PROJECT CYCLE

Design

To understand the need for producing DHA-PC and why biological synthesis is the most suitable method, a plan is made to collect consumer and industry data. This includes exploring market demand for PC-DHA, evaluating the challenges of krill oil extraction, and assessing public perceptions of synthetic versus natural products.

Build

Two key actions are implemented:

1. Survey: Distribute a questionnaire to consumers, targeting their knowledge and preferences regarding DHA-PC. The survey aims to measure demand, pricing concerns, and potential resistance to chemically synthesized products.
2. Industry Consultation: Engage with experts at Liaoyu Group, a leading fisheries company, to discuss the krill oil supply chain. Gather data on supply limitations, labor costs, environmental issues like overfishing, and the inefficiency of capturing krill for DHA-PC production.

Test

• Survey results: Analyzing the data from the questionnaire, we found that in the feedback, more than 80% of the people believe that krill oil products have a certain role in cardiovascular and cerebrovascular aspects, and more than 60% of the people think that the reason to prevent their purchase of corresponding products is too high price.
• Liaoyu Group feedback: Collect insights on the economic and environmental challenges of krill fishing, including labor-intensive operations (ships must remain in Antarctica for 2-3 years), bycatch issues, and the fact that 1,000 krill are needed for a single bottle of krill oil.

Learn

The findings reveal several critical points:

1. High demand, limited supply: There is strong consumer demand for PC-DHA, but the supply chain for krill oil cannot meet this demand due to high costs and sustainability concerns.
2. Resistance to chemical synthesis: Survey participants express distrust of chemically synthesized PC-DHA, highlighting the need for safer, more natural alternatives.
3. Biological synthesis required: Given these insights, biological synthesis of PC-DHA emerges as the most viable solution, offering a sustainable and environmentally friendly way to meet growing demand while overcoming the challenges of krill oil extraction.

Design

• Market Survey: Since we have identified PC-DHA demand of the market, we also wondered whether there are people or products providing solutions already. We managed to conduct a market survey to learn about existing solutions.
• Literature Search: We want to search in natural pathways for solutions for manufacturing PC-DHA. Therefore, we sought solutions in literature, aiming to find characterization of enzyme-catalyzed reactions that produce PC-DHA.

Build

• Identify drawbacks of current products: The human practice group contacted companies with PC-DHA products to survey existing problems and future prospects on the product. Two major types of PC-DHA products were identified, each showed several shortages. Krill oil raised concerns of poor extracting technology, having excessive costs and causing environmental harm. PC-DHA egg does not ensure PC-DHA content and has limited production.
• Identify possible bio-manufacture solution: In literature [1], we discovered previous characterization of the fatty acid metabolic pathway in Schizochytrium Limacinum, which included PC-DHA synthesizing reactions.

Test

• Validating bio-manufacture as a problem-addressing solution: Constructing a biological system to manufacture PC-DHA can avoid the occurrence of environmental problems and ethical issues related to harvesting. Meanwhile, another product in the market—algal oil—reports low content of PC-DHA but environmentally friendly procedures, further inspiring us to consider biomanufacture as having the potential to greatly increase purity and reduce costs of current methods, with proper engineering performed.
• Precisely locating the enzyme to be engineered: In PC-DHA synthesizing reactions of Schizochytrium Limacinum, we studied the enzyme LACS (long-chain Acyl-Coa Synthetase), which directly catalyzes the DHA reaction. We searched on LACS and discovered that its selectivity of DHA is currently limited while having higher activity towards other fatty acids of different lengths. This indicated a potential that engineering this protein can possibly alter its selectivity towards different substrates, increasing its affinity with DHA, and further resulting in a significant improvement of performance in PC-DHA synthesis.

