iGEM 2024 — Team tjusls-china

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{% block title %}Notebook{% endblock %}
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Notebook

| Click |
| --- |
| [Chen Kewei](https://static.igem.wiki/teams/5292/notebook/kewei-chen-notebooks.pdf) |
| [Li Jiening](https://static.igem.wiki/teams/5292/notebook/igem-notebook-lijiening.pdf) |
| [Liu Xingyu](https://static.igem.wiki/teams/5292/notebook/igem-liuxingyu-notebook.pdf) |
| [Ng Yujing](https://static.igem.wiki/teams/5292/notebook/2024-igem-notebook-ng-yu-jing.pdf) |
| [Sun Haoyue](https://static.igem.wiki/teams/5292/notebook/2024-igem-notebook-haoyue-sun.pdf) |
| [Tan Peiwei](https://static.igem.wiki/teams/5292/notebook/igem-notebook-tan-pei-wei.pdf) |
| [Wang Wenjie](https://static.igem.wiki/teams/5292/notebook/notebook-wenjie-wang.pdf) |
| [Wang Liangrui](https://static.igem.wiki/teams/5292/notebook/2024igem-notebook-liangruiwang.pdf) |
| [Wang Dong](https://static.igem.wiki/teams/5292/notebook/2024-igem-notebook-dongwang.pdf) |
| [Yang Zhuoran](https://static.igem.wiki/teams/5292/notebook/igem-notebook-zhuoran-yang.pdf) |

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one
iteration of the engineering design cycle. This achievement should be distinct from your Contribution for Bronze.{%
endblock %}
{% block page\_content %}

Engineering

# Overview

In our project, we optimized promoters, signal peptides, strains, and vectors through multiple rounds of
engineering cycles, ultimately successfully constructing a highly efficient system for ICCG expression.

![Link to example2](https://static.igem.wiki/teams/5292/engineering/ov.png)

## Chassis and Vector Screening

In the optimization of strains and vectors, we conducted one round of the "Design-Build-Test-Learn" cycle. We
screened the performance of different strains and vectors and selected the most suitable expression system.

## Culture Condition Screening

To further improve expression efficiency, we conducted two rounds of the "Design-Build-Test-Learn" cycle on
culture conditions. By adjusting the culture conditions, we optimized the expression levels of ICCG.

## Promoter Optimization

We performed three rounds of the "Design-Build-Test-Learn" cycle for promoter optimization. In the first round,
we generated the Mtac promoter library through random mutagenesis and trained a promoter prediction model using
the screening data. In the second round, we designed new promoter mutants based on the model, validated their
expression efficiency through experiments, and used the results to optimize the model. In the third round, we
further refined the model, designed and tested more mutants, and eventually obtained a highly efficient promoter
sequence and an optimized prediction model, providing a solid foundation for ICCG expression.

## Signal Peptide Optimization

The optimization of signal peptides also went through three rounds of the "Design-Build-Test-Learn" cycle. By
improving the signal peptide sequences and combining experimental data, we gradually enhanced the efficiency of
signal peptides in ICCG expression. Ultimately, we built a signal peptide design model and obtained an optimized
signal peptide sequence.

Through these engineering cycles, we not only successfully optimized the promoters and signal peptides but also
constructed a highly efficient ICCG expression system, providing a solid foundation for future expression studies.

# Engineering 1 - Chassis and Vector Screening

## Cycle 1.1

### Design

We selected four common Escherichia coli protein expression host strains and eight commonly used expression
plasmids to conduct a univariate experiment.

Strain Table

| Strain | Source | Genotype | Description | Antibiotic Resistance |
| --- | --- | --- | --- | --- |
| BL21(DE3) | B | F– ompT hsdSB (rB–mB–) gal dcm (DE3) | Common expression host strain | None |
| Solu BL21(DE3) | B | F– ompT hsdSB (rB–mB–) gal dcm (DE3) | Enhances the solubility of target proteins, improving the solubility of many recombinant proteins | None |
| BL21(DE3)pLysS | B | F– ompT hsdSB (rB–mB–) gal dcm (DE3)pLysS(CmR) | Tight control expression host strain | Chloramphenicol |
| Origami2(DE3) | K-12 | F− λ− ilvG− rfb-50 rph-1 Δgor ΔtrxB lacY1 ompT hsdS(rB− mB−) gal dcm lacI(DE3) | An E. coli strain specialized for enhancing disulfide bond formation within the cell. Provides an oxidative environment for proper folding of disulfide bond-containing proteins. | Tetracycline, Streptomycin |

