation conditions
10
11
12
13
14
15
16
17
18
19
20
21
22
The microorganisms and vectors used in this research are shown in Table 1. Competent Cell Preparation
Kit and enzymes were supplied by TaKaRa (Dalian, China). With the exception of MSG and αꢀKG (Sigmaꢀ
Aldrich, Shanghai, China), all chemical reagents were purchased from Shanghai Sangon Biological
Engineering Technology and Services Co. Ltd. (Shanghai, China). All microorganisms were grown at 37 °C in
Luria–Bertani broth (LB; 10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl) or on LB agar plates.
Mutant library construction by epꢀPCR
GeneMorph II Random Mutagenesis Kit (Agilent Technologies, Santa Clara, CA, USA) was used to perform
epꢀPCR. Each amplification reaction (50 ꢁL) contained 0.5 ꢁM each primer (pm1_F1 and pm1_R1; Table 1),
200 ꢁM deoxyribonucleotide triphosphates, and 2.5 U of Mutazyme II DNA polymerase in Mutazyme II
reaction buffer. Mutagenic amplifications were conducted by two separate processes. In the first process, the
quantities of template amplicon were varied (0.001 ng, 0.1 ng, 1 ng, 10 ng, or 100 ng,) and in the second one,
the number of amplification cycles was not the same (one, three, five, seven, ten, fifteen, or twenty cycles).
After the process optimization, it was observed that 1 ng of template and fifteen cycles of amplification were
5

RSC Advances
Page 6 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
suitable condition for the production of two or three amino acids containing mutants. After the application
under optimal conditions, PCR products were digested with BamHI and SmaI, purified, and ligated into the
expression vector pHT43 (MBio, Carlsbad, Germany) which was digested by the same enzymes. To generate
the mutant library, the ligation products were used to transform competent E. coli JM109 cells by
electroporation. The resulting colonies were washed down with sterile water, and plasmid DNA was extracted
from the pooled E. coli library and subsequently transformed into the engineered B. subtilis (BSUC1) by
electroporation.
3
4
5
6
7
8
Mutant library construction by DNA shuffling
9
DNA family shuffling experiment was performed by the following steps: parental templates preparation,
DNase I digestion, primerꢀless PCR and PCR with primers. At first, two DNA fragments containing the LAAD
genes from P. mirabilis (accession number: EU669819.1) and P. (accession number: AB030003)) were
amplified by PCR from plasmids pETꢀpm1338 and pETꢀpvLAAD using the primers pm1_F/pm1_R and
pvLAAD_F/pvLAAD_R, respectively (Table 1). The PCR amplified fragments were purified and identical
quantities (a total of about 4 ꢁg) of the two gene preparations were mixed and equilibrated for 5 min at 15°C.
DNA digestion (by the addition of 0.5 U of DNase) was completed in the presence of 2 mM Mn2+ for 2 min at
15 °C and terminated the digestion reaction by incubating at 90 °C for 10 min. The digested DNA was
electrophoresed in a 2% agarose gel to isolate the desired DNA fragments between 100 and 200 bp and
subsequently purified. After that, PCR without primers was performed in mixture containing 2.5 U Taq Plus
DNA Polymerase, 2 ꢁg of the fragments mixture, 1X Taq Plus buffer, 0.2 mmol/L dNTP with a total volume of
50 ꢁL. The reaction conditions was as follows: 95 °C for 1 min, 35 cycles of 95 °C for 30 s, 50°C for 30 s and
72 °C for 30 s, and a final incubation at 72 °C for 8 min. Then, 1 ꢁL of this PCR reaction was used as a
template to amplify the fullꢀlength genes using different set of primers (pm1_F/pm1_R or
10
11
12
13
14
15
16
17
18
19
20
21
22
6

Page 7 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
pvLAAD_F/pv_LAADR or pm1_F/ pv_LAADR or pm1_F /pv_LAADR). Finally, the mutated PCR products of
the fullꢀlength gene were digested by BamHI and SmaI and the digested product was ligated into the vector
pHT43 which was digested by the same enzymes. The ligation products were used to transform competent E.
coli JM109 cells. All the resulting colonies were washed down with sterile water, and then plasmid DNA was
extracted from the pooled E. coli library and subsequently transformed into the engineered B. subtilis (BSUC1)
by electroporation. Approximately, 3.5X103 clones were screened in each round of shuffling experiment.
Siteꢀdirected mutagenesis
3
4
5
6
7
8
The MutanBEST Kit was used for the siteꢀdirected mutagenesis and single step PCR method was done using
PrimeSTAR HS DNA polymerase with the plasmid pm133g4/pHT43 as the template DNA. The amino acids at
position 278, 317, 320, 415 and 417 of pm1338g4 were replaced by the following amino acids: threonine
(ACC), alanine (GCG), tyrosine (TAC), histidine (CAC), phenylalanine (TTC), leucine (CGT), isoleucine (ATC),
methionine (ATG), valine (GTT), serine (TCT), proline (CCG), cysteine (TGC), tryptophan (TGG), arginine
(CGT), glycine (GGT), lysine (AAA), asparagine (AAC), aspartic acid (AAC), glutamic acid (GAA), and
glutamine (CAG).
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Biocatalyst preparation and assay of wholeꢀcell biocatalytic activity
For seed preparation, recombinant B. subtilis cells were grown at 37 °C on a shaker (200 rpm) in LB
medium containing chloramphenicol (10 mg/L) for 12 h. Fermentation was executed in a 3ꢀL vessel
(BioFlo115; New Brunswick Scientific Co., Edison, NJ, USA) with a working volume of 1.8 L. A seed culture
was inoculated at a concentration of 1% (v/v) into Terrific broth for cultivation. When the optical density at 600
nm (OD600) reached 0.6, 0.4 mM isopropyl βꢀDꢀ1ꢀthiogalactopyranoside was added to initiate LꢀAAD
production. The agitation speed, aeration rate, and temperature were controlled at 400 rpm, 1.0 vvm, and 28
°C, respectively, to avoid the formation of inactive inclusion bodies. After induction for 5 h, the cells were
7