Learn

• Confirming the employment of target algae and enzyme: While discovering existing problems of current products by market survey, we built confidence in a biomanufacture solution's potential to address them. With promising inspiration from literature, we made the decision to engineer the target enzyme LACS (Long-chain Acyl-Coa Synthetase in target algae Schizochytrium Limacinum). However, we learnt that a certain type of enzyme usually consists of a whole family of subtypes from different species. This fact calls for the next step to identify specific LACS subtypes to be engineered to better catalyze DHA.

Design

Underlining the key concerns when making subtype selection:
Now that we have confirmed to engineer the enzyme of LACS to provide a novel PC-DHA synthesizing solution, we are to find a specific LACS as our target to further optimize.
The first factor we considered is homology to the target algae Schizochytrium Limacinum. A protein with a specific function may have different subtypes in different species, with varying sequence differences. These differences in sequence may further lead to differences in structures, with a possible result that a protein from one species cannot successfully be expressed in another species. Though we aim to use Schizochytrium Limacinum as the production chassis, the LACS of Schizochytrium Limacinum has not been characterized. Therefore, we hope that the object of our protein engineering is a subtype that can have better homology to the LACS of Schizochytrium Limacinum.
Another factor we took into consideration was the subtype's affinity to DHA. LACS, short for Long chain Acyl-CoA Synthetase, catalyzes the reaction of CoA with several types of fatty acids that differ in length and degree of unsaturation, varying from 16 carbon (16C) to 24 carbon (24C). A certain subtype of LACS shows different selectivity towards different substrates, and we wanted to ensure that our candidate had shown enough affinity to DHA (22C).

Build

Constructing a Phylogenetic tree to screen homologous subtype: LACS consists of a whole family of enzymes from different species, and we want to select candidate LACS subtypes in species with the closest relation to our target algae. As LACS serves as an important role in fatty acid metabolic pathways, its existence has been proven in a variety of species, ranging from prokaryotes like bacteria, to eukaryotes like plants and mammals. Having our target algae, Schizochytrium Limacinum, identified to be used in manufacturing, we want the engineer object enzyme to be a subtype of LACS that has close phylogeny to our target algae. A large set of characterized subtypes of LACS in protein databases were screened out, and we first conducted phylogenetic analysis. A phylogenetic tree was constructed to study the evolutionary relationships of selected subtypes, from which we selected a few subtypes with the closest phylogeny to Schizochytrium Limacinum.

Test

Verifying subtypes' affinity and selecting subtypes that show preference to DHA:
Our second round of selection seeks solid evidence of selectivity. LACS, which is the abbreviation for long-chain acyl-CoA synthetase, shows different activity towards fatty acids of varying lengths, ranging from C16 to C24. DHA is a fatty acid containing 22 carbon atoms (22C). Before we finally opt for a certain subtype, we want to verify that it has shown activity towards 22C in previous research. From the selected set of subtypes from our first round of selection, we then launched a test with reference to the literature. For each subtype with close phylogeny to our target algae, we read through the literature to seek past conducted experiments on 22C with tangible results of 22C activity as proof.

Learn

Confirming the selection of LACS1, 5, and 6:
To sum up our efforts in screening subtypes, we constructed a phylogenetic tree as the first round and verified affinity to DHA as the second round. The first round screened out a few LACS homologous to Schizochytrium limacinum. The second round of literature review helped us to identify three subtypes with evident activity, including one from Arabidopsis thaliana, one from Hondaea fermentalgiana, and one from Chromochloris zofingiensis, marked as LACS1, LACS6, and czLACS5 respectively.

Design

The objective of this cycle is to qualitatively analyze the growth curve of the YB525 yeast strain (which we borrowed from Professor Zhu's lab) and assess the substrate affinity of three enzymes:

• LACS1 (BBa_K5255000)
• Czlacs5 (BBa_K5255001)
• LACS6 (BBa_K5255002)
  for different fatty acids. Specifically, the goal is to determine the affinity of the three target genes to different fatty acids by the growth of YB525.

