iGEM 2025 — Team tji-seoul

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{% block title %}Notebook{% endblock %}
{% block lead %}A Transparent Record of Our Scientific Journey{% endblock %}
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![notebook Header](https://static.igem.wiki/teams/5909/4labnotebook/header-notebook.webp)

## Notebook

Laboratory notes are an essential part of any iGEM project. They serve not only as a record of what we did in the lab but also as a reference for others who may want to repeat, build upon, or evaluate our work. According to the **iGEM 2025 official guidelines**, good documentation should be clear, organized, and reproducible. This means that every experiment should include its purpose, materials, methods, results, and reflections on what was learned.

For our team, the lab note acted as the **central record of experimental progress**. Every member contributed by documenting their own work immediately after each session. Notes were written in detail, including all steps, concentrations, incubation times, and any unexpected results or troubleshooting we encountered. By doing this consistently, we created a transparent record that allows others to understand not only what worked but also what challenges we faced and how we overcame them.

## Organization of Our Lab Notes

We organized our lab notes into **three main experimental phases**:

* **Molecular Biology and Cloning** (pipetting accuracy, plasmid transformation, plasmid extraction)
* **Protein Expression and Characterization** (colony selection, induction, SDS-PAGE analysis)
* **Protein Quantification and Binding Assays** (BCA assay, refolding of MHC-peptide complexes, fluorescence detection for peptide binding)

Each entry contains:

1. **Objective** -- why the experiment was performed.
2. **Materials and Methods** -- the reagents and step-by-step procedures used.
3. **Results** -- observations, gel images, or quantification data.
4. **Purpose and Learning** -- how the results connected back to our project goals.

All notes were consolidated into a **single digital document**, which we updated weekly. This ensured consistency and allowed us to track progress over time. The final lab note has been uploaded here as a PDF for full transparency.

## Accessing the Lab Note

View our complete lab note below — flip through pages using the arrows.

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[Download Full PDF](%7B%7B%20url_for%28%27static%27%2C%20filename%3D%27assets/4%29lab_notebook/Lab%20Note.pdf%27%29%20%7D%7D)

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{% block title %}Engineering Success{% endblock %}
{% block lead %}Engineering in iGEM is not just about having an idea --- it's about making that idea work in the lab through iteration. In our project, we faced multiple challenges, and solving them through the Design--Build--Test--Learn cycle was key to making real progress.{% endblock %}
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![engineering success Header](https://static.igem.wiki/teams/5909/3projectengineeringsuccess/header-engineering-success.webp)

Engineering in iGEM is not just about having an idea --- it's about making that idea work in the lab through **iteration**. iGEM encourages teams to show how they applied the **Design--Build--Test--Learn (DBTL) cycle**: identifying problems, designing possible solutions, testing them, and learning from the results. Our project followed this principle closely. Each time we encountered a challenge, we treated it not as a failure, but as feedback. This way, we could keep moving forward, refining our system step by step.

![The Design--Build--Test--Learn cycle we applied to our project](https://static.igem.wiki/teams/5909/3projectengineeringsuccess/figure-engineering-success-1.webp)

*Figure 1: The Design--Build--Test--Learn cycle we applied to our project. Each engineering challenge followed this iterative framework.*

At the start, our designs were simple: clone the gene, express the protein, and test its function. But reality is never that straightforward. Protein yields were lower than expected, expression sometimes stressed the bacteria, and assembling functional MHC complexes required more than just following textbook protocols. Each time, we went back to the DBTL cycle --- redesigning parts of the workflow, changing experimental conditions, or rethinking our approach entirely.

We want to highlight two important things about our engineering journey:

1. **Iteration matters more than perfection.** Our first attempts rarely worked as planned, but each attempt gave us data to improve the next step.
2. **Decisions were guided by measurable outcomes.** Whether it was SDS-PAGE bands, BCA assay readouts, or fluorescence signals, we always tested our assumptions against real results.

This page documents how we turned setbacks into progress. From rescuing low protein expression by testing multiple colonies, to improving antigen detection by building MHC tetramers instead of relying on monomers, each engineering step reflects the DBTL cycle in action. Our experience shows that even as high school students, with systematic problem-solving, we can engineer complex biological systems in ways that align with professional research practices.

## Challenge 1: Low Protein Expression in E. coli

When we first tried expressing the HLA-A\*02:01 heavy chain in *E. coli*, the outcome was disappointing. Colonies that carried our plasmid grew more slowly than controls, and protein yields were very low. On SDS-PAGE, the heavy chain band appeared weak and irregular, instead of the clear bands we expected. This suggested two possibilities: either the protein was not being expressed efficiently, or the expression burden was toxic to the bacteria (Rosano & Ceccarelli, 2014).

This became our first major engineering obstacle. Without enough protein, purification, refolding, and binding assays would not be possible.

#### Iterative Solution: Splitting Colonies and Selecting Survivors

Instead of repeating the same culture from a single colony, we used the **Design--Build--Test--Learn (DBTL)** cycle to approach the problem systematically.

