(45 intermediate revisions by 5 users not shown) | |||
Line 25: | Line 25: | ||
<![endif]--> | <![endif]--> | ||
<style> | <style> | ||
+ | .nav-pills>li.active>a, | ||
+ | .nav-pills>li.active>a:hover, | ||
+ | .nav-pills>li.active>a:focus { | ||
+ | color: #fff; | ||
+ | background-color: #A0A0A0; | ||
+ | } | ||
+ | |||
+ | .list-group-item.active>.badge, | ||
+ | .nav-pills>.active>a>.badge { | ||
+ | color: #A0A0A0; | ||
+ | background-color: #fff; | ||
+ | } | ||
</style> | </style> | ||
</head> | </head> | ||
Line 150: | Line 162: | ||
<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"> | + | <div class="col-md-12" style="padding-top:70px"> |
− | <h2 style="text-align:center">Introduction</h2> | + | <ul id="myTab" class="nav nav-pills nav-justified" style="margin:0; padding:0; "> |
− | + | <li class="active"> | |
− | + | <a href="#service-one" data-toggle="tab"> | |
− | + | <h2>Metabolic flow modeling</h2> | |
− | + | </a> | |
− | + | </li> | |
− | + | <li class=""> | |
− | + | <a href="#service-two" data-toggle="tab"> | |
− | + | <h2>AEMD</h2> | |
− | + | </a> | |
− | + | </li> | |
− | + | </ul> | |
− | + | <div id="myTabContent" class="tab-content"> | |
− | + | <div class="tab-pane fade active in" id="service-one"> | |
− | + | <h2 style="text-align:center">Introduction</h2> | |
− | + | <h4>This year our project is the introduction of acrylic synthetic routes in Escherichia coli or | |
− | + | Saccharomyces cerevisiae to produce acrylic acid.</h4> | |
− | + | <br> | |
− | + | ||
− | + | <center> | |
− | + | <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"> | |
− | + | <h5 style="text-align:center"> Primitive metabolic path map in E.Coli</h5> | |
− | + | </center> | |
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | ||
− | + | <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). | |
− | + | <br> | |
− | + | ||
− | + | <center> | |
− | + | <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> | |
− | + | </center> | |
− | + | ||
− | + | ||
− | + | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | <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> | ||
+ | |||
+ | |||
+ | |||
+ | <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. | ||
+ | |||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/e/e5/Model-6.2.png" class="img-responsive" width="20%" height="20%"> | ||
+ | </div> | ||
+ | |||
+ | |||
+ | |||
+ | <br> 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: | ||
+ | <br> The reaction path of the original pathway is Gly to Gly-3-p and then to DAHP | ||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/d/da/Model-7.png" class="img-responsive" width="40%" height="40%"> | ||
+ | </div> | ||
+ | |||
+ | |||
+ | <br> 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, The concentration of Gly-3-p 0 mM and the concentration of DHAP 0 mM at the | ||
+ | same time. | ||
+ | <br> | ||
+ | <br> In this reaction, we make the following assumptions about our model: | ||
+ | <br> 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. | ||
+ | <br> 2. Assume that the substrate involved in the reaction does not participate in other reactions. | ||
+ | <br> 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 ceaS2 enzyme data. | ||
+ | <br> | ||
+ | </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> | ||
+ | <h4 style="text-align:center">KATA Sensitivity Test before Modification</h4> | ||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/8/89/Model-12.1.png" class="img-responsive" width="60%" height="60%"> | ||
+ | </div> | ||
+ | |||
+ | |||
+ | <br> | ||
+ | <h4 style="text-align:center">KATA Sensitivity Test after Modification</h4> | ||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/e/e9/Model-12.2.png" class="img-responsive" width="60%" height="60%"> | ||
+ | </div> | ||
+ | |||
+ | |||
+ | <h4> | ||
+ | <br> 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. | ||
+ | <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> | ||
+ | <h4 style="text-align:center">Before transformation</h4> | ||
+ | |||
+ | |||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/9/91/Model-14.1.png" class="img-responsive" width="60%" height="60%"> | ||
+ | </div> | ||
+ | <br> | ||
+ | <h4 style="text-align:center">After trransformation</h4> | ||
+ | |||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/d/d8/Model-14.2.png" class="img-responsive" width="60%" height="60%"> | ||
+ | </div> | ||
+ | <h4> | ||
+ | <br> 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. | ||
+ | <br> But when we adjust the standard deviation of the normal distribution random variable to | ||
+ | 0.05, the result is shown below. | ||
+ | <div align="center"> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/f/f2/Model-15.1.png" class="img-responsive" width="60%" height="60%"> | ||
+ | </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"> | ||
+ | |||
+ | <h2 style="text-align:center"> Mutation Design of ceaS2 by using AEMD</h2> | ||
+ | <h3 style="text-align:center">Abstract</h3> | ||
