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| <div class="batu" style="background: url('https://static.igem.org/mediawiki/2017/f/fe/Npu-background.png') no-repeat fixed; overflow: hidden;"> | | <div class="batu" style="background: url('https://static.igem.org/mediawiki/2017/f/fe/Npu-background.png') no-repeat fixed; overflow: hidden;"> |
− | <img class="img-responsive" src="https://static.igem.org/mediawiki/2017/9/97/%E9%A2%98%E7%9B%AE%E5%B0%8F%E9%80%9A%E6%A0%8Fmodel.jpg"> | + | <img class="img-responsive" src="https://static.igem.org/mediawiki/2017/4/46/%E9%A2%98%E7%9B%AE%E5%B0%8F%E9%80%9A%E6%A0%8Fdesign.jpg"> |
| <div class="container" style="padding-top:70px"> | | <div class="container" style="padding-top:70px"> |
| <div class="row"> | | <div class="row"> |
− | <div class="col-md-12" style="padding-top:70px"> | + | <div class="col-md-12"> |
− | <ul id="myTab" class="nav nav-pills nav-justified" style="margin:0; padding:0; "> | + | <h3>Introduction</h3> |
− | <li class="active">
| + | <h4>The essence of biochemical synthesis is the catalytic reaction with enzyme as the catalyst. Creating |
− | <a href="#service-one" data-toggle="tab">
| + | new biochemical reactions is an important research direction of synthetic biology. |
− | <h2>Metabolic flow modeling</h2>
| + | <br> ceaS2, whose full name is N2-(2-carboxyethyl)arginine synthase2, is a kind of enzyme in Streptomyces |
− | </a>
| + | clavuligerus. The mentor of our team, Jiang Huifeng, has confirmed the new functions of ceaS2 |
− | </li>
| + | <br> with the help of TPP (Thiamine pyrophosphate) and magnesium ions, ceaS2 enzyme can catalyze the |
− | <li class="">
| + | production of acrylic acid with DHAP (dihydroxy acetone phosphate) or G3P (glyceraldehyde 3-phosphate) |
− | <a href="#service-two" data-toggle="tab">
| + | as substrate. |
− | <h2>AEMD</h2>
| + | <br> Cell factory of acrylic acid (GAACF) 1.0: |
− | </a>
| + | <br> DHAP and G3P are the central metabolic secondary products which can be easily found in various organisms. |
− | </li>
| + | They are the carbon flow nodes that must be passed in the glycerol metabolic pathway in most organisms. |
− | </ul> | + | With ceaS2 enzyme as the core part, it is possible to create a new pathway to synthesize acrylic |
− | <div id="myTabContent" class="tab-content">
| + | acid from glycerol metabolic pathway in organisms and construct a cell factory with high yield of |
− | <div class="tab-pane fade active in" id="service-one"> | + | acrylic acid. |
− | <h2 style="text-align:center">Introduction</h2>
| + | <br> First, we took E. coli BL21 (DE3) as the chassis cells and constructed engineering bacteria carrying |
− | <h4>This year our project is the introduction of acrylic synthetic routes in Escherichia coli or
| + | the gene of ceaS2 enzyme with pET-28a plasmid as the vector. We constructed a new pathway to synthesize |
− | Saccharomyces cerevisiae to produce acrylic acid.</h4>
| + | acrylic acid from any carbon source by transforming ceaS2 directly into the chassis cells. This new |
− | <br>
| + | approach is the shortest compared to other pathways. Take the glycerol metabolic pathway of E. coli |
− | <div class="col-md-12">
| + | as an example, we only need three enzymes to achieve the synthesis of acrylic acid from glycerol. |
− | <div class="col-md-6">
| + | So this pathway is a more flexible and has more development prospects. |
− | <img src="https://static.igem.org/mediawiki/2017/2/21/%E5%A4%A7%E8%82%A0%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2.png" class="img-responsive">
| + | <br> 【E.coli图+路径图+质粒图】 |
− | <h5 style="text-align:center"> Primitive metabolic path map in E.Coli</h5>
| + | |
− | </div>
| + | |
− | <div class="col-md-6">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/7/7b/%E9%85%B5%E6%AF%8D%E5%8E%9F%E5%A7%8B%E4%BB%A3%E8%B0%A2%E5%9B%BE.png" class="img-responsive">
| + | |
− | <h5 style="text-align:center"> Primitive metabolic path map in S.Cerevisiae</h5>
| + | |
− | </div>
| + | |
− | </div>
| + | |
− |
| + | |
− | <h4>
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br> Therefore, through the literature review, we found two enzymes which can achieve efficient
| + | |
− | conversion of glycerol to generate DHAP the same way.