* **Design**: Hypothesis --- different colonies from the same plate might vary in how well they tolerated heavy chain expression.
* **Build**: We inoculated multiple flasks with different colonies, rather than relying on just one.
* **Test**: Growth curves (OD₆₀₀) were measured for each flask. Some showed slow growth and high stress, while others grew more steadily.
* **Learn**: Colonies differed significantly in their ability to express the protein. By scaling up the healthier colonies, we obtained higher protein yields.

This adjustment turned a stalled experiment into usable protein production. It was our first clear success in applying engineering principles to a real biological problem.

![By iterating the engineering cycle, healthier colonies were selected to enhance protein yield](https://static.igem.wiki/teams/5909/3projectengineeringsuccess/figure-engineering-success-2-new.webp)

*Figure 2: By iterating the engineering cycle, healthier colonies were selected to enhance protein yield.*

## Challenge 2: From Monomers to Tetramers

Once we successfully purified the heavy chain and β2-microglobulin, we faced a decision. Should we test antigen binding with monomeric MHC--peptide complexes, or go further and form tetramers?

We chose the more ambitious option: **tetramer formation**. This required extra steps, but the benefits outweighed the effort.

* **Higher Sensitivity**: Monomers bind T-cell receptors very weakly and transiently. Tetramers, made by linking four biotinylated MHC molecules to streptavidin, have much stronger avidity (Altman et al., 1996). This makes binding more detectable and reliable.
* **Established Method in Immunology**: MHC tetramers are widely used in immunology labs to identify antigen-specific T cells (Davis & Bjorkman, 1989). By following this path, our project aligned with professional standards.
* **Better Testing of Engineering Impact**: Since our goal was to improve antigen delivery, we needed a system sensitive enough to reveal differences between wild-type and engineered MHC molecules. Tetramers gave us that level of resolution, while monomers might not have.

Choosing tetramers was not the easiest route, but it represented a deliberate engineering choice: building a more advanced testing system so our results could be meaningful and trusted.

## Challenge 3: Measuring Binding Affinity

The next step was to confirm whether our engineered MHC molecules showed improved peptide presentation compared to wild-type. For this, we needed a **quantitative and reproducible assay**.

We selected a **fluorescence-based plate assay**, similar in principle to ELISA (Crowther, 2000). In our setup:

* MHC tetramers carrying tumor-specific peptides were immobilized on a nickel-coated plate.
* Detection reagents, such as streptavidin--fluorophore conjugates, were used to measure binding strength.
* Fluorescence intensity was measured using a microplate reader, with stronger signals corresponding to stronger or more stable peptide binding.

This method was ideal for two reasons:

1. It provided a **direct quantitative measurement** to compare wild-type vs. engineered variants.
2. It was **accessible and reproducible** for a high school team, while still reflecting how professional labs validate protein interactions.

Integrating this assay into our DBTL cycle ensured that our engineering decisions were backed by measurable outcomes, not just assumptions.

![Binding affinity analysis set-up](https://static.igem.wiki/teams/5909/3projectengineeringsuccess/figure-engineering-success-3.webp)

*Figure 3. Binding affinity analysis set-up*

## Integration of Data and Iteration

At the heart of our engineering workflow was a continuous data-driven feedback loop that connected every stage of the Design–Build–Test–Learn cycle. Each experimental outcome—whether a gel band, absorbance value, or fluorescence signal—was treated as both a result and a guidepost for the next iteration.

Our approach began with SDS-PAGE analysis, which revealed irregular band patterns and inconsistent expression levels across colonies. Rather than dismissing this as experimental noise, we treated the heterogeneity as meaningful data. The observation that some colonies produced sharper, more defined bands prompted us to investigate colony-level expression variability—a decision that ultimately doubled our usable protein yield. This exemplified the first layer of iteration: moving from observational data to informed selection.

Next, the BCA protein quantification assay (Smith et al., 1985) transformed these qualitative improvements into quantitative insight. By establishing a standard curve and measuring absorbance at 562 nm, we could normalize protein concentrations across different preparations. This normalization was critical for downstream comparability. Without equalized inputs, interpreting differences in peptide-binding strength between wild-type and mutant HLA-A\*02:01 would have been unreliable. Quantification ensured that every subsequent measurement reflected genuine biochemical behavior, not uneven sample loading.

In the binding affinity experiments, fluorescence-based assays provided the most direct evidence that our engineering was effective. The data revealed that Mutant 1 (W167A) maintained a stable binding signal over time, while Mutant 2 (Y7A/Y99A/Y159A/Y171A) exhibited broader signal variability—suggesting that the second set of mutations increased flexibility at the expense of stability. These results, though subtle, were vital in confirming that our computationally suggested modifications were biochemically relevant.

Crucially, these quantitative datasets were not analyzed in isolation. They were systematically compared across cycles to guide subsequent design decisions. Each “Learn” phase produced a distinct hypothesis, which then re-entered the “Design” phase as an improved construct or refined assay condition. For example, the misfolding identified in SDS-PAGE prompted a redesign of expression conditions; the protein quantification results informed how much sample to use per binding test; and the fluorescence data indicated which mutant should be prioritized for extended peptide docking simulations.