Vectors：

| Vectors |
| --- |
| pET-22b(+) |
| pET-26b(+) |
| pET-28a(+) |
| pET-28a-SUMO |
| pET-32a(+) |
| pGEX-6p-1 |
| pMAT9-SUMO |
| pCold-TF |

### Build & Test

The ICCG-GFP fusion protein gene (BBa\_K5292405) was constructed into eight different vectors, and the
successfully
constructed recombinant plasmids were respectively transformed into the BL21 (DE3) strain. After screening through
a
high-throughput screening system, the optimal vector was determined to be pET-26b(+).

The pET-26b-ICCG-GFP recombinant plasmid was then transformed into four different host strains. Based on a
comprehensive consideration of the screening data and culture efficiency (induction time, induction temperature,
etc.), we selected the BL21 (DE3) strain as the chassis for subsequent mutant screening.

For detailed experimental data, please refer to [Results](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27Results%27%29%20%7D%7D)
page, and for the high-throughput screening experiment protocol, refer to the [Experiments](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27Experiments%27%29%20%7D%7D) page. The same applies to subsequent sections.

### Learn

After this round of chassis and vector screening, we have determined that the chassis for subsequent experiments
will be BL21 (DE3) and the vector will be pET-26b(+).

# Engineering 2 - Culture Condition Optimization

## Cycle 2.1

### Design

We optimized the culture conditions (induction duration, induction temperature, and inducer concentration) by
designing an orthogonal experiment. The experimental conditions are shown in the table below:

Table 1 Cycle 2.1 Orthogonal Experiment Table

|  | induction temperature/℃ | | | induction duration/h | | | inducer concentration/mM | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 16 | 25 | 30 | 8 | 12 | 16 | 0.2 | 0.5 | 1 |
| 1 |  | + |  |  | + |  | + |  |  |
| 2 |  | + |  |  | + |  |  | + |  |
| 3 |  | + |  |  | + |  |  |  | + |
| 4 |  | + |  | + |  |  |  | + |  |
| 5 |  | + |  |  |  | + |  | + |  |
| 6 | + |  |  |  | + |  |  | + |  |
| 7 |  |  | + |  | + |  |  | + |  |

### Build & Test

We constructed a high-throughput screening system and characterized protein expression levels using fluorescence
intensity.

### Learn

Based on the characterization results, we designed the second round of orthogonal experiments.

## Cycle 2.2

### Design

We designed orthogonal tests to optimise the culture conditions (induction duration, induction temperature,
inducer
concentration) and the experimental conditions are shown in the table below:

Table 2 Cycle 2.2 Orthogonal Experiment Table

|  | induction temperature/℃ | | | induction duration/h | | | inducer concentration/mM | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 16 | 25 | 30 | 12 | 16 | 24 | 0.5 | 1 | 1.5 |
| 1 |  | + |  |  | + |  | + |  |  |
| 2 |  | + |  |  | + |  |  | + |  |
| 3 |  | + |  |  | + |  |  |  | + |
| 4 |  | + |  | + |  |  |  | + |  |
| 5 |  | + |  |  |  | + |  | + |  |
| 6 | + |  |  |  | + |  |  | + |  |
| 7 |  |  | + |  | + |  |  | + |  |

### Build & Test

We established a high-throughput screening system and characterized protein expression levels using fluorescence
intensity.

### Learn

After the second round of orthogonal experiments, we determined the culture conditions as follows: induction
temperature of 25°C, induction time of 12 hours, and final IPTG concentration of 1mM.

# Engineering 3 - Promoter Optimization

## Cycle 3.1

### Design

In order to construct the CNN-LSTM model (Convolutional Neural Network - Long Short-Term Memory Network) to
predict tac promoter mutants, we first needed to perform random mutagenesis on the tac promoter. Based on several
literature reports, we selected the 16bp region between the -10 and -35 elements for mutagenesis. The mutant tac
promoters were designed as shown in the figure below, where the red 16bp represents the mutation region, and the
blue regions represent the fixed 15bp sequences flanking it.

![Link to example2](https://static.igem.wiki/teams/5292/engineering/31.png)

Table.1 Mutation sequence design

### Build & Test

To construct the Mtac promoter mutants, we used a random mutagenesis approach targeting the 16 bp sequence
between the -35 and -10 regions of the tac promoter, a critical area for RNA polymerase binding and transcription
initiation. We designed a pair of degenerate primers, with the mutation region encoded by 16 consecutive N
nucleotides,
to introduce diversity into this sequence, generating a series of different promoter mutants.