Build

Several experimental groups are set up with YB525 strain cultures:

1. 16-carbon and 18-carbon fatty acids: Supplement groups with C16 (palmitic acid) and C18 (stearic acid) to evaluate their impact on growth recovery.
2. DHA (22-carbon fatty acid): A group with DHA supplementation to assess its effects on growth compared to shorter-chain fatty acids.

Test

Multiple growth curves are generated under the various conditions:

• Control (no fatty acids): YB525 is expected to grow very slowly without any external fatty acids.
• C16 and C18 fatty acids: Growth is expected to recover when these shorter-chain fatty acids are provided, indicating their role in cellular metabolism and membrane synthesis.
• DHA (22C fatty acid): Growth recovery is also expected, but it may be slower compared to C16 and C18 supplementation, potentially indicating differences in enzyme affinity or metabolic processing.

Learn

The experiment reveals several insights:

1. LACS1 affinity for DHA: Preliminary analysis suggests that LACS1 has a higher affinity for DHA, as growth recovery is observed when DHA is supplemented, though not as fast as with C16 and C18.
2. Metabolic preference: The faster recovery in growth with C16 and C18 suggests that YB525 or the LACS enzymes may preferentially utilize these shorter-chain fatty acids more efficiently than DHA. However, the wild-type LACS1 shows the highest preference compared to Czlacs5 and LACS6. Therefore, we choose to use LACS1 as our target gene.

Design

Our project aims to enhance the catalytic activity of the enzyme LACS through conducting single-site mutation. We plan to apply protein modeling approaches to analyze which site to mutate. We will approach this from three perspectives:

1. A novel approach of optimizing mRNA.
2. Based on the basic properties of enzymatic reactions.
3. Based on the specific mechanism of our target enzyme, LACS.

Build

Approaches Based on mRNA

We applied several traditional approaches together with novel analytic methods for optimizing mRNA and verified improvement in structure by running computations.

Approaches Based on the Basic Properties of Enzymatic Reactions

We adopted several analytic methods to find promising mutagenesis sites by exploiting the protein's structural features and other important properties. Our methods included optimizing sites approximately 7 Å to 10 Å away from the active site. See Modeling/Integrated Protein Engineering/Semi-Rational Approaches for details.

Approaches Based on the Specific Mechanism of Our Target Enzyme LACS

We conducted a literature review to understand and determine the specific methods for selecting mutagenesis sites corresponding to the specific mechanism.

Test

To make the enzyme more specific for substrate catalysis, we decided to focus on optimizing sites based on known reaction mechanisms. For further screening of effective site mutations, we verified several properties of LACS, such as the stability of the protein after single-site mutagenesis, and simulated the process of substrate binding to select sites that have a higher possibility of improving from the previously chosen candidate sites.

Learn

Chosen Mutagenesis Plots

In this cycle, we have successfully chosen several single-site mutation sequences of LACS1 that showed a consistent increase in activity in our multiple modeling approaches. These will be subjected to subsequent enzymatic activity assays.

Systematic Analysis of Mutagenesis Candidates

Additionally, for czLACS5 and LACS6 that were excluded in the previous cycle due to insufficient affinity, we have also identified several promising single-site mutation plots that show increased activity in modeling simulations. We attached candidates that have not yet been verified in vitro together with corresponding modeling analytic results, which can be characterized in the future to further enhance LACS's catalytic activity.

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Design

The goal of this cycle is to select the most suitable methods for measuring LACS enzyme activity. After evaluating several options, two methods were chosen:

1. NADH-coupled enzyme assays
2. LC-MS for directly measuring DHA consumption

The NADH-coupled assay is based on linking the LACS reaction to a subsequent reaction that consumes NADH, allowing for indirect measurement of LACS activity by monitoring NADH depletion. LC-MS will provide a direct quantification of DHA substrate levels, giving a more accurate picture of LACS's ability to catalyze DHA.

