In vitro enzyme production

In vitro enzyme production

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I need to express a protein in vitro but I don't know where to start. I will likely do a T7 transcription protocol but for translation I am not sure what to do. Are there any good kits?

New England Biolabs, which has an excellent reputation for well-tested, well-documented, and robust products, has a line of protein expression and purification products that I'd definitely take a look at. I've used the pMAL system for expression of fusion proteins in E. coli, and the PURExpress system looks just like what you're looking for. I have a personal (but not financial) connection with them, and I can vouch for their quality and technical support.

Pierce/Thermo is generally a good place to look when dealing with protein stuff. I've had good results with lots of their stuff, but I haven't done in vitro expression in ages, so I can't remember what I used. (BTW, I have no connection with them, I just tend to like their products.)

As Alan Boyd mentioned, lots of companies like Life Technologies, Promega, Qiagen, and Sigma have in vitro expression kits. Which one you pick will ultimately be influenced by what exactly you need your protein to do, the desired scale of expression, the type of tag(s) you want to put on it, what equipment and expertise you have in your lab, and how much time and money you want to invest.

Substrate Colonization, Strain Competition, Enzyme Production In Vitro, and Biocontrol of Pythium ultimum by Trichoderma spp. Isolates P1 and T3

The antagonistic Trichoderma spp. isolates P1 and T3 differed in their ability to colonize and to compete in sphagnum peat moss and on wood chips. In peat supplemented with straw, isolate T3 produced twice as many colony forming units (cfu) as isolate P1. On wood chips, the two isolates formed a similar number of cfu. When the two Trichoderma isolates were cultivated together approximately 85–90% of the cfu were from T3 on both substrates. The presence of Pythium ultimum in peat amended with straw did not influence the number of Trichoderma cfu formed. The two Trichoderma isolates produced different amounts of hydrolytic enzymes both in liquid cultures and in peat. Seven different enzyme activities were tested. Enzyme production by T. harzianum isolate T3 was less influenced by the type of carbon source amendment than that of isolate T. atroviride P1. Culture filtrates of isolate P1 grown on complex carbon sources were high in endochitinase activity, whereas cellulase and endo-1,3-β-glucanase activities were more pronounced in filtrates of isolate T3. There was no significant difference between the two isolates in their ability to protect cucumber seedlings against P. ultimum while the combination of the two fungi resulted in significantly less biocontrol than each isolate alone.

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In vitro and in vivo production of pectolytic enzymes by some phytopathogenic fungi

Five fungal plant pathogens were investigated for their in vitro and in vivo ability to produce pectolytic enzymes. Only Fusarium solani did not produce any appreciable amount of enzymes in vivo and in vitro. Alternaria solani, Collectotrichum truncatum, Colletotrichum capsici and Curvularia pallescens produced pectolytic enzymes in vitro which were detected after ten-fold concentration of the cultures by ammonium sulphate precipitation. These same four fungi produced pectolytic enzymes in vivo which were detected after twenty-fold concentration of blended infected host tissue. The pH values of peak enzyme activity of 3.0 and 5.0 indicate that the enzymes produced were polygalacturanaes. The monogalacturonide end-product of sodium polypectate and cell wall degradation indicate that exopolygalacturonases were produced by the pathogens. The low concentration of these enzymes in infected host plant tissues and the slow pectate-splitting action of the exopolygalacturonases produced may account for the slow development and the dry necrotic nature of the disease these fungi cause.

Starch Structure and Metabolism

Starch granules comprise linear α-1,4-glucans with periodic α-1,6-branches, giving long chains capable of wrapping around each other to form double helical arrangements, which stack side by side, forming alternating layers of highly ordered liquid crystalline lamella interspersed with amorphous regions (Figure ​ (Figure1) 1 ) (Waigh et al., 1998). This self-organizing nanostructure makes the surface of a starch granule highly resistant to enzymatic attack, requiring specialized enzymes to initiate degradation.

