iGEM 2025 — Team xjtlu-ai-china

{% extends "layout.html" %}
{% block title %}Lab Notebook{% endblock %}
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# Lab Notebook

Detailed documentation of our experimental journey

#### Contents

* [Literature Review Phase](#literature-review)
* [Sequence Screening](#sequence-screening)
* [Plasmid Construction](#plasmid-construction)
* [Transformation Verification](#transformation-verification)
* [Expression Optimization](#expression-optimization)
* [Activity Testing](#activity-testing)
* [Protein Analysis](#protein-analysis)

## Literature Review Phase

### 6.9 ~ 6.20

Twelve days dedicated to wet-lab related literature retrieval and reading.

## Sequence Screening

### 6.21 ~ 6.23

Screen and identify five target sequences for subsequent validation.

| Number | PET\_01 | PET\_02 | PET\_03 | PET\_04 | PLA\_01 |
| --- | --- | --- | --- | --- | --- |
| **Enzyme\_name** | Poly(ethylene terephthalate) hydrolase | putative cutinase 1 precursor | Triacylglycerol lipase | Leaf-branch compost cutinase | PLA depolymerase |
| **Class** | Hydrolase | Hydrolase | Hydrolase | Hydrolase | Hydrolase |
| **Type of degradable plastic** | PET | PET | PET | PET | PLA |
| **Source type** | BCT | FUN | BCT | Unknown | Unknown |
| **Source species** | Piscinibacter sakaiensis (Ideonella sakaiensis) | Fusarium mangiferae | Caldimonas brevitalea | Unknown prokaryotic organism | Paenibacillus amylolyticus |
| **UniProt/NCBI ID** | A0A0K8P6T7 | A0A1L7TAX9 | A0A0G3BI90 | G9BY57 | Q83VD0 |
| **Original sequence length** | 290 | 230 | 298 | 293 | 201 |
| **signal peptide** | yes (1-27) | yes (1-16) | yes (1-29) | yes (1-34) | Unknown |
| **Disulfide linkage** | yes (203↔239, 273↔289) | yes (48↔126, 188↔195) | None | yes (275↔292) | Unknown |
| **Ligand type** | None | None | None | None | None |
| **AlphaFold/PDB model** | 5XFY | AF-A0A1L7TAX9-F1 | AF-A0A0G3BI90-F1 | 4EB0 | Unknown |
| **Design recommendation** | Remove the original signal peptide | Remove the original signal peptide | Remove the original signal peptide | Remove the original signal peptide | Remove the original signal peptide |

## Plasmid Construction

### 6.24 ~ 6.26

Select pET22b(+) as the vector plasmid. Design and construct five novel recombinant plasmids. Commission a company for plasmid synthesis.

## Transformation Verification

### 7.13

Perform plasmid transformation of the plastic-degrading enzyme gene constructs according to the established protocol. Resuspend the bacterial cells evenly and plate onto LB agar plates supplemented with 100 μg/mL ampicillin. Incubate inverted at 37°C overnight.

### 7.14

**LB Plate Overnight Culture Results:**

Colonies were observed on all 100 μg/mL Amp LB plates, indicating successful preliminary transformation.

![LB plate transformation result 1](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-1.webp)
![LB plate transformation result 2](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-2.webp)
![LB plate transformation result 3](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-3jpg.webp)
![LB plate transformation result 4](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-4.webp)
![LB plate transformation result 5](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-5.webp)
![LB plate transformation result 6](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-14-6.webp)

**Colony Selection and PCR Setup:**

* Select five distinct colonies from each plate and label them 1-30 (across 5 plates x 6 replicates each).
* Prepare the PCR reaction mixture according to the protocol.
* In a 1.5mL microcentrifuge tube, add ddH₂O, forward and reverse primers, and high-fidelity enzyme in equal proportions. Aliquot into PCR tubes.
* Prepare an empty Amp LB plate and evenly label it with numbers 1-30.
* Using a sterile pipette tip, pick individual colonies. First, streak a short line on the corresponding marked area on the plate to preserve the bacterial strain. Subsequently, gently transfer a small amount of bacterial cells from the streak into the PCR tube to serve as the template.

**PCR Amplification:**

95°C for 3 min ->(94°C for 25s, 55°C for 25s, 72°C for 15s) x 35 cycles ->72°C for 5 min -> 4°C indefinitely

Perform agarose gel electrophoresis on the PCR amplification products. No other non-specific bands were observed. The molecular weights of all five amplified fragments matched the expected sizes.

![PCR gel electrophoresis result 1](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/pcr1.webp)
![PCR gel electrophoresis result 2](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/pcr2.webp)

![PCR gel electrophoresis result 3](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/pcr3.webp)

Excise the DNA bands corresponding to samples 5, 8, 11, 17, and 22 from the agarose gel. Submit these gel slices to the company for Sanger sequencing.

## Expression Optimization

### 7.15

**Plasmid Transformation and Expression Optimization:**

**Culture Preparation for Induction:**

Inoculate bacteria from the preserved single colony plates into LB liquid culture medium containing ampicillin (100 μg/mL). Incubate at 37°C on a shaker for 3 hours.

**IPTG Induction and Expression Optimization:**

Subsequently, add IPTG to final concentrations of 0, 0.05, 0.1, 0.5, and 1 mM for induction. This is to screen for the optimal expression concentration. Incubate the induced bacterial cultures at 25°C on a shaker overnight.

| IPTG concentration（mM） | 5mg/ml IPTG original liquid（ul） | Bacteria solution(ul） | 5mg/ml IPTG original liquid（ul） | Bacteria solution（ul） |
| --- | --- | --- | --- | --- |
| 0 | 0 | 1000 | 0 | 750 |
| 0.05 | 2.383 | 997.671 | 1.787 | 748.253 |
| 0.1 | 4.766 | 995.234 | 3.574 | 746.425 |
| 0.5 | 23.83 | 976.17 | 17.872 | 732.127 |
| 1 | 47.66 | 952.34 | 35.745 | 714.255 |

### 7.16

Bacterial cultures were cryopreserved on July 15th. Sanger sequencing confirmed that the sequences of the recombinant plasmids transformed into E. coli were fully consistent with the design.