Build

The experimental protocols are established for both selected methods:

1. NADH-coupled assay: This method uses NADH as an indicator of the enzymatic reaction. As LACS catalyzes the conversion of DHA to DHA-CoA, the subsequent reaction depletes NADH, which can be measured spectrophotometrically through absorbance at 340 nm.
2. LC-MS analysis: This method directly measures the depletion of DHA in the reaction mixture, allowing for precise quantification of the substrate's consumption.

Test

The protocols are tested to measure LACS activity:
The NADH-coupled assay is performed to monitor NADH consumption over time, providing an indirect but rapid measurement of enzyme activity.

LC-MS is used to directly quantify DHA reduction in the reaction mixture, allowing for a more detailed and accurate assessment of LACS efficiency.

Learn

The LC-MS method is finally chosen for its rapid, simple application and more direct results. However, the results reveal some limitations:

• The data obtained is in vitro, which might not fully reflect LACS activity under physiological conditions.
• To get more biologically relevant data, the system must be scaled to an in vivo production system, potentially in organisms like Schizochytrium limacinum to study LACS activity within a natural metabolic context.

Design

For time limitation, in the lab we only verified our modeling result of enzymatic modification. So this section focuses on how to estimate the production of PC-DHA in Schizochytrium limacinum. With reference to common practices of cellular global simulation, we use a model that characterizes the metabolic network for Schizochytrium called iCY1170_DHA, and estimated the production based on this model, hoping to use the algorithm of optforce to find potential knockout targets.

Build

We use the iCY1170_DHA model to run flux balance analysis, setting the PC-DHA production as the objective function, aiming to find the maximum PC-DHA production rate in this organism. Then we run the optforce algorithm to get the MustL and MustLL of Schizochytrium, based on these "Must" sets to figure out the strategy to perform gene knockout.

Test

After running the FBA, the PC-DHA production is 0.0245 (mmol/gDW/h). Optforce returns 2 MustL and MustLL targets. According to our result, after knocking out the MAT, the production of PC-DHA increased to 250% of the original, with the balanced production at 0.0623 (mmol/gDW/h), compared to the wild type at 0.0245 (mmol/gDW/h). This computation shows that performing a knockout of the bypass will significantly increase the production of our target product, DHA-PC.

Learn

By using GEMS, we can roughly estimate the production of PC-DHA in Schizochytrium limacinum after knocking out the bypass. In the future, we will conduct experiments with Schizochytrium limacinum to determine if this engineering method is useful.

EXPERIMENT CYCLE

Design

The aim of this experiment is to quantitatively assess the growth curve of the YB525 yeast strain and investigate the substrate affinity of three enzymes (BBa_K5255000) (BBa_K5255001) (BBa_K5255002) towards different fatty acids. Specifically, the study evaluates whether these LACS enzymes demonstrate higher affinity for DHA (22-carbon fatty acid) compared to shorter-chain fatty acids like C16 and C18.

Build

The experimental setup includes the following groups, each subjected to different fatty acid conditions:

a. Control Group: No external fatty acids are added to the medium to establish baseline growth.

b. C16 and C18 Groups: Palmitic acid (C16) and stearic acid (C18) are supplemented to examine their effects on promoting cell growth and recovery.

c. DHA Group: DHA (docosahexaenoic acid, a 22-carbon fatty acid) is added to evaluate the enzyme affinity for longer-chain polyunsaturated fatty acids.

Growth curves for YB525 under these various conditions will be recorded at regular time intervals to analyze how quickly the strain adapts and grows in each environment.

Test

Growth data was collected in the form of OD600 measurements over time to generate multiple growth curves:

• Control (no fatty acids): YB525 is expected to show significantly slower growth without external fatty acid supplementation.
• C16 and C18 groups: Growth is anticipated to recover quickly, as these fatty acids provide essential substrates for membrane synthesis and cellular energy production.
• DHA group: While growth recovery is expected, it may occur at a slower rate than in the C16 and C18 groups, suggesting that DHA might be processed less efficiently or the enzyme affinity for DHA might be lower.

