Introduction
This year our project is the introduction of acrylic synthetic routes in Escherichia coli
or Saccharomyces cerevisiae to produce acrylic acid.
Primitive metabolic path map
Primitive metabolic path map
We have a rational new design and transformation of the core enzyme ceaS2, at the same
time, we also want to be optimized to improve the acrylic acid production in the metabolic
flow.
We know that for Escherichia coli, the carbon flow rate of its original glycerol metabolic
pathway may not be sufficient, and if the new glycerol metabolic pathway can be used
to increase the carbon flow of DHAP or G3P, the substrate of the core enzyme ceaS2 can
be increased Concentration to increase acrylic acid production.
Therefore, through the literature review, we found two enzymes which can achieve efficient
conversion of glycerol to generate DHAP the same way.
In our new approach, Glycerol dehydrogenase (Gly DH) is capable of efficiently converting
glycerol to 1,3-Dihydroxyacetone (DHA) and then phosphorylates DHA to DHAP via Dihydroxyacetone
kinase (DAK).
New route map
Before the implementation of the formal experiment, we need to model it to analyze the
impact of the introduction of new routes on the original metabolic flow, especially the
two intermediates of DHAP or G3P. Specifically, we care about the following two issues:
1. Has the DHAP or G3P's carbon flow improved after the introduction of new metabolic
pathways? Is it compared to the previous increase in production?
2. The introduction of new pathways after the entire metabolic pathway is stable and
robust. How is it?
In order to answer these two questions, we established a carbon metabolic flow model.
The overall workflow is as follows:
Parameter estimation
There are many parameters to be determined in the model. Most of these kinetic parameters
can be found in the literature or in the database, but at the same time, there are some
kinetic parameters of the enzyme we are looking for. Its organic matter, or the temperature
and ph of the enzyme are different. Therefore, we need to re-estimate this part of the
parameters.
In the process data link, we cited the method using the data point weighting of University
of Manchester in year 2016 . The weighting of the samples is as follows:
1. When the sample PH is the same, the sample is weighted by 4 .2 when they are close.
1 when they differ much.
2. When the sample temperature is the same, the pH is the same.
3. When the samples are from the same species , the weight of the sample is 4.When they
are the non-identical species and are the prokaryotes,or the corresponding species mutated
to the corresponding species, the weight is 2. When they are the non-identical species
and are the eukaryotes, the weight is 1.
4. Try to delete the missing data. If there are some essential samples of the temperature
and PH missing, then the corresponding weight is 2.
1.kernel density estimate 2. Gaussian mixed model.
The fourth point reflects our point of view of Bayesian. In the absence of prior knowledge
of the case, we take as much as possible the weight of neutrality.
Based on the points above, we get a new parametric vector after weighting.In our model,
we do the fitting of the probability density according to the two methods of the parameter
vector: 1.kernel density estimatebsp;2. Gaussian mixed model.
The basic workflow of parameter estimation:
The Gaussian mixture model can be approximated to any real probability distribution in theory.
The EM algorithm is used to estimate the parameters required for the model. And we use
the Gaussian mixture model to estimate the probability density of the possible distribution
of parameters.
After making the probability distribution, we select Bin randomly, which meet the conditions
of width = len / 10.And we select the most possible bin based on the CDF, and estimate
the corresponding parameters when the bin reach average value.
Finally, we get the estimated parameter values, as well as the corresponding parameters
of the original PDF. The specific form and parameter values are as follows:
The reaction path of the original pathway is Gly to Gly-3-p and then to DAHP
The original pathway belongs to the reaction of a single channel, and there is a random
bibi reaction and an irreversible Mickey equation reaction. The reaction involves two
enzymes paticipating - glpk and glpD. We assume that the reaction concentration of these
two enzymes is 0.01 mM, assuming that the initial [Gly] concentration is 10 mM, the initial
concentration of ATP 10 mM, Gly-3 The concentration of -p 0 mM and the concentration
of DHAP 0 mM at the same time.
In this reaction, we make the following assumptions about our model:
1. The ATP of the E.coil system is given externally completely, assuming that the culture
conditions given externally are sufficient and ATP maintains a stable constant.
2. Assume that the substrate involved in the reaction does not participate in other
reactions.
