iGEM 2025 — Team shanghaitech-china

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## Treatment of Rhinosinusitis

### Prokaryotic Expression of GZMK

### Eukaryotic Expression of GZMK

### GZMK Properties Related Experiments

### The Activity Assay of GZMK

### Small Molecule Screening

## Diagnosis of Rhinosinusitis

### Binder Purification and Affinity Measurement

### Construction of Colloidal Gold Test Strip

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Wet Lab
Modeling
Software
Hardware

## Expression of Activated GZMK

### Cycle 1: Acquisition of GZMK DNA

#### Design

To express GZMK, we need to obtain its DNA sequence. Considering the long synthesis cycle and high cost of synthesizing DNA sequences through commercial services, we propose to enrich and clone the GZMK DNA from a human genomic library.

#### Build

We designed two primers that bind to sequences flanking the GZMK gene on human chromosomes to enrich the GZMK gene from the genomic library. To enhance success rates, we developed primers of varying lengths (annealing temperatures) to balance nonspecific binding and yield.

![](https://static.igem.wiki/teams/5553/description/add/2wetlabwiki-01.webp)

#### Test

Using the human genome as a template, PCR was performed with the designed primers.

For shorter primers with lower specificity, numerous nonspecific bands were observed due to nonspecific binding. For longer primers with higher specificity, no specific binding was detected at various temperatures, and the target GZMK band was not obtained.

![](https://static.igem.wiki/teams/5553/engineer/image.webp)

PCR Results for GZMK

#### Learn

The PCR results showed that low-specificity primers generated multiple nonspecific bands, while high-specificity primers failed to amplify the target band under various temperature conditions. This suggests that the GZMK gene is present at very low abundance or even absent in the genomic library used, rather than the issue being solely due to primer design. Therefore, we concluded that enrichment from the genomic library is not feasible and shifted to commercial DNA synthesis to ensure a correct and usable GZMK template.

### Cycle 2: Prokaryotic Expression

#### Design

To screen for inhibitors and binding proteins of GZMK, obtaining active GZMK is critical. Based on literature review, GZMK can be expressed using either prokaryotic or eukaryotic vectors. We prioritized prokaryotic expression due to its lower requirements for equipment and reduced cost.

#### Build

We removed the signal peptide from the human GZMK gene, retained the proenzyme fragment, and performed codon optimization for Escherichia coli before cloning it into the pET-28a vector for GZMK protein expression in E. coli. As GZMK is a human-derived protein, expression in E. coli leads to incorrect folding and the formation of inclusion bodies, necessitating denaturation and refolding procedures for the inclusion bodies.

![](https://static.igem.wiki/teams/5553/description/add/2wetlabwiki-02.webp)

#### Test

We transformed the plasmid into Escherichia coli and cultured until turbidity was observed. Induction was performed at 37°C for 4–5 hours, followed by centrifugation to collect the bacterial cells. After cell lysis and centrifugation, a white precipitate was obtained, which was confirmed to contain the target protein GZMK via SDS-PAGE and Western Blot analysis.

![](https://static.igem.wiki/teams/5553/engineer/image-1.webp)

Results of GZMK Prokaryotic Expression

The inclusion bodies were collected and dissolved in a denaturation buffer. The solution was then slowly pumped into a refolding buffer, followed by dialysis to exchange the buffer, yielding a GZMK solution. The presence of the target protein GZMK was confirmed via SDS-PAGE and Western Blot analysis.

![](https://static.igem.wiki/teams/5553/engineer/image-2.webp)

Results of GZMK Denaturation and Refolding

GZMK was activated using Cathepsin C (CTSC), and its enzymatic activity was subsequently measured, revealing no expected activity.

#### Learn

SDS-PAGE and Western Blot confirmed that GZMK protein with the correct molecular weight and tag was obtained. However, the absence of enzymatic activity indicated that the problem was not at the genetic construction or translation level, but rather during the refolding process. As a human-derived protein, GZMK tends to form inclusion bodies in E. coli, and improper refolding conditions may result in misfolded or disordered protein that lacks activity. Thus, we concluded that optimizing refolding conditions is essential to obtain functional GZMK.

### Cycle 3: Optimization of Prokaryotic Expression Conditions

#### Design

Based on Cycle 2, we need to modify the refolding conditions to obtain correctly folded GZMK.

#### Build

Through literature and textbook review, we found that the temperature during the refolding process affects the formation of disulfide bonds, which are critical for stabilizing the GZMK structure and generating its activity. Therefore, based on the literature, we increased the refolding temperature to promote the formation of correct disulfide bonds.

#### Test

Inclusion bodies were collected, dissolved in denaturation buffer, and slowly pumped into refolding buffer, with the refolding temperature raised from 4°C to room temperature (approximately 25°C) and a refolding duration of 30 hours.

After 30 hours of refolding at room temperature, a large amount of white flocculent precipitate formed in the solution, which was identified as denatured GZMK protein.

#### Learn

The refolding temperature experiments revealed that at low temperatures, GZMK failed to form correct disulfide bonds and proper structure, while at higher temperatures, it denatured and precipitated. Considering these results along with time and cost constraints, we recognized that the prokaryotic system is unlikely to meet the requirements for correct folding and enzymatic activity. Hence, we decided to abandon further optimization in the prokaryotic system and shift to eukaryotic cell expression, which naturally supports proper folding and modifications.

### Cycle 4: Signal Peptide Selection

#### Design

GZMK is a human protein, and its expression in mammalian cells naturally facilitates the formation of correct disulfide bonds and proper folding, eliminating the need for denaturation and refolding processes. However, due to its dual intracellular and extracellular toxicity, we need to express it in the form of a secreted zymogen.

#### Build

We utilized HEK 293F cells to express GZMK via secretory expression. To this end, we constructed the following plasmids. The choice of signal peptide affects secretion efficiency, and we selected three signal peptides reported in the literature and constructed corresponding plasmids.

![](https://static.igem.wiki/teams/5553/description/add/2wetlabwiki-03.webp)

#### Test

Primers were first designed for PCR, and the results showed that the GZMK-1 vector could not be cloned, likely due to template contamination or degradation. Consequently, GZMK-2 and GZMK-3 were transfected into HEK 293F cells for protein expression.

![](https://static.igem.wiki/teams/5553/engineer/image-3.webp)

PCR Results for Eukaryotic Expression of GZMK

Cells were periodically sampled, and Western Blot was used to assess protein expression levels. The secretion level of GZMK-2 was extremely low, whereas GZMK-3 exhibited high secretion levels, allowing GZMK to be obtained from the culture medium.

![](https://static.igem.wiki/teams/5553/engineer/image-4.webp)

Expression Status of Eukaryotic GZMK

The culture medium of GZMK-3 was collected and subjected to affinity chromatography followed by size-exclusion chromatography. High-purity GZMK solution was obtained, as verified by SDS-PAGE.

![](https://static.igem.wiki/teams/5553/engineer/image-5.webp)

Purification Results of GZMK

#### Learn

The comparison of three