We used PCR amplification to obtain a linearized vector and seamless cloning to obtain recombinant plasmids
carrying the mutated tac promoters. The mixture of plasmids generated by random mutagenesis was then transformed
into
the expression strain BL21 (DE3). After expression screening through a high-throughput screening system, we
selected
samples for sequencing of the mutated regions, resulting in the identification of the Mtac sequence.

### Learn

After obtaining the dataset of tac mutant sequences and their expression levels from the experiment, we
constructed
a CNN-LSTM model to serve as a predictor for promoter strength.

## Cycle 3.2

### Design

After constructing the predictor, we used a GA (Genetic Algorithm) to design a generator, integrating the
CNN-LSTM
predictor as the evaluation module for the genetic algorithm. The previously obtained tac mutant sequences were
selected as the initial population, and through operations such as crossover and mutation in the genetic
algorithm,
along with evaluation by the predictor, a new set of tac mutant promoters was designed based on the initial tac
sequences.
These promoters included both high-performing and low-performing variants, with the purpose of validating our
model.

### Build & Test

To construct the DMtac mutants, we designed single-point mutation primers and performed PCR amplification on the
template to obtain a linearized vector. Seamless cloning technology was used to obtain recombinant plasmids
carrying
the DMtac mutants. The recombinant plasmids were then transformed into DH5α for plasmid amplification, followed by
plasmid sequencing to confirm the correctly constructed recombinant plasmids.

To characterize the activity of the DMtac promoters, we constructed an ICCG-GFP fusion protein, using
fluorescence
intensity as a quantitative measure of expression. The following steps were taken to characterize the mutants:

The constructed DMtac promoter mutant plasmids were transformed into Escherichia coli BL21 (DE3) expression
strains.
The transformed strains were cultured in a high-throughput screening system, and the expression levels of
different
mutants were compared by measuring fluorescence/OD600.

### Learn

The experimental results showed that while our GA generator had a certain level of accuracy, it was still not
satisfactory and lacked interpretability. Therefore, we decided to modify the generator.

## Cycle 3.3

### Design

The experimental results showed that although our GA generator had a certain level of accuracy, it was still not
satisfactory and lacked interpretability. Therefore, we replaced the generator with a statistical model based on
binomial distribution, as the accuracy of this statistical model has been validated in several studies. Through
operations such as confidence intervals and hypothesis testing, we identified a series of high-frequency
combinations
of strong promoter site-base pairs. Clearly, the statistical model offers higher interpretability.

![Link to example2](https://static.igem.wiki/teams/5292/engineering/33.png)

Table.2 Mutation sequence design by binomial statistical model

### Build & Test

Same as Cycle 3.2.

### Learn

The expression level of the mutants generated using the binomial statistical model was ultimately increased to
108%
of the tac promoter in the experiment. Considering that the predictor used Z-score normalization to avoid the
influence
of extreme values, the range of expression levels it can predict is relatively conservative, making it unable to
predict
mutants with significantly higher expression than the tac promoter. Additionally, we unexpectedly discovered that
our
model predicted a hidden outlier. These results are consistent with expectations and are quite encouraging.

# Engineering 4 - Signal Peptide Optimization

## Cycle 4.1

### Design

In this experiment, structural and physicochemical features of the N-region, H-region, and C-region were
extracted.
Based on the RF (Random Forest) regression training model developed by Stefano Grasso et al. in 2023, which has
high
evaluation accuracy for Gram-positive bacterial signal peptides, we scored and screened Escherichia coli signal
peptides
from the signal peptide library. The selected high-scoring signal peptides were then constructed and expressed
with
ICCG.

### Build & Test

We used PCR amplification to obtain a linearized vector and seamless cloning to obtain recombinant plasmids
carrying the signal peptide. The recombinant plasmids were then transformed into the *Escherichia coli*
strain
*DH5α*. We then sequenced the amplified recombinant plasmid and obtained the correctly constructed
recombinant
plasmid.
To characterize the activity of the signal peptide, we constructed an ICCG-GFP fusion protein, using fluorescence
intensity as a quantitative measure of expression.