Structure of the starch granule. Starch granules are composed of α-1,4-glucans with some α-1,6-branches, placing them parallel to each other, allowing double helix formation. The branches are specifically placed, giving regions of branching, known as amorphous lamella, and regions of exclusively linear chains which form crystalline lamella, and these lamella form defined growth rings. Specific enzymes are needed to synthesize and degrade this highly ordered insoluble structure. Coultate (2002) – Reproduced by permission of The Royal Society of Chemistry.

In order to engineer precisely defined starch structures, we need to understand the metabolic enzymes involved, insights into which are provided by continued research in the model plant Arabidopsis thaliana (Santelia and Zeeman, 2011). Multiple enzymes are required to create the correct morphology of the starch granule, and the specific role of individual enzyme isoforms is not well-understood (Ball and Morell, 2003 Tetlow, 2011). For example, the correct synthesis of granular starch counter-intuitively requires two degradative isoamylases (Bustos et al., 2004). Storage starch is formed in the amyloplasts of specialist storage organs, such as the grain or tuber, while transitory starch is stored in the leaf chloroplasts, as a carbon source for photosynthetic cells at night. The two plastids use different suites of enzymes to attack the starch granule (Smith et al., 2005 Smirnova et al., 2015). In the storage organs, the granule is enzymatically hydrolyzed (Radchuk et al., 2009), ultimately releasing glucose in photosynthetic organs, the transitory starch granule is initially phosphorylated by specific glucan kinases, before being broken down into short maltooligosaccharides (Fettke et al., 2009). There are several isoforms of each class of enzyme involved in starch synthesis and breakdown, but differences in their action are generally interpreted in terms of the expression of the gene and protein in question, rather than of the enzyme activity per se. The complication of enzymatic reactions taking place on an insoluble starch substrate accounts for these crude approximations, a situation that needs to be addressed in order to inform plant engineering/synthetic biology studies.


Amidst growing concerns about global energy and anthropogenic climate change, biofuels are believed to be promising alternatives to replace traditional fossil fuels 29 . Accompanying the rapid expansion of biofuel industry is surplus of glycerol, the main byproduct of biofuel production 30 . The low price and the large quantity availability of glycerol have made it an ideal feedstock for the production of various chemicals 30,31,32 . Herein, we have constructed an in vitro biosystem for the biotransformation of glycerol into value-added chemicals with different degrees of reduction.

Glycerol can be converted into pyruvate by an artificial enzymatic reaction cascade composed of ALDO, DHAD, and catalase. Through reactions not involving the participation of NAD + /NADH, pyruvate can be directly used as the substrate for the production of various chemicals such as N-acetylneuraminate, acetoin, and l -tyrosine, the latter of which is illustrated in this study (Figs. 1 and 2). The oxidation of glycerol by O2 does not provide the reducing power required for the production of chemicals with higher degrees of reduction such as lactate, alanine, 2,3-butanediol and ethanol. Thus, coupling the enzymatic pathway with an NADH-regeneration system using formate and formate dehydrogenase was thought to be a potential solution to this problem. The production of both l -lactate and d -lactate in high yields and optical purities verified this assumption (Figs. 1 and 3). During the production of pyruvate from glycerol, glycerate would be produced as an intermediate product. Besides being dehydrated to produce pyruvate, glycerate can also be dehydrogenated to produce 3-hydroxypyruvate and NADH, which might be useful for chemical production processes involving self-sufficient NADH recycling. Herein, the manufacturing of l -serine from glycerol and ammonium is achieved by applying this concept (Figs. 1 and 4).

Both l -tyrosine and l -serine serve as valuable precursors with multiple applications in the food, chemical, pharmaceutical, and cosmetic industries 33,34 . However, the fermentative syntheses of l -tyrosine and l -serine are restricted by the multistep reactions required and complex regulatory processes. For example, the cellular synthesis of l -serine from glycerol requires at least eight enzymes. Complicated phosphorylation and inhibitory feedback mechanisms are also involved in the metabolic processes (Supplementary Fig. 9). In this study, two artificially designed enzymatic cascades were constructed to manufacture l -tyrosine and l -serine from glycerol, using the minimum number of enzymes. l -Tyrosine, with an ee greater than 99.9%, was produced in 77.1% yield. Further, l -serine, with an ee of greater than 99.9%, was produced from glycerol in 71.3% yield, which is higher than any microbial fermentation processes reported to date 35 .

