iGEM 2024 — Team hbut-wuhan

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Notebook

January

February

March

April

May

June

July

August

September

# January

## Project preparation and recruit team members

On January 12th, our team recruited members with various professional backgrounds in Hubei University of Technology (HBUT). The team members had a brainstorming session to discuss and specify the critical question that our project trying to figure out. Finally, we want to externally express a high value-added product. Our team members have conducted extensive literature reading research to find a suitable product in two weeks.

Finally, team leader and group leaders consulted to Dr. Yao and Liu (Associated professor in HBUT, PI of our team), and initially determined to make one of the exogenous expression of P37 family peptides for type 2 diabetes (T2D) therapy.

# February

## Modeling

1.Firstly, our bioinformatic group try to find out all the homological peptides derived from plants, and we
utilized the Blast function in the NCBI database to search for protein sequences with high similarity to the
Vg general sequence in the database. We obtained 50 proteins with a similarity higher than 71.3% to Vg from
all species and removed 17 duplicate records. These 33 protein sequences were then aligned using the Align by
ClustalW function in the MEGAX.(Figure 1)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-1.jpg)

**Figure 1. The conserved regions of Vg evolution.**Utilized the align by
clustalw function of megax software, where the yellow regions represent conserved domains, and the white areas
indicate mutation sites.

2.Using the MEGAX software, we constructed the corresponding phylogenetic tree by employing the Maximum
Likelihood algorithm with the Jones-Taylor-Thornton (JTT) model.(Figure 2)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-2.jpg)

**Figure 2. Phylogenetic tree of all the selected peptide.**

3. Use the online tool cnsknowall (https://cnsknowall.com/) to optimize the phylogenetic tree. (Figure 3)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-3.jpg)

**Figure 3. Phylogenetic tree of all the selected peptide.**The data in this
section is sourced from the blast in the NCBI database, employing the maximum likelihood algorithm of the
Jones-Taylor-Thornton (JTT) model in megax software to construct a phylogenetic tree, which was beautified
using cnsknowall (https://cnsknowall.com/). The red star marks Vg, the species in the green branches belong to
Fabaceae, and those in the blue branches belong to Euphorbiaceae. No protein sequences with high overall
similarity were found in animals and microorganisms.

# March

## Modeling

Our team utilized the deep learning architecture and neural networks of AlphaFold3 to predict the
three-dimensional structures of Vg and its receptor. To better visualize these structures, we employed pymol
for rendering. We observed disulfide bonds between the amino acids at positions 7 and 22, 3 and 15, and 20 and
32. (Figure 4)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-4.jpg)

**Figure 4. Three-dimensional structures of Vg.**(A) Vg has a spherical
structure, which provides a basis for its oral availability. (B) it can be observed that there are disulfide
bonds between the amino acids at positions 7 and 22, 3 and 15, and 20 and 32, which are crucial for the
structural stability of Vg.

By using the latest Jpred4 algorithm in the JNet algorithm series, we predicted the secondary structure of
VG. Utilizing the branch-site algorithm of PAML, we identified positive selection sites. In the NCBI database,
we used the Conserved Domain Database (CDD) and tools such as BLAST (Basic Local Alignment Search Tool) and
CD-Search to find a highly reliable Albumin. I superfamily functional domain in VG. By integrating the
evolutionary strategies of different amino acids at positions 25, 27, and 28 in the 33 similar peptides, we
finally formed a total of 26 mutants, which we named（R25A，R25A\_V27F，R25A\_V27F\_V28A, R25A\_V27F\_V28I, R25A\_V27L,
R25A\_V27L\_V28A, R25A\_V27L-V28I, R25A\_V28A,R25A\_V28I, R25G, R25G\_V27F, R25G\_V27F\_V28A, R25G\_V27F\_V28I,
R25GV27L, R25G\_V27L\_V28A, R25G\_V27L\_V28I, R25G\_V28A,R25G\_V28I, V27F, V27F\_V28A, V27F\_V28I, V27L, V27L\_V28A,
V27L\_V28I, V28A, V28I). (Figure 5-7)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-5.jpg)

**Figure 5. Utilizing machine learning to predict the secondary structure of
Vg.**The JPred4 algorithm exports predicted secondary structures of Vg, where the green regions indicate
β-fold regions, with β-fold regions present at positions 21-31.

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-6.jpg)

**Figure 6. The branch-site algorithm of PYMAL.**

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-7.jpg)

**Figure 7. Using machine learning to predict the functional domain
determination of Vg.**The conserved domain database (CDD) from the NCBI database was used, and through
CD-search, a highly reliable albumin-I superfamily functional domain of Vg was identified. According to the
annotations from the conserved domain database (CDD), the albumin-I superfamily functional domain can bind to
the 43 kDa 3 eceptor protein VDAC-1.

## Project · preparation

We held regular iGEM discussions to refine the design of the project. At the meeting, each of us was assigned corresponding tasks and each performed his or her own duties.

# April

## Modeling

To evaluate the binding potential of small molecule ligands, we used the chemScore to assess the activity,
with results exported within a range of 0-1. The Hex scoring function and Ligand Docking were utilized to
obtain Glide Score data, which provided insights into the binding free energy. For ease of data visualization
and processing, we artificially converted all the binding free energy values to their negative equivalents,
concentrating all the values into the first quadrant. We then used the Origin software to plot a
three-dimensional graph representing these data. (Figure 8).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-8.jpg)

**Figure 8. Using three abbreviated algorithms to evaluate the binding of Vg to
the receptor VDAC-1.**The x-axis represents the results derived from the chemscore algorithm in Maestro
software, the y-axis represents the hex scoring function from Discovery Studio software, and the z-axis
represents the glide score results from Maestro software. The binding site of VDAC-1 with Vg was again
determined based on the binding positions obtained from the autodocking blind docking algorithm. In the
figure, vdac-1 is displayed in stick form, the binding sites are shown as white surfaces, and the binding
pockets are represented as purple cubes. The red spheres indicate Vg, while the blue spheres represent the
other 32 evolutionary strategies. In the data processing, since the chemscore results are within the range of
(0-1), and both the hex scoring function and glide score represent binding free energy, we artificially took
the negative values of all binding free energy data for easier visualization and observation of conclusions.

## Project preparation

Through the results of model, the teacher's suggestion and the current social needs, we decided to biological synthesize the hypoglycemic peptide (Vglycin, Vg) in Pichia pastoris.

## Safety training

Before we start our wet experiment, all of our team members were subjected to the safety training by the lab
manager, including personal protective equipment (PPE), the standard operating procedure of instrument, the
physical biosecurity, information security and cybersecurity. Team members were required to pass certain exams
before entering the laboratory for experiments.

# May

## Experiment

1.We designed the first plasmid, pPIC9K-His-DDDDK-Vg (Figure 9). *Pichia pastoris* was chosen as the
expression system because it is a kind of methanotrophic yeast. We will send the designed plasmid to the
company for synthesis, and start our experiment after getting the plasmid.

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-10.jpg)

**Figure 9. The construction of the plasmid pPIC9K-His-DDDDK-Vg.**

2.Plasmid pPIC9K-His-DDDDK-Vg was transformed into *Pichia pastoris* GS115. We performed single enzyme
digestion, linearization, agarose gel electrophoresis verification and DNA purification of the newly
constructed plasmids. PCR amplification and electrophoresis verification. (Figure 10)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-11.jpg)

**Figure 10. Coated plate sieve.**

3.Bands were observed on SDS-PAGE through Western Blot experiment. But the molecular weight didn't match our
expectations.

4.We conducted nickel column affinity chromatography to isolate the target protein with His label, and found
that there were no bands.(Figure 11)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-12.jpg)

**Figure 11. The expression of His-Vg detected by western blotting.**

# June

## Modeling

Using the Vina force field in conjunction with the Autodocking algorithm, as well as the OPLS-AA and AMBER
force fields, we conducted calculations with all three methods employing the TIP4P water model and the solvent
models GBSA (Generalized Born Solvent Accessible model) and MMGBSA (Molecular Mechanics Generalized Born
Surface Area) algorithm. To integrate the results from these three algorithms, we used Origin software to plot
a three-dimensional graph. To maintain consistency in our data representation, we also converted all the
binding free energy values to their negative equivalents, ensuring that all the values were concentrated in
the first quadrant of the graph.