### Learn

We obtained a series of signal peptide sequences and their corresponding expression levels based on feature
values.
Among them, the signal peptide \*\*nfaA\*\*, which we predicted to have the best expression, indeed showed the highest
expression in the experiment. Although the overall accuracy was somewhat unsatisfactory due to poor mobility, the
model
still provided valuable guidance for the experiment.

## Cycle 4.2

### Design

A comprehensive scoring matrix was constructed based on the standard amino acid Blosum 62 substitution matrix and
hydrophobicity matrix. Using the well-performing signal peptide nfaA from the previous round, we applied the
Markov
transfer frequency matrix to build a generator based on a Hidden Markov Model (HMM). This generator was used to
modify
the H-region of the signal peptide, generating artificially designed and optimized mutant signal peptides.

![Link to example2](https://static.igem.wiki/teams/5292/engineering/421.png)

Table 1. Comprehensive score matrix for amino acid substitutions

![Link to example2](https://static.igem.wiki/teams/5292/engineering/422.png)

Table 2.Markov transfer frequency matrix

### Build & Test

We used PCR amplification to obtain a linearized vector and seamless cloning to obtain recombinant plasmids
carrying
the mutated nfaA signal peptide. The recombinant plasmids were then transformed into the *Escherichia coli*
strain
*DH5α*. We then sequenced the amplified recombinant plasmid and obtained the correctly constructed
recombinant
plasmid.

To characterize the activity of the MnfaA signal peptide, we constructed an ICCG-GFP fusion protein, using
fluorescence intensity as a quantitative measure of expression.

### Learn

We realized that relying solely on the Hidden Markov generator to modify signal peptides is insufficient; it is
still
necessary to screen them through a predictor.

## Cycle 4.3

### Design

We generated a large number of mutant signal peptides using the Hidden Markov Model and evaluated them with the
previous prediction model. We then selected mutant signal peptides that performed better than the original nfaA
for
experimentation.

### Build & Test

Same as Cycle 4.2.

### Learn

We once again confirmed that the model's transferability from Gram-positive bacteria to Gram-negative bacteria is
not strong, so adjustments to the model are needed. Fortunately, we have obtained sufficient data from two rounds
of
testing to support the next step of model fine-tuning.

We trained our own Random Forest model using the large dataset provided by Stefano Grasso et al., and then
integrated
the aforementioned data to build a new small dataset. Using this small dataset, we fine-tuned the larger model to
design a predictor more suited to the type of signal peptides used in our experiments—specifically, those better
suited
for Gram-negative bacteria. After applying transfer learning, we obtained a model that can accurately predict the
protein
secretion capacity of Gram-negative bacteria. When we evaluated previous signal peptides using the new model, the
results
showed a satisfactory fit. For a more detailed introduction to the signal peptide models in the three design
sections
mentioned above, please refer to our [Model page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27Model%27%29%20%7D%7D).

# References

1. Zhang, S., Liu, D., Mao, Z., Mao, Y., Ma, H., Chen, T., Zhao, X., & Wang, Z. (2018). Model-based
reconstruction
of synthetic promoter library in Corynebacterium glutamicum. Biotechnology Letters, 40(5), 819–827.

2. Van Brempt, M., Clauwaert, J., Mey, F., Stock, M., Maertens, J., Waegeman, W., & De Mey, M. (2020). Predictive
design of sigma factor-specific promoters. Nature Communications, 11, 5822.

3. Li, Z.-J., Zhang, Z.-X., Xu, Y., Shi, T.-Q., Ye, C., Sun, X.-M., & Huang, H. (2022). CRISPR-based construction
of
a BL21 (DE3)-derived variant strain library to rapidly improve recombinant protein production. ACS Synthetic
Biology,
11(1), 343-352.

4. Cui-Fang G, Sen W, Feng-Wei T, et al. An Artificial Design Technique to Optimize Signal Peptide [J]. American
Journal of Biochemistry and Biotechnology, 2017, 13(3).

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
{% block page\_content %}

Experiments

# Overview

TJUSLS-China iGEM team is dedicated to addressing the plastic pollution crisis. Based on previous successes,
we
are deploying a series of strategies to enhance the production of PETase (variant: LCC-ICCG). We conducted
extensive literature research and followed the iGEM engineering cycle (Design, Build, Test, Learn) to form a
full
workflow of plasmid construction and random mutation through PCR; followed by protein fluorescence
quantitation
and final high through-put screening and mutant design.