Learn

Analysis of the growth curves reveals key insights into LACS enzyme functionality and substrate preference:

a. LACS1’s affinity for DHA: Preliminary findings may indicate that LACS1 (BBa_K5255000) has a measurable affinity for DHA, as growth does recover, although at a slower pace compared to C16 and C18.

b. YB525’s reliance on external fatty acids: The slow growth in the control group underscores the strain’s dependence on external fatty acids for robust growth and metabolic functions.

c. Metabolic processing of fatty acids: Faster growth recovery in the presence of C16 and C18 implies that YB525 or the LACS enzymes favor shorter-chain fatty acids for cellular metabolism, potentially making DHA a less preferred substrate in comparison.

Design

The goal is to express LACS1 (BBa_K5255000) in Saccharomyces cerevisiae InvSC1 and detect the resulting products using LC-MS to validate LACS1's enzymatic function. The experiment constructed a plasmid with LACS1 (BBa_K5255000) under the GAL1 inducible promoter for expression in yeast. LC-MS analysis was designed to detect and quantify the specific metabolites produced by LACS1 (BBa_K5255000). Positive and negative controls were established to compare metabolic differences.

Build

The plasmid was then transformed into the YB525 strain, and yeast transformants were selected using uracil-deficient medium.

Test

Protein Expression and Induction:

• Induce LACS1 expression in yeast and monitor protein production by analyzing samples at different time points.

LC-MS Product Detection :
Prepare yeast cell extracts and analyze them using LC-MS. Identify and quantify specific metabolites related to LACS1 (BBa_K5255000) activity by comparing results from positive and negative controls.

Learn

Through the data, we found that when the same amount of internal standard is added, the amount of C16-CoA detected in NC is about 10 times that in the sample, while the corresponding amount of DHA-CoA detected is 1.5 times. It can therefore be inferred that new DHA-CoA is produced in the sample.

However, the process of extracting and purifying the protein in Saccharomyces cerevisiae is extremely challenging, so in order to save time for the experiment, we decided to re-express the protein in E. coli and carry out subsequent protein purification as well as testing for enzyme activity.

Design

The purpose of this cycle is to verify the function of LACS1 (BBa_K5255000) protein. We construct the plasmid pET21a(+)-LACS1 to express our target protein. There is a C-terminal His-tag for easy purification via fast protein liquid chromatography.

Build

• Transformation:

  • Transform the constructed pET21a(+)-LACS1 plasmid into BL21(DE3) cells via heat-shock.
  • Plate transformed cells on LB agar with ampicillin (100 µg/mL) to select for successful transformants.

• Verification:

  • Perform colony PCR to confirm proper plasmid insertion.
  • Perform purification to obtain the target protein.

Test

• Induce protein expression:
  • Grow BL21(DE3) transformants in LB media to an OD600 of 0.6.
  • Induce expression by adding IPTG (final concentration 400 μM).
  • Incubate for 18 hours at 18°C.

Purify Protein

• Harvest the cells and lyse them via sonication.
• Purify the protein using fast protein liquid chromatography.
• Analyze protein purity using SDS-PAGE.

Learn

It can be seen from the results of SDS and western blot that after preliminary purification, the protein in our sample is not pure. Therefore, we need to perform anion chromatography to obtain a more pure target protein (77.7 kDa).

Design

In this step, the aim is to improve the purity of the target protein through secondary purification using anion exchange chromatography.

Build

Once the protein is expressed and initially purified using a primary method, it must be prepared for secondary purification. This preparation involves buffer exchange or dialysis into the chosen low-salt binding buffer to remove any excess salt or impurities that may interfere with binding. After preparation, the anion exchange column is packed and equilibrated with the binding buffer to ensure the column is ready to retain the target protein.