## Experiment

1.As for the problem of the first plasmid we constructed, we speculated through literature review that the
signal peptide in the front end covered up the expression of His-tag, resulting in the failure of the
recombinant protein to hang on the nickel column. Therefore, we replaced DDDDK with Asp-Pro to improve the
expression of target protein.

2.Therefore, we optimized the plasmid to obtain the second plasmid, pPIC9K-3Vg-His. (Figure 12)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-13.jpg)

**Figure 12. The construction of the plasmid pPIC9K-3Vg-His.**

3.After designing the second plasmid, we send it to the company for synthesis.

4.After getting the constructed plasmid, we carried out experiments, including plasmid extraction,
linearization, DNA purification, PCR amplification and nucleic acid gel electrophoresis.

5.However, in the final western blotting experiment, we did not detect expected bands by the western
blotting. (Figure 13)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-14.jpg)

**Figure 13. The expression of Vg-His was detected by western blotting.**

## Human practices

In order to understand the current domestic hypoglycemic drug market and diabetic patients' expectations for
hypoglycemic drugs, we came to the retired staff activity Center to conduct voluntary blood glucose screening
for retired staff. (Figure 14)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-15.jpg)

**Figure 14. Blood glucose testing for elderly people in the community.**

# July

## Experiment

1.On the basis of previous experiments, we considered that the low molecular weight of the recombinant
protein made it difficult to be detected.

2.Therefore, we added a nattokinase NK in front of Vg, and designed the third plasmid pPIC9K-NK-3Vg-His
(Figure 15), and sent it to the company for synthesis.

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-16.jpg)

**Figure 15. Construction of the plasmid pPIC9K-NK-3Vg-His.**

3.Through the experimental operation, we carried out plasmid extraction, linearization, DNA purification, PCR
amplification, nucleic acid gel electrophoresis. Finally, we make experimental results and successfully detect
bands on SDS-PAGE.

4.The above experiment is repeated, and the protein we need can still be detected. (Figure 16)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-17.jpg)

**Figure 16. The expression of His-tag target protein was detected by western
blotting.**

## Human practices

1.Our team members visited BGI and heard that BGI's gene therapy program for chronic diseases is to treat
diabetes by adjusting the nutritional structure of food and adding b vitamins. We also reported our work to
BGI, and one of our strengths- the homology of medicine/food, which coincided with BGI's program and it was
highly praised by Chen Liu, an expert from BGI. (Figure 17)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook18-18.jpg)

**Figure 17. Presenting our project to BGI.**

2.Our team members participated in the 11th iGEMer Exchange Meeting (CCiC) in China. At the meeting, teacher
Ziwen Xie put forward a valuable suggestion that our project needs an independent innovation iteration
process. (Figure 18)

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook19-19.jpg)

**Figure 18. Participating in the 11th iGEMer Exchange Meeting in China
(CCiC).**

3.To further explore the latent value of the existing achievements, our team selected the backbones to
establish an entrepreneurial team. We are guided by the spirit of iGEM to make real and inexpensive
hypoglycemic drugs for more than 140 million diabetic patients in our country.

# August

## Experiment

1.Due to the successful expression of the protein, we enlarged our expression system (200 mL of LB) to
improve Vg yield (Figure 19).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-20.jpg)

**Figure 19. Vg expression induced by methanol in *Pichia pastoris*.**

2.To further increase the stability of Vg, we redesigned our construction of Vg expression system. By
reviewing the literature, we found that the stability of disulfide bonds can be maintained by adding molecular
chaperones. We hope that by adding molecular chaperones, such as Asf1, we can help boost the activity of DNA
helicase during cell division.

3.At the same time, enhancers such as heptapeptide Qa, which has been reported to enhances viral protein
expression, was added to increase the expression of recombinant protein.

## Human practices

1.Our instructor Dr. Chenguang Yao introduced us to Ms. Hongrong Liu, the boss of Wuhan Lanzhi Biotechnology
Co., Ltd. Our team immediately rushed to Wuhan Lanzhi Biotechnology Co., Ltd. We emphasized that we have
determined the unique certainty of drug ingredients, the homology of medicine and food, and the advantages of
increasing production capacity by using synthetic biology. I received enthusiastic
support from Ms. Hongrong Liu and her company (Figure 20).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook21-21.jpg)

**Figure 20. Visiting and communication with Wuhan Lanzhi Biotechnology Co.,
Ltd.**

2.We interviewed Kanghong Hu, the director of the Sino-German Biomedical Center of Hubei University of
Technology. We asked some questions and received some useful advice from Professor Hu (Figure 21).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-22.jpg)

**Figure 21. Interview with Professor Kanghong Hu.**

3.Our team also participated in the synthetic biology competition held in China and won the silver prize
(Figure 22).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-23.jpg)

**Figure 22. Our team won the silver Prize in Chinese Synthetic Biology
Competition.**

# September

## Modeling

Using the Vina force field in conjunction with the Autodocking algorithm, as well as the OPLS-AA and AMBER
force fields, we conducted calculations with all three methods employing the TIP4P water model and the solvent
models GBSA (Generalized Born Solvent Accessible model) and MMGBSA (Molecular Mechanics Generalized Born
Surface Area) algorithm. To integrate the results from these three algorithms, we used Origin software to plot
a three-dimensional graph. To maintain consistency in our data representation, we also converted all the
binding free energy values to their negative equivalents, ensuring that all the values were concentrated in
the first quadrant of the graph. (Figure 23).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-9.jpg)

**Figure 23. Further, using three precise algorithms to evaluate the binding of
Vg to the receptor VDAC-1.** The x-axis represents the results derived from the vina force field combined
with the autodocking algorithm, the y-axis represents the results from the mmgbsa calculation scheme using the
opls-aa force field, and the z-axis represents the results from the mmgbsa calculation scheme using the amber
force field. The red spheres indicate Vg. The orange spheres represent the top three mutation strategies in
terms of binding effectiveness with vdac-1 excluding Vg. The blue spheres represent the other 29 mutation
strategies.

## Experiment

1.Through the construction of plasmids from generation to generation, the addition and updating of
components, we finally successfully obtained the ideal band and realized the large-scale expression of
hypoglycemic peptide P37 by *Pichia pastoris*.

2.We have sorted out the experiments during this period. Under the previous experimental results, the protein
was purified by nickel column and concentrated. The purified protein was detected by western blotting assay.

## Human practices

In order to popularize students' understanding of biology and synthetic biology, our team visited Wuhan Yucai
Senior High School to introduce the concept of synthetic biology and its role in the pharmaceutical industry
and in maintaining human health (Figure 24).