Through this cyclical integration of data and learning, our project advanced from uncertain early-stage expression to reproducible, quantitative measurement of engineered antigen presentation. This tight coupling between measurement and design exemplifies what iGEM defines as Engineering Success: reproducible, data-informed iteration that transforms uncertainty into reliable performance. It also provided a foundation for future reproducibility—any lab following our documented workflow could repeat, validate, and extend our findings without ambiguity.

![Decorative design separator](https://static.igem.wiki/teams/5909/3projectengineeringsuccess/figure-engineering-success-4.webp)

## Reflection and Future Directions

The iterative engineering process not only shaped our technical results but also reshaped our team’s understanding of what it means to “engineer biology.” Initially, we approached the project with a conventional research mindset—testing hypotheses and seeking positive results. Over time, however, we began to see the deeper value of iteration: every unexpected result was a form of communication from the biological system itself.

We learned that failure is feedback. When our early protein yields were low, it wasn’t merely a setback—it was an indication that our design assumptions about bacterial tolerance were incomplete. By systematically mapping OD₆₀₀ growth rates against expression patterns, we learned to recognize subtle correlations between stress response and yield optimization. This insight has since become a reusable design rule within our lab: variability across colonies can be leveraged as a natural selection tool for protein production.

Moreover, we recognized that measurement is inseparable from design. Every assay we performed—SDS-PAGE, BCA, fluorescence—wasn’t just a test but an extension of the engineering process itself. Each method introduced constraints that shaped how we thought about our system. For example, the resolution limit of the SDS-PAGE gel informed our choice of molecular weight markers and buffer composition, while the sensitivity of the fluorescence reader determined our minimum measurable peptide-binding threshold. This alignment between data and decision-making refined our ability to design with quantitative foresight rather than reactive troubleshooting.

From a broader perspective, the experience cultivated a new level of systems thinking. Engineering MHC molecules is inherently complex because it sits at the intersection of structural biology, immunology, and synthetic design. By combining AI-based mutation prediction (via DiffDock modeling) with iterative wet-lab validation, we began to bridge computational inference with experimental reality. This hybrid workflow not only accelerated our learning but also demonstrated how computational biology can guide empirical engineering in education-focused research environments like iGEM.

Looking forward, we aim to extend this iterative philosophy beyond the current project. Our immediate next step is to integrate computational modeling feedback directly into the wet-lab cycle—for instance, using predictive structural analysis to suggest new stabilizing mutations before synthesis. This would create a closed-loop engineering system where data from fluorescence assays automatically inform new simulation parameters, enabling real-time design optimization.

Additionally, we plan to transform our experimental pipeline into an open-source educational module. By adapting our DBTL documentation into a digital teaching format (complete with annotated protocols, data templates, and visualization guides), we hope to empower future iGEM teams—particularly high school teams—to replicate and iterate upon our work. This aligns directly with the iGEM Foundation’s mission of knowledge dissemination and community reproducibility.

In the longer term, our modular framework could be expanded for broader immunological applications, such as antigen discovery in infectious diseases or autoimmunity studies. The workflow of iterative design, measurement-driven feedback, and AI-supported prediction is not limited to MHC engineering; it represents a scalable, generalizable approach to rational protein design.

Ultimately, this journey redefined how we perceive “engineering success.” It is not the end product that defines success but the resilience of the process—the willingness to redesign, retest, and relearn. By combining data integration, computational reasoning, and educational outreach, we turned a challenging biological question into a repeatable, teachable model of scientific iteration.

## References

1. Altman, J. D., Moss, P. A. H., Goulder, P. J. R., Barouch, D. H., McHeyzer-Williams, M. G., Bell, J. I., McMichael, A. J., & Davis, M. M. (1996). Phenotypic analysis of antigen-specific T lymphocytes. Science, 274(5284), 94–96.
2. Ando, D., Garcia Martin, H., & Keasling, J. D. (2016). Engineering cycles in synthetic biology: from DNA assembly to models. Current Opinion in Biotechnology, 39, 139–144.
3. Crowther, J. R. (2000). The ELISA Guidebook. Humana Press, Totowa, NJ.
4. Rosano, G. L., & Ceccarelli, E. A. (2014). Recombinant protein expression in Escherichia coli: advances and challenges. Frontiers in Microbiology, 5, 172.
5. Smith, P. K., Krohn, R. I., Hermanson, G. T., Mallia, A. K., Gartner, F. H., Provenzano, M. D., Fujimoto, E. K., Goeke, N. M., Olson, B. J., & Klenk, D. C. (1985). Measurement of protein using bicinchoninic acid. Analytical Biochemistry, 150(1), 76–85.

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![experiment Header](https://static.igem.wiki/teams/5909/4labexperiment/header-experiment.webp)

## Introduction

The experimental work in our project was designed to move step by step from DNA to functional protein complexes, allowing us to test our engineered MHC Class I molecules. Each experiment builds on the previous one, forming a logical workflow that starts with molecular cloning and ends with assays measuring binding efficiency.