To start with, the pET26b-ICCG (without pelB signal peptide) was chosen to be a reform target and that is
constructed the GFP fusion protein. Moreover, we also tested with variety of vectors (pET22b, pET28a, pET28as,
pET32a, pMAT9s, pGEX-6p-1, and pCold-TF) and E. coli expression systems (BL21(DE3), BL21(DE3) pLysS,
SoluBL21(DE3), Origami2) to enhance the protein yield. Better parameterized signal peptides were obtained by
model
prediction of the signal peptide database. A high-throughput screen on expression levels following plasmid
construction revealed the best nfaA signal peptide and further design optimized by computational modeling and
empirical validation. Additionally, we modified the T7 promoter and introduced random mutations into the tac
promoter via PCR. The experimental data from wet lab work informed our models, allowing us to refine and
complete
the engineering cycle.

Our efforts have yielded promising results, and we are now pursuing further exploration toward
industrial-scale
fermentation. Our ultimate goal is to increase the production of highly active PETase, contributing to the
fight
against plastic waste. We remain committed to discovering new possibilities for this solution in the future.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one
iteration of the engineering design cycle. This achievement should be distinct from your Contribution for Bronze.{%
endblock %}
{% block page\_content %}

Results

## Overview

In this project, through multiple rounds of engineering cycles, we optimized the promoter, signal peptide, and
culture conditions, successfully constructing a highly efficient ICCG expression system. First, we created an
ICCG-GFP fusion protein and used fluorescence intensity to characterize ICCG protein expression levels. During
promoter optimization, we generated a Mtac promoter library through random mutagenesis and used a CNN-LSTM model
to design and optimize the novel DMtac promoter. For the signal peptide, we screened and optimized the nfaA signal
peptide and designed MnfaA mutants using a Markov transfer frequency matrix model. By combining high-throughput
screening systems with predictive models, we significantly improved the expression efficiency of ICCG, providing
valuable components and tools for future synthetic biology research.

## 1. Establishment of a High-Throughput Screening System

### 1.1 Construction of Fusion Protein

We used seamless cloning to attach the GFP protein to the C-terminus of ICCG through a GS linker.

![PCR Amplification Results](https://static.igem.wiki/teams/5292/result/11.png)

Figure 1.1 PCR Amplification Results. According to the agarose gel electrophoresis
results, the
PCR amplification yielded fragments of the expected length.

Through shake flask cultivation and protein purification, we obtained ICCG-GFP fusion protein samples.

![SDS-PAGE Results of Purified Protein Products](https://static.igem.wiki/teams/5292/result/12.png)

The above results indicate that we have successfully
constructed the correct ICCG-GFP fusion protein.

### 1.2 Construction of a High-Throughput Screening System for Protein Yield Characterization Based on Fluorescence Intensity

#### 1.2.1 Screening Results of Culture Conditions

After two rounds of orthogonal experiments, considering that increasing inducer concentration and extending
induction time would raise protein production costs (which is unfavorable for large-scale production of ICCG), we
selected an induction temperature of 25°C, an induction time of 12 hours, and a final IPTG concentration of 1 mM
as the culture conditions for our subsequent high-throughput screening system.

#### 1.2.2 Creation of the Fluorescence Intensity-Protein Concentration Standard Curve

After purifying the ICCG-GFP fusion protein, we measured the fluorescence intensity of protein samples at final
concentrations of 0 μg/mL, 2 μg/mL, 4 μg/mL, 8 μg/mL, 16 μg/mL, 32 μg/mL, and 64 μg/mL under the conditions of an
excitation wavelength of 488 nm and an emission wavelength of 507 nm. The resulting fluorescence intensity-protein
concentration standard curve is shown below:

![Fluorescence Intensity-Protein Concentration Standard Curve](https://static.igem.wiki/teams/5292/result/13.png)

Figure 1.3 Fluorescence Intensity-Protein Concentration Standard Curve

## 2. Screening of Strain and Vector

We characterized the expression efficiency of four strains and eight vectors and the results are as follows:

![Strain and Vector Screening Results](https://static.igem.wiki/teams/5292/result/21.png)

Figure 2.1 Strain and Vector Screening Results

In the end, we selected \*Escherichia coli\* BL21(DE3) as the chassis and pET-26b(+) as the vector to construct the
high-throughput screening system.

## 3. Promoter Optimization

### 3.1 Construction and Characterization of the Wild-Type tac Promoter

We constructed the tac promoter in the pET26b vector carrying the ICCG-GFP fusion protein gene using seamless
cloning and characterized its activity.