![](https://static.igem.wiki/teams/5384/wet-lab/notebook/notebook-24.jpg)

**Figure 24. Popularizing students' understanding of biology and synthetic
biology.**

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Engineering

Introduction

Cycle 1: Preliminary Screening

Cycle 2: pPIC9K-His-DDDDK-Vg

Cycle 3: pPIC9K-3Vg-His

Cycle 4: pPIC9K-NK-3Vg-His

Cycle 5: Mutation model

Conclusions & Future Prospects

References

# Introduction

Every engineering process consists of multiple milestones that together build the final system. Engineering a
biological system is not straightforward and often requires many customizations and fine tunings to achieve
optimization. In Figure 1. you can see the engineering cycle that describes the main stages in our every
engineering process. We used the Design-Build-Test-Learning (DBTL) loop to continuously improve our
experimental design and methods, making our experimental process easier to understand so that future iGEM
teams can build on our achievements.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering1-1.jpg)

**Figure 1. Engineering success cycle**

# Cycle 1: Preliminary Screening

In 1995, while conducting diabetes research at Carlinloskar Medical School in Sweden, Dr. Zhengwang Chen
isolated Aglycin, a 37-amino acid peptide that acts as an incretin, from the small intestine of pigs. Between
2002 and 2016, advancements in sequencing technology enabled Chen's team to discover that the genes encoding
Aglycin family peptides originate from leguminous plants rather than mammals, identifying four distinct groups
of Aglycin family peptides in soybeans. This finding underscores the potential for these peptides to be
further developed into antidiabetic drugs. However, it is important to note that polypeptides with similar
structures are also present in various plants, including the traditional Chinese medicine licorice and the
significant oil crop castor. Following discussions with Dr. Chenguang Yao, a member of chen’group, we posit
that through cross-species research, synthetic biology, and comparisons of artificially concentrated
advantageous traits across reproductive isolation, Vg within the Aglycin family peptide holds considerable
promise as a drug development solution (Figure 2).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-2.png)

**Figure 2. Evolution and homology analysis of Vg based on the NCBI.**The data
in this section is sourced from the BLAST in the NCBI database, employing the maximum likelihood algorithm of
the Jones-Taylor-Thornton (JTT) model in megax software to construct a phylogenetic tree, which was beautified
using cnsknowall (https://cnsknowall.com/). The red star marks Vg, the species in the green branches belong to
Fabaceae, and those in the blue branches belong to Euphorbiaceae. No protein sequences with high overall
similarity were found in animals and microorganisms.

## Design

With JPred4 revealed a distinct secondary structure, a β-sheet, spanning amino acids 21 to 31 in P37 family.
Further investigation through the NCBI database indicated that this β-sheet constitutes an Albumin\_I
superfamily functional domain, which is capable of specifically binding to the 43 kDa protein 2jk4.
Consequently, we identified the Albumin\_I superfamily functional domain formed by amino acids 21 to 31 as a
candidate site for mutation (Figure 3).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-3.png)

**Figure 3. Prediction of the conservation of P37 family.**The conserved
domain database (CDD) from the NCBI database was used, and through CD-search, a highly reliable albumin-I
superfamily functional domain of Vg was identified. According to the annotations from the conserved domain
database (CDD), the albumin-I superfamily functional domain can bind to the 43 kDa receptor protein VDAC-1.

By comparing the homologous sequences of amino acids 21 to 31, we obtained the conserved domain. This
analysis, combined with the phosphorylation site assessment of the hypoglycemic peptide P37 using NetPhos3.1,
our team selected the NetPhos3.1 algorithm because it has extensively and systematically learned to capture
the relationship between protein three-dimensional structure and function among the many algorithms using
Artificial Neural Networks (ANNs). This aspect is often overlooked in traditional sequence-based prediction
tools[1]. By integrating this information, NetPhos 3.1 provides a more comprehensive perspective
for the prediction of phosphorylation sites. Furthermore, the evaluation of disulfide bond sites and steric
hindrance indicated that the structure of hypoglycemic peptide P37 was stable. From an evolutionary
perspective, we utilized the branch-site algorithm and likelihood ratio tests to analyze the accelerated rate
of non-synonymous substitutions (dN) in specific genes as a measure of positive selection[2]. This
research on positive selection provides an important theoretical basis for protein engineering, which can
assist in designing proteins with specific functions[3]. In terms of algorithm selection, we chose
PAML because compared to other software such as HyPhy, PAML can process large datasets more quickly when
analyzing selection pressure[4] and also incorporates model-based inference. This approach makes
the selection process more scientific and reduces the risk of selection bias[5].

Through the comprehensive analysis of the above methods, our team established the mutation sites and schemes
(Figure 4). Thus, we integrated the different amino acid evolutionary strategies at positions 25, 27, and 28
from 32 similar peptides (Figure 5).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-4.png)

**Figure 4. The conserved regions of Vg evolution.**Utilized the align by
clustalw function of megax software, where the yellow regions represent conserved domains and the white areas
indicate mutation sites.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-5.png)

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-6.png)

**Figure 5. Prediction of phosphorylation sites in Vg.**NetPhos3.1 exports
predicted phosphorylation sites, with points marked as "yes" or exceeding the purple boundary indicating
phosphorylation sites predicted with higher confidence by machine learning, resulting in predicted
phosphorylation sites at positions 18 and 36.

## Build

Through the design section, our team established the mutation sites and plans, so we integrated different
amino acid evolution strategies of 33 similar polypeptides at positions 25, 27, and 28, forming a total of 26
mutation products, which we named For（R25A, R25A\_V27F, R25A\_V27F\_V28A, R25A\_V27F\_V28I, R25A\_V27L,
R25A\_V27L\_V28A, R25A\_V27L-V28I, R25A\_V28A, R25A\_V28I, R25G, R25G\_V27F, R25G\_V27F\_V28A, R25G\_V27F\_V28I,
R25G\_V27L, R25G\_V27L\_V28A, R25G\_V27L\_V28I, R25G\_V28A, R25G\_V28I, V27F, V27F\_V28A, V27F\_V28I, V27L, V27L\_V28A,
V27L\_V28I, V28A, V28I) (Figure 6).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-7.png)

**Figure 6. The prediction of the mutation sites in Vg.**(A), (B), and (C)
respectively display the mutation sites 2, 5, 2, 7, and 2, 8 of Vg. The sites 2, 5, 2, 7, and 2, 8 are
represented in stick form, while the remaining parts are depicted in cartoon form.

## Test

We next planed to use chemSocre to evaluate the binding potential of small molecule ligands, distinguish
active compounds from inactive compounds through an empirical scoring mechanism based on machine learning
technology, and eliminate some mutation schemes that are inactivated by random mechanical combinations (Figure
7). Our team then used the Hex scoring function as the earliest stage of drug virtual screening, which can
save us time and computing power, but it has limitations such as the inability to accurately predict all types
of protein-ligand interactions. Finally, our team used molecular docking in Ligand Docking to collect the
exported Glide Score data. The three data were combined and origin was used to draw a three-dimensional graph.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-8.png)

**Figure 7. Calculating the free binding of Vg to the VDAC-1 receptor using
three sophisticated algorithms.**The x-axis represents the results derived from the chemscore algorithm in
Maestro software, the y-axis represents the hex scoring function from Discovery Studio software, and the
z-axis represents the glide score results from Maestro software. The binding site of VDAC-1 with Vg was again
determined based on the binding positions obtained from the autodocking blind docking algorithm. In the
figure, vdac-1 is displayed in stick form, the binding sites are shown as white surfaces, and the binding
pockets are represented as purple cubes. The red spheres indicate Vg, while the blue spheres represent the
other 32 evolutionary strategies. In the data processing, since the chemscore results are within the range of
(0-1), and both the hex scoring function and glide score represent binding free energy, we artificially took
the negative values of all binding free energy data for easier visualization and observation of conclusions.

For the convenience of data visualization, we artificially take negative values for all data of binding free
energy and concentrate all values into the first quadrant.

## Learn

By learning three indicators, we could preliminarily obtain the activity of Vg and the calculation of free
binding energy by two preliminary scoring functions. It could be considered that the binding effect of Vg and
2jk4 has significant advantages compared with other evolutionary strategies. However, these algorithms have
simplified and ignored some parts in order to save computing power. In the next step, we used more complex
algorithms to perform more precise prediction calculations.

# Cycle 2: pPIC9K-His-DDDDK-Vg

In the first cycle, through preliminary screening, our team found that some mutation schemes have excellent
performance under certain specific molecular positions and scoring functions.

## Design

In the whole project, our goal was to make Vg production cost-effective and high yield. In order to select a
proper expression vector, our team gathered information on various microorganisms (including *Escherichia coli,
Saccharomyces cerevisiae*, mammalian cells …) and ultimately chosen *Pichia pastoris* as the expression
host. *Pichia pastoris* offers significant advantages over other expression systems in terms of protein
processing, secretion, post-translational modification, and glycosylation[6]. As one of the most
widely protein expression systems, *Pichia pastoris* is recognized as a prominent host in molecular
biology for the purpose of generating recombinant proteins. The advantages of utilizing the *Pichia
pastoris* system for protein production encompass the proper folding process occurring within the
endoplasmic reticulum[7]. Initially, we intended to use the most common exogenous expression
system-Escherichia coli. Although Vg has three disulfide bonds, a small amount of disulfide bond formation can
also form in E. coli. However, we learned that there have been previous attempts to express Vg in E. coli, but
the Vg produced lacked biological activity. Therefore, we turned to *Pichia pastoris*.