RSC Advances
Page 8 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
harvested via centrifugation at 8,000 × g for 10 min at 4 °C and then washed twice with 20 mM TrisꢀHCl buffer
(pH 8.0). The cell pellet was resuspended in 20 mM TrisꢀHCl buffer (pH 8.0) and maintained at 4 °C for further
studies. Biomass concentrations were analyzed spectrophotometrically (UVꢀ2450 PC; Shimadzu Co.; Kyoto,
3
4
Japan) at OD620 and converted to dry cell weight (DCW) using the Eq. (1)47
DCW = {(0.4442 × OD600) − 0.021} (g/L)
:
5
(1)
6
Wholeꢀcell biocatalytic activity was assayed by measuring the αꢀKG titer in the reaction solution. To measure
the initial production rate, the reaction solution (100 g/L MSG; 20.0 g [DCW]/L wholeꢀcell biocatalyst in 20 mM
Tris, pH 8.0; 10 ꢁM carbonyl cyanideꢀ3ꢀchlorophenylhydrazone; and 5 mM MgCl2) was performed at 40 °C on
a shaker for 4 h. To investigate the time profile study for αꢀKG production, biotransformation was performed in
the 3 L fermenter (BioFlo115; New Brunswick Scientific Co.) with 1.2 L reaction solution for 36 h. The agitation
speed, aeration rate, pH, and temperature were maintained at 400 rpm, 1.5 vvm, 8.0, and 40°C, respectively.
Samples were collected at different time and then centrifuged at 8,000 × g for 10 min to stop the reaction, and
the αꢀKG concentration in the supernatant was then measured with highꢀperformance liquid chromatography
(HPLC). The biotransformation ratio was determined using the Eq. (2):
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
15
(2)
where M1 is mol MSG before transformation and M2 is remaining mol MSG after the conversion.
Biochemical characterization at different temperature and pH
Biochemical characterization of all variables was performed with 20 mL of reaction mixture in a 250ꢀmL
Erlenmeyer flask. Reactions were performed using the standard wholeꢀcell biocatalytic reaction conditions
described above. To characterize the temperature, the reaction was performed at pH 8.0 and 200 rpm with
temperatures varying between 25 and 45 °C. For pH characterization, the conditions were 40 °C, 200 rpm,
and Na2HPO4–KH2PO4 buffer pH 5–9. The reactions were stopped by centrifugation at 8,000 × g for 10 min,
8

Page 9 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
and the supernatant was recovered subsequently for measurement of αꢀKG by HPLC.
Determination of kinetic parameters of the evolved mutants
3
The evolved mutants were grown with induction, cells were centrifuged at 8, 000 × g for 10 min and pellets
were suspended in purification buffers containing nꢀdodecylꢀβꢀDꢀmaltoside (0.01 %). After disruption with
ultrasonication for 20 min on ice (sonication for 1 s and intermission for 2 s) by VivraꢀCell (Sonics, Newtown,
CT, USA), the solution was filtered through a 0.45ꢀꢁM pore size membrane. Then, the filtrate was purified on a
HisTrap™ FF 5 mL column with AKTA Explorer (GE Healthcare, Piscataway, NJ, USA) and desalted with an
Ultraꢀ4 Centrifugal Filter Device (Amicon, Shanghai, China). Protein concentration was measured with a BCA
protein assay kit (TianGen, Beijing, China).
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
The kinetic analysis of αꢀKG production by the evolved mutants were performed by measuring αꢀKG titers
with different concentrations of MSG (10, 20, 40, 80, 120, 160, 200, and 240 mM) as substrate at 40 °C for 30
min. The kinetic parameters Km and Vmax were determined using the LineweaverꢀBurk plots with the plotting
method shown in Eq. (3):
1 /V = (Km /Vmax Χ 1/[ S] + 1 /Vmax
)
(3)
where V is the reaction rate (the amount of αꢀKG produced by 1 mg of evolved mutant per min; ꢁmol αꢀKG/
min/mg/protein), Km is the Michaelis constant (mM), Vmax is the maximum reaction rate (ꢁmol αꢀKG
min/mg/protein), and [S] is the concentration of MSG (mM).
Batch and Fedꢀbatch biotransformation
Batch and fedꢀbatch biotransformation were conducted in the 3ꢀL vessel (BioFlo115; New Brunswick
Scientific Co.) with a working volume of 1.3 L. For batch culture, the reaction was started with 100 g/L of MSG,
whereas for fed batch culture, the reaction was started with 50 g/L of MSG. During the interval feeding,
substrate was added at every 6ꢀh interval intervals (25 g or 40 g per every time; for three times). During the
9