| + | |
− | <br> 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).
| + | |
− | </h4>
| + | |
− | <br>
| + | |
− | <div class="col-md-12">
| + | |
− | <div class="col-md-6">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/1/10/%E5%A4%A7%E8%82%A0%E8%B7%AF%E5%BE%84%E5%9B%BE.png" class="img-responsive">
| + | |
− | <h5 style="text-align:center"> New route map in E.Coli</h5>
| + | |
− | </div>
| + | |
− | <div class="col-md-6">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/b/b0/%E9%85%B5%E6%AF%8D%E8%B7%AF%E5%BE%84%E5%9B%BE.png" class="img-responsive">
| + | |
− | <h5 style="text-align:center"> New route map S.Cerevisiae</h5>
| + | |
− | </div>
| + | |
− | </div>
| + | |
− |
| + | |
− | <h4>
| + | |
− | <br> 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:
| + | |
− | <br> 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?
| + | |
− | <br> 2. The introduction of new pathways after the entire metabolic pathway is stable and robust.
| + | |
− | How is it?
| + | |
− | <br> In order to answer these two questions, we established a carbon metabolic flow model.
| + | |
− | </h4>
| + | |
− | <h4>The overall workflow is as follows:</h4>
| + | |
− | <h2 style="text-align:center">Parameter estimation</h2>
| + | |
− | <h4>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.
| + | |
− | <br>
| + | |
− | <br> 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:
| + | |
− | <br> 1. When the sample PH is the same, the sample is weighted by 4 .2 when they are close. 1
| + | |
− | when they differ much.
| + | |
− | <br> 2. When the sample temperature is the same, the pH is the same.
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br>
| + | |
− | <br> 1.kernel density estimate 2. Gaussian mixed model.
| + | |
− | <br>
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/3/32/Model-4.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | <br>
| + | |
− | <br>
| + | |
− | <br>
| + | |
− | <h2 style="text-align:center">The basic workflow of parameter estimation:</h2>
| + | |
− | <h4>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.
| + | |
| | | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/f/f7/Model-5.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/b/b2/Model-6.1.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
| | | |
− | | + | https://static.igem.org/mediawiki/2017/1/10/%E5%A4%A7%E8%82%A0%E8%B7%AF%E5%BE%84%E5%9B%BE.png |
− | | + | |
− | <br> 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.
| + | Through the whole cell catalysis and HPLC (High Performance Liquid Chromatography), |
− | | + | the results show that the engineering bacteria can use glycerol as carbon source to produce acrylic |
− | <div align="center">
| + | acid. However, the yield of the cell factory 1.0 is not high, only about 1mg / L. |
− | <img src="https://static.igem.org/mediawiki/2017/e/e5/Model-6.2.png" class="img-responsive" width="20%" height="20%">
| + | <br> It is known that acrylic acid can not be metabolized in the cell, so we analyzed the possible reasons |
− | </div>
| + | as the following: |
− | | + | <br> 1. The activity and the catalytic efficiency of wild type ceaS2 is low. |
− | | + | <br> 2. The low carbon flow rate of glycerol metabolic pathway in E. coli leads to the low concentration |
− | | + | of DHAP and G3P. |
− | <br> Finally, we get the estimated parameter values, as well as the corresponding parameters
| + | <br> 3. Acrylic acid is toxic to the chassis cells. |
− | of the original PDF. The specific form and parameter values are as follows:
| + | <br> 4. The reaction conditions such as carbon source, pH, temperature and reaction time are not suitable. |
− | <br> The reaction path of the original pathway is Gly to Gly-3-p and then to DAHP
| + | <br> Based on the analyzing results, we have made improvements and built a new cell factory. |
− | <div align="center">
| + | <br> |
− | <img src="https://static.igem.org/mediawiki/2017/d/da/Model-7.png" class="img-responsive" width="40%" height="40%">
| + | <br> Cell factory of acrylic acid (GAACF) 2.0: |
− | </div>
| + | <br> We built a new cell factory of acrylic acid through the four part: CO-PART, SYSTEM, PATHWAY, PRODUCTION! |
− | | + | <br> |
− | | + | </h4> |
− | <br> The original pathway belongs to the reaction of a single channel, and there is a random
| + | <div id="COREPART" style="padding-top:50px;margin-top:-50px;"> |
− | bibi reaction and an irreversible Mickey equation reaction. The reaction involves two enzymes
| + | <h2 style="text-align:center">CORE PART</h2> |
− | paticipating - glpk and glpD. We assume that the reaction concentration of these two enzymes
| + | <h4>Acrylic acid is a byproduct of CEAS2 enzyme, the catalytic effect of wild type ceaS2 enzyme is very |
− | is 0.01 mM, assuming that the initial [Gly] concentration is 10 mM, the initial concentration
| + | weak, and acrylic acid production is only 1mg / L. So it is necessary to improve the catalytic |
− | of ATP 10 mM, Gly-3 The concentration of -p 0 mM and the concentration of DHAP 0 mM at the
| + | effect of this core factor, ceaS2 enzyme. |
− | same time.