Then, we optimized the sequence of Vg to enhance heterologous expression in *Pichia pastoris*. In
design circuit, we planned to extract Vg in a simplified manner, but how can we make it? Firstly, we conducted
extensive literature review. We added 6x-His tag for subsequent protein separation and purification. This tag
not only facilitates affinity purification through the Ni column, but also does not significantly interfere
with the folding of small peptides. In the *Pichia pastoris* expression system, a lot of projects
purified the target protein by adding His-tag, which proved to be feasible and easy to purify[8].
Then we need to select an endopeptidase to separate our target protein from other proteins. We learned that
commonly used protease cleavage sites include SUMO protease (Ulp1), HRV 3C protease, enterokinase, TEV
protease, caspase-3-like proteases, and trypsin. Because our Vg is ultimately required to be produced as a
hypoglycemic drug, all our end products need to ensure that no residues are left after cleavage to maintain
the activity or structural integrity of the protein. At the same time, we also need to avoid non-specific
cutting, to ensure the integrity of the Vg function, so we finally chose Enterokinase (EK). We inserted the
DDDDK segment specifically recognized by Enterokinase between the Vg and other components. The recombinant
plasmid was identified by DNA sequencing (Figure 8).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-10.png)

**Figure 8. Visualization of the pPIC9K-His-DDDDK-Vg.** The expression of P37
gene in the recombinant plasmid was strictly regulated by methanol using AOX promoter, and α factor secretion
signal was an extracellular signal peptide mediating Vg fermentation and secretion into culture medium. EK
(Enterokinase) can recognize DDDDK sequence in protein efficiently and specifically, and separate P37 from
other non-target proteins to obtain P37 with high purity[9].

## Build

We subjected the plasmid map to Beijing Tsingke Biotech Co., Ltd. for synthesis.

## Test

After inducing the expression of the target protein by methanol, we carried out Coomassie Brilliant blue
staining and WB experiments to detect the product. The results showed that His-Vg band was detected
successfully, but the molecular weight did not accord with the theoretical value. Our theoretical molecular
weight is 16.9 kDa, but Coomassie Brilliant blue test did not show a significant difference in the band (25
kDa), while molecular positive signal can be detected at size of about 66 kDa by western blotting (Figure 9).
After the recombinant protein was successfully obtained, we performed nickel column affinity chromatography on
the supernatant of culture medium to explore whether His-tag could bind to nickel column successfully. It was
found that the recombinant protein could not be attached to the nickel column.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering9-1.jpg)

**Figure 9. The expression of Vg was detected by Coomassie Brilliant Blue
staining and western blotting.** The difference between GS115 and the culture supernatants of strains
2,3,5,6,7 was observed, and the size was inconsistent with the theory(16.9 kDa). G was negative control group
of *Pichia pastoris* strain without transforming P37 plasmid.

## Learn

Given that we detected the expression of the target protein, but did not enrich the target protein with
Ni-NTA. We hypothesized that *Pichia pastoris* may be involved in glycosylation of the target protein,
which Posttranslational modification a significant increase in the molecular weight of the protein. Especially
for secretory proteins, glycosylation is a common phenomenon, which may lead to the actual molecular weight
greater than the theoretical value[10]. Our analysis indicated that the His-tag was likely masked
by the N-terminal part of the hypoglycemic peptide, preventing it from interacting with the nickel ions on the
column.

# Cycle 3: pPIC9K-3Vg-His

## Design

To prevent His-tag from being masked, we moved it to the C-terminal and ran the experiment again. By
reviewing the literature, we learned that we could increase the expression level of our Vg by increasing the
copy number of the target gene[11], and therefore, we changed the original single-copy Vg to three
copies. We also learned that, in addition to DDDDK being suitable for protein purification, Asp-Pro is also a
good choice and is more economical and efficient to use[12,13]. So our plasmid design turns out to
be like this (Figure 10).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-14.png)

**Figure 10. Visualization of the designed plasmid pPIC9K-3Vg-His.**

## Build

The new plasmid was successfully constructed.

## Test

After inducing the expression of the target protein successfully, we carried out Coomassie Brilliant blue
staining and WB experiments to detect the product. No obvious target band was detected by SDS-PAGE and
Coomassie Brilliant blue staining.

## Learn

We suspected that the molecular weight of the target protein we expressed was still too small, and that the
small polypeptides were easily dispersed during protein separation, making them less detectable in western
blotting experiments.

# Cycle 4: pPIC9K-NK-3Vg-His

## Design

To make the recombinant protein easier to detect, we inserted a fusion gene of Nattokinase (NK) in front of
the Vg to expand the molecular weight of the target product. NK, an alkaline serine endopeptidase secreted by
Bacillus subtilis (natto), has the advantages of long half-life, high specificity, low side effects and direct
oral administration with molecular weight of 27.7 kDa[14] . We added the gene of NK to the original
plasmid, which can compensate for the low molecular weight defect of our target protein. Besides, in the
process of late purification, we could collect Vg and NK, respectively, as our expression products. The
plasmid element design can be seen in Figure 11.

## Build

We gave the plasmid map to Nanjing Genco Biotechnology Co., Ltd. for synthesis.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-15.png)

**Figure 11. Visualization of the open reading frame of
pPIC9K-NK-3Vg-His.**pPIC9K-NK-3vg-3xhis

## Test

After inducing the expression of the target protein successfully, we carried out Coomassie Brilliant blue
staining and WB experiments to detect the product. Finally, the obvious target bands were successfully
detected by western blotting at the corresponding sites of 55 kDa, indicating the successful expression of the
recombinant protein after western blotting (Figure 12).

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering12-1.jpg)

**Figure12. Detection of P37 expression by Western blotting.**SDS-PAGE
electrophoresis and His-antibody incubation showed that there was a weak signal band between GS115 and the
supernatants of No. 1,3,4,5,7 in 50 kDa. His was negative control group. 1-6 was recombinant P37 expressed by
*Pichia pastoris* strain. G was negative control group of *Pichia pastoris* strain without
transforming P37 plasmid.

## Learn

We suspected that it may be due to the continuous passage of the strain, resulting in the loss of our target
protein plasmid, thus changing our experimental results.

# Cycle 5: Mutation model

## Design

In order to maximize the advantages of the mutation scheme, we selected three molecular force fields and two
more accurate calculation methods. For design, we chose Vina force field with Autodocking algorithm, OPLS-AA
force field and AMBER force field, all using TIP4P water and solvent model, GBSA solvent model and MMGBSA
algorithm, to conduct comprehensive calculation and comparison of these three algorithms.

## Build

Our team still used the previous round of 26 mutation products and Vg, a total of 27 solutions for
calculation and prediction, and used the Origin software to draw a three-dimensional map.

## Test

Our team put 27 sets of data into the program and found that the top four were R25A\_V28A, VG, R25G\_V27F\_V28A
and R25G\_V28A.

![](https://static.igem.wiki/teams/5384/wet-lab/engineering/engineering-9.png)

**Figure 13. Evaluation of the free binding energy between Vg variants and
VDAC1.**The x-axis represents the results derived from the vina force field combined with the autodocking
algorithm, the y-axis represents the results from the mmgbsa calculation scheme using the opls-aa force field,
and the z-axis represents the results from the mmgbsa calculation scheme using the amber force field. The red
spheres indicate Vg, the orange spheres represent the top three mutation strategies in terms of binding
effectiveness with vdac-1 excluding Vg, and the blue spheres represent the other 29 mutation strategies.