RSC Advances
Page 10 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
continuous feeding, substrate was added at a rate of 8.1 ml/L/h (from 4.37 M stock solution of MSG, from 4 h
to 23 h) during the fedꢀbatch process.
3
Computational modeling of the tertiary structure of pm133 and pm1338g4
To generate a model, the sequence of pm133 and pm1338g4 was submitted to the IꢀTASSER web server
(http://zhanglab.ccmb.med.umich.edu/IꢀTASSER/)16 to generate a homology model using pmaLAAD (5FJM)
as a template.48 The complete results, together with coordinates of the models and Z score impact are
available in the database (http://zhanglab.ccmb.med.umich.edu/ITASSER/output) using the IDs
S270391 and S269887 for pm133 and pm1338g4, respectively. Computationallyꢀderived structures were
viewed as well as showed using PyMOL.49 Enzymeꢀsubstrate docking with the help of Patch dock server.50
Statistical analysis
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
All experiments were performed at least three times, and the results are expressed as mean ± standard
deviation (n=3). Data were analyzed using the Student’s t test. P values less than 0.05 were considered
statistically significant.
Results
EpꢀPCRꢀbased directed evolution and influence of mutations on the reaction kinetics of pm133
In the earlier study, we devised a protocol to screen expected mutants effectively and showed that
engineered pm133 is functionally expressed in E. coli and B. subtilis at a reproducible and constant
expression level, with the titer of αꢀKG reaching 12.79 g/L.15 Based on this heterologous expression method, a
library of pm133 mutants with a regular mutation rate of two or three amino acid changes per protein was
produced using epꢀPCR. This library of mutants was localized to the membrane of the heterologous host and
screened for mutants with better biotransformation efficacy in a 96ꢀwell plate format. Only mutants with MSGꢀ
10

Page 11 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
toꢀαꢀKG biotransformation efficacy higher than that of the pm133 enzyme were selected for further
confirmation. 15 After screening the eight rounds of approximately 8×104 epꢀPCR clones, we identified an
evolved mutant (pm1338) with considerably improved titer after the biotransformation of MSG; the titer was
increased to 37.23 g/L (Fig. 2). pm1338 resulted in mutation of residues 147, 150, 193, 246, 259, 271, 278,
291, 295, 317, 320, 340, 362, 374, 383, 408, 415, 437, and 445 (Figure 1B). Supplemental Figure S1 shows
the similar amount of expression of parent pm133 and evolved mutants pm1338 and pm1338g4. Among the
mutants, acidic residues of D147, D362, D374, and E383 were changed to neutral and basic or aliphatic
3
4
5
6
7
8
amino acids. In the LꢀAAD mutant pm1338, Vmax was increased from 56.7 ꢁM/min to 167.2 ꢁM/min, and the
9
substrate affinity, Km (8.83 mM), was also increased simultaneously (Table 3). In many examples of directed
evolution, mutations have improved desired enzyme properties in which amino acid residues located close
either to the active center, substrateꢀbinding pockets, or both are often targeted in rational enzyme design
because these alterations are more likely to affect the active site architecture, thereby affecting the catalytic
reaction.46 We applied a structural modeling method using the IꢀTASSER program to predict threeꢀdimensional
structure as described in the Materials and Methods (Figure 1A). The model that exhibited the highest C score
and TM score was used for further study, and the rootꢀmeanꢀsquare difference value of this model was 2.68 Ǻ.
In the mutant pm1338, mutations were identified in the Nꢀterminal, central, and Cꢀterminal regions of the
enzyme (Figure 1B).
10
11
12
13
14
15
16
17
18
19
20
21
22
Gene shuffling based directed evolution and influence of mutations on the reaction kinetics of pm1338
The in vitro recombination of homologous progenitor genes presumably allows for the generation of chimeric
sequences which are more diverse than the typical epPCR without resorting to high polymeraseꢀdependent
mutation rates; this method is usually referred to ‘gene shuffling.17 Gene shuffling was used to further improve
the titer of pm1338 with another deaminase from P. vulgaris which showed 65% similarity with the pm1.18 Four
11

RSC Advances
Page 12 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
rounds of shuffling resulted in the mutant pm1338g4, harboring mutation of residues 141, 208, 242, 251, 258,
267, 269, 275, 285, 286, 366, 378, 389, 400, and 418. Importantly, this mutant exhibited improved
biotransformation efficiency compared with that of the pm1338 mutant, although the supplemental Figure S1
shows the similar amount of expression of pm1338 and pmi1338g4. Molecular modeling showed that these
residues were also located on the surface, at the center and less structured loop regions of the enzyme, which
are traditionally considered highly flexible (Figure 1B). The αꢀKG titer by the evolved pm1338g4 with these
mutations reached 54.55 g/L within 30 h (Figure 2). The mutant pm1338g4 also exhibited a higher
biotransformation efficiency compared with that of pm1338 (Figure 2). In addition, kinetic analysis showed that
the substrate specificity, Km (6.56 mM), and reaction rate, Vmax (241.8 ꢁM/min), increased in the pm1338g4
mutant relative to those of pm1338 (Table 3).
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Influence of reaction pH and temperature on αꢀKG production by the evolved enzymes
The crude enzymes produced by the pm133, pm1338 and pm1338g4 were purified and no difference was
found in the molecular masses (about 51 kDa) between the different mutant variants on sodium dodecyl
sulfateꢀpolyacrylamide gel electrophoresis gels, consistent with the results of a previous report.19 The effects
of reaction pH and temperature on αꢀKG production rate by the pm133, pm1338 and pm1338g4 from MSG are
shown in supplemental Figure S2A. The pH range from 5.0 to 9.0 was analyzed for the mutants, since they
showed a significant decrease in activity below pH 5.0 and above 9.0. Moreover, the mutants had the same
optimal activity at pH 8.0, (supplemental Figure S2A), similar to that of wildꢀtype pm1.19 All the mutants
exhibited the maximum production rate at 40 °C (supplemental Figure S2B); among them, pm1338g4 showed
the highest production rate of 3.56 g/L/h. The optimal biochemical character between the wild type and mutant
proteins were similar, although the production rate was higher in the mutants. These results confirmed the
beneficial results of the mutagenesis during the directed evolution.
12