We began by introducing our recombinant plasmid into *E. coli* to produce sufficient DNA for downstream experiments. From there, we purified plasmids, expressed proteins, and validated their presence using techniques such as SDS-PAGE and BCA protein assays. To reconstitute functional MHC Class I molecules, we refolded heavy chains, β2-microglobulin, and peptides into stable complexes. Finally, we performed binding assays to compare the efficiency of wild-type and engineered variants.

![Several people in a lab](https://static.igem.wiki/teams/5909/4labexperiment/image-experiment-1.webp)

Together, these experiments form the backbone of our project. They not only demonstrate our ability to take an idea from computational design to laboratory testing, but also show how synthetic biology methods can be applied systematically to address complex biological challenges. Each step highlights both technical skills and scientific reasoning, which are essential for validating our approach to improving antigen presentation in cancer immunotherapy.

## Experiment 1: Transformation of Recombinant Plasmid DNA

#### Purpose

In experiments that use microorganisms as hosts, DNA is the most important material. To carry out molecular biology research, we need a sufficient amount of DNA. Transformation is a method that introduces foreign DNA into bacterial cells. By applying physical or chemical treatments, we make the bacterial membrane permeable so that plasmid DNA can enter the cell. Transformed bacteria can then be cultured, allowing large-scale DNA preparation.

Beyond simply obtaining DNA, transformation is widely used in the laboratory to introduce recombinant plasmids that carry new genes for research purposes.

#### Principle

Transformation introduces a special DNA molecule called a plasmid into host cells.
Plasmids are small, circular DNA molecules naturally found in many bacteria. They replicate independently of chromosomal DNA and often carry useful genes, such as antibiotic resistance. Researchers can insert new genes into plasmids, creating recombinant plasmids for protein expression or genetic studies.

##### What Cloning Means

Molecular biology cloning doesn't mean creating a complete animal. Instead, it's about making many copies of a specific piece of DNA, such as a gene. Researchers may want to study a gene or use it to make a protein. To do this, the gene is copied and placed inside a plasmid, which is a tiny, ring-shaped piece of DNA in bacteria. Plasmids are especially useful because they can carry foreign DNA and produce many copies inside bacterial cells.

To insert a gene into a plasmid, scientists use enzymes. Restriction enzymes work like molecular scissors, cutting DNA at specific sequences, while DNA ligase acts like glue, sealing the gene into the plasmid. Once this recombinant plasmid is made, it is called a "vector" because it carries the gene into bacteria. When bacteria take up the plasmid, they multiply and produce millions of gene copies.

This process is important because it allows bacteria to produce proteins for us. For example, inserting the MHC protein gene into bacteria means the bacteria can generate the protein in large amounts. This same strategy is also used to make insulin and other medicines. DNA cloning in this way gives scientists the ability to study genes, make proteins, and even design new drugs.

##### Competent Cells and Transformation

To make bacteria receptive to DNA, we prepare competent cells. This is usually done with *E. coli* grown to log phase, the stage where the bacteria are actively dividing. At this point, their surface receptors make them more likely to take up DNA.

One commonly used strain is *E. coli* DH5-α. After growing to log phase, cells are washed and suspended in calcium chloride. Calcium ions neutralize the negatively charged cell membrane, helping the negatively charged DNA bind to the cell surface.

Next, we use a heat shock step: rapidly heating the cell--DNA mixture, then cooling it on ice. This creates a temporary pore in the bacterial membrane, allowing plasmid DNA to enter. After recovery in nutrient-rich medium, the transformed cells express the plasmid's genes.

By plating the cells on agar containing antibiotics (such as kanamycin), only bacteria that successfully took up the plasmid (and thus the resistance gene) will grow into colonies. Each colony originates from a single transformed cell, making it a genetic clone.

#### Materials

* 37 °C shaking incubator
* 42 °C water bath
* Ice and ice bucket
* 1.5 mL microtubes
* Sterile spreader
* LB medium and LB agar plates (with kanamycin)
* Competent *E. coli* DH5-α
* Recombinant plasmid DNA (NUSA\_pET28a)

#### Procedure

1. Keep competent cells on ice.
2. Prepare LB agar plates with kanamycin.
3. Mix 5 μL plasmid DNA with competent cells by tapping the tube (do not pipette).
4. Incubate on ice for 20 min.
5. Heat shock: 42 °C for 90 sec, then immediately place on ice for 2--3 min.
6. Add 1 mL LB medium, incubate 1 h at 37 °C shaking incubator.
7. Centrifuge briefly, remove most supernatant, leave pellet with small medium.
8. Spread onto kanamycin agar plate.
9. Incubate overnight, then observe colonies next day.

![Transformed E. coli on agar plate](https://static.igem.wiki/teams/5909/4labexperiment/figure-experiment-1.webp)

*Figure 1. Transformed E. coli on agar plate*

## Experiment 2: Extraction and Purification of Plasmid DNA

#### Purpose

To extract and purify plasmid DNA from transformed *E. coli*. Purified plasmid DNA is essential for downstream applications such as sequencing, cloning, and protein expression.