+ | <h4>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. | ||
+ | <br> ceaS2 enzyme is the most important enzyme in our entire acrylic acid synthesis pathway, | ||
+ | but the activity of wild type is not high. So it is exceedingly necessary to modify it on | ||
+ | the basis of the "part" level to improve its catalytic reactivity. We used the AEMD platform | ||
+ | to conduct the mutational design for ceaS2 enzyme in order to figure out a more accurate | ||
+ | scheme of mutation, which can also exert great beneficial impact on the later experiments. | ||
+ | <br> We have totally identified 32 mutational sites, and its point mutation transformation. The | ||
+ | experimental results show that there are 11 sites, where the enzyme activity gets boosted, | ||
+ | after the transformation. Compared to wild type ceaS2 enzyme, the highest activity has increased | ||
+ | by 11 times, whose effect is obviously noticeable. This also demonstrates the ability of | ||
+ | this designing platform. </h4> | ||
+ | <h3 style="text-align:center">Introduction</h3> | ||
+ | <h4>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. | ||
+ | </h4> | ||
+ | |||
+ | <h3 style="text-align:center">What is AEMD?</h3> | ||
+ | |||
+ | <h4>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. </h4> | ||
+ | |||
+ | |||
+ | |||
+ | <h4>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 | ||
+ | <a href="https://static.igem.org/mediawiki/2017/0/09/AEMD_Supplementary_materials.pdf">Supplementary methods</a> in more detail.</h4> | ||
+ | |||
+ | <h3 style="text-align:center">Process</h3> | ||
+ | |||
+ | <h4>This time we utilized AEMD's Stability mode (click here for AEMD user's guide) to screen for | ||
+ | mutational sites that benefit the ceaS2 enzyme activity. | ||
+ | <br> Because of the complexity of enzyme catalysis, it’s difficult to predict point mutation | ||
+ | improving protein activity accurately. How AEMD work? | ||
+ | <br> Firstly,the development team of AEMD recently described a method which is able to identify | ||
+ | desired mutations by analyzing the coevolution information of protein sequences (Liu, et | ||
+ | al., 2016). In the AEMD-web, some point mutations are suggested by this method. Besides, | ||
+ | AEMD’s analysis generated some residues close to active center and transport tunnels which | ||
+ | are recommended to saturated mutation to improve activity (Fig. 1C). For the input of target | ||
+ | protein sequence, AEMD first obtain the PDB file using RosettaCM (Song, et al., 2013). Next, | ||
+ | the substrate of template PDB was mapped into target PDB using the “struct_align” funciton | ||
+ | of Schrodinger software (QikProp, 2015). The spatial location of substrate in target PDB | ||
+ | can help to determine the ligand-binding pocket of target enzyme. If all potential template | ||
+ | PDB had no substrate in the PDB file, AEMD predicted the ligand-binding pocket by a Rosetta | ||
+ | script (gen_apo_grids.linuxgccrelease) (Zanghellini, et al., 2006). After the determination | ||
+ | of ligand-binding pocket, AEMD generated the possible catalytic sites by search local Catalytic | ||
+ | Site Atlas (Furnham, et al., 2014); the residues within 5Å distance from ligands by calculating | ||
+ | the minimum distance between residue and substrate; and the residues located within 3 Å distance | ||
+ | from transport tunnels by CAVER (Chovancova, et al., 2012).(see the Fig.1 (C)) | ||
+ | <br> We submitted the amino acid sequence and PDB file of ceaS2 online and got the prediction | ||
+ | result in half an hour.As shown below, you can also | ||
+ | <a href="https://static.igem.org/mediawiki/2017/5/57/CeaS2_analysis_report.pdf">download PDF version</a>. | ||
+ | </h4> | ||
+ | <br> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/e/e2/Model_result.png" class="img-responsive"> | ||
+ | <br> | ||
+ | <br> | ||
+ | <h3 style="text-align:center">Result</h3> | ||
+ | <h4> | ||
+ | <br> We first selected the mutations within the 5Å distance of active site, altogether 33 kinds, | ||
+ | and then used point mutation to conduct molecular cloning operation. Next step was to synthesize | ||
+ | the acrylic acid using the whole cell catalysis and determined the acrylic acid yield by | ||
+ | HPLC. The results are as follows: | ||
+ | <br> | ||
+ | <center> | ||
+ | <img src="https://static.igem.org/mediawiki/2017/e/e4/NPU-26.png" class="img-responsive"> | ||
+ | </center> | ||
+ | In these total 33 mutations of mutational sites, there are 11 mutations' acrylic acid yield higher than that of the wild | ||
+ | type, which indicates a higher activity. The highest mutational site F438M presents a yield | ||
+ | 11 times the wild type. Therefore, it is valid and tangible for us to implement AEMD to design | ||
+ | the mutational sites! | ||
+ | </h4> | ||
+ | |||
+ | |||
+ | </div> | ||
+ | |||
+ | </div> | ||
+ | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
</div> | </div> |
Latest revision as of 03:00, 2 November 2017