Page 13 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
Influence of fedꢀbatch biotransformation process for αꢀKG production
In the batch experiment aiming at maximal αꢀKG productivity, considerable accumulation of incompletely
oxidized MSG was observed. Many experiments have shown that the fedꢀbatch substrate addition could be
more efficient for the biotransformation where high substrate concentration have negative effects on the
process. Therefore, a fedꢀbatch setup was applied for efficient biotransformation of MSG to αꢀKG. Controlled
feeding of MSG prevents the accumulation of substrate and blocks its inhibitory effect. Two strategies for αꢀKG
production, pulse feeding and continuously constant rate feeding, were applied for achieving maximum
specific productivity and maximal αꢀKG titers. In order to achieve a maximum specific αꢀKG production rate,
MSG was fed at appropriate amount to maintain maximum specific productivity and at more than the optimal
amount. After an initial phase and extend the cultivation time, the MSG feeding rate was adjusted in order to
maintain slightly saturating levels of MSG. In the pulse feeding approach, the reaction process was initially
started by adding 50g/L of MSG; MSG feed was then performed at a rate of 25 g/L every 6h for a total of three
times, resulting in increased production of αꢀKG to approximately 64.63 g/L (Figure 3). Similarly, feeding of 40
g/L MSG after every 6h for a total of three times yielded accumulation of αꢀKG to 83.33 g/L (Figure 3). Then,
we devised an alternative feeding strategy (continuously constant rate feeding) to achieve higher final αꢀKG
titers. Continuously MSG feeding at a feeding rate of 6 g/L/h for 20 hours (from 4h to 23 h) resulted in better
biotransformation efficiency, and αꢀKG titer reached 89.11 g/L (Figure 3). Thus, the maintenance requirements
were met, and extensive substrate accumulation during the biotransformation was avoided.
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Structure modeling analysis of evolved variant pm1338g4
Since, pm1338g4 is a FADꢀcontaining enzyme and produces αꢀketo acid and ammonia (Scheme 1) without
forming hydrogen peroxide, thus this enzyme is different from known oxidases. In addition, this enzyme shows
a broad substrate specificity 51, therefore, the substrateꢀbinding pocket is usually lined by hydrophobic
13

RSC Advances
Page 14 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
residues. The entrance site of this type enzyme for substrate is unusually wide (15ꢀ20 Å) and mostly occupied
with negatively charged amino acids (Glu108, Glu145, Glu149, Asp156, Glu340, Asp416, & Glu417)49 (Figure
4A). In addition, the substrateꢀbinding pocket of this type of enzyme is also about 20 Å deep, and mostly
hydrophobic.49 In case of pm1338g4, the substrateꢀbinding pocket is lined by Leu279, Phe318, Met412,
Val438 & Trp439 (Figure 4B). Some amino acids which are close to these sites were changed by the epPCR
and gene shuffling. Among them, amino acids at position 278, 317, 415 and 418 were subjected to sideꢀ
directed mutagenesis. Supplemental figure 3 shows that amino acids changed by the evolution were the best
among the other amino acids. In addition, epPCR and gene shuffling also have changed the distances
between the substrate and actives sites comparatively in a positive manner (Figure 4C and 4D) and as a
results, substrate binding affinity was improved in the evolved pm1338g4 (Table 3).
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
For the investigation of the location and accessibility of the mutations identified and to elucidate the
topological distribution of these mutated residues in variant pm1338g4, the relative solvent accessibility scores
were predicted by the tool that represent solvent accessibility in proteins.20 The results from the ASAView
showed that three mutated residues, A285G, L378T and E389Q, were located on the surface and had a
higher calculated solvent accessibility value (Figure 5A). Comparison of pm133 with pm1338g4 revealed some
different kinetic properties toward glutamate, i.e., mutations both close to and far away from the active site can
effectively improve catalytic activity (Table 3). Therefore, our results established the substrate specificity and
reaction velocity of the deaminase enzyme could be modulated by the mutations of residues for better solvent
accessibility.21, 22
To identify the possible molecular basis for the enhancement of deaminase activity against glutamate, we
constructed a model of the pm1338g4ꢀglutamate complex based on the homology model and PSIPRED server
was used to predict the secondary structure.45 The result showed that six valuable mutations introduced into
14

Page 15 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
the protein (i.e., D147A, N150K, F208P, E383H, E389Q, and V445A) were located on the loop helixes (Figure
5B), which may be close to the active site and could therefore established a progressive interaction with the
substrate. Replacing an acidic residues with a neutral, aliphatic and basic residue in the coil (e.g., D362N,
D374V, and E400K), contributed to the improved catalytic activity of pm1338g4, mainly because the loop
helixes could possess a high mobility and cause corresponding slight conformation changes near the active
site.23 However, the mutations close to the active site may be responsible for improving the catalytic efficiency
of pm1338g4 on glutamate mainly by decreasing the Km value (Table 3). In the gene shuffling mutagenesis,
the replacement of 15 amino acids resulted in a corresponding increase in the Vmax and decrease in the Km
(Table 3), thus, we can assume that the new substitution of pm1338 could optimize the conformation at the
active site entrance, thus improving catalysis of pm1338g4 on glutamate (Table 3). These mutations may
enhance the affinity for glutamate by contributing to the stabilization of glutamate binding and could also
increase the cofactor’s redox potential owing to the introduction of polar residues among the mutations.24
Additionally, mutations of pm1338g4 that were far away from the active site may cause conformational
changes and could contribute to the promotion of catalytic activity on glutamate by forming a lid to cover the
substrateꢀbinding site.25, 26
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Discussion
The results from the present study demonstrate that the directed evolution by integration of epPCR and
DNA shuffling can improve the production of αꢀKG. To the best of our knowledge, this is the first report on
improving the production of αꢀKG by directed evolution based on epPCR and DNA shuffling. Ultimately, we
obtained a maximum αꢀKG yield of 89.11 g/L in a 3ꢀL fermenter by combining optimization of biochemical
characteristics and optimum substrate feeding. Thus, the αꢀKG yield from the newly obtained deaminase
15