#### Principle

* **Why purify plasmid DNA?** It's widely used for recombinant DNA, gene cloning, and protein expression.
* **Amplification in E. coli:** Plasmids replicate in bacteria, producing large DNA yields.
* **Alkaline lysis method:**
  + Lyse bacterial cells with alkaline detergent.
  + Both chromosomal and plasmid DNA denature.
  + Neutralization step → plasmids reanneal (small, circular DNA), chromosomal DNA cannot.
  + Centrifugation removes chromosomal DNA and debris.
  + Plasmids bind to filter membrane → washed → eluted in buffer.

#### Materials

* Plasmid purification kit (Enzynomics)
* Microcentrifuge

#### Procedure

1. Collect bacterial culture.
2. Lyse cells with alkaline buffer.
3. Neutralize to allow plasmid reannealing.
4. Centrifuge to pellet debris and chromosomal DNA.
5. Transfer supernatant to spin column.
6. Wash with washing buffer.
7. Elute purified plasmid DNA into new tube.
8. Store at --20 °C.

## Experiment 3: Protein Expression

#### Purpose

To express recombinant proteins in *E. coli* using IPTG induction.

#### Principle

* **IPTG Induction**:
  + IPTG mimics lactose and activates the lac operon.
  + This leads to T7 RNA polymerase production, which drives transcription from the T7 promoter on the plasmid.
  + Stable induction = overexpression of target protein.
* **Growth Monitoring**:
  + Optical density (OD600) indicates bacterial growth phase.
  + Induction is optimal at mid-log phase (OD ~0.5--0.7).

#### Materials

* 37 °C shaking incubator
* LB medium (with kanamycin)
* 1 M IPTG
* Spectrophotometer

#### Procedure

1. Inoculate a colony into LB broth + kanamycin; incubate overnight.
2. Transfer 1--2% of pre-culture into fresh 50 mL LB medium.
3. Measure OD600 hourly.
4. At OD 0.5--0.7, add IPTG to 0.5 mM.
5. Incubate 4 h further.
6. Harvest by centrifugation.
7. Discard supernatant, freeze pellet at --20 °C.

![Diagram of IPTG induction principle](https://static.igem.wiki/teams/5909/4labexperiment/figure-experiment-2.webp)

*Figure 2. Diagram of IPTG induction principle*

## Experiment 4: SDS-PAGE Gel Electrophoresis

#### Purpose

To separate proteins based on molecular weight using SDS-PAGE. By observing protein bands on the gel, we can check the expression level and approximate size of the target protein.

![A person wearing gloves and holding a plastic container](https://static.igem.wiki/teams/5909/4labexperiment/image-experiment-2.webp)

#### Principle

##### What is SDS-PAGE?

*Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), in simple terms, is a laboratory technique for analyzing proteins based on their size. The process begins with SDS, which is a detergent that denatures proteins and gives proteins negative charges. This treatment makes proteins lose their original shapes and structures, and makes them linear. Also, SDS coats proteins with a negative charge that is proportional to individual protein's length. As a result, the separation that occurs in the gel depends mostly on protein size rather than shape or charge.*

*The proteins are then loaded into a polyacrylamide gel and subjected to an electric current. Since the proteins are all negatively charged, they move toward the positive electrode. The gel itself acts as a molecular filter: smaller proteins move faster and farther, while larger proteins move slower and remain closer to the top. After electrophoresis, the gel is treated with stain, which binds to proteins and makes the separated bands visible. Each band represents proteins of a specific size, allowing researchers to check protein expression and compare purification results. Overall, SDS-PAGE enables scientists to visualize and analyze proteins in a clear and convenient way.*

* SDS-PAGE = Sodium Dodecyl Sulfate -- Polyacrylamide Gel Electrophoresis.
* Acrylamide gels are denser than agarose gels, making them ideal for separating proteins.
* SDS coats proteins with negative charges and denatures them into linear chains.
* When voltage is applied, proteins migrate toward the positive electrode, separated by size:
  + Small proteins → move faster (downward).
  + Large proteins → move slower (remain higher).

##### SDS (Detergent)

* SDS disrupts secondary, tertiary, and quaternary protein structures, unfolding them into linear chains.
* It also gives proteins a uniform negative charge so that separation depends only on size, not charge or shape.

##### Polyacrylamide Gel Structure

* **Stacking gel (upper layer):** compresses all proteins into a sharp starting band.
* **Separating gel (lower layer):** separates proteins by molecular weight.
* Glycine, Cl⁻ ions, and proteins migrate differently, creating conditions that align proteins in the stacking gel before separation.

#### Materials

* Protein sample (10 μL)
* 2× Laemmli sample buffer (Tris-HCl, SDS, glycerol, bromophenol blue, β-mercaptoethanol/DTT)
* SDS-PAGE gel kit
* SDS running buffer (Tris-Cl, glycine, SDS)
* Staining and destaining solutions

#### Procedure

1. Prepare SDS-PAGE gel using the kit.
2. Mix protein sample with 2× Laemmli buffer.
3. Heat mixture at 95°C for 5 minutes to denature proteins.
4. Load samples into wells and run at 180 V for ~1 hour.
5. Remove gel and stain with staining solution for 15 minutes.
6. Destain for ~30 minutes until background is clear.
7. Observe protein bands and take a photo.