In order to determine the yield of the target product, we chose to observe the efficiency
of the DHAP yield estimation system in view of the lack of basic Deas2 enzyme data.
We can observe that the DHAP stops growing close to 50 minutes.
Then, we need to test the carbon pathway through the modified pathway, And add a metabolic
pathway enzyme-catalyzed by GlyDH enzyme and DAK in the original path, while the need
for NOX enzyme and CAT enzyme from the role of NAD + supplement, resulting in DHA, and
finally Phosphorylation produces DHAP.
metabolic flow after the transformation of the reaction model. According to the actual
situation of the reaction, we make the following assumptions:
1. In the reaction , due to the process of hydrogen peroxide to the production of O2,
that is, the process of generating acceptor, is faster, we will regard the reaction of
NOX enzyme NADH catalytic as an ordinary Michael's equation, rather than ordered sequence
reaction.
2. Random pairs of sequence reactions and ordered sequence reaction equations are identical.
So we substrate which is identified as the [A] substrate depending on the integrity of
the data.
Metabolic pathways after transformation
We can see that the rate of DHAP is faster after changing the metabolic pathway, which
means that the higher the output per unit time after being put in use, the sooner the
reaction is done. Compared to the pre-improved pathway ,the reaction finishes roughly
five minutes ahead.
Sensitive analysis
In the previous pathway study, we noted that the Kcat values of glpK and GlyDH enzymes
are unknown (we do not have a large deviation in the absence of a sample expressed in
E.coil).
The Kcat value of the reaction of propanal is the Kcat value of the reaction of Gly
and GlyDH. It is also assumed that the K63 ratio of the two enzymes is 2: 1.
We often use Kcat / Km to describe the catalytic efficiency of different enzymes for
the same substrate, and as a result of our experiments, GlyDH exhibited higher catalytic
efficiency than glpK. Thus we assume that the GlyDH enzyme and the glpK enzyme satisfy
the following relationship:
GlyDH enzyme activity and the Km value for gly and the Km value of the glpk enzyme to
gly have been known in previous studies. Next we adjust the alpha coefficient to study
the effect of different ratios on the overall metabolic flow.
KATA Sensitivity Test before Modification
KATA Sensitivity Test after Modification
We show the highest ratio (1000) and the lowest ratio (0.001) in yellow and blue lines
respectively. The pre-transformation pathway is most sensitive to the change of Kcat
in glpK enzyme, and the metabolic pathway of the target substrate is transformed with
the change of α Rate is always higher than the pre-transformation pathway, even when
the glpK Kcat / Km value is 100 times the GlyDH, the reason may be DAK enzyme catalytic
efficiency’s higher than glpD.
As we can see, the previous reaction is dependent on ATP, and in the previous hypothesis,
we make ATP stable in the constant .In order to analyze the ATP concentration changes
on the impact of glycerol conversion, we add a Standard deviation of 0.05, mean of the
normal distribution of variables to disturb the timing of the concentration of ATP in
ODE.
For the sake of us to observe the significant results, we assume that the initial concentration
of ATP is 0 and is always greater than zero.
And the change curve of the concentration in 60min is shown below:
Before transformation
After trransformation
We found that the random change of ATP concentration had a significant effect on the
pathway after transformation, and the rate of DHAP synthesis was lower than that before
transformation.
But when we adjust the standard deviation of the normal distribution random variable
to 0.05, the result is shown below.
Thus, we found that even if ATP had a greater perturbation, the overall level was relatively
high in 0-60 min compared to the previous standard deviation of 0.02. While the transformation
of the metabolic pathway also reflects a more stable curve of change. At this point the
concentration of DHAP is not significantly affected by changes in ATP concentration.
Thus, in actual production, we only need to keep the ATP concentration at a slightly
higher level, not only to ensure the production of the target product, but to increase
the stability of the system as well.
Abstract
Engineering for the desired enzyme catalytic properties plays an important role in the biosynthesis
of bulk chemicals and natural products. However, it is a time-consuming task to improve
enzyme catalysis by traditional random mutagenesis. And the utility of rational design
based on protein structure often was limited by the lack of protein structure for target
enzymes and professional backgrounds of bioinformatics.