RSC Advances
Page 16 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
(pm1338g4) by directed evolution was 6.97ꢀfold higher than that from the previously engineered deaminase
(pm133).15 For the improvement of the catalytic performance of enzymes, directed evolution is one of the
utmost powerful tools currently available.27, 28 Indeed, this method has been effectively applied to explore
structureꢀfunction relations and advance enzyme properties; therefore, it has been effectively applied. 29ꢀ31
Nevertheless, in most cases, replacements are presented at a rate of one to two amino acid substitutions per
round of directed evolution.32 Upper mutagenic rates are not usually used since they frequently abolish
enzyme function and are associated with an increased tendency to negate positive mutations. Additionally, a
larger library size is produced by greater mutagenic rates, which eventually necessitates an impracticable
robust screening and selection method to recognize positive variants. Although, 34 amino acids were found to
be mutated in the pm1338g4 the mechanism through which these mutations affect the production of αꢀKG
remains to be elucidated. However, mutations distant from the active site of other enzymes can increase the
catalytic properties of enzymes.33ꢀ37 For example, Ryu et al. (2006) used epꢀPCR to generate a
Photobacterium lipolyticum lipase mutant exhibited 75% increased activity at 25 °C.38 In another example,
aspartate aminotransferase from E. coli was transformed into a valine aminotransferase by epꢀPCR after 17
amino acid substitutions and evolved enzyme exhibited an important increase in the catalytic efficiency for a
nonꢀnative substrate, valine.33 Usually, saturation mutagenesis is performed to produce a library by designing
degenerate primers for the residue position in screening the resultant library to conclude which of the twenty
amino acids shows the maximum enhanced effect at that position. In our case, however, saturation
mutagenesis in the four sites showed that evolved mutants displayed the maximum enhanced effect
(supplemental Figure S3).
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
In the improved mutant pm1338g4, the reaction velocity was increased significantly (Table 3). This result
could be explained by two ways. First, there could be more open binding sites on mutant protein, which could
16

Page 17 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
result in increased substrate binding. Second, the binding may be modulatory and allosteric in nature,
although in pm133 proteins amino acids in active site region may differ from those of the natural protein, which
do not provide perfect binding (Figure 1). Similar results were obtained in other directed evolution experiment
using epPCR or DNA shuffling. For example, gene shuffling of a family of human interferonꢀalpha (HuꢀIFNꢀ
alpha) genes was used to derive variants with increased activities, and after a second round of selective gene
shuffling, the most active clone exhibited a 285,000ꢀfold improvement relative to the wild type HuꢀIFNꢀ
alpha2a.39 Similarly, using DNA shuffling and saturation mutagenesis, the activity and substrate specificity of
cytochrome c peroxidase from Saccharomyces cerevisiae were significantly improved. 40, 41
3
4
5
6
7
8
9
To eliminate inhibition due to the higher substrate concentration, we added MSG at systematic intervals and
examined the effect of this intervention on αꢀKG production. MSG addition at intervals ensued in higher αꢀKG
production because of the higher biocatalyst activity than that in the batch bioconversion method (Figure 3A).
In addition, feeding MSG at a constant rate appeared to increase the αꢀKG yield further and reduce the
substrate inhibition, allowing αꢀKG yield to peak (Figure 3B). The αꢀKG titer reached 89.11 g/L when MSG was
added continuously, while the glutamate bioconversion ratio reached to 60.6%. Therefore, the continuous
supply method may favor higher biocatalytic activity than the interval or pulse supply method. Moreover, the
bioconversion rate was also higher than that detected with the batch bioconversion method. Thus, the
continuous feeding method is more effective and suitable for the production of αꢀKG.
10
11
12
13
14
15
16
17
18
19
20
21
22
During the random mutagenesis, the entire coding sequence of the enzyme is targeted; however, only a few
mutated residues form the substrate binding site (Figure 4D). As a result, most mutated residues are far away
from active site.22 A delicate disruption in the spatial configuration of the active site and some minor alterations
in the protein side chain and backbone can be caused by these distant mutations, which can affect the protein
secondary structure and cause elusive variations in the organization of the protein tertiary structure, resulting
17

RSC Advances
Page 18 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
in dramatic changes in the catalytic supremacy of enzyme (Figure 4 and Table 3).42 Hence, based on the
structural model, while the mutations adjacent to the active site seemed to be more valuable in alteration of an
enzyme’s catalytic activity and substrate selectivity, these distant mutations could also play a role in refining or
adapting the catalytic properties of the enzyme (Figure 5B).
3
4
5
During the last decade, directed evolution has become a prominent method for engineering improved
proteins, however, there is still great opportunity for producing experimentally modest and more effective
strategies and techniques. We used successive rounds of epꢀPCR and gene shuffling experiment to generate
variants of P. mirabilis pm1338g4 exhibiting increased biotransformation titer. Our newly developed variants
exhibited increased production of αꢀKG titers from wholeꢀcell biocatalysis, reaching 53.74 g/L, compared with
those of wild type P. mirabilis pm1 (4.65 g/L) and pm133 (12.79 g/L). The fedꢀbatch strategy was optimized,
further increasing the αꢀKG titer to 89.11 g/L. Thus, the productivity obtained in this study was much higher
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
than those described in previous reports. In particular, with additional engineering of LꢀAAD, the production
level of αꢀKG may be more increased by reducing the product inhibition In addition, in situ product removal
technique can be applied for the removal of product inhibiting the biocatalysis process.43, 44 Overall, we have
shown that combining epꢀPCR and gene shuffling is an effective strategy for the molecular engineering of
industrial enzymes.
Acknowledgements
This work is supported by a project funded by the Priority Academic Program Development of Jiangsu Higher
Education Institutions, the 111 Project (No. 111ꢀ2ꢀ06), and the Jiangsu province "Collaborative Innovation
Center for Advanced Industrial Fermentation" industry development program.
Conflict of Interest
18