## Experiment 5: BCA Protein Assay

#### Purpose

To measure total protein concentration of samples.

#### Principle

* Proteins reduce Cu²⁺ → Cu⁺ in alkaline solution.
* Cu⁺ reacts with BCA to form purple complex (λ = 562 nm).
* Absorbance intensity ∝ protein concentration.
* Requires standard curve with BSA.

![Graphical illustration of BCA assay principle](https://static.igem.wiki/teams/5909/4labexperiment/figure-experiment-3.webp)

*Figure 3. Graphical illustration of BCA assay principle*

* **Note**: BCA measures *total protein*, not whether the proteins are correctly folded or functional.

#### Materials

* BSA standard
* BCA assay kit
* 96-well plate and plate reader (562 nm)

#### Procedure

1. Prepare BSA standards across concentration range.
2. Mix Reagent A and B = Working Reagent (50:1).
3. Add 25 μL sample + 200 μL WR per well.
4. Incubate 30 min at 37 °C.
5. Read OD at 562 nm.
6. Plot standard curve, calculate unknown concentrations.

![A close-up of a plastic container](https://static.igem.wiki/teams/5909/4labexperiment/image-experiment-3.webp)

## Experiment 6: Refolding of MHC/Peptide Complexes

#### Purpose

To refold recombinant MHC class I heavy chain (HC) and β2m with tumor-specific peptide into functional complexes.

#### Principle

* HC and β2m expressed in *E. coli* are denatured in urea.
* Refolding buffer with L-arginine and redox system prevents aggregation.
* **Order of addition**: Peptide + β2m first, then HC (added gradually).
* Low temperature and slow stirring enable correct folding.

#### Materials

* Refolding buffer (Tris, L-arginine, glutathione redox system)
* Guanidine solution
* Protease inhibitors (PMSF, pepstatin, leupeptin)
* Recombinant HC, β2m, peptide

#### Procedure

1. Prepare HC (18.6 mg, ~3 μM) and β2m (13.2 mg, ~6 μM).
2. Cool 200 mL refolding buffer to 10 °C. Add inhibitors.
3. Dissolve peptide (12 mg) in buffer.
4. Dilute proteins with guanidine solution.
5. Inject β2m, then HC slowly near stir bar.
6. Incubate 8 h at 10 °C with stirring.
7. Add second aliquots → incubate 6--12 h.
8. Add final aliquots → incubate 24 h.
9. Store at 4 °C or proceed to next step.

## Experiment 7: Antigen Binding Affinity Assay

#### Purpose

To compare binding efficiency of wild-type vs. engineered MHC--peptide complexes using a fluorescence-based assay.

#### Principle

* Fluorescence: molecules absorb excitation light, emit longer-wavelength light (Stokes shift).
* Intensity ∝ number of bound complexes.
* Enables comparison between different complexes.

#### Materials

* Nickel-coated 96-well plate
* MHC--peptide complex solutions (WT, mutant variants)
* Refolding buffer
* Microplate reader (fluorescence mode)

#### Procedure

1. Equilibrate wells with buffer for 5 min.
2. Load complexes (WT, mutants) + blanks.
3. Incubate 2 h to overnight at RT.
4. Wash wells 5× with buffer.
5. Add 100 μL buffer; read fluorescence.
6. Compare fluorescence intensities.

![Illustration of MHC-antigen binding affinity assay set-up using nikel-coated plate](https://static.igem.wiki/teams/5909/4labexperiment/figure-experiment-4.webp)

*Figure 4. Illustration of MHC-antigen binding affinity assay set-up using nikel-coated plate*

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{% block title %}Results{% endblock %}
{% block lead %}From Computational Design to Experimental Validation: Our Journey Through the DBTL Cycle{% endblock %}
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![results Header](https://static.igem.wiki/teams/5909/4labresults/header-results.webp)

## Results

The Results section brings together all the work we performed --- from **computational protein modeling** to **experimental validation** --- and shows how each part of the project fits into the larger iGEM cycle of design, build, test, and learn. Our overall goal was to explore whether we could **engineer MHC Class I molecules to enhance binding affinity for tumor-associated peptides**. This required not only designing mutations in silico, but also cloning, expressing, purifying, and testing recombinant proteins in the lab.

We started with **in silico modeling**, where we mapped the peptide-binding groove of HLA-A\*02:01 and identified key residues responsible for peptide stabilization. Using alanine scanning and docking simulations, we designed two mutant variants that were predicted to alter peptide binding. This computational phase established a rational basis for our experiments and reduced the guesswork in deciding which mutations to test.