Following lysate collection, the initial 4-pNPA enzyme activity assay revealed an optimal enzyme activity profile at 0.1 mM IPTG. However, the PET\_03 and PET\_04 groups exhibited significantly lower enzyme activity compared to the empty vector control.

![4-pNPA enzyme activity assay results](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-16.webp)

### 7.17

**IPTG Induction Culture Preparation and OD Measurement:**

**Inoculation:** Transfer 1 μL of the bacterial culture from July 15th into fresh 15 mL of LB medium supplemented with Ampicillin (100 μg/mL).

**Pre-induction Culture:** Incubate for 1-2 hours.

| Sample | OD600 |
| --- | --- |
| PLA\_01 | 0.608 |
| PET\_01 | 0.637 |
| PET\_02 | 0.618 |
| PET\_03 | 0.526 |
| PET\_04 | 0.671 |
| Plasmid | 0.727 |

Expression was induced using 0.1 mM IPTG (prepared from a 5 mg/mL stock solution, 24 μL added to the culture), followed by incubation at 20°C on a shaker starting at 18:50. However, due to the reuse of the same measurement vessel without proper cleaning when measuring OD values and the practice of returning the measured bacterial liquid to its original tube, contamination was suspected. The following day, the empty LB tube (without bacterial inoculation) appeared turbid, leading to the discard of this set of bacterial cultures.

### 7.19

A 5 μL aliquot from the cryopreserved glycerol stock of bacterial cells was used to inoculate 15 mL of fresh LB medium supplemented with Ampicillin (100 μg/mL). After 2 hours of incubation, the culture medium remained clear with no significant visible changes. Overnight incubation was then performed.

### 7.20 ~ 7.22

The bacterial colonies cultured overnight on July 19th were measured for their OD600 using a spectrophotometer, yielding a value above 5.0. As the cell density was too high for immediate use, the cultures were consequently cryopreserved in glycerol stocks.

Following a discussion, it was determined that an operational error had occurred. The correct procedure dictates that glycerol stocks should first be used to inoculate LB agar plates containing ampicillin (100 μg/mL) and incubated overnight. The resulting colonies should then be picked with a pipette tip and used to inoculate 15 mL of fresh LB Amp (100 μg/mL) liquid medium.

After 3 hours of incubation, samples were collected for OD measurement, with the following results:

| Sample | Nanodrop | Spectrophotometer |
| --- | --- | --- |
| PLA\_01 | 0.96 | 0.715 |
| PET\_01 | 0.91 | 0.597 |
| PET\_02 | 1.27 | 0.946 |
| PET\_03 | 0.82 | 0.670 |
| PET\_04 | 0.91 | 1.056 |
| Plasmid | 0.69 | 0.489 |

12 μL of IPTG was added to the bacterial cultures from July 20th, followed by overnight incubation at 20°C on a shaker.

## Activity Testing

The bacterial cultures from July 20th were processed according to the protocol. In wells A-F, columns 1-9 of the 96-well plate, 135 μL of sodium phosphate buffer (pH 8.0), 15 μL of crude enzyme solution, and 50 μL of 4-pNPA stock solution were added. For wells G, columns 1-9, 150 μL of sodium phosphate buffer (pH 8.0) and 50 μL of 4-pNPA stock solution were added.

The template is as shown in the table:

|  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **PLA\_01** | - | - | - | - | - | - | - | - | - |
| **PET\_01** | - | - | - | - | - | - | - | - | - |
| **PET\_02** | - | - | - | - | - | - | - | - | - |
| **PET\_03** | - | - | - | - | - | - | - | - | - |
| **PET\_04** | - | - | - | - | - | - | - | - | - |
| **Plasmid** | - | - | - | - | - | - | - | - | - |
| **BLANK** | - | - | - | - | - | - | - | - | - |

Following procedural improvements, secreted proteins from all strains exhibited degradation capabilities towards plastic analogs.

![Activity testing results July 20-22](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-20-7-22.webp)

Bacterial cultures from July 20th were used to inoculate LB agar plates supplemented with ampicillin (100 μg/mL) via streaking. The plates were then incubated overnight at 37°C.

### 7.23 (The first attempt to measure enzyme activity)

**PLA Solution Preparation:** Dissolve 0.25 g of PLA plastic fragments in 20 mL of dichloromethane. Place the solution on a magnetic stirrer within a fume hood, under ice bath conditions, and in the dark, allowing it to evaporate for 3 hours.

**Medium Preparation:** Autoclave 500 mL of LB agar medium at 121°C.

**Top Layer (5 mL per plate):** Mix 87.5 mL of the autoclaved LB agar medium with 42 μL of IPTG stock solution (1 mM; 50 mg/mL) and 100 mL of the prepared PLA solution.

**Bottom Layer (15 mL per plate):** Combine the remaining 400 mL of LB medium with 1.9 mL of IPTG stock solution (0.1 mM; 5 mg/mL) and 400 μL of Ampicillin stock solution (100 mg/mL).

Then inoculation of bacterial cultures from July 22nd was performed by streaking or spot inoculation onto newly prepared media (in triplicate). The plates were subsequently incubated overnight at 37°C in an oven.

Overnight cultures of bacterial colonies from July 23rd showed no discernible clear zones.