## Learn

Through this round of calculations, our team noticed that position 28 was mutated to A, which has great
potential. In the follow-up, our team is going to try to conduct further research around position 28, which
may provide a better solution. However, the current virtual prediction results confirmed that we will directly
use Vg without mutation to explore the next wet experimental synthesis method.

# Conclusions & Future Prospects

We have successfully established an expression system for the exogenous protein Vg in *Pichia
pastoris* GS115. Subsequently, we will conduct small-scale fermentation experiments in the laboratory,
continuously optimizing the medium formulation and cultivation protocols to enhance the economic viability of
the culture. We will also improve strain stability to provide better conditions for future production
expansion. In later stages, we will perform activity identification experiments using mice, comparing
recombinant protein with naturally extracted proteins to assess the biological expression of Vg activity.

In the subsequent experimental design, we plan to incorporate molecular chaperones and enhancers into the
plasmid to further increase the yield of Vg and enhance its biological activity. Molecular chaperones are
beneficial for maintaining the stability of the disulfide bonds in Vg, ensuring its activity. We intend to
include hsp70 in our experimental design, as literature indicates that there have been successful experiments
utilizing hsp70 in *Pichia pastoris* to maintain the disulfide bonds of the product[15],
which is very similar to our experiment, thus demonstrating the feasibility of our experimental design.
Additionally, the enhancer we plan to add is exin21/Qα booster. Literature has shown that the addition of this
enhancer significantly increases protein expression levels and promotes the secretion of recombinant proteins,
making our product easier to collect and purify[16].

# References

[1] Ismail, H.D., Jones, A., Kim, J.H., Newman, R.H., Kc, D.B. RF-Phos: A Novel General Phosphorylation Site
Prediction Tool Based on Random Forest. BioMed Research International. 2016:2016:3281590. doi:
10.1155/2016/3281590. Epub 2016 Mar 15.

[2] Yang Z. “Statistical properties of the branch-site test of positive selection.” Molecular Biology and
Evolution 28, no. 3 (2011): 1217-28. doi: 10.1093/molbev/msq303. Epub 2010 Nov 18. PMID: 21087944; PMCID:
PMC3030880

[3] Anderson, G., Hare, K.J., Jenkinson, E.J. “Positive selection of thymocytes: the long and winding road.”
Immunology Today 20, no. 10 (1999): 463-468. Epub [Epub date not available in the sources]. PMID: 10500294

[4] Ramasamy, S., et al. “paPAML: An Improved Computational Tool to Explore Selection Pressure on
Protein-Coding Sequences.” Genes 13, no. 6 (2022): 1090. doi: 10.3390/genes13061090. Epub 2022 Jun 18. PMID:
35741852;

[5] Álvarez-Carretero, S., Kapli, P., & Yang, Z. “Beginner’s Guide on the Use of PAML to Detect Positive
Selection.” Molecular Biology and Evolution 40, no. 4 (2023): msad041. doi: 10.1093/molbev/msad041. Epub 2023
Apr 4.

[6] Wen-Jing Zhou, Jiang-Ke Yang, Lin Mao, Li-Hong Miao. Codon optimization, promoter and expression system
selection that achieved high-level production of Yarrowia lipolytica lipase in *Pichia pastoris*.
Enzyme and Microbial Technology. Volume 71, April 2015, Pages 66-72

[7] Chronopoulou S, Tsochantaridis I, Tokamani M, Kokkinopliti KD, Tsomakidis P, Giannakakis A, Galanis A,
Pappa A, Sandaltzopoulos R. Expression and purification of human interferon alpha 2a (IFNα2a) in the
methylotrophic yeast *Pichia pastoris*. Protein Expr Purif. 2023 Nov;211:106339. doi:
10.1016/j.pep.2023.106339. Epub 2023 Jul 17. PMID: 37467825.

[8] Chronopoulou S, Tsochantaridis I, Tokamani M, Kokkinopliti KD, Tsomakidis P, Giannakakis A, Galanis A,
Pappa A, Sandaltzopoulos R. Expression and purification of human interferon alpha 2a (IFNα2a) in the
methylotrophic yeast *Pichia pastoris*. Protein Expr Purif. 2023 Nov;211:106339. doi:
10.1016/j.pep.2023.106339. Epub 2023 Jul 17. PMID: 37467825.

[9] Prachayasittikul V, Isarankura Na Ayudhya C, Piacham T, Kiatfuengfoo R. One-step purification of chimeric
green fluorescent protein providing metal-binding avidity and protease recognition sequence. Asian Pac J
Allergy Immunol. 2003 Dec;21(4):259-67. PMID: 15198344.]

[10] ZHANG Xinran, LING Yan, YANG Ying. Molecular level strategy for high expression of foreign protein in
*Pichia pastoris* [J] . Food and Fermentation Industries,2022,48(17) :321 – 328

[11] Xinran Zhang, Yan Ling, Yin Yang. Molecular level strategy for high expression of foreign protein in
*Pichia pastoris* [J]. Food and Fermentation Industries ,2022,48(17):321-328.
DOI:10.13995/j.cnki.11-1802/ts.029940.

[12] Mollaev M, Zabolotskii A, Gorokhovets N, Nikolskaya E, Sokol M, Tsedilin A, Mollaeva M, Chirkina M,
Kuvaev T, Pshenichnikova A, Yabbarov N. Expression of acid cleavable Asp-Pro linked multimeric AFP peptide in
E. coli. J Genet Eng Biotechnol. 2021 Oct 14;19(1):155. doi: 10.1186/s43141-021-00265-5. PMID: 34648110;
PMCID: PMC8517049.

[13] Thambi T, Jung JM, Lee DS. Recent strategies to develop pH-sensitive injectable hydrogels. Biomater Sci.
2023 Mar 14;11(6):1948-1961.

[14] Chen H, McGowan EM, Ren N, Lal S, Nassif N, Shad-Kaneez F, Qu X, Lin Y. Nattokinase: A Promising
Alternative in Prevention and Treatment of Cardiovascular Diseases. Biomark Insights. 2018 Jul
5;13:1177271918785130. doi: 10.1177/1177271918785130. PMID: 30013308; PMCID: PMC6043915.

[15] Cai Yian, Zhang Yiqun, Yang Zixuan, Liu Yexue, Liu Wenlong, Lu Fuping, Li Yu. Enhanced Expression of
Protease K in *Pichia pastoris* through Molecular Chaperones and Analysis of Its Effect on Wool Scale
Layer. Biotechnology Bulletin. 2024, 40(7):307-313.

[16] Yuanjun Zhu, A. Sami Saribas, Jinbiao Liu, Yuan Lin, Brittany Bodnar, Ruotong Zhao, Qian Guo, Julia
Ting, Zhengyu Wei, Aidan Ellis, Fang Li, Xu Wang, Xiaofeng Yang, Hong Wang, Wen-Zhe Ho, Ling Yang, Wenhui Hu.
Protein expression/secretion boost by a novel unique 21-mer cis-regulatory motif (Exin21) via mRNA
stabilization. Molecular Therapy. 2023, Volume 31, Issue 4, p1136-1158

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Experiments

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Results

Screening of Target Proteins

Overexpression of Genes for Increased Vg Production

References

In our project, we simultaneously pursued two experimental fronts: modeling and biomanufacturing. Below, we
present the results achieved thus far in each area.

# Screening of Target Proteins

## Reconstruction of Ancestral Sequences of Vg

Our team initially utilized the BLAST function in the NCBI database to identify protein sequences with high
similarity to the overall sequence of Vg. We obtained 50 protein sequences from various species, all
exhibiting a similarity greater than 71.3% to Vg. After removing 17 duplicate records, we aligned the
remaining 33 protein sequences using the ClustalW function in MEGA X software (Figure 1 A) and constructed a
corresponding phylogenetic tree using the maximum likelihood algorithm based on the Jones-Taylor-Thornton
(JTT) model in MEGA X software (Figure 1 B).