Next, we moved into the **wet lab phase**, where we produced wild-type HLA-A\*02:01, Mutant 1, Mutant 2, and β2-microglobulin in *E. coli*. At first, protein expression was limited by low yields, but by applying **colony selection** (screening and choosing the best-expressing bacterial colonies), we significantly improved production. Although the proteins localized mostly to inclusion bodies, we successfully purified and solubilized them using urea-based denaturation. This confirmed that bacterial systems can produce engineered human proteins at scale, provided that proper refolding strategies are applied.

The purified proteins were then verified through SDS-PAGE and quantified using BCA assays, which showed that we achieved milligram-per-milliliter levels of recombinant proteins --- enough for downstream testing. Finally, we tested functionality with a **fluorescence-based peptide binding assay**. This experiment confirmed that wild-type HLA-A\*02:01 bound strongly to the pp65 peptide, while Mutant 1 and Mutant 2 both showed reduced binding affinity. While these results did not enhance antigen presentation as originally intended, they proved that **structural modifications of MHC molecules can tune binding affinity**.

Taken together, our results demonstrate the power of combining computational design with experimental testing. The project highlighted both the potential and the challenges of rational protein engineering: although our specific mutations reduced binding, the findings confirm that **binding properties are not fixed, but tunable** through deliberate design choices. This insight is crucial for future synthetic biology work aimed at fine-tuning immune responses.

## In Silico Results: Structural Analysis of HLA-A\*02:01

To begin, we analyzed the **structure of HLA-A\*02:01**, a common allele of MHC Class I. Using the high-resolution crystal structure **PDB ID: 6Q3K**, we examined the complex of HLA-A\*02:01 bound to β2-microglobulin (B2M) and the viral peptide pp65 (sequence: NLVPMVATV).

The α chain of HLA-A\*02:01 is divided into three domains. The **α1 and α2 domains form the peptide-binding groove**, a slot-like pocket that secures short peptides. The **α3 domain** stabilizes the structure, while **B2M** ensures proper folding and surface presentation. Inside the groove, we identified **23 residues** interacting directly with the peptide, including conserved aromatic residues like Tyr7 and Tyr116 that stabilize hydrophobic anchors, and charged residues like Glu63 and Lys66 that provide electrostatic stabilization.

![3D ribbon illustration of the HLA-A02:01 peptide-binding groove with bound peptide](https://static.igem.wiki/teams/5909/4labresults/figures-results-1.webp)

*Figure 1. 3D ribbon illustration of the HLA-A02:01 peptide-binding groove with bound peptide. Key contact residues highlighted.\**

This structural map guided our mutagenesis strategy, helping us design Mutant 1 and Mutant 2 by modifying residues predicted to influence peptide stability.

## Alanine Scanning and Mutagenesis Predictions

Next, we performed **in silico alanine scanning** to systematically test the contribution of each residue to peptide binding. Residues that showed significant predicted loss of binding when mutated to alanine were considered essential. In contrast, residues with smaller predicted contributions were considered candidates for modification. This analysis gave us a short list of residues where substitutions could potentially **improve peptide affinity** without disrupting overall structure.

## Docking Simulations with DiffDock

We then employed **DiffDock**, an AI-based molecular docking program, to simulate peptide--MHC interactions. Using 6Q3K as the receptor and the pp65 peptide as the ligand, we compared the wild-type binding mode with our proposed mutants. DiffDock produced predicted binding poses and affinity scores, allowing us to visualize how mutations might shift the peptide's position or strengthen stabilizing contacts.

Our results suggested that certain mutations in the groove could **increase the predicted binding affinity** for tumor-associated peptides such as NY-ESO-1 or WT1, which share anchor residue preferences with pp65. These computational predictions served as the basis for designing our experimental constructs.

## Protein Expression in E. coli

We next expressed wild-type HLA-A\*02:01, Mutant 1, Mutant 2, and B2M in *E. coli*. Expression was induced with IPTG at mid-log phase growth. SDS-PAGE analysis confirmed robust production of all proteins.

![Expression of recombinant proteins in E. coli](https://static.igem.wiki/teams/5909/4labresults/figures-results-2.webp)

*Figure 2. Expression of recombinant proteins in E. coli. SDS-PAGE gel showing lanes for induced lysates, soluble fraction, and inclusion body fraction for HLA-A02:01 and B2M.*

Most proteins were present in the **inclusion body fraction**, a common outcome when expressing eukaryotic proteins in bacteria. However, we noted that not all colonies expressed protein at equal levels. By performing **colony selection**---screening multiple colonies for expression efficiency and choosing the highest-producing clones---we significantly improved yields. This step reflects the iterative "design--build--test--learn" approach emphasized in iGEM: even small optimizations, like colony choice, can greatly influence experimental success.

## Protein Purification and Solubilization

To purify the proteins, inclusion body pellets were isolated by centrifugation and washed multiple times with detergent buffer to remove membrane contaminants. The washed pellets were then solubilized in **8 M urea**, which unfolded the proteins completely. Overnight incubation ensured full solubilization, and centrifugation removed any remaining debris.

This approach yielded large amounts of purified, denatured HLA-A\*02:01 and B2M. Although the proteins were not yet in a functional, refolded state, this step was critical for obtaining sufficient quantities for downstream refolding and binding studies.