![Activity testing results July 20-22](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-23.webp)
![July 23rd clear zone test results](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-23-2.webp)

### 7.24 (The second attempt to measure enzyme activity after the adjustment)

Add 0.25 g of PLA plastic fragments to 20 mL of dichloromethane. Process the mixture on a magnetic stirrer for 1.5 hours. Subsequently, add 100 mL of double-distilled water (ddH₂O) and homogenize for 1.5 hours to facilitate evaporation.

However, after the dichloromethane evaporated, the PLA was unable to form a stable suspension in pure water. Instead, it aggregated into sheet-like structures and floated on the water surface.

![PLA suspension aggregation July 24th](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-24.webp)

Prepare a 2x concentrated LB agar medium by dissolving 20 g of LB agar powder in 250 mL of double-distilled water (ddH₂O). After sterilizing the LB agar medium, add the mixture of PLA and water to the sterile medium. Pour this mixture into two plates. Following solidification of the medium, spot inoculate with PLA-degrading bacteria.

### 7.25

The overnight cultures from July 24th showed contamination by unwanted microorganisms. Subsequently, 5 mg/mL IPTG was added to the PLA-degrading bacterial colonies, and 100 mg/mL Ampicillin was added to a blank area.

![July 25th contamination and IPTG treatment](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-25.webp)
![July 25th bacterial inoculation setup](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-25-2.webp)

Inoculating bacterial strains from the July 22nd streak plates into 5 mL of LB medium supplemented with 5 µL of ampicillin stock solution and incubated overnight at 37°C.

### 7.26 ~ 7.28 (The third attempt to measure enzyme activity)

To the overnight cultures from July 25th, 24 µL of IPTG was added, followed by incubation at 20°C with shaking.

Then, preparing the PLA degradation screening plates, 0.25 g of PLA plastic fragments were dissolved in 5 mL of chloroform and stirred on a magnetic stirrer for 1 hour. Subsequently, 100 mL of sterile double-distilled water (ddH₂O) was added, and the mixture was homogenized. This resulting suspension was then combined with 100 mL of 2x concentrated LB medium. Plates were poured at intervals of 15 min, 30 min, 1 h, and 1.5 h from this mixture, with a control group consisting of 100 mL ddH₂O mixed with 100 mL of 2x concentrated LB medium. Inoculation was performed by streak plating or spot inoculation onto the prepared plates with both PLA-degrading bacteria and empty vector control bacteria. All inoculated plates were incubated overnight at 37°C in an oven.

The results for the July 27th inoculation are shown below:

![July 26-27 PLA degradation test plate 1](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-26-1jpg.webp)
![July 26-27 PLA degradation test plate 2](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-26-2.webp)
![July 26-27 PLA degradation test plate 3](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/7-26-3.webp)

After three days of incubation and observation, no clear zones were detected around the bacterial colonies, indicating a lack of detectable plastic degradation enzyme activity. Therefore, the experiment to verify enzymatic activity on PLA-containing agar plates has been discontinued.

## Protein Analysis

### 7.30

Following the teacher's advice, we revised the experimental plan and decided to attempt using WB for supplementary verification.

Bacterial liquid was processed according to the protocol, commencing with SDS-PAGE electrophoresis at 140 V for 45 minutes using pre-cast gels, with 10 µL of sample loaded per well. Following electrophoresis, protein transfer was performed at 100 V for 1 hour. For antibody incubation, the membrane was initially blocked for 1.5 hours at room temperature with 5% non-fat dry milk in 25 mL of TBST, followed by a single wash with TBST. Primary antibody incubation was conducted overnight at 4°C with shaking in a cold room (lid unsealed) using a 1:1000 dilution in 5 mL of 0.5% non-fat dry milk solution in TBST; this was followed by four TBST washes, each lasting approximately 30 minutes. Subsequently, the membrane was incubated with the secondary antibody at a 1:5000 dilution in 10 mL of TBST for 50 minutes at room temperature, followed by four 10-minute washes with TBST.

**The results are as follow:**

![Western Blot result - Protein content in supernatant](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/wb1.webp)
![Western Blot result - Protein content in bacterial cells](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/wb2.webp)

Left: Protein content in the supernatant. Right: Protein content in the bacterial cells.

### 8.15

Finally, following the professor's suggestion and adhering to the protocol, the crude enzyme preparation obtained by sonication was added to Oxford cups on agar plates containing a plastic emulsion. The results showed only a faint ring, which, after discussion, was not considered a genuine indicator of plastic degradation.

![Oxford cup plastic degradation test result 1](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/8-15-1.webp)
![Oxford cup plastic degradation test result 2](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/notebook/8-15-2.webp)

up

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering{% endblock %}
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# Engineering