Optically pure lactate is a platform chemical that can be utilized in many industrial applications 36,37 . Nowadays, lactate isomers can be produced with high optical purity through microbial processes. However, a novel technology that can produce lactate from inexpensive raw materials and in high optical purity is still desirable for supporting the potential applications of optically pure lactate. In this study, optically pure l -lactate and d -lactate (ee 100%) were produced from glycerol in high yields. NADH regeneration was achieved through coupling the system with a thermostable formate dehydrogenase, which avoided the production of unwanted byproducts.

The yields and production rates of the systems developed in this study still need to be improved before their industrial applications. When 50 g L − 1 glycerol was added in the reaction system for l -lactate production, only 34.4 mM l -lactate was produced in 72 h with a productivity of 0.48 mM h − 1 (Supplementary Fig. 10). The low activities of some key enzymes such as ALDO (0.26 U mg − 1 ) and DHAD (0.011 U mg − 1 ) may be the limiting factors requiring future studies. New biocatalysts with high activities, obtained through systematic screening or directed evolution, will be necessary to expand the potential applications of the in vitro synthetic systems. Considering the instability of NAD + at high temperatures, enzymes that are capable of using thermostable and cheap NAD + analogs might promote the application of the in vitro biosystem. Systematic optimization of the reaction conditions may also improve the performance of these systems 14 .

In summary, the large surplus of glycerol derived from the dramatic growth of the biofuel industry has caused economic and environmental concerns regarding its disposal. Using selected thermostable enzymes, we designed a completely artificial in vitro biosystem involving different enzymatic cascades to biotransform glycerol into value-added chemicals with different degrees of reduction. Manufacturing of l -tyrosine was achieved through the condensation of glycerol, ammonium, and phenol without the assistance of NAD + /NADH-related redox reactions. Optically pure l -lactate and d -lactate were produced through coupling with an NADH regeneration system. Production of l -serine from glycerol and ammonium was achieved using a four-enzyme cascade. The in vitro enzymatic system may be a versatile and useful platform for the production of value-added chemicals from glycerol.

Expression of storage lipid biosynthesis transcription factors and enzymes in Jatropha curcas L. cell suspension cultures and seeds

Members of Biotechnology laboratory at Universidad de Antioquia, Colombia, including the authors. First row from left to right: Laura Michell Carmona (first author), Dr. Aura Urrea , Dr. Natalia Pabón (Evo-Devo in Plants), Dr. Lucia Atehortua (Director). Second row from left to right: Ana Maria Henao . Tatiana Osorio, Liliana Monsalve, Juan Felipe Tamayo. Third row from left to right: Catalina Botero, Sandra Macias, Maria Isabel Quintero, Anngy Amaya. Fourth row from left to right: Erika Obando, Dr. Adriana Gallego, Liuda Sepulveda, Monica Arias.

The oleaginous plant Jatropha curcas has been proposed as a promising source for biodiesel production, using its seeds or in vitro production in cell cultures. In this paper, we showed a new approach to understand the regulation of storage lipids. We compared gene expression between endosperm cells in planta and endosperm-derived cell suspension cultures (EDCCs). We found a unique expression of some transcription factors that could participate in regulating different processes, among them LEC1, FUS3, ABI3, and WRI1. They could play a pivotal role in regulating the early stages of seed development and control the accumulation of storage compounds during the maturation of J. curcas seeds. Conversely, these genes displayed a lower genetic expression in cell suspensions and less content of total lipids, except for WRI1, which had comparable expression levels in the two systems. The remarkable differences between both types of cells allowed us to establish the important role of those genes in regulating storage lipids biosynthesis. Our results provide valuable information to understand the regulation processes carried out during seed development. Moreover, plant suspension cultures have proven to be an invaluable system to carry out studies at biochemical and molecular levels for lipids and other metabolites and future biotechnological applications. In this study, we test the Jatropha cell suspensions as an oil-producing platform in vitro and provide evidence of changes in response to the carbon-nitrogen ratio. We lay the foundation for future studies on carbon flow towards the biosynthesis of both pathways, starches, and oils in this oleaginous plant. The main areas of the Biotechnology laboratory include biodiversity, biotechnology of plants, microalgae, and fungi. Currently, our lab is working on several projects, like the establishment of embryogenic suspension cultures of Theobroma cacao as a model to study the gene expression of transcription factors involved in the acquisition of embryogenic potential and the optimization of the storage compounds accumulation on somatic embryos to improve plant conversion rates.