| + | <br> The gene of ceaS2 enzyme consists of 1719 deoxynucleotides and the protein sequence consists |
− | <br>
| + | of 573 amino acids. We need to use bioinformatics to analyze and simulate, in order to help us |
− | <br> In this reaction, we make the following assumptions about our model:
| + | decide the correct proposal. |
− | <br> 1. The ATP of the E.coil system is given externally completely, assuming that the culture
| + | <br> We constructed ceaS2 enzyme mutants using the AEMD (Auto Enzyme Mutation Design) platform. We |
− | conditions given externally are sufficient and ATP maintains a stable constant.
| + | constructed the ceaS2 wild-type sequence on pET-28a plasmid. We used pET-28a-ceaS2 plasmid as |
− | <br> 2. Assume that the substrate involved in the reaction does not participate in other reactions.
| + | a template to create point mutation, and then transformed the plasmid into BL21. Then, we did |
− | <br> In order to determine the yield of the target product, we chose to observe the efficiency
| + | the whole cell catalysis to get the products. Finally, we screened for ceaS2 mutants with high |
− | of the DHAP yield estimation system in view of the lack of basic Deas2 enzyme data.
| + | catalytic efficiency by HPLC (High Performance Liquid Chromatography) (Learn more about HPLC!)and |
− | <br>
| + | HTS (High throughput screening) (Learn more about HTS!). |
− | </h4>
| + | |
− | | + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/9/95/Model-8.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | | + | |
− | | + | |
− | <h4>
| + | |
− | <br> We can observe that the DHAP stops growing close to 50 minutes.
| + | |
− | <br>
| + | |
− | <br> 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.
| + | |
− | <br> metabolic flow after the transformation of the reaction model. According to the actual situation
| + | |
− | of the reaction, we make the following assumptions:
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | <h4 style="text-align:center">Metabolic pathways after transformation</h4>
| + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/3/33/Model-10.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | | + | |
− | | + | |
− | <h4>
| + | |
− | <br> 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.
| + | |
− | <br> Sensitive analysis
| + | |
− | <br> 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).
| + | |
− | <br> 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.
| + | |
− | <br> 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:
| + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/5/50/Model-11.1.png" class="img-responsive" width="30%" height="30%">
| + | |
− | </div>
| + | |
− | | + | |
− | | + | |
− | <br> 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.
| + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/9/9c/Model-11.2.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | | + | |
− | </h4>
| + | |
| <br> | | <br> |
− | <h4 style="text-align:center">KATA Sensitivity Test before Modification</h4>
| + | </h4> |
− | <div align="center">
| + | </div> |
− | <img src="https://static.igem.org/mediawiki/2017/8/89/Model-12.1.png" class="img-responsive" width="60%" height="60%">
| + | 【ceaS2酶结构图+5埃范围内活性中心示意图】 |
− | </div>
| + | |
| | | |
| | | |
| + | <div id="PATHWAY" style="padding-top:50px;margin-top:-50px;"> |
| + | <h2 style="text-align:center">PATHWAY</h2> |
| + | <h4>The carbon flow rate of the glycerol metabolic pathway is low. In order to solve the problem, we |
| + | need reconstruction and optimization of the original metabolic pathway. |
| <br> | | <br> |
− | <h4 style="text-align:center">KATA Sensitivity Test after Modification</h4> | + | <br> RE-Construction:We designed the GDC (GlyDH-DAK-Ceas2) pathway to produce acrylic acid from glycerol. |
− | <div align="center"> | + | In this pathway, GlyDH(Glycerol dehydrogenase) can efficiently convert Glycerol into DHA(1,3-Dihydroxyacetone). |
− | <img src="https://static.igem.org/mediawiki/2017/e/e9/Model-12.2.png" class="img-responsive" width="60%" height="60%">
| + | Then DAK (Dihydroxyacetone kinase) converts DHA into DHAP. Finally, ceaS2 converts DHAP into |
− | </div> | + | acrylic acid. In addition, because GlyDH depends on NAD+, we added two reduction models, NOX |
− | | + | (NADH dehydrogenase )and CAT(Catalase), to the pathway, with the purpose of providing the required |
− | | + | reduction force for GLY DH through the two layers of substrate level cycle. At last, we construct |
− | <h4> | + | a new pathway for acrylic acid synthesis- GNCDC(GlyDH-NOX-CAT-DAK-ceaS2) |
− | <br> We show the highest ratio (1000) and the lowest ratio (0.001) in yellow and blue lines respectively.
| + | <br> The genes of GlyDH and DAK were constructed on two MCS (multiple cloning sites) on the backbone |
− | The pre-transformation pathway is most sensitive to the change of Kcat in glpK enzyme, and
| + | of pCDFDuet-1 plasmid. NOX and CAT were constructed on two MCSs on the backbone of pETDuet-1 |
− | the metabolic pathway of the target substrate is transformed with the change of α Rate is
| + | plasmid. ) (质粒图注释) |
− | 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.
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br> And the change curve of the concentration in 60min is shown below:
| + | |
− | </h4> | + | |
| <br> | | <br> |
− | <br>
| + | </h4> |
− | <h4 style="text-align:center">Before transformation</h4>
| + | 【E.coli新路径图(含旧路径部分),区别主要途径和还原力模块+质粒图】 |
| + | </div> |
| | | |
− | | + | <div id="SYSTEM" style="padding-top:50px;margin-top:-50px;"> |
− | <div align="center">
| + | <h2 style="text-align:center">SYSTEM</h2> |
− | <img src="https://static.igem.org/mediawiki/2017/9/91/Model-14.1.png" class="img-responsive" width="60%" height="60%">
| + | <h4>The choice of the chassis organism is vital to the efficiency of the cell factory. Acrylic acid may |
− | </div> | + | do damage to the cell membrane. So we need to choose an organism which has high tolerance of |
| + | acrylic acid. Escherichia coli and Saccharomyces cerevisiae are two model organisms which can |
| + | be easily modified in the prokaryotic and eukaryotic. |
| + | <br> Therefore, in the choice of the chassis organism, we tested two organisms, E. coli MG1655 and |
| + | Saccharomyces cerevisiae BY4741. BY4741 has a great ability to metabolize glycerol. According |
| + | to GAACF1.0, we used the YCPlac33 plasmid with URA defect screening marker as the backbone and |
| + | used the pTDH3 constitutive promoter and tPFK1 constitutive terminator to construct ceaS2 plasmid. |
| + | <br> 【S.C图+路径图+质粒图】 We confirmed the proposal can make S.cerevisiae produce acrylic acid, but the |
| + | yield is low, so we decided to optimize it. |
| + | <br> First, according to GNCDC(GlyDH-NOX-CAT-DAK-ceaS2) in E.coli, we added NOX to the pathway(the |
| + | CAT enzyme is active in S.cerevisiae). So we designed a pathway, GNDC(GlyDH-NOX -DAK-ceaS2), |
| + | for S.cerevisiae. |
| + | <br> 【新途径+质粒图】 The genes of GlyDH and DAK were constructed on the backbone of YCPlac33 plasmid with |
| + | URA marker. We used the ADH1 promoter and TGPD1 terminator for GlyDH, the PGK1 promoter and the |