Page 19 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
The authors declare that they have no conflict of interest.
Ethical approval
3
This article does not contain any studies with human participants or animals performed by any of the authors.
4
5
Notes and references
6
1. C. Jäckel, P. Kast and D. Hilvert, Annu. Rev. Biophys., 2008, 37, 153ꢀ173.
2. S. Fong, T.D. Machajewski, C.C. Mak and C. Wong, Chem. Biol., 2000, 7, 873ꢀ883.
3. S. Melzer, C. Sonnendecker, C. Föllner and W. Zimmermann, FEBS Open Bio., 2015, 5, 528ꢀ534.
4. H.E. Schoemaker, D. Mink and M.G. Wubbolts, Science, 2003, 299, 1694ꢀ1697.
5. A.J. Straathof, S. Panke and A. Schmid, Curr. Opin. Biotechnol., 2002, 13, 548ꢀ556.
6. N. Buschke, R. Schäfer, J. Becker and C. Wittmann, Bioresour. Technol., 2013, 135, 544ꢀ554.
7. A.R. Fernie, F. Carrari and L.J. Sweetlove, Curr. Opin. Plant Biol., 2004, 7, 254ꢀ261.
8. U. Stottmeister, A. Aurich, H. Wilde, J. Andersch, S. Schmidt and D. Sicker, J. Ind. Microbiol. Biotechnol.,
2005, 32, 651ꢀ664.
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
9. C. Otto, V. Yovkova and G. Barth, Appl. Microbiol. Biotechnol., 2011, 92, 689ꢀ695.
10. S. Verseck, A. Karau and M. Weber, Evonik Degussa GmbH, Patent WO200905348, 2009.
11. T.V. Finogenova, I.G. Morgunov and O.G. Chernyavskaya, Appl Biochem Microbiol, 2005, 41, 418ꢀ425.
12. D.D. Zhang, N. Liang, Z.P. Shi, L.M. Liu, J. Chen and G.C. Du, Biotechnol. Bioprocess Eng., 2009, 14,
134ꢀ139.
13. J.W. Zhou, H.Y. Zhou, G.C. Du, L.M. Liu and J. Chen, Lett. Appl. Microbiol., 2010, 51, 264ꢀ271.
14. P. Niu, X. Dong, Y. Wang and L. Liu, J. Biotechnol., 2014, 179, 56ꢀ62.
15. G.S. Hossain, J. Li, H.D. Shin, L. Liu, M. Wang, G. Du and J. Chen, 2014, J. Biotechnol., 187, 71ꢀ77.
19

RSC Advances
Page 20 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
16. Y. Zhang, 2008, BMC Bioinformatics, 9, 40.
17. A. Crameri, S.A. Raillard, E. Bermudez and W.P. Stemmer, Nature, 1998, 391, 288ꢀ291.
3
18. G.S. Hossain, J. Li, H.D. Shin, G. Du, M. Wang, L. Liu and J. Chen, PLoS One, 2014, 9, e114291.
19. G.S. Hossain, J. Li, H.D. Shin, L. Liu, M. Wang, G. Du and J. Chen, J. Biotechnol., 2014, 169, 112ꢀ120.
20. S. Ahmad, M. Gromiha, H. Fawareh and A. Sarai, BMC Bioinformatics, 2004, 5, 51ꢀ56.
21. G.P. Horsman, A.M. Liu, E. Henke, U.T. Bornscheuer and R.J. Kazlauskas, Chem. Eur. J., 2003, 9, 1933ꢀ
1939.
4
5
6
7
8
22. S. Park, K.L. Morley, G.P. Horsman, M. Holmquist, K. Hult and R.J. Kazlauskas, Chem. Biol., 2005, 12,
45ꢀ54.
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23. M. Pedotti, E. Rosini, G. Molla, T. Moschetti and C. Savino, J. Biol. Chem., 2009, 284, 36415ꢀ36423.
24. M.W. Fraaije and A. Mattevi, Trends Biochem. Scil., 2000, 25, 126ꢀ132.
25. E.C. Settembre, P.C. Dorrestein, J.H. Park, A.M. Augustine and T.P. Begley, Biochemistry, 2003, 42,
2971ꢀ2981.
26. M. Mörtl, K. Diederichs, W. Welte, G. Molla and L. Motteran, J. Biol .Chem., 2004, 279, 29718ꢀ29727.
27. K.E. Jaeger, T. Eggert, A. Eipper and M.T. Reetz, Appl. Microbiol. Biotechnol., 2001, 55, 519ꢀ530.
28. T.W. Wang, H. Zhu, X.Y. Ma, T. Zhang, Y.S. Ma and D.Z. Wei, Mol. Biotechnol., 2006, 34, 55ꢀ68.
29. S. Bershtein and D.S. Tawfik, Curr. Opin. Chem. Biol., 2008, 12, 151ꢀ158.
30. A.V. Shivange, J. Marienhagen, H. Mundhada, A. Schenk and U. Schwaneberg. Curr. Opin. Chem. Biol.,
2009, 13, 19ꢀ25.
31. A.V. Shivange, A. Serwe, A. Dennig, D. Roccatano, S. Haefner and U. Schwaneberg, Appl. Microbiol.
Biotechnol., 2011, 95, 405ꢀ418.
32. C.A. Tracewell and F.H. Arnold, Cur. Opin. Chem. Biol., 2009, 13, 3ꢀ9.
20