Laura Carmona-Rojas, Aura Urrea-Trujillo, Daniel Gil-Arrendondo, Lucia Atehortúa-Garcés, Natalia Pabón-Mora. Expression of storage lipid biosynthesis transcription factors and enzymes in Jatropha curcas L. cell suspension cultures and seeds. In Vitro Cellular & Developmental Biology – Plant, 57, 164-177, 2021.

Key findings

Programmable DNA network based Compartmentalized Self-Replication:

Dramé-Maigné et al. developed a DNA based molecular network for linking the activity of a DNA nicking enzyme to the replication of its encoding gene through a programmed feedback loop. Nt.BstNBI (NBI) was chosen as the target because of its wide-spread usage in biotechnological industries in its natural form. Bacteria expressing a library of mutagenized NBI variants are encapsulated in emulsion droplets. The molecular program consists of three modules. The first module senses the activity of the NBI variant and generates short oligos linearly over time. The second module amplifies the incoming signal from the first module and exponentially generates the amount of DNA strands necessary to trigger the third module. The third and the final primer-generating module generates forward and reverse primers for amplification of the encoding gene, and the amount of primers generated via this molecular program is correlated with the activity of the nicking enzyme. Genes encoding for the active nickase variants get enriched in the selection pool by virtue of the availability of sufficient primers for PCR amplification of encoding gene sequence. The authors name this in vitro, screening-free approach Programmable External Network-based Compartmentalized Self-Replication (PEN CSR).

This high-throughput method screens up to ten million gene variants simultaneously, with the reported selection efficiency of the selection procedure very close to the theoretical maximum.

Deep mutational scanning for unraveling the fitness landscape of proteins

Current usage of NBI in molecular diagnostics and other biotechnological applications are in its natural form. Having NBI variants with increased catalytic rate and higher thermal stability are desirable for improving the applicability of NBI. The authors exposed the protein to two different kinds of selective pressure: kinetic stress, by reducing incubation time for primer amplification, and thermal stress, by applying a heat shock at 65 C. They were able to identify favorable mutations necessary for optimal functioning of the protein under the applied selective pressure. The majority of the mutations that performed better under kinetic stress were primarily clustered around the DNA binding pocket of the nickase. While these mutations can be rationalized based on the available crystal structures, other identified favorable mutations were distributed over surface exposed residues. This suggests that structural studies of proteins are not always sufficient to identify residues imminent to the functioning of the protein and a deep mutational scanning of proteins is necessary for a thorough understanding of the fitness landscape of proteins.

Article information

Toward low-cost biomanufacturing through in vitro synthetic biology: bottom-up design

Y.-H. P. Zhang, S. Myung, C. You, Z. Zhu and J. A. Rollin, J. Mater. Chem., 2011, 21, 18877 DOI: 10.1039/C1JM12078F

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In vitro enzyme production - Biology

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Expression and purification of enzymes involved in precorrin-2 synthesis

Native PBGS, PBGD, UROS, SUMT, and precorrin-2 dehydrogenase from Sinorhizobium meliloti or Bacillus megatherium fused to an N-terminal His-tag were produced in recombinant E. coli after incubation at 30°C overnight. Purified proteins were analyzed by SDS-PAGE (S1 Fig). The molecular weights of the enzymes ranged from 25 to 36 kDa.