| + | tPFK1 terminator for DAK. NOX and ceaS2 were constructed on the backbone of the other YCPlac33 |
| + | plasmid. We replaced URA marker with LEU marker to screen for two plasmids easily. We used the |
| + | TEF2 promoter and tRPS2 terminator for GlyDH, the same promoter and terminator as the original |
| + | pathway for ceaS2. (质粒图注释) |
| <br> | | <br> |
− | <h4 style="text-align:center">After trransformation</h4>
| + | </h4> |
| + | </div> |
| | | |
− | <div align="center">
| + | <div id="PRODUCTION" style="padding-top:50px;margin-top:-50px;"> |
− | <img src="https://static.igem.org/mediawiki/2017/d/d8/Model-14.2.png" class="img-responsive" width="60%" height="60%">
| + | <h2 style="text-align:center">PRODUCTION</h2> |
− | </div>
| + | <h4>To make the engineering bacteria produce acrylic acid, it takes two stages. First, bacteria must |
− | <h4>
| + | grow and express the enzyme, then use carbon source to synthesize acrylic acid. To screen for |
− | <br> We found that the random change of ATP concentration had a significant effect on the pathway
| + | engineering bacteria, it is a waste of time and reagents to use the traditional fermentation |
− | after transformation, and the rate of DHAP synthesis was lower than that before transformation.
| + | method. We used whole cell catalysis to carry out the reaction for acrylic acid production |
− | <br> But when we adjust the standard deviation of the normal distribution random variable to
| + | <br> After the enzyme is expressed, the bacteria solution will be centrifuged and concentrated 10 |
− | 0.05, the result is shown below.
| + | times with buffer before the reaction. Therefore, we optimized the reaction process, selected |
− | <div align="center">
| + | the carbon source, Buffer, temperature, pH, reaction time and other conditions to optimize the |
− | <img src="https://static.igem.org/mediawiki/2017/f/f2/Model-15.1.png" class="img-responsive" width="60%" height="60%">
| + | production process of the cell factory. |
− | </div>
| + | |
− | | + | |
− | <div align="center">
| + | |
− | <img src="https://static.igem.org/mediawiki/2017/c/ce/Model-15.2.png" class="img-responsive" width="60%" height="60%">
| + | |
− | </div>
| + | |
− | | + | |
− | | + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | | + | |
− | </div>
| + | |
− | <div class="tab-pane fade" id="service-two">
| + | |
− | | + | |
− | <h3 style="text-align: center;">Abstract</h3>
| + | |
| <br> | | <br> |
− | <h4>Engineering for the desired enzyme catalytic properties plays an important role in the biosynthesis
| + | </h4> |
− | of bulk chemicals and natural products. However, it is a time-consuming task to improve enzyme
| + | 【筛选条件组合表,分为E.coli和S.C的】 |
− | catalysis by traditional random mutagenesis. And the utility of rational design based on
| + | <br> |
− | protein structure often was limited by the lack of protein structure for target enzymes and
| + | <h3>PS. We also made Hardware |
− | professional backgrounds of bioinformatics.
| + | <a href="https://2017.igem.org/Team:NPU-China/Hardware">(Click Here)</a> to simulate the industrial production process of acrylic acid!</h3> |
− | <br>
| + | |
− | <br>
| + | |
− | </h4> | + | |
− | <h3 style="text-align: center;">Introduction</h3>
| + | |
− | <h4>
| + | |
− | <br> 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.
| + | |
− | <br> 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.
| + | |
− | <br>
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | <h3 style="text-align: center;">What is AEMD?</h3>
| + | |
− | <h4>
| + | |
− | <br> 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).
| + | |
− | <br> 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.
| + | |
− | <br>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.
| + | |
− | <br>
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | <h3 style="text-align: center;">Process</h3>
| + | |
− | <h4>
| + | |
− | <br> 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).
| + | |
− | <br> 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.
| + | |
− | <br>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.
| + | |
− | <br>
| + | |
− | <br>
| + | |
− | </h4>
| + | |
− | </div>
| + | |
− | | + | |
| </div> | | </div> |
| | | |
| + | </div> |
| | | |
− |
| |
− | </div>
| |
| </div> | | </div> |
| <!-- Blog Post Row --> | | <!-- Blog Post Row --> |