Page 21 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
33. S. Oue, A. Okamoto, T. Yano and H. Kagamiyama, J. Biol. Chem., 1999, 274, 2344ꢀ2349.
34. H.F. Xu, X.E. Zhang, Z. Zhang, Y.M. Zhang and A.E.G. Cass, Biocatal. Biotransform., 2003, 21, 41ꢀ47.
35. Y. Fan, W. Fang, Y. Xiao, X. Yang, Y. Zhang, M.J. Bidochka and Y. Pei, Appl. Microbiol. Biotechnol.,
2007, 76, 135ꢀ139.
3
4
5
36. D.E. Stephens, K. Rumbold, K. Permaul, B.A. Prior and S. Singh, J. Biotechnol., 2007, 127, 348ꢀ354.
37. Z.Y. Zuo, Z.L. Zheng, Z.G. Liu, Q.M. Yi and G.L. Zou, Enz. Microbiol. Technol., 2007, 40, 569ꢀ577.
38. H.S. Ryu, H.K. Kim, W.C. Choi, M.H. Kim, S.Y. Park, N.S. Han, T.K. Oh and J.K. Lee, Appl. Microbiol.
Biotechnol., 2006, 70, 321ꢀ326.
6
7
8
9
39. C.C. Chang, T.T. Chen, B.W. Cox, G.N. Dawes, W.P. Stemmer, J. Punnonen and P.A. Patten, Nat.
Biotechnol., 17, 1999, 793ꢀ797.
10
11
12
13
14
15
16
17
18
19
20
21
22
40. A. Iffland, P. Tafelmeyer, C. Saudan and K. Johnsson, Biochemistry, 2000, 39, 10790ꢀ10798.
41. A. Iffland, S. Gendreizig, P. Tafelmeyer and K. Johnsson, Biochem. Biophys. Res. Commun., 2001, 286,
126ꢀ132.
42. K.L. Morley and R.J. Kazlauskas, 2005, Trends Biotechnol., 23, 231ꢀ237.
43. D. Stark, and U. von Stockar, Adv. Biochem. Eng. Biotechnol., 80, 2003, 149ꢀ175.
44. B. Zelić, S. Gostovic, K. Vuorilehto, D. VasićꢀRacki and R. Takors, Biotechnol. Bioeng., 2004, 85, 638ꢀ
646.
45. L.J. McGuffin, K. Bryson and D.T. Jones, Bioinformatics 2000, 16, 404ꢀ405.
46. M.T. Reetz, M. Bocola, J.D. Carballeira, D. Zha and A. Vogel, Angew. Chem. Int. Ed., 2005, 44, 4192ꢀ
4196.
47. G. Bratbak, I. Dundas, Appl. Environ. Microbiol., 1984, 48, 755ꢀ777.
48. P. Motta, G. Molla, L.Pollegioni and M. Nardini, J. Biol. Chem,. 2016, M115.703819.
21

RSC Advances
Page 22 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
49. L.L.C. Schrodinger. The PyMOL Molecular Graphics System. Version 1.3r1. 2010.
50. D. SchneidmanꢀDuhovny, Y. Inbar, R. Nussinov and H.J. Wolfson, 2005, Nucl. Acids. Res., 33, W363ꢀ
3
367.
4
51. J.O. Baek, J.W. Seo, O. Kwon, S.I. Seong, I.H. Kim and C.H. Kim, 2011, J. Basic Microbiol., 51, 129ꢀ135.
5
6
Figure captions
7
Figure 1. A. Schematic view of the domain spatial arrangement in pm1338g4 (the substrate binding domain in
yellow and the FAD binding domain is in red; αꢀhelices, β ꢀstrands, and coils are represented by helical
ribbons, arrows, and ropes, respectively). B. Mutant amino acids are shown in ribbon structure of pm1338g4.
Figure 2. Time profile for the production of αꢀKG from MSG by the whole cell biocatalyst containing the
pm133, epꢀPCR evolved pm1338 and then gene shuffling evolved pm1338g4 in the batch culture starting with
100 g of MSG (round sign for pm133, square sign for pm1338 and triangle sign for pm1338g4). Data were
analyzed using the Student’s t test. P values less than 0.05 were considered statistically significant.
Figure 3. Time profile of fedꢀbatch biotransformation for the production of αꢀKG by the whole cell biocatalyst
with the evolved pm1338g4. Feeding every 6 hour with 25 g/L of MSG (round sign), feeding every 6 hour with
40g/L of MSG (square sign ) and continuous feeding with 6 g/L/h of MSG (triangle sign). Data were analyzed
using the Student’s t test. P values less than 0.05 were considered statistically significant.
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Figure 4. Substrate entrance sites in pm1338g4 and distances of substrate from active sites. A. Substrate
entrance sites, blue spheres shows the negative charged amino acids, orange spheres shows the mutant
amino acids and surface structure represents the substrate, glutamate; B. Substrate entrance sites, blue sticks
shows the hydrophobic amino acids, orange sticks shows the mutant amino acids and surface structure
22