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/31.png)

Figure 3.1 Agarose Gel Electrophoresis Results of tac Promoter Construction

![Characterization Results of tac Promoter](https://static.igem.wiki/teams/5292/result/32.png)

Figure 3.2 Characterization Results of tac Promoter

Compared to the T7 promoter, the tac promoter increased ICCG-GFP expression by 37.94%.

### 3.2 Construction and Characterization of Mtac

To construct the Mtac promoter mutants, we employed a random mutagenesis approach targeting the 16 bp sequence
between the -35 and -10 regions of the tac promoter. We designed a pair of degenerate primers with the mutation
region consisting of 16 consecutive N nucleotides to introduce diversity into that sequence, resulting in a series
of different promoter mutants. We used PCR amplification to obtain a linearized vector and generated recombinant
plasmids through seamless cloning. A total of 12 PCR amplification reactions were conducted, yielding 160 µL of
recombinant product.

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/33.png)

Figure 3.3 Agarose Gel Electrophoresis Results

We picked a total of 1,037 single clones from 16 plate cultures for trial expression in the high-throughput
screening system. Based on the fluorescence intensity of the mutant proteins, we selected samples with a uniform
distribution of data and those with fluorescence intensity higher than that of the wild-type tac promoter for
sequencing, resulting in 88 Mtac promoter data points.

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/mtac-result.png)

Figure 3.4 Normalized Fluorescence of Mtac Mutants

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/mnfaa.png)

The obtained mutant sequences are as follows:

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/mtac513.png)

Figure 3.5Mtac513

Compared to the wild-type tac promoter, Mtac662 increased ICCG-GFP expression by 36.84% and by 77.68% compared to
the T7 promoter.

## 3.3 Construction and Characterization of DMtac

We generated the Mtac promoter library through random mutagenesis, and by combining experimental screening data,
we trained a promoter prediction model. Based on this machine learning model, we designed the DMtac promoter,
predicting mutants that could potentially enhance ICCG expression efficiency. Using seamless cloning, we
constructed recombinant plasmids and successfully obtained correctly constructed recombinant plasmids. The trial
expression results are as follows:

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/dmtac-result.png)

Figure 3.6 Normalized Fluorescence of DMtac Mutants

Compared to the original tac promoter, the DMtac promoter significantly improved expression efficiency.
Fluorescence/OD600 data indicated that ICCG expression increased by 8.2% under the DMtac promoter. Compared to the
T7 promoter, DMtac increased ICCG expression by 30%.

## 3.4 Promoter Design and Prediction Model

Given the limited amount of data, we used various regularization techniques (e.g., Dropout, Batch Normalization,
and Early Stopping) to prevent overfitting and conducted multiple training sessions to find the optimal model.

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/37.png)

Figure 3.7 Model Training Results

Final Model Performance on the test set:

* R² = 0.68
* MAE = 0.21
* PCC = 0.85

These results significantly outperform the predictor reported by Wang et al. in Nucleic Acids Research in 2018
(PCC = 0.514).

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/38.png)

Figure 3.8 Heatmap of the Binomial Statistical Mode

The mutants generated using the Binomial Statistical model ultimately demonstrated an expression level of 108%
compared to the tac promoter in our experiments. It's important to note that the predictor utilized Z-score
normalization to reduce the impact of extreme values, resulting in a relatively conservative predicted expression
range. This approach made it less likely for the model to forecast mutants with expression levels significantly
higher than that of the tac promoter. Nonetheless, the outcome meets our expectations and is very promising.

One specific data point stood out to us: a mutated tac promoter with an expression level exceeding 40,000 was
consistently predicted by our model to be around 30,000 during several training sessions. Initially, we suspected
this discrepancy was due to poor model fitting. However, in the final round of experimental validation, the
protein expression level for this promoter was measured at 30,279, confirming that the previous value of over
40,000 was indeed erroneous. This result closely aligns with our model's prediction of 30,000, indicating that our
model successfully identified a hidden data anomaly! This finding is undoubtedly exciting and further validates
the effectiveness of our model. Details of this part can be found in the Model section.

## 4. Signal Peptide Optimization

### 4.1 Construction and Characterization of 40 Signal Peptides

We constructed 40 signal peptides into the pET26b vector carrying the ICCG-GFP fusion protein gene using seamless
cloning, and we characterized their activity.