Establishment of a multiple enzyme system to produce precorrin-2

Initially, ALA, NAD, and all enzyme concentrations were set at 1 μM. To verify if precorrin-2 was successfully produced in the reaction mixture, we conducted an assay using precorrin-2 dehydrogenase. Sirohydrochlorin, produced from precorrin-2 by precorrin-2 dehydrogenase, has a known absorption peak of 376 nm [24, 25]. When precorrin-2 dehydrogenase was added to the reaction mixture, an absorption peak at 376 nm appeared, indicating that precorrin-2 had been transformed into sirohydrochlorin (Fig 2). To ensure that all of the precorrin-2 had been converted to sirohydrochlorin, we explored the proper precorrin-2 dehydrogenase concentration for the reaction. Varying concentrations of precorrin-2 dehydrogenase were added to the reaction mixture. The initial velocity of the reaction did not differ with precorrin-2 dehydrogenase concentrations from 0.5 μM to 10 μM. We therefore chose to use 1 μM precorrin-2 dehydrogenase for further experiments.

Precorrin-2 was produced from ALA by the tandem-enzyme reaction system containing purified, recombinant PBGS, PBGD, UROS, SUMT, and SAM (dotted line) precorrin-2 was then converted into sirohydrochlorin by precorrin-2 dehydrogenase in the presence of NAD (solid line).

Optimization of SAM cofactor concentration

SAM is the cofactor for precorrin-2 synthesis. SUMT is sensitive to inhibition by SAH and demonstrates a competitive relationship with SAM [26]. In order to determine the optimal SAM concentration for facilitating a forward reaction, SAM was titrated into the reaction mixture. In the initial experiment, ALA, NAD, and all enzyme concentrations were set at 1 μM. For determining the optimal concentration of SAM, we carried out the reaction using 20 μM, 50 μM, 200 μM, 500 μM, and 2 mM SAM. As SAM concentration increased from 20 μM to 200 μM, precorrin-2 productivity rose sharply. As SAM concentration rose above 200 μM, however, precorrin-2 productivity began to decline gradually (Fig 3A).

(A), Optimization of SAM concentration. The reaction mixture contained: 1 mM ALA, 200 μM NAD, 1 μM each enzyme, and various concentrations of SAM (20 μM, 50 μM, 200 μM, 500 μM, and 2 mM SAM) (B), Optimization of ALA concentration. The reaction mixture contained: 200 μM SAM, 200 μM NAD, 1 μM each enzyme, and various concentrations of ALA (0.5 mM, 1 mM, 5 mM, 20 mM, and 100 mM). Results are presented as mean ± SD. Error bars represent standard deviations of three biological replicates.

Optimization of ALA and NAD concentrations

SUMT is inhibited at uroporphyrinogen III concentrations above 2 μM [26]. As the concentration of ALA has a direct effect on uroporphyrinogen III concentration, we sought the optimal ALA concentration. For this experiment, all enzyme concentrations were 1 μM, SAM concentration was 200 μM, and NAD concentration was 200 μM. The concentrations of ALA tested were 0.5 mM, 1 mM, 5 mM, 20 mM, and 100 mM. Similar to the pattern observed with varying SAM concentrations, precorrin-2 productivity rose with increasing ALA initially and peaked when the ALA concentration reached 5 mM. Precorrin-2 productivity then declined as ALA concentration rose further. Notably, when ALA concentration arrived at 100 mM, no precorrin-2 was detected. Additionally, we determined that precorrin-2 productivity at a NAD concentration of 1 μM exceeded productivity at 200 μM NAD (with all other component concentrations kept constant). Thus, NAD concentration was fixed at 1 μM in subsequent assays.