We conducted an evolutionary analysis of Vg based on the phylogenetic tree and homology comparison results
(Figure 1 A, B). From the perspective of natural evolution, conserved domains and mutation sites were
identified (Figure 1 A). By examining different positions within the mutation intervals, we categorized the
mutation sites into two types: functional domain mutation sites and non-functional domain mutation sites
(Figure 1 C). Since the pharmacology of the Vg we developed is associated with the VDAC-1 receptor protein,
the functional domain corresponds to the region that directly interacts with the receptor (Figure 1 D).
Therefore, we focused our mutation studies solely on the functional domain mutation sites.

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-1.jpg)

**Figure 1. Evolution and homology analysis of Vg based on reports.**(A)
Utilized the align by clustalw function of megax software, where the yellow regions represent conserved
domains and the white areas indicate mutation sites. (B) The data in this section is sourced from the blast in
the NCBI database, employing the maximum likelihood algorithm of the Jones-Taylor-Thornton (JTT) model in
megax software to construct a phylogenetic tree, which was beautified using cnsknowall
(https://cnsknowall.com/). The red star marks Vg, the species in the green branches belong to Fabaceae, and
those in the blue branches belong to Euphorbiaceae. No protein sequences with high overall similarity were
found in animals and microorganisms. (C) The conserved domain database (CDD) from the NCBI database was used,
and through CD-search, a highly reliable albumin-I superfamily functional domain of Vg was identified.
According to the annotations from the conserved domain database (CDD), the albumin-I superfamily functional
domain can bind to the 43 kDα receptor protein VDAC-1. (D) The image depicting the binding of Vg to the
receptor VDAC-1 was generated using Autodocking Vina, showing the conformation when the free binding energy of
Vg to the receptor VDAC-1 is minimized at -8.9 kcal/mol. The visualization was enhanced using pymol, where
VDAC-1 is displayed in surface form, the functional domain of Vg is shown in mesh form, and the non-functional
domain of Vg is represented in cartoon form. The figure clearly illustrates the direct interaction of the
functional domain of Vg with the molecular binding pocket of the receptor. VDAC-1 is displayed in surface
form, the functional domain of Vg is shown in mesh form, and the non-functional domain of Vg is represented in
cartoon form. The figure clearly illustrates the direct interaction of the functional domain of Vg with the
molecular binding pocket of the receptor.

## Mutation Site Strategy of Vg Guided by Machine Learning

We plan to subsequently synthesize Vg in *Pichia pastoris* eukaryotic heterologous expression system, which is
quite different with plant expression systems. In order to achieve successful expression of Vg in *Pichia pastoris*, we employed the artificial neural network (ANN) algorithm within the netphos3.1 algorithm to analyze
and predict the key domain in all the selected sequences. This approach leverages extensive and systematic
learning to capture the relationship between protein three-dimensional structure and function, enabling
precise prediction of phosphorylation sites[1] (Figure 2 A). By processing the results derived from
the NetPhos3.1 algorithm (Figure 2 B), we identified phosphorylation sites at positions 18 and 36, which can
influence the intracellular localization of the protein. Phosphorylation can alter the hydrophilicity of the
protein or its interaction with the cell membrane, leading to the translocation of the protein from the
cytoplasm to the cell membrane or nucleus, which is crucial for cellular function and signal transduction. We
confirmed the presence of phosphorylation sites at positions 18 and 36, which can affect the protein's
localization within the cell. Through changes in the protein's hydrophilicity or interactions with the cell
membrane, phosphorylation can result in the movement of the protein from the cytoplasm to the cell membrane or
nucleus, which is vital for cellular functions and signaling pathways[2].

The selection of mutation sites mentioned above is based on the arrangement of amino acids, considering the
primary structure level. Therefore, our team utilized the deep learning architecture and neural networks of
AlphaFold3 (Figure 2 C) to predict the three-dimensional structure of Vg, employing Pymol to visualize the
three-dimensional conformation of Vg. Since Vg is a polypeptide without a quaternary structure, we will select
mutation sites based on the secondary and tertiary structures.

We predicted the secondary structure of Vg using the latest jpred4 algorithm from the jnet algorithm series.
The results indicate that Vg has three β-sheet regions at positions 21 to 31, while other regions consist of
β-turns and random coils (Figure 2 D). In the annotation of the albumin superfamily functional domain of Vg,
there is a correlation between the function of this domain and the three β-sheet regions (Figure 1 C), which
allows us to narrow the research area to the mutation sites from 21 to 31.

In the three-dimensional structure of Vg, the significant advantage of Vg as an oral hypoglycemic agent is
primarily considered, a property determined by its globular three-dimensional structure. The key to Vg forming
a stable globular structure lies in three pairs of disulfide bonds (Figure 2 E). Observations reveal that
disulfide bonds exist between amino acids at positions 7 and 22, 3 and 15, as well as 20 and 32. Consequently,
the sites that form disulfide bonds in the mutation sites were eliminated.

Finally, considering that the essence of biological evolution is gene mutation, we performed tblastn
operations on 33 proteins with high overall similarity to Vg in the NCBI database for gene prediction. For the
predicted genes, we utilized the branch-site model in the PAML software, employing likelihood ratio tests to
analyze the accelerated changes in nonsynonymous substitutions in specific genes, obtaining values for the
constant. The identified positive selection sites were then traced back to the proteins, serving as important
references for valuable mutation sites in proteins. For the predicted genes, we utilized the branch-site model
in the PAML software, employing likelihood ratio tests to analyze the accelerated changes in nonsynonymous
substitutions in specific genes, obtaining values for the constant. The identified positive selection sites
were then traced back to the proteins, serving as important references for valuable mutation sites in proteins
(Figure 2 F)

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-20.jpg)

**Figure 2. Machine learning-assisted selection of Vg mutation sites.**(A)
Illustrates the architecture of the artificial neural network (ANN) algorithm, which simulates the cognitive
and learning processes of the human brain, showcasing a large and systematic learning framework that captures
the relationship between protein three-dimensional structure and function. (B) NetPhos3.1 exports predicted
phosphorylation sites, with points marked as "yes" or exceeding the purple boundary indicating phosphorylation
sites predicted with higher confidence by machine learning, resulting in predicted phosphorylation sites at
positions 18 and 36. (C) Displays a simple architecture of AlphaFold3, divided into three modules: blue
represents MSA & template, green represents PairFormer, and pink represents the diffusion model. The data
transmission route is divided into three parts: yellow indicates input data, blue indicates abstract network
activations, and green indicates output data. (D) The JPred4 algorithm exports predicted secondary structures
of Vg, where the green regions indicate β-fold regions, with β-fold regions present at positions 21-31. The
data transmission route is divided into three parts: yellow indicates input data, blue indicates abstract
network activations, and green indicates output data. (E) Displays the three-dimensional structure of Vg
predicted by AlphaFold3, with disulfide bonds colored red using PyMOL software. (F) Shows the branch-site
algorithm of PYMAL.

Through the guidance of the aforementioned machine learning, we developed mutation strategies and plans,
focusing on the evolutionary strategies of different amino acids at positions 25, 27, and 28 (Figure 3 A, B,
C). We combined various mutation schemes at these sites, resulting in a total of 26 mutant products, which we
named (R25A, R25A\_V27F, R25A\_V27F\_V28A, R25A\_V27F\_V28I, R25A\_V27L, R25A\_V27L\_V28A, R25A\_V27L\_V28I, R25A\_V28A,
R25A\_V28I, R25G, R25G\_V27F, R25G\_V27F\_V28A, R25G\_V27F\_V28I, R25G\_V27L, R25G\_V27L\_V28A, R25G\_V27L\_V28I,
R25G\_V28A, R25G\_V28I, V27F, V27F\_V28A, V27F\_V28I, V27L, V27L\_V28A, V27L\_V28I, V28A, V28I), and utilized
AlphaFold3 to depict the three-dimensional structures of these various mutant products.

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-3.png)

**Figure 3. Mutations site in Vg.**(A), (B), and (C) respectively display the
mutation sites 2, 5, 2, 7, and 2, 8 of Vg. The sites 2, 5, 2, 7, and 2, 8 are represented in stick form, while
the remaining parts are depicted in cartoon form.