#### Contents

* [Cycle 1 — Data Integration & Benchmark Preparation](#cycle-1)
  + [1.1. Status Quo](#status-quo)
  + [1.2. Goal](#goal)
  + [1.3. Sub-Cycle 1A — Data Standardization &
    Cleaning](#sub-cycle-1a)
    - [Design](#design-1a)
    - [Build](#build-1a)
    - [Test](#test-1a)
    - [Learn](#learn-1a)
  + [1.4. Sub-Cycle 1B — Benchmark Design](#sub-cycle-1b)
    - [Design](#design-1b)
    - [Build](#build-1b)
    - [Test](#test-1b)
    - [Learn](#learn-1b)
  + [1.5. Sub-Cycle 1C — Biological Validation](#sub-cycle-1c)
    - [Design](#design-1c)
    - [Build](#build-1c)
    - [Test](#test-1c)
    - [Learn](#learn-1c)
  + [1.6. Conclusion](#conclusion-1)
* [Cycle 2 — Baseline ML with ESM Embeddings](#cycle-2)
  + [2.1. Goal](#goal-2)
  + [2.2. Sub-Cycle 2A — Embedding Comparison](#sub-cycle-2a)
    - [Design](#design-2a)
    - [Build](#build-2a)
    - [Test](#test-2a)
    - [Learn](#learn-2a)
  + [2.3. Sub-Cycle 2B — Handling Class Imbalance](#sub-cycle-2b)
    - [Design](#design-2b)
    - [Build](#build-2b)
    - [Test](#test-2b)
    - [Learn](#learn-2b)
  + [2.4. Conclusion](#conclusion-2)
* [Cycle 3 — Dual-Tower with Plastic Features](#cycle-3)
  + [3.1. Goal](#goal-3)
  + [3.2. Sub-Cycle 3A — Plastic Embedding](#sub-cycle-3a)
    - [Design](#design-3a)
    - [Build](#build-3a)
    - [Test](#test-3a)
    - [Learn](#learn-3a)
  + [3.3. Sub-Cycle 3B — Interactive Model](#sub-cycle-3b)
    - [Design](#design-3b)
    - [Build](#build-3b)
    - [Test](#test-3b)
    - [Learn](#learn-3b)
  + [3.4. Conclusion](#conclusion-3)
* [Cycle 4 — Structure-Aware Backbones](#cycle-4)
  + [4.1. Goal](#goal-4)
  + [4.2. Sub-Cycle 4A — Structure Sources](#sub-cycle-4a)
    - [Design](#design-4a)
    - [Build](#build-4a)
    - [Test](#test-4a)
    - [Learn](#learn-4a)
  + [4.3. Sub-Cycle 4B — Graph Representation](#sub-cycle-4b)
    - [Design](#design-4b)
    - [Build](#build-4b)
    - [Test](#test-4b)
    - [Learn](#learn-4b)
  + [4.4. Sub-Cycle 4C — GNN vs. GVP](#sub-cycle-4c)
    - [Design](#design-4c)
    - [Build](#build-4c)
    - [Test](#test-4c)
    - [Learn](#learn-4c)
  + [4.5. Conclusion](#conclusion-4)
* [Cycle 5 — User-Friendly Web Application](#cycle-5)
  + [5.1. Goal](#goal-5)
  + [5.2. Design](#design-5)
  + [5.3. Build](#build-5)
  + [5.4. Test](#test-5)
  + [5.5. Learn](#learn-5)

## Cycle 1 — Data Integration & Benchmark Preparation

### Status Quo

Existing plastic-degrading enzyme databases were fragmented, inconsistent, and often incomplete. Many entries
lacked standardization, with missing sequences, structures, or annotations, making them difficult to use for
systematic modeling.

### Goal

To construct a standardized, unified, and large-scale dataset for enzyme–plastic interactions, with rigorous
train/test isolation, difficulty-stratified benchmarks, and biological validation, so that downstream models
can be developed and evaluated objectively.

### Sub-Cycle 1A — Data Standardization & Cleaning

#### Design

Before dataset expansion, we first needed to define what counts as a valid entry. Many existing databases
contained fragmented or inconsistent records, with missing EC annotations, structures, or partial sequences.
We asked: what is the minimal requirement for inclusion?

#### Build

We set enzyme sequence as the minimal requirement, since it carries sufficient biological information and is
the easiest to retrieve reliably. To achieve this, we used a specialized database of plastic-degrading
enzymes as the primary reference. From this starting point, we developed multiple retrieval scripts to query
large-scale resources such as UniProt and NCBI, aiming to supplement missing information (e.g., structures,
metadata). All retrieved data were then cross-validated against the original annotations to detect
inconsistencies and remove redundancies. This systematic approach allowed us to reconstruct a coherent and
enriched dataset.

#### Test

We ensured that no duplicate sequences remained and that every plastic-degradation entry contained at least
its corresponding enzyme sequence. Sequence length distributions became more coherent, and all retrievable
metadata (e.g., structures, annotations) were systematically added back.

#### Learn

We learned that defining a minimal entry standard (sequence) early not only prevented data collapse but also
ensured feasibility for downstream model training. Through automated cross-database cleaning combined with
manual review, we identified and corrected multiple issues in raw resources, including mismatched IDs,
misannotated plastic types, and uneven classification granularity. This process improved dataset quality and
reproducibility, laying a robust foundation for future cycles.

### Sub-Cycle 1B — Benchmark Design (Isolation + Stratification)

#### Design

Since our ultimate goal is to build a predictive model for plastic-degrading enzymes, the first step must be
to fix an independent test set from the very beginning. This is a necessary step for engineering-grade
projects, ensuring that evaluation metrics remain objective and not artificially inflated by models
indirectly "seeing" similar data during training. Once the test set is fixed, the next challenge is how to
split it more meaningfully: a purely random hold-out cannot capture the true biological challenge of enzyme
prediction. To address this, we proposed using sequence similarity as the guiding metric, since similarity
reflects evolutionary and functional relationships between enzymes. This allows the test set to represent
real biological diversity and stratified difficulty levels, rather than being just a leftover subset.

#### Build

Following the design principle, we developed an algorithmic pipeline for test set stratification based on
sequence similarity. For each candidate enzyme, we computed its nearest sequence identity to the training
set using global alignment. This ensured that classification into difficulty tiers reflected the hardest
comparison it would face in practice. Using iterative calibration, we finalized two thresholds—0.9 and
0.7—which divided the benchmark into three levels:

* Easy: identity ≥ 0.9 (high similarity to training sequences)
* Medium: 0.7 ≤ identity < 0.9 (moderate similarity)
* Hard: identity < 0.7 (low similarity, distant from training data)

In addition, the algorithm included balancing constraints, ensuring that the number of samples across the
three tiers was as even as possible. The entire process was automated and reproducible, generating
stratified FASTA files and metadata tables for downstream modeling.