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/41.png)

Figure 4.1 Signal Peptide Characterization Results

The characterization results of the signal peptides confirmed the model's prediction for the nfaA signal peptide:
nfaA was the best-performing signal peptide during the characterization process. Although the secretion efficiency
of these signal peptides did not significantly improve in the experiments, they provided valuable reference data
for further optimizing PETase expression.

### 4.2 Construction and Characterization of MnfaA

The experimental data showed that while the MnfaA signal peptide did slightly enhance ICCG secretion, the
secretion level did not achieve the expected significant improvement. Compared to the control group, the
fluorescence signal exhibited only minor changes, indicating that MnfaA's effect on improving ICCG secretion
efficiency was limited.

![Figure 4.2 Normalized fluorescence intensity characterization of the MnfaA mutants.](https://static.igem.wiki/teams/5292/result/update.webp)

Figure 4.2 Normalized fluorescence intensity characterization of the MnfaA mutants.

This suggests that MnfaA may require further optimization, particularly in the context of the *Escherichia
coli* expression system, where unforeseen factors may be limiting its efficiency.

### 4.3 Discussion of Experimental Results and Model Optimization

The final training outcomes of the dataset are as follows:

* Mean Absolute Error (MAE): 0.2627
* Root Mean Square Error (RMSE): 0.3143
* Coefficient of Determination (R²): 0.6061
* Pearson Correlation Coefficient (PCC): 0.8275

![Agarose Gel Electrophoresis Results of tac Promoter Construction](https://static.igem.wiki/teams/5292/result/43.png)

Picture 4.3 Fitted curve optimized with the new dataset

These metrics indicate that the model exhibits both high accuracy and predictive capability. The low MAE and RMSE
values suggest that the model effectively minimizes prediction errors, while the relatively high R² demonstrates
its substantial capacity to explain the variance within the data. Additionally, the Pearson Correlation
Coefficient of 0.8275 indicates a strong positive correlation between the predicted and actual values. Taken
together, these results validate the model as a robust tool for predicting *Escherichia coli* protein
secretion efficiency.

This innovative approach significantly advances a relatively underexplored field, opening new avenues for the
study and development of *Escherichia coli* protein secretion models while offering valuable insights for
future research.

## Summary and Discussion of Project Results

Throughout the project, we successfully optimized several key components, including promoters, signal peptides,
and culture conditions, to significantly enhance ICCG expression. Each phase of the project yielded important
results, contributing to the overall success of the system.

### Promoter Optimization

Using random mutagenesis, we generated the Mtac promoter library and identified mutants that significantly
improved expression levels compared to the wild-type tac promoter. By integrating machine learning models, such as
CNN-LSTM and binomial statistical models, we further optimized the promoter sequences, leading to the development
of the DMtac series. These optimized promoters demonstrated higher expression levels, validating the effectiveness
of our approach.

### Signal Peptide Optimization

In the signal peptide optimization process, the nfaA peptide was identified as the top performer from a library
of \*E. coli\* signal peptides. Further refinement using Markov transfer frequency matrix generated MnfaA mutants,
which were experimentally tested for their impact on protein secretion. Although improvements in secretion
efficiency were observed, some mutants did not meet the expected results, highlighting areas for further
optimization.

### High-Throughput Screening and Expression System

We successfully constructed a high-throughput screening system, using fluorescence intensity to quantify ICCG
expression levels via ICCG-GFP fusion proteins. This system allowed for rapid screening of various genetic
constructs, facilitating efficient identification of high-performing variants.

### Culture Condition Optimization

Two rounds of orthogonal experiments were conducted to optimize the induction temperature, time, and IPTG
concentration. The optimal conditions—25°C, 12-hour induction, and 1mM IPTG—were identified as the most suitable
for ICCG expression, balancing production cost and efficiency.

## Discussion

Our results indicate that combining random mutagenesis with machine learning models can lead to significant
improvements in gene expression. The DMtac promoter series and optimized signal peptides present new opportunities
for enhancing protein production in synthetic biology. However, while we achieved higher expression levels and
improved secretion efficiency, some of the experimental results did not fully align with model predictions,
particularly in the case of signal peptide mutations. This highlights the need for further refinement of
predictive models and optimization of genetic components.

Overall, the project successfully demonstrated the potential of integrating experimental and computational
approaches to optimize genetic elements, providing a robust framework for future research in protein expression
and synthetic biology.

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