Introduction
Enzyme engineering has been extensively used to optimize biocatalysts in industrial
biotechnology since most of enzymes in nature prefer to organisms adaptation but not
industrial production (Alvizo, et al., 2014; Ma, et al., 2009; Savile, et al., 2010).
Traditionally, optimized enzymes were obtained by random site-directed or saturated mutagenesis
such as Error Prone PCR, DNA shuffling and so on (Kabumoto, et al., 2009; Qi, et al.,
2009; Reetz and Carballeira, 2007; Yep, et al., 2008). Due to the immense possibility
of sequence mutation at amino acids level, it is a time-consuming and low efficiency
task to obtain a high efficient biocatalyst by random mutation.
With the availability of an increasing number of protein structural and biochemical
data, rational design of enzymatic mutation has become more and more popular (Bloom,
et al., 2005; Chica, et al., 2005; Kiss, et al., 2013; Li, et al., 2012; Steiner and
Schwab, 2012). Many strategies have been used to obtain evolutionary information, catalytic
sites and substrate channels by integrating sequence and structural features of enzymes.
Previous studies have developed many effective computational tools for enzyme engineering,
such as the enzyme design software Rosetta (Leaver-Fay, et al., 2011) and stability design
software Foldx (Van, et al., 2011) and so on (Table S2). However, most of them only focus
on one feature, like the thermo-stability based on the known PDB structure, and often
request professional backgrounds in protein structure, biochemistry, bioinformatics and
so on.
What is AEMD?
AEMD is a web-based pipeline, which integrates several approaches together for enzyme
stability, selectivity and activity engineering. This pipeline can generate comprehensive
reports, which include the recommended mutation for improving enzyme catalytic property.
Specifically, users can get the recommended mutation only inputting sequence information
of target enzymes, which is very useful in the situation without professional knowledge
and the known protein structure, since AEMD contains a functional module that can automatically
predict structure of the target enzyme based on the known structures in Protein Data
Bank (PDB).
AEMD-Web provides a web interface, enabling users to conveniently predict mutants which
could improve the stability, selectivity and activity of enzymes. Users can obtain the
suggestion of mutations for almost all enzyme even without protein structure. In the
future, we will construct a comprehensive enzymatic mutant database and integrate new
computing technology, to improve the efficiency of enzyme engineering in industrial biotechnology.
Fig.1 Workflow of the Stability analysis (A), Selectivity analysis (B) and Activity analysis
(C). The blue color rectangle blocks represent the inputs of sequence or PDB file, and
the output of recommended mutation sites. The green and gray color rectangle blocks represent
the evolution- and energy-based analysis process, respectively. The yellow color diamond
blocks represent the use of other softwares and approaches. The processes were shown
in Supplementary methods 【click here】in more detail. AEMD is freely available for non-commercial
use at www.AEMD.tech:8181.
Process
AEMD is a web-based pipeline, which integrates several approaches together for enzyme
stability, selectivity and activity engineering. This pipeline can generate comprehensive
reports, which include the recommended mutation for improving enzyme catalytic property.
Specifically, users can get the recommended mutation only inputting sequence information
of target enzymes, which is very useful in the situation without professional knowledge
and the known protein structure, since AEMD contains a functional module that can automatically
predict structure of the target enzyme based on the known structures in Protein Data
Bank (PDB).
AEMD-Web provides a web interface, enabling users to conveniently predict mutants which
could improve the stability, selectivity and activity of enzymes. Users can obtain the
suggestion of mutations for almost all enzyme even without protein structure. In the
future, we will construct a comprehensive enzymatic mutant database and integrate new
computing technology, to improve the efficiency of enzyme engineering in industrial biotechnology.
Fig.1 Workflow of the Stability analysis (A), Selectivity analysis (B) and Activity analysis
(C). The blue color rectangle blocks represent the inputs of sequence or PDB file, and
the output of recommended mutation sites. The green and gray color rectangle blocks represent
the evolution- and energy-based analysis process, respectively. The yellow color diamond
blocks represent the use of other softwares and approaches. The processes were shown
in Supplementary methods 【click here】in more detail. AEMD is freely available for non-commercial
use at www.AEMD.tech:8181.