Optimization of enzyme concentrations

In order to determine the optimal enzyme concentrations for each of the four enzymes, we titrated each enzyme across a range of concentrations, one enzyme at a time, in order of their function in the pathway. Specifically, PBGS, PBGD, UROS, and SUMT were tested at concentrations 0.02–3 μM, 0.1–6 μM, 0.1–10 μM and 0.1–35 μM, respectively. SAM and ALA concentrations were fixed at 200 μM and 5 mM, respectively. For the first enzyme, PBGS, all other enzyme concentrations were fixed at 1 μM. After each enzyme’s optimal concentration was determined, however, the optimal value was used in the reaction mixture when optimizing subsequent enzymes. All enzymes showed a similar bell curve pattern (Fig 4). The optimum concentrations were found to be: 0.1 μM PBGS, 1 μM PBGD, 1 μM UROS, and 10 μM SUMT.

The reaction mixture contained: 5 mM ALA, 200 μM SAM, 1 mM NAD, 1 μM precorrin-2 dehydrogenase, and various concentrations of titrated enzymes. (A), Optimization of PBGS concentration. The reaction mixture contained 1 μM PBGD, 1 μM UROS, 1 μM SUMT, and various concentrations of PBGS from 0.02–3 μM. (B), Optimization of PBGD concentration. The reaction mixture contained 0.1 μM PBGS, 1 μM UROS, 1 μM SUMT, and various concentrations of PBGD from 0.1–6 μM. (C), Optimization of UROS concentration. The reaction mixture contained 0.1 μM PBGS, 1 μM PBGD, 1 μM SUMT, and various concentrations of UROS from 0.1–10 μM. (D), Optimization of SUMT concentration. The reaction mixture contained 0.1 μM PBGS, 1 μM PBGD, 1 μM UROS, and various concentrations of SUMT from 0.1–35 μM. Results are presented as the mean of 3 replicates. Error bars indicate SD.

Model fitting and statistical analyses

Though the above experiment determined each enzyme’s optimal concentration when all other reaction ingredients were kept constant, being able to only vary the concentration of one enzyme at a time limits our ability to truly optimize precorrin-2 productivity. We therefore applied response surface methodology (RSM) to attempt to optimize the four independent variables further. Using the above preliminary experiment to determine optimal concentration ranges, experimental designs with four independent variables were created (Table 1). Each variable was assessed at three levels: -1 (the concentration immediately preceding the optimal value in the previous experiment), +1 (the concentration immediately following the optimal value in the previous experiment), and 0 (the average of the -1 and +1 concentrations). The observed and predicted initial velocities were also determined for each run of the model. The highest precorrin-2 productivity was Run 23, with all variables at the 0 level. These concentrations are very similar to the individual optimal concentrations determined in the previous experiment.

According to the sequential sum of squares, the quadratic model was the best fit among the linear, 2FI, quadratic, and cubic models (P<0.0001). Since this model was also determined to be the best fit according to lack of fit tests (P = 0.1474) and R 2 summary statistics (adjusted R 2 = 0.8638, predicted R 2 = 0.6410), we chose to apply the quadratic model for further data analyses. We assessed the significance of our RSM model by conducting an analysis of variance (ANOVA) for all independent variables and interactions (Table 2). We found all linear and quadratic effects of PBGS, PBGD, UROS, and the quadratic effect of SUMT to be significant (P<0.05). None of the interaction effects between these enzymes were significant. We also studied the interaction between precorrin-2 productivity and various combinations of different enzyme concentrations in which two variables were kept constant at the 0 concentration level while the other two variables were varied across their experimental ranges. These interactions were depicted using three-dimensional response surface plots (Fig 5). For example, when PBGS concentrations fluctuate from 0.02 μM to 0.5 μM, precorrin-2 productivity first increases and then declines (Fig 5A). PBGD has a similarly strong effect on precorrin-2 productivity. UROS and SUMT, however, have less of an effect on precorrin-2 productivity (Fig 5B–5D), consistent with the ANOVA analysis (Table 2).

(A), Effect of PBGS and PBGD concentrations. (B), Effect of PBGD and UROS concentrations. (C), Effect of UROS and SUMT concentrations. (D), Effect of PBGS and SUMT concentrations.