## Virtual Screen for Vg Binding Molecular

Our team plans to first evaluate the binding potential of molecular ligands for Vg through a machine
learning-based empirical scoring mechanism, distinguishing between active and inactive compounds to eliminate
some mutation schemes that become inactive due to random mechanical combinations. We utilized the Chemscore
algorithm in Maestro software, which decomposes the total energy of the protein-ligand complex into multiple
energy terms, removing unreasonable geometric configurations and mutual positions, ultimately obtaining the
activity of the molecular ligands through a scoring function. Subsequently, our team assessed from the
perspective of the stability of the protein-ligand complex, selecting the Hex scoring function from Discovery
Studio software. By collecting and calculating various energy terms of the protein-ligand complex after
virtual docking, we weighted and combined these terms using the scoring function. Finally, to achieve the
speed of reaching the binding energy equilibrium of the protein-ligand complex, we selected the Glide module
in the Glide score of Maestro software, employing the OPLS all-atom force field. We collected data derived
from the three methods using the all-atom model mechanical analysis in the OPLS all-atom force field,
processed the data, and plotted a three-dimensional graph using Origin (Figure 4 B).

We proceeded to use protein-ligand blind docking to simulate molecular behavior in a real biological
environment. By analyzing the results of molecular docking, we identified the binding pocket of the receptor
VDAC-1 (Figure 4 A), which provided an important parameter for the next step of specific docking. In the
protein-ligand blind docking, we selected the Vina force field in conjunction with the AutoDock algorithm. We
utilized the Vina force field to derive a mathematical expression[3] that describes the
relationship between the system's energy and its particle coordinates, calculating the total energy of the
molecular system to assess the stability of different conformations. This allowed us to obtain the
three-dimensional structure of the most stable conformation and the system energy, which served as an
important parameter for the next step of specific docking. Finally, we employed the global optimization
algorithm of AutoDock to draw conclusions. We also utilized the same MMGBSA calculation scheme along with the
TIP4P water and solvent model, and the GBSA solvent model, using different molecular force fields,
specifically the Amber force field and the OPLS-AA force field. The free binding energy calculation results
from the two force fields were designated as Amber and Prime MMGBSA. We collected data derived from the three
methods and processed it, using Origin to create a three-dimensional graph (Figure 4 C).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-4.png)

**Figure 4. Virtual Screen for Vg binding moleculars.**(A) First, the binding
pocket of vdac-1 was predicted using the sitmap function in Maestro software, resulting in ten binding
pockets. The binding site of vdac-1 with Vg was then determined based on the binding positions obtained from
the autodocking blind docking algorithm. In the figure, vdac-1 is displayed in stick form, the binding sites
are shown as white surfaces, and the binding pockets are represented as purple cubes. (B) The x-axis
represents the results derived from the chemscore algorithm in Maestro software, the y-axis represents the hex
scoring function from Discovery Studio software, and the z-axis represents the glide score results from
Maestro software. The binding site of VDAC-1 with Vg was again determined based on the binding positions
obtained from the autodocking blind docking algorithm. In the figure, vdac-1 is displayed in stick form, the
binding sites are shown as white surfaces, and the binding pockets are represented as purple cubes. The red
spheres indicate Vg, while the blue spheres represent the other 32 evolutionary strategies. In the data
processing, since the chemscore results are within the range of (0-1), and both the hex scoring function and
glide score represent binding free energy, we artificially took the negative values of all binding free energy
data for easier visualization and observation of conclusions. (C) The x-axis represents the results derived
from the vina force field combined with the autodocking algorithm, the y-axis represents the results from the
mmgbsa calculation scheme using the opls-aa force field, and the z-axis represents the results from the mmgbsa
calculation scheme using the amber force field. The red spheres indicate Vg, the orange spheres represent the
top three mutation strategies in terms of binding effectiveness with vdac-1 excluding Vg, and the blue spheres
represent the other 29 mutation strategies. In the data processing for figures (B) and (C), we took the
negative values of all binding free energy data to maintain consistency.

After six rounds of calculations, two methods, the empirical function and mmgbsa, utilized four types of
molecular force fields: the OPLS all-atom force field, the Vina force field, the Amber force field, and the
OPLS-AA force field. This comprehensive evaluation demonstrated that Vg has a significant advantage in binding
to the receptor VDAC-1 compared to various evolutionary strategies, allowing for the direct use of the
wild-type Vg in the subsequent exploration of wet lab synthesis methods.

# Overexpression of Genes for Increased Vg Production

To correctly express Vg, a hypoglycemic polypeptide derived from soybean consisting of 37 amino acids. We
chose *Pichia pastoris* GS115 as our chassis microorganism. *Pichia pastoris* is a kind of yeast
that can utilize methanol as its sole carbon and energy source. It offers significant advantages over other
expression systems in terms of protein processing, secretion, post-translational modification, and
glycosylation[4]. To achieve our goal, we have constructed three plasmids and then transformed them
into GS115 for expression. The plasmid, parts we used are shown in [parts](https://2024.igem.wiki/hbut-wuhan/parts).

## pPIC9K-his-DDDDK-Vg

Our goal is to consistently produce Vg using GS115. As mentioned in the [engineering](https://2024.igem.wiki/hbut-wuhan/engineering) page. We already constructed three
plasmids in our project. For expressing Vg successfully, the vector we selected is pPIC9K, which codes a
signaling peptide that makes the expressed protein secret into the culture environment. There is an AOX 1
promoter on the pPIC9K vector, encoding an alcohol oxidase that breaks down methanol as a carbon
source[5].

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-5.png)

**Figure 5. Visualization of the pPIC9K-his-DDDDK-Vg for
overexpression.**Recombinant plasmid profile of PIC9K-3xhis-DDDDK-Vg. The expression of P37 gene in the
recombinant plasmid was strictly regulated by methanol using AOX promoter, and α factor secretion signal was
an extracellular signal peptide mediating Vg fermentation and secretion into culture medium. Ek (Enterokinase)
can recognize DDDDK sequence in protein efficiently and specifically, and separate P37 from other non-target
proteins to obtain P37 with high purity[6].

Since we required a large number of plasmids, we transferred the recombinant pPIC9K plasmid to DH5α to obtain
a large number of plasmids. After the plasmid was extracted, the plasmid was linearized by single digestion
with Sac I (Figure 6 A), and we transferred the linearized plasmid into *Pichia pastoris* by chemical
transformation. After the dilution of the transformed bacterial solution, we cultivated the single colonies.
Meanwhile, the positive clones were further selected by dual resistance to kanamycin and ampicillin.
Subsequently, we first performed colony PCR to select the positive clones. We designed the PCR results for two
pairs of primers (Figure 6 B), and all the selected plasmids were at the expected position, consistent with the
position of the positive control.

After obtaining the validated positive clones, we induced the expression of the target protein by methanol,
and used Western blotting (Figure 6 C) and Coomassie Brilliant Blue staining (Figure 6 D) to verify the successfully
expressed recombinant protein in the supernatant.

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-6.png)

**Figure 6. Biosynthesis of Vg in *Pichia pastoris*.**(A) Linearization
of pPIC9K-his- DDDDK-Vg recombinant plasmid. The pPIC9K-his-DDDDK-Vg recombinant plasmid was linearized by Sac
I, and it was identified that the dissected band should run slower with the intact plasmid running ahead,
consistent with the theory. Lane Sac I was the experimental group of linearized pPIC9K-3xhis-DDDDK-Vg
recombinant plasmid, and Vector was the group of undigested plasmid. (B) Screening positive clones stably
expressing Vg. The extracted *Pichia pastoris* genomic DNA was analyzed with vector universal upstream
and downstream primers 5, AOX-F, and 3, AOX-R about 670 bp in size (up panel) , and P37-F and 3, AOX-R,
upstream and downstream of the target fragment, about 300 bp in size down panel . Except sample 10, the other
samples are in accordance with expectation. 1-20 was the recombinant GS115 strain, G was the negative control
of *Pichia pastoris* strain without transforming P37 plasmid. P was the positive control using
pPIC9K-his-DDDDK-Vg as the template. (C) The expression of P37 was detected by Coomassie Brilliant Blue
staining. The difference between GS115 and the culture supernatants of strains 2,3,5,6,7 was observed, and the
size was inconsistent with the theory(16.9 kDα). G was negative control group of *Pichia pastoris*
strain without transforming P37 plasmid.