#### Test

After constructing the benchmark, we carried out additional validation to ensure its correctness. Using
independent scripts, we confirmed that no duplicate entries existed between training and test sets,
eliminating any risk of data leakage. We also re-calculated the sequence similarity values to cross-check
the automated pipeline, ensuring that each sample was assigned to the correct difficulty tier. Finally,
analysis of the distribution across tiers showed that the benchmark achieved a nearly balanced ratio
(~1:1:1) among easy, medium, and hard levels. Together, these results indicate that the benchmark not only
preserves biological meaning but also provides strong evaluation capacity, capable of reliably
distinguishing true performance differences between models.

#### Learn

From this sub-cycle, we learned that designing a benchmark is not just about splitting data into training and
test sets, but about embedding biological logic into evaluation. By using sequence similarity as the
splitting criterion, we ensured that the test set represents realistic levels of biological diversity. The
near-balanced distribution across easy, medium, and hard tiers confirmed the robustness of our pipeline.
Importantly, this setup enables tier-specific evaluation: by separately analyzing performance on each
difficulty level, we can better judge a model's generalization ability and its biological interpretability,
rather than relying on a single aggregated score.

### Sub-Cycle 1C — Biological Validation of Dataset

#### Design

To ensure that our dataset is not only computationally consistent but also biologically reliable, we designed
a two-pronged validation strategy: (1) wet-lab experiments to confirm that selected enzymes truly exhibit
degradation activity, and (2) bioinformatic analyses to examine family composition and evolutionary
structure.

#### Build

**Wet-lab:**

We selected five representative enzyme sequences using a semi-random stratified sampling strategy. On the one
hand, we included well-documented entries from the literature to cover established representatives; on the
other, we incorporated underexplored candidates to ensure representativeness and diversity. The chosen genes
were cloned into expression plasmids and heterologously expressed in the standard E. coli host via the pelB
signal peptide pathway (widely used and previously reported in literature for periplasmic secretion). The
expressed proteins were subsequently purified for downstream validation.

![PET plasmid construction and expression strategy](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/petplasmid.webp)

**Bioinformatics:**

All entries were standardized and redundancy-removed before analysis. Family assignments were performed using
Pfam/InterPro domain and HMM-based annotations. Representative sequences were then aligned with
MAFFT/Clustal Omega, and a phylogenetic tree was constructed using the maximum-likelihood method with
bootstrap support, to assess the consistency of family composition and evolutionary structure.

#### Test

**Wet-lab:**

Soluble expression of the target proteins was confirmed by SDS-PAGE, which showed clear bands at the expected
molecular weights. Western Blot (WB) further validated the presence of the expressed enzymes. The purified
proteins were subjected to activity assays using substrates relevant to ester-bond and polyester hydrolysis.
All five samples exhibited significant enzymatic activity compared with negative controls, with distinct
differences in substrate degradation patterns, confirming that the selected dataset entries correspond to
biologically functional enzymes.

**Bioinformatics:**

Statistical profiling confirmed that the dataset is consistent and biologically meaningful: sequence length
distributions, taxonomic coverage, and plastic-category coverage were coherent, and redundancies were
successfully removed. Family assignment revealed that α/β-hydrolases constitute the majority, accompanied by
cutinase-like and lipase-like families known for polyester degradation. Phylogenetic analysis further
supported this: representative sequences clustered into clades consistent with annotated families, and the
overall evolutionary structure was in line with prior literature on plastic-degrading enzymes.

#### Learn

This validation confirmed that our curated entries are biologically meaningful: the selected enzymes showed
real activity, and bioinformatic analysis revealed family and evolutionary structures consistent with
literature. Importantly, we observed that enzyme sequences are often dominated by conserved,
structure-maintaining regions, reflecting evolutionary constraints. At the same time, we recognized that
plastic-degrading enzymes are not products of purposeful evolution but rather cases of fortuitous adaptive
gain, where the natural substrates of hydrolases share structural similarity with synthetic plastics. This
helps explain why small mutations in catalytic regions can sometimes unlock unexpected plastic-degrading
activity.

### Conclusion

Through this cycle, we successfully constructed a standardized, unified, and large-scale dataset for
enzyme–plastic interactions. The dataset was built with rigorous train/test isolation, designed
difficulty-stratified benchmarks using sequence similarity, and validated through both wet-lab experiments
and bioinformatic analyses. As a result, we achieved a dataset of high completeness, reliability, and
biological grounding, which can now be packaged and shared as a milestone deliverable. This foundation
ensures that downstream models can be developed and evaluated in an objective, reproducible, and
biologically meaningful manner.

## Cycle 2 — Baseline ML with ESM Embeddings

### Goal

To design and implement the first predictive baseline for enzyme–plastic interactions by framing the problem
as a supervised classification task (enzyme sequence → plastic type). This cycle focused on exploring
different enzyme sequence embeddings within a fixed ML framework and on testing strategies to handle class
imbalance between common and rare plastics. The objective was to assess the feasibility of prediction,
establish an initial baseline, and prepare insights for further model development.

### Sub-Cycle 2A — Embedding Comparison

#### Design

In our first modeling attempt, we adopted the most straightforward design: treating plastics as labels and
using enzyme sequences as inputs. The central question was whether sequence information alone is sufficient
to predict the corresponding plastic type. To make the model truly "understand" sequences, we needed to
transform the amino acid strings into numerical embeddings. Therefore, we tested multiple embedding
approaches to explore which representation would be most suitable as the starting point for baseline
prediction.