Test

During the testing phase, the protein sample was loaded onto the anion exchange column, allowing the negatively charged protein to bind to the resin. A salt gradient was applied to the column, causing the bound proteins to elute. Fractions were collected throughout the elution process, and western blot was used to detect the purity of the protein.

Learn

We found relatively higher purity after anion chromatography purification. Afterwards, we combined three samples that have a high concentration of protein.

Cycle 5: Construction of Expression System in C43(DE3)

Design

According to the literature, the C43(DE3) strain is more suitable for expressing membrane proteins compared to other strains like BL21(DE3), as it reduces toxicity and allows for higher yields of functional proteins. Based on this, we chose to use C43(DE3) to express a mutant form of the target protein. The gene encoding the mutant protein is cloned into a suitable expression vector, such as pET21a(+), under the T7 promoter. A His-tag is included for subsequent purification steps.

Build

The C43(DE3) cells are transformed with the constructed pET21a-mutant Lacs1 plasmid following the same protocol as with BL21(DE3). The cells are grown in LB medium and selected with ampicillin. Once the transformants are confirmed by colony PCR, protein expression is induced using IPTG. After induction, the cells are harvested, and the protein is purified using fast protein liquid chromatography, similar to what was done with BL21(DE3).

Test

Protein expression is analyzed by SDS-PAGE to determine whether the mutant protein is expressed at the desired level. Due to the relatively high purity of MLACS1 (BBa_K5255003) , we only performed fast protein liquid chromatography to obtain the target protein.

Learn

The protein expressed by the C43(DE3) strain was relatively pure and did not require further purification. The protein obtained in this process was suitable for the subsequent enzyme activity determination process.

Cycle 6: Liquid Chromatography-Mass Spectrometry for LACS1 Activity

Design

The aim was to compare the activity of LACS1 (BBa_K5255000) and MLACS1 (BBa_K5255003) in catalyzing the formation of DHA-CoA from DHA. A method with high sensitivity and resolution that allows both qualitative and quantitative analysis was chosen: liquid chromatography-mass spectrometry (LC-MS). Liquid chromatography (LC) uses a column to separate the components of a sample based on properties such as polarity and molecular weight, so that different components reach the detector at different times. Mass spectrometry (MS) detects the separated compounds by ionizing the sample molecules to produce charged ions, which are separated and analyzed based on the mass-to-charge ratio (m/z) to quantify the components in the sample.

Build

The samples used for LC-MS consisted of a reaction system of the purified enzymes LACS1 (BBa_K5255000) and MLACS1 (BBa_K5255003), which reacted for two minutes with the substrates DHA, ATP, and coenzyme A, respectively, before the reaction was terminated. After extraction of the mixture, DHA was found in the bottom layer of the extract, while the product DHA-CoA was in the upper layer. Th

Learn

The findings of the study indicated that the system comprising MLACS (BBa_K5255003) exhibited a markedly enhanced reaction rate in comparison to the system containing the LACS enzyme. Consequently, it was postulated that the proteins generated by the modeling group demonstrated a considerably elevated level of activity against DHA.

MODELING CYCLE

Cycle 1: Unknown Reaction Mechanism Single-Site Mutation

Design:

We have conducted a literature review to understand how to optimize proteins in cases where the reaction mechanism is unknown, and how to apply alanine scanning in the optimization of proteins for enzymes with an unknown reaction mechanism.

Build:

• Alanine Scanning: Utilizing the repairPDB tool, we will optimize the protein model to achieve a rational structure while minimizing the number of amino acids.
• Active Site Optimization: We will predict the structure of the LACS protein using AlphaFold and identify key sites within 7 to 10 angstroms (Å) of the active site using PyMOL software.

Test:

We found that the specificity was not strong, which may indicate that the current optimization strategy does not adequately consider the key amino acids involved in the specific binding with the substrate DHA.