After the recombinant protein was successfully obtained, we performed nickel column affinity chromatography
on the supernatant of culture medium to explore whether His-tag could bind to nickel column successfully. It
was found that the recombinant protein could not be attached to the nickel column. We suspect that the signal
peptide at the front of the nickel column may mask the binding of His-tag to the nickel column (Figure 7).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-7.png)

**Figure 7. Vg enrichment and purification with Ni-NTA.**Proteins from culture
supernatant (S), flow through (FT), wash (W) obtained by Ni-NTA affinity chromatography were separated by
SDS–PAGE. M protein markers (kDα). 10 nm, 10 mm, 50 mm, 200 mm, 500 mM means that we used the corresponding
concentration of imidazole to wash the column, and then washed the protein with low concentration [(10 mM) of
imidazole first, repeated three times, and then eluted the target protein with high concentration gradient (50
mM, 100mM, 200mM of imidazole)].

## pPIC9K-3Vg-His

To prevent His-tag from being masked, we moved it to the C-terminal and ran the experiment again. By
reviewing the literature, we learned that we could increase the expression level of our Vg by increasing the
copy number of the target gene, and therefore, we changed the original single-copy Vg to three
copies[7]. We also learned that, in addition to DDDDK being suitable for protein purification,
Asp-Pro is also a good choice and is more economical and efficient to use[8,9]. So our plasmid
design turns out to be like this (Figure 8).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-8.png)

**Figure 8. Visualization of the pPIC9K-3Vg-His for overexpression.**We were
able to send the newly designed plasmid to the company for synthesis, and this is our plasmid sequence (Figure 9
A). Then we carried out Sac I digestion (Figure 9 B). The digested plasmid was transferred into *Pichia
pastoris* by chemical transformation, and the obtained recombinant bacterial solution was diluted and
coated. The positive clones of GS115 strain 1,2,4,7,8,9 were selected and identified by PCR (Figure 9 C).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-9.png)

**Figure 9.** (A) the peak diagram of the recombinant plasmid pPIC9K-3Vg-His,
which is consistent with the theoretical sequence as shown in the peak diagram. (B) linearization of
pPIC9K-3Vg-His recombinant plasmid. The recombinant plasmid pPIC9K-3Vg-His was linearized at Sac l site. It
was identified that the cut band should run slowly and the intact plasmid ran ahead, which was consistent with
the theory. Lane Sac l was the experimental group of linearized pPIC9K-3Vg-His recombinant plasmid, and Vector
was the control group without restriction enzyme digestion. (C) Screening positive clones stably expressing
Vg. The extracted *Pichia pastoris* genomic DNA was used to judge whether *Pichia pastoris* expressed P37 with
primers upstream and downstream of the target fragment P37-F and P37-R, and the size was about 1,900 bp (Figure
5, C, Left). Using vector universal upstream primer 5, AOX-F and target gene downstream primer P37-R, one
primer carrier, one primer on the target gene, to determine whether the target gene is transferred to Pichia
pastoris, about 570 bp in size (Figure 5, C, Right). 1-9 is the recombinant GS115 sample expressed by Pichia
pastoris strain. G is the negative control of *Pichia pastoris* strain without transforming P37 plasmid.

After obtaining the validated positive clones, we induced the expression of the target protein by methanol,
and used Western blotting (Figure 10 A) and Coomassie Brilliant Blue staining (Figure 10 B) to detect expressed
recombinant protein in the supernatant. Unfortunately, we still have not successfully detected the target
band, considering that our recombinant protein may be too small molecular weight, not easy to express or
detect.

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-10.png)

**Figure 10.** (A) the expression of Vg was detected by Coomassie Brilliant
Blue staining. There was no significant difference between GS115 and the culture supernatant of experimental
group. (B) Vg expression was detected by Western blotting. The results of SDS-PAGE electrophoresis and His
antibody incubation showed that the recombinant plasmid was not expressed or detected in *Pichia pastoris*. His
was negative control group, 1-6 was recombinant Vg sample expressed by *Pichia pastoris* strain, G was negative
control group of *Pichia pastoris* strain without transforming Vg plasmid.

## pPIC9K-NK-3Vg-His

We found nattokinase (NK), an alkaline serine endopeptidase secreted by Bacillus subtilis (natto), has the
advantages of long half-life, high specificity, low side effects and direct oral administration with molecular
weight of 27.7 kd[10] . We added it to the original plasmid, which can compensate for the low
molecular weight defect of our target protein. At the same time, in the process of late purification, we can
collect Vg and NK, respectively, as our expression products (Figure 11).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-11.png)

**Figure 11. Visualization of the pPIC9K-NK-3Vg-His for overexpression.**

Then we carried out Sac I digestion (Figure 12 A). The digested plasmid was transferred into *Pichia
pastoris* by chemical transformation, and the obtained recombinant GS115 solution was diluted and
coated. The positive clones of GS115 strain 1,3,8 were selected and identified by PCR (Figure 12 B).

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-12.png)

**Figure 12.** (A) pPIC9K-3Vg-His recombinant plasmid linearization. The
recombinant plasmid pPIC9K-3Vg-His was linearized at Sac I site. It was identified that the cut band should run
slowly, and the intact plasmid ran ahead, which was consistent with the theory. Lane Sac I was the experimental
group of linearized pPIC9K-NK-3Vg-His recombinant plasmid, and Vector was the control group without restriction
enzyme digestion. (C) Screening positive clones stably expressing P37. The genomic DNA of *Pichia
pastoris* was extracted to examine the signature sequence with universal upstream primers (AOX-F and
AOX-R), and the size of product was about 1700 bp (b, upper panel). The PCR product amplificated by primers
Vg-F and AOX-R, was about 1300 bp in size (b, lower panel), which is indistinguishable from the expected
sample 1,3,8. 1-20 was the recombinant P37 expressed by *Pichia pastoris*.

After obtaining the validated positive clones, we induced the expression of the target protein by methanol,
and used Western blotting (Figure 13 A, B) to verify the successfully expressed recombinant protein in the
supernatant. Finally, we successfully constructed the GS115 expression system of Vg. The effect of P37
concentration is obvious, so we can extract P37 from large-scale induction culture.

![](https://static.igem.wiki/teams/5384/wet-lab/results/result-13.png)

**Figure 13.** (A) Detection of P37 expression by Western blotting. SDS-PAGE
electrophoresis and His-antibody incubation showed that there was a weak signal band between GS115 and the
supernatants of No. 1,3,4,5,7 in 50 kDα. 1-6 was recombinant P37 expressed by *Pichia pastoris* strain.
G was negative control group of *Pichia pastoris* strain without transforming P37 plasmid. (B) P37
expression was detected by Western blotting after concentration.

## Conclusion

Our team has noted that the mutation at positions 28 to 'A' holds significant potential. In the future, we
can further investigate these positions using synthetic biology methods to explore more theoretical mechanisms
of molecular binding. In selecting mutation strategies, our team utilized the local alignment algorithm of the
NCBI BLAST function. Subsequently, we can learn from the profile-HMM algorithm mentioned by the 2023 NUU-China
team, which will enable us to identify more proteins that have different structures but similar functions.
Through comprehensive analysis, we aim to uncover the essential mechanisms of the docking between Vg and
VDAC-1.

The aim of our project is to construct a microbial expression system that can efficiently express peptide Vg.
Our major achievements and findings include the successful construction of a Vg-expressing GS115 strain, which
contributes to the industrialization of Vg. Our achievements can enable the world's diabetes patients to enjoy
the Vg-related treatment, and improve their daily lives.

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