#### Build

![Embedding comparison methodology](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/complex102.webp)

* One-hot encoding: each amino acid is treated as an isolated symbol, without any notion of biochemical
  similarity.
* Physicochemical descriptors: each amino acid is described by properties such as charge, hydropathy, or
  secondary structure tendencies, giving more biological meaning.
* ESM embeddings: large protein language models (ESM) are trained on millions of sequences, learning
  evolutionary context — e.g., conserved regions, motifs, and functional patterns — and output rich
  numerical vectors for each sequence.
* All three representations were used as inputs to a fixed Histogram-based Gradient Boosting (HistGB)
  classifier.

#### Test

The comparison showed a consistent ranking: ESM embeddings > physicochemical features > one-hot. More complex
representations like ESM contained richer hidden information, which allowed the classifier to achieve higher
F1 and AUROC scores on the same dataset.

![F1 score comparison across different embedding methods](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/f1.webp)

#### Learn

We learned that advanced embeddings such as ESM capture hidden evolutionary and structural signals that
simpler encodings miss, leading to stronger performance. This highlights that in biological prediction
tasks, the choice of embedding method can substantially influence model outcomes. At the same time, the
experiment validated that our dataset (from Cycle 1) supports meaningful learning, providing a solid
baseline for future model iterations.

### Sub-Cycle 2B — Handling Class Imbalance (SMOTE)

#### Design

In plastic-degradation research, a few plastics such as PET or PLA have been extensively studied, resulting
in many training samples, while most plastics remain rarely represented. This long-tail distribution caused
the classifier to overfit frequent plastics and underperform on rare ones. Our goal in this sub-cycle was to
reduce imbalance so that rare plastics could be predicted more fairly.

#### Build

We applied SMOTE (Synthetic Minority Oversampling Technique) on the training set. SMOTE generates synthetic
samples by interpolating between existing minority-class samples in feature space. Conceptually, this is
like imagining plausible intermediate enzymes between rare sequences. Importantly, the validation and test
sets remained untouched to preserve a realistic distribution for evaluation.

#### Test

After applying SMOTE, rare plastics achieved clear improvements in F1 scores, while frequent plastics
remained stable. Confusion matrices showed fewer cases of rare labels being systematically misclassified as
common ones.

#### Learn

We confirmed that class imbalance strongly reduces rare-class performance, and that SMOTE can partially
compensate for this effect. However, treating plastics solely as labels still limits biological
interpretability, especially given the large number of small classes.

### Conclusion

In Cycle 2, we completed the first end-to-end predictive baseline for enzyme–plastic interactions. By testing
different embeddings under a fixed ML framework and applying SMOTE to handle imbalance, we achieved solid
predictive performance and confirmed the feasibility of our dataset for modeling. This established a
practical baseline benchmark. Building on this success, our next step is to move beyond the "labels-only"
framing and design models that incorporate biochemical principles and interpretable structures, enabling
deeper biological insight.

## Cycle 3 — Dual-Tower with Plastic Features

### Goal

In earlier attempts, plastics were treated only as labels, which caused imbalance and limited
interpretability. In reality, degradability arises from the interaction between enzyme properties and
plastic properties. The goal of this cycle is to design a model framework that can explicitly incorporate
plastic information, so predictions move beyond label-only learning and become more biochemically
grounded.

### Sub-Cycle 3A — Plastic Embedding Representation

#### Design

Molecular descriptors are widely used for chemical modeling tasks — they are numerical summaries of a
molecule's properties (e.g., charge, hydrophobicity, or atom counts). These usually perform well on small
molecules. However, most existing descriptors (e.g., RDKit features) were originally designed with small
molecules in mind. When applied directly to polymers, they show strong correlation with molecular size,
making dimensionality reduction maps cluster by chain length rather than true chemical similarity between
plastics. The central challenge became: how to design embeddings that reflect the intrinsic chemical
properties of plastics, instead of trivial size bias?

#### Build

![Plastic polymer embedding methodology](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/plasticpolymer.webp)

To address this, we introduced density normalization, dividing descriptors by the number of heavy atoms,
which reduced the dominance of molecular weight. Intuitively, this means turning descriptors into an
"average per atom" representation, so that embeddings focus on chemical composition rather than sheer size.
Additionally, we leveraged co-degradation relationships as a weak supervision signal: plastics that are
reported to degrade together were encouraged to form closer embeddings. This was implemented by pre-training
a plastic encoder with contrastive-style objectives, allowing embeddings to reflect both chemical logic and
biological relevance.

#### Test

![PCA analysis of plastic polymer embeddings](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/pca.webp)

Dimensionality reduction with PCA and UMAP revealed that normalized embeddings produced more chemically
coherent clusters. Polyesters grouped closely, polyolefins formed a separate cluster, and importantly,
members of the PHA (polyhydroxyalkanoate) family began to cluster together in line with their shared
biochemical properties. Unlike raw RDKit descriptors, the embeddings no longer simply followed molecular
weight gradients.

#### Learn

We learned that polymer-specific adjustments are essential for meaningful representations. Density
normalization effectively reduced molecular-weight artifacts, while co-degradation pre-training aligned
embeddings with known biological behavior. Together, these strategies provided a chemically interpretable
and biologically relevant representation of plastics.

### Sub-Cycle 3B — Interactive Model Architecture

#### Design

In previous cycles, plastics were treated purely as labels, which meant the model learned only from enzyme
features. However, degradability is the result of an interaction between enzyme and plastic properties. This
raised a key design question: instead of predicting labels, can we build a framework where both enzymes and
plastics are explicitly represented, and their compatibility is learned through interaction?

#### Build

Inspired by recommendation systems (e.g., user–item models), we constructed a dual-tower architecture. The
enzyme tower encodes proteins using backbones, while the plastic tower encodes polymers using the newly
developed embeddings (density-normalized + co-degradation pre-trained). An interaction head was then
implemented to combine the two towers, with different strategies tested:

* Dot product (measuring similarity directly)
* Bilinear layer (capturing feature-specific interactions)
* MLP fusion (nonlinear combination of features)

This design allowed enzyme–plastic compatibility scores to be learned in a structured and interpretable
manner.