Page 23 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
represents the substrate, glutamate; C. & D. The distances, in Å, of glutamate (surface structure) from its
active sites (blue, gray and red elements) position in pm133 and in pm1338g4, respectively.
Figure 5. Solvent accessibility and solventꢀexposed surface of pm1338g4. A. The spiral view, which shows
amino acid residues of pm1338g4, in the order of their solvent accessibility. Most accessible residues come on
the outermost ring of this spiral. Blue, red, green, gray colors are used for positively charged, negatively
charged, polar and nonꢀpolar residues respectively. Yellow color is used for cysteine residues. Radius of the
solid circles representing these residues corresponds to the relative solvent accessibility; B. The mutants,
shown in orange color spheres, that are present in the loop helixes, shown in red color, in the model of
pm1338g4 which was docked with the substrate glutamate.
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
23

RSC Advances
Page 24 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
Table 1. Microorganisms, vectors and primers used in this study
Name
Characteristics
References
Strains
Escherichia coli JM109
recA1, endA1, gyrA96, thi, hsdR17, supE44, relA1, ∆(lacꢀpro
AB)/F′ (traD36, proAB+, lacIq, lacZ∆M 15)
Takara, Otsu, Japan
15
B. subtilis 168 derivate, ∆sucA::lox72
Bacillus subtilis
BSUC1
Proteus mirabilis KCTC
2566
Korea Collection for Type
Cultures
Plasmids
pHT43
MoBiTec, Göttingen,
Germany
Primers
Pm1_F1
5′ꢀCGCGGATCCATGGCAATAAGTAGAAGAAAATTTAꢀ3′
5′ꢀTCCCCCGGGTTAGAAACGATACAGACTAAATGGTꢀ3′
5′ꢀCGCGGATCCATGGCAATAAGTAGAAGAAAATTTAꢀ3′
5′ꢀTCCCCCGGGTTAGAAACGATACAGACTAAATGGTꢀ3′
BamHI
SmaI
Pm1_R1
pvLAAD_F
pvLAAD_R
BamHI
SmaI
3
4
5
6
7
8
9
10
11
24

Page 25 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
3
Table 2. List of pm133 mutants generated by errorꢀprone polymerase chain reaction (epꢀPCR) and geneꢀ
shuffling mutation.
Round of epꢀPCR
Mutations present
First epꢀPCR (pm1331)
Second epꢀPCR (pm1332)
Third epꢀPCR (pm1333)
Fourth epꢀPCR (pm1334)
Fifth epꢀPCR (pm1335)
G259W / D362N
W259G / D362N / N150K / Q278L / G437V
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A
Sixth epꢀPCR (pm1336)
Seventh epꢀPCR (pm1337)
Eighth epꢀPCR (pm1338)
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F / G291R /S408G
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F / G291R / S408G / E366K /
N418L / V269I
1
st round Gene shuffling
(pm1338g1)
2nd round Gene shuffling
(pm1338g2)
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F / G291R / S408G/ E366K/
N418L / V269I / E400K / P275N / V258I / L378T
3rd round Gene shuffling
(pm1338g3)
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F / G291R / S408G /E366K /
N418L / V269I / E400K / P275N / V258I / L378T / L267M / E389Q / A285G / A286V /
R251Q
4th round Gene shuffling
(pm1338g4)
W259G / D362N / N150K / Q278L/ G437V / G193A / P320S / P246A / D374V / D340E /
V271I / V445A / A295H / P415F / E383H / D147A / I317F / G291R / S408G / E366K /
N418L / V269I / E400K / P275N / V258I / L378T / L267M / E389Q / A285G / A286V /
R251Q / V141A/ F208P / T242S
4
5
Mutations in bold indicate new mutations identified in each round of directed evolution.
6
7
8
9
10
25

RSC Advances
Page 26 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
3
Table 3. Apparent kinetic parameters of αꢀKG production using wholeꢀcell biocatalysts containing pm133 and
its mutants
Strains/mutants
Pm133
Vmax (ꢁM/min)
Km (mM)
Vmax/ Km (per min)
56.7 ± 1.11
23.58 ± 0.97
0.0024
Pm1331
Pm1332
Pm1333
79.2 ± 0.92
92.6 ± 1.34
105.3 ± 1.65
21.32 ± 1.78
18.11 ± 0.69
16.72 ± 0.82
0.0037
0.0051
0.0063
Pm1334
142.7 ± 1.52
168.8 ± 1.09
159.1 ± 1.31
155.9 ± 1.11
167.2 ± 0.81
184.6 ± 0.43
207.1 ± 0.57
223.8 ± 0.32
241.8 ± 0.11
13.26± 0.46
11.69 ± 0.23
10.53 ± 0.32
10.17 ± 0.13
8.83 ± 0.17
8.12 ± 0.09
7.54 ± 0.12
6.91 ± 0.23
6.56 ± 0.23
0.0107
0.0144
0.0151
0.0153
0.0189
0.0226
0.0274
0.0322
0.0368
Pm1335
Pm1336
Pm1337
Pm1338
Pm1338g1
Pm1338g2
Pm1338g3
Pm1338g4
4
5
6
7
8
Each value was calculated from three independent experiments. The cell concentration of the reaction system
was 20.0 g/L (dry cell weight). The total volume of each reaction mixture was 20 mL (50 mM TrisꢀCl buffer, pH
8.0), and the ʟꢀamino acid deaminase concentration in each reaction solution was equal (15.6 ꢁmol/min/mg);
Vmax, maximum rate of αꢀKG production; Km, Michaelis constant.
9
10
11
12
13
14
15
26

Page 27 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
3
4
(Scheme 1)
5
6
7
8
9
10
11
12
27

RSC Advances
Page 28 of 32
View Article Online
DOI: 10.1039/C6RA02940J
1
2
3
(Figure 1)
4
5
6
7
8
9
10
28

Page 29 of 32
RSC Advances
View Article Online
DOI: 10.1039/C6RA02940J
1
2
3
4
5
6
7
8
9
(Figure 2)
10
11
12
13
14
15
16
17
29