![Workflow of protein purification](https://static.igem.wiki/teams/5909/4labresults/figures-results-3.webp)

*Figure 3. Workflow of protein purification.*

## Confirmation of Purified Proteins

The purified fractions were analyzed by SDS-PAGE to verify protein recovery. Distinct bands corresponding to the expected molecular weights confirmed the presence of **HLA-A\*02:01 (25--308 a.a.)** and **B2M (21--119 a.a.)** . Compared to the crude lysates, these purified samples showed much cleaner backgrounds, indicating successful removal of bacterial proteins.

This step demonstrated that, even though the proteins were expressed in inclusion bodies, our purification workflow was effective in producing high-purity samples suitable for biochemical assays.

![SDS-PAGE of purified proteins](https://static.igem.wiki/teams/5909/4labresults/figures-results-4.webp)

*Figure 4. SDS-PAGE of purified proteins. Gel image with lanes showing purified HLA-A02:01 and B2M alongside molecular weight markers.*

## Protein Quantification with BCA Assay

We quantified protein concentrations using a **BCA assay**, which measures the reduction of Cu²⁺ ions by peptide bonds, leading to a color change that can be measured spectrophotometrically. A standard curve was generated using BSA at known concentrations, with the following regression formula:

**y = 0.0014x -- 0.0083**,

where *y* is the absorbance and *x* is the protein concentration .

![Graph showing standard curve and measured concentrations for HLA-A02:01 and B2M](https://static.igem.wiki/teams/5909/4labresults/figure-results-5.webp)

*Figure 5. Graph showing standard curve and measured concentrations for HLA-A02:01 and B2M.*

For **HLA-A\*02:01**, absorbance values across dilutions (1/100--1/400) corresponded to an average concentration of **19.3 mg/mL**. For **B2M**, the average concentration was **16.7 mg/mL** . These concentrations are relatively high, confirming that our purification yielded sufficient protein for binding assays.

These high concentrations provided ample material for binding assays, showing that our bacterial system was not only efficient but also scalable.

## Fluorescence-Based Binding Assay

The final stage of our experiments aimed to confirm whether our purified proteins were capable of binding peptide antigens, and to compare binding strength between wild-type and mutant constructs. To achieve this, we used a **fluorescence detection method** that allowed us to quantify peptide binding in vitro.

We immobilized our recombinant proteins onto **nickel-coated plates**. This method takes advantage of the **His-tag** engineered onto our constructs: the histidine residues have strong affinity for nickel ions, enabling stable and specific attachment of the protein to the plate surface. This ensured that the orientation of the proteins was consistent across wells and that the peptide-binding grooves were accessible for testing.

This assay design provided a direct readout of binding strength: **higher fluorescence intensity indicated stronger binding affinity**, while lower intensity reflected weaker interactions.

![Fluorescence detection assay setup](https://static.igem.wiki/teams/5909/4labresults/figure-results-6.webp)

Finally, we tested peptide-binding functionality using a **fluorescence detection assay**. The wild-type HLA-A\*02:01 + B2M complex bound the pp65 peptide with strong fluorescence, confirming correct folding and functional binding.

In contrast, **Mutant 1 and Mutant 2 both showed lower fluorescence signals compared to wild type**, indicating reduced binding affinity. This was a key finding: while we did not achieve enhanced binding strength as intended, we demonstrated that structural modifications can indeed alter binding.

This result is significant because it shows that the **binding affinity of MHC molecules is tunable through 3D structural changes**. Even though our mutations weakened binding, the very fact that binding was altered validates our engineering approach.

![Fluorescence detection of peptide binding](https://static.igem.wiki/teams/5909/4labresults/figure-results-7.webp)

*Figure 7. Fluorescence detection of peptide binding. Bar graph comparing fluorescence intensity of wild-type HLA-A\*02:01, Mutant 1, and Mutant 2 complexes with peptide.*

## Conclusion

Our combined computational and experimental work provided several key insights:

1. **In silico modeling** successfully identified peptide-binding residues and guided the design of two mutant constructs.
2. **Colony selection improved expression**, highlighting how even practical optimizations contribute to engineering success.
3. **Expression and purification were robust**, producing large amounts of recombinant wild-type and mutant proteins despite inclusion body formation.
4. **BCA quantification confirmed high yields**, ensuring material availability for downstream studies.
5. **Functional binding was demonstrated**: wild-type HLA-A\*02:01 bound pp65 peptide strongly, while Mutant 1 and Mutant 2 showed reduced affinity.

Although we did not achieve the intended enhancement of binding strength, our results clearly demonstrate that **the binding properties of MHC molecules can be modulated by engineering their 3D structure**. This finding is important for future synthetic biology applications, where precise control of antigen presentation could be used to design novel cancer immunotherapies.

Our project therefore stands as a proof-of-concept: computational predictions combined with iterative wet lab optimization can not only reproduce native function but also deliberately alter it. The next challenge is to refine our approach to identify mutations that enhance rather than weaken peptide binding.

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