#### Test

Evaluation showed that the dual-tower framework achieved more balanced performance across easy, medium, and
hard tiers, compared with the label-only baseline. This indicates stronger generalization: the model was not
only fitting frequent cases but also extending to more challenging, less-represented plastics.

#### Learn

We learned that enzyme–plastic interactions are best modeled as a two-sided problem, where both enzymes and
plastics contribute features. This interactive framework moved beyond simple label classification and laid
the foundation for biochemically interpretable predictions, bridging molecular representation with
functional outcomes.

### Conclusion

In this cycle, we moved beyond treating plastics as mere labels and introduced dedicated plastic embeddings.
With density-normalized descriptors and co-degradation signals, the embeddings captured chemical and
biological relevance more faithfully. Integrated into a dual-tower framework, enzymes and plastics were
encoded separately and combined through interaction heads, which improved interpretability and yielded more
balanced performance across difficulty tiers. However, the increased complexity of the architecture also
highlighted the limitations of current backbones, as overall metrics still showed room for improvement.

## Cycle 4 — Structure-Aware Backbones

### Goal

In the previous cycle, we improved interpretability by introducing plastic features. However, the enzyme side
was still represented only by sequences, which inevitably lose critical information. Since enzyme activity
is fundamentally determined by its 3D structure—including folds, catalytic pockets, and binding sites—this
cycle shifts focus to building structure-aware backbones, aiming to capture spatial and geometric features
essential for plastic degradation.

### Sub-Cycle 4A — Structure Sources & Reliability

#### Design

In the previous cycle, we realized that relying on sequences alone leaves out critical information. Enzyme
activity is fundamentally tied to 3D structure, such as folding, catalytic pockets, and binding residues. To
incorporate structural knowledge into modeling, we first had to decide where these structures would come
from. Protein structures can be obtained either from experiments (e.g., X-ray, Cryo-EM) or from
computational prediction (e.g., AlphaFold). For plastic-degrading enzymes, which mostly belong to
well-studied hydrolase families, AlphaFold provides high-confidence predictions. Since its method is based
on residue–residue contact patterns, naturally aligned with our graph construction, we asked: Can
AlphaFold-predicted structures serve as a reliable default for introducing structural backbones into our
model?

#### Build

We systematically retrieved AlphaFold models for enzymes in our dataset. Confidence scores (pLDDT) were
inspected, showing consistently high values for plastic-degrading families. Where available, experimental
PDBs were retained as gold standards, but the majority of entries were supplemented with AlphaFold
structures. All structures were processed into residue contact graphs, ensuring consistency regardless of
origin.

#### Test

![AlphaFold pLDDT confidence scores](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/plddt.webp)
> **pLDDT-colored predicted enzyme structure.**
>
> The predominance of deep blue regions (mean pLDDT = 97.7) indicates a highly confident
> and accurate structural prediction.

Benchmark comparisons showed that models trained with AlphaFold-predicted structures performed on par with
those using experimental PDBs. This demonstrated that AlphaFold is reliable for our dataset. Moreover, the
graph construction pipeline worked seamlessly across both sources, producing consistent node/edge
features.

#### Learn

We confirmed that AlphaFold can serve as a robust default source of structures, freeing the project from the
bottleneck of limited experimental PDB coverage. This not only increased the dataset's completeness but also
ensured scalability, as any new enzyme sequence can directly be assigned a predicted structure. Thus,
structure-aware modeling becomes practical at scale, without being constrained by experimental
availability.

### Sub-Cycle 4B — Graph Representation of Enzyme Structures

#### Design

To move beyond sequence-only inputs, we needed a way to represent enzyme 3D structures in a machine-readable
format. Proteins can be naturally modeled as graphs, where residues are nodes and contacts define edges.
This raised the central question: how can we construct residue-level contact graphs that preserve both
biochemical and spatial information?

#### Build

Instead of discarding sequence embeddings, we reorganized them into a graph representation. Each residue's
initial embedding was obtained from ESM, preserving evolutionary and biochemical context. These embeddings
were then assigned to graph nodes, while edges encoded spatial proximity (e.g., Cα–Cα distances within 10Å).
In this way, the model could jointly leverage sequence-derived residue embeddings and their 3D spatial
relationships, making the enzyme backbone structure-aware.

![Protein graph representation with ESM embeddings](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/engineering/protein-graph-representation.webp)

#### Test

Initial experiments showed that models with structural graphs achieved more balanced performance across
difficulty tiers compared to sequence-only baselines. Notably, enzymes with low sequence similarity but
similar folds were better recognized, suggesting that structural context improved generalization.

#### Learn

From an engineering synthetic biology perspective, this step confirmed that proteins can be represented not
just as linear sequences but as residue contact graphs, where biochemical properties are grounded in a
physical 3D scaffold. By embedding structural constraints into the model, we move closer to capturing the
true determinants of enzyme activity — pockets, binding sites, and catalytic environments — which sequences
alone cannot fully describe. This validates that structural embeddings add essential information missing
from pure sequence models, and provides a foundation for building interpretable, design-oriented predictive
frameworks in future cycles.

### Sub-Cycle 4C — GNN vs. GVP Backbones

#### Design

After confirming that residue contact graphs provide structural value, the next question was: is it enough to
only know which residues are in contact, or do we also need to encode their 3D orientation? Standard GNNs
(GCN, GraphSAGE, GAT) propagate scalar features across graph edges, but they cannot directly represent
vector information such as bond directions or spatial orientation. Geometric Vector Perceptrons (GVPs)
extend this idea by introducing both scalar and vector channels, enabling models to capture structural
geometry.