Learn:

To enhance specificity, we will delve into the intrinsic reaction mechanism of LACS and consult relevant literature. This will assist us in more accurately selecting optimization sites specific to the substrate DHA, thereby improving the protein's affinity for the substrate and catalytic efficiency.

Cycle 2: Known Reaction Mechanism Single-Site Mutation

Design:

Through literature research, we have identified strategies for protein optimization, including mutations in key structural motifs such as the P-motif, A-motif, and L-motif, as well as changes in gating amino acids and adjustments to charge properties to enhance substrate affinity.

Build:

• By sequence alignment, we have excluded the highly conserved P-motif, A-motif, and L-motif as optimization sites.
• We have identified the gating amino acids of LACS1, czLACS5, and LACS6 and plan to mutate them to aromatic amino acids.
• Using Caver software to analyze the protein model, we have identified potential channel optimization sites.
• We have selected conserved amino acid sites at the bottom of the channel and plan to mutate them to amino acids with better charge and hydrophobic properties.

Test:

After selecting mutation sequences with FoldX, we simulate the binding of mutant proteins with substrates through AutoDock and select single-point mutation sequences with better binding performance.

Learn

A multi-step screening process ensures that the mutation sequences obtained from modeling are highly reliable.

Cycle 3: In Vitro Enzymatic Activity Assay

h3>Design:

We intend to analyze the enzymatic activity data obtained from our experiments to determine the Michaelis constant (Km) for both the wild-type and mutant enzymes.

Build:

We will derive the double reciprocal form of the Michaelis-Menten equation, which will facilitate the simplification of the experimental data analysis process.

Test:

We plan to perform graphical fitting of the experimental data to ascertain the optimal model parameters.

Learn:

The Km values measured in our experiments are obtained through in vitro assays of purified enzymes, a method that eliminates the interference of potential intracellular substances, thus providing a more accurate reflection of the enzyme's intrinsic property changes. However, as our project is aimed at producing this enzyme within Schizochytrium sp., these Km values may not directly correspond to the enzyme's Km when reacting within the organism. Nonetheless, this data remains invaluable, offering insights into the enzyme's property alterations and aiding us in further optimizing the enzyme's expression and activity within Schizochytrium sp.

Cycle 4: Enzyme Activity from In Vitro to In Vivo

Design:

Our objective is to develop a formula that can translate the in vitro determined Km of an enzyme into its in vivo Km within Schizochytrium sp., thereby providing an approximation of the enzyme's Km within the organism.

Build:

Adhering to the assumptions of the Michaelis-Menten equation and considering the presence of multiple potential substrates in the target pathway, we w

Test

We will review literature to find relevant data and, for areas lacking direct data, make reasonable order-of-magnitude assumptions based on analogous data, which will then be incorporated into the formula.

Learn

Given that the determination of Km and the consideration of substrates that significantly impact the reaction, the data obtained from the formula holds substantial significance and allows for a rough estimation of the target product's yield. Although the formula has its limitations, such as being based on the Michaelis-Menten equation and requiring compliance with its assumptions, and also overlooks substrates that have a minor impact on the selectivity of the reaction, the final data obtained from the formula may deviate from the actual situation. Nevertheless, overall, the data derived from the formula is of great reference value for the final yield of the target within Schizochytrium sp.

1. X.-H. Yue, W.-C. Chen, Z.-M. Wang, P.-Y. Liu, X.-Y. Li, C.-B. Lin, S.-H. Lu, F.-H. Huang, and X. Wan, Journal of Agricultural and Food Chemistry, 2019, 67, 9683–9693.
2. T. Baumgarten, S. Schlegel, S. Wagner, M. Löw, J. Eriksson, I. Bonde, M. J. Herrgård, H. J. Heipieper, M. H. H. Nørholm, D. J. Slotboom, and J.-W. De Gier, Scientific Reports, DOI:10.1038/srep45089.