#### Build

We implemented two enzyme backbones:

* GNN-based: residue graphs with scalar node features (AA identity, charge, hydrophobicity) and
  distance-based edges.
* GVP-based: same residue graphs, but augmented with 3D vector channels (residue coordinates, orientation
  vectors) to encode spatial geometry.

Both were trained within the dual-tower framework, ensuring a fair comparison.

#### Test

Both GNN and GVP backbones outperformed sequence-only baselines, especially on medium/hard tiers. Among them,
GVP showed the most consistent improvements, yielding higher F1 and AUROC, and more stable predictions
across rare plastics.

#### Learn

We learned that while GNNs capture who contacts whom, GVPs go further by encoding how residues are oriented
in 3D. This geometric sensitivity not only improved accuracy but also enhanced interpretability, as
important catalytic residues were better highlighted. From an engineering perspective, this confirmed that
integrating geometric structure is essential for biochemically meaningful models.

### Conclusion

By integrating structure-aware backbones—retrieving reliable 3D structures (with AlphaFold as default),
representing them as residue contact graphs, and adopting geometry-aware GVP networks—our model achieved its
best F1 scores to date, with more stable performance across difficulty tiers. More importantly, it gained
biochemical interpretability, as predictions naturally highlighted catalytic residues and structural motifs.
At this stage, our framework has matured into its most complete form: accurate, robust, and biologically
consistent. However, the project remains highly specialized, and the current technical complexity still sets
a high barrier for many intended users in the biological community.

## Cycle 5 — User-Friendly Web Application

### Goal

To evolve the specialized research framework into a more accessible and user-friendly "one-stop" online
platform, making it easier for researchers across diverse backgrounds — including those primarily engaged in
wet-lab or experimental work — to explore enzyme–plastic interactions. The goal was to create an intuitive
web application that integrates both model prediction and database exploration, offering a seamless,
installation-free experience for users worldwide.

### Design

The platform's design process was grounded in extensive feedback from its target users — including
researchers in microbiology, environmental science, synthetic biology, and chemistry. These discussions
revealed a shared need for an integrated "one-stop" online environment that combines predictive modeling
with a comprehensive enzyme–plastic database. The guiding design principle was to achieve usability and
efficiency without compromising scientific rigor. The platform had to be approachable for users unfamiliar
with computational tools, yet powerful enough to meet the standards of professional research.

### Build

![](https://static.igem.wiki/teams/5580/xjtluaichina2025/assets/images/matching-pictures/webapp/model-selector-function.avif)

The platform is deployed on AWS cloud servers to ensure global accessibility and stable performance. It
consists of two primary components: the Plaszyme model platform and the PlaszymeDB online database.

The model platform integrates two complementary predictors: PlaszymeAlpha, a sequence-based model for rapid
predictions directly from FASTA files, and PlaszymeX, a structure-informed model designed for higher
interpretability.

To simplify user interaction, we built an automated backend workflow that integrates colabfold\_batch for 3D
structure prediction. This design follows the "less is more" principle — users only need to provide
sequences once, and the system automatically handles structure generation and model inference. It also
supports direct prediction from user-uploaded PDB files when available.

To meet the needs of large-scale research scenarios, the platform also includes a metagenomic analysis
module, which employs a custom HMM pipeline to discover potential enzyme candidates from environmental
datasets for prediction. All model outputs follow a standardized format, providing degradation
probabilities, confidence scores, an interactive 3D molecular viewer (Mol\* API), and CSV export options for
further analysis.

The PlaszymeDB online database complements the model platform by serving as a centralized, searchable
knowledge base. It offers a high-performance search algorithm based on traversal logic, interactive data
visualizations, and a locally developed BLAST alignment tool tailored to our curated database. Phylogenetic
relationships are dynamically displayed via an embedded iTOL tree, while detailed enzyme pages integrate
multiple interactive modules through iframes — including a 3D structure viewer (Mol\* API), a chemical
formula renderer (Ketcher API), full amino acid sequences, literature citations, and EC annotations derived
from both curated references and our DeepEC-based functional predictions.

### Test

We conducted a two-stage validation process to ensure both technical correctness and user usability.

**(1) Internal functional testing:** Using established benchmark datasets, we performed
end-to-end predictions directly on the live platform. The system successfully reproduced expected outcomes,
verifying the model's accuracy and confirming that the backend workflow operated as intended. Additional
simulations were conducted for the metagenomic input pipeline, confirming that large-scale sequence inputs
were correctly processed from structure prediction to output generation.

**(2) User acceptance testing:** We invited several researchers from our earlier user
interviews—representing the microbiology, environmental, synthetic biology, and chemistry communities—to
test the public platforms (http://plaszyme.org/plaszyme and http://plaszyme.org/plaszymedb). They completed
full prediction and database exploration workflows without prior instruction, providing direct usability
feedback. Their responses consistently highlighted that the system was intuitive, efficient, and practical
for real laboratory workflows, validating that our design successfully met its accessibility goals.

### Learn

Throughout this process, we realized that developing an effective scientific tool must begin with
understanding the real-world research contexts it is meant to serve—such as the metagenomic analysis of
environmental samples and microbial communities.

Continuous dialogue with microbiology, environmental, chemistry, and synthetic biology researchers helped us
recognize that accessibility and professionalism must coexist. Their feedback shaped our guiding principle:
a platform should be easy to use without oversimplifying the science behind it.

The consistently positive responses to the platform's clarity and smooth workflow validated this approach.
Ultimately, we learned that the real value of computational frameworks lies not only in prediction accuracy,
but in bridging advanced algorithms with practical usability, enabling researchers to focus on discovery
rather than deployment. This experience also revealed the platform's potential to expand toward more
specialized applications in the future.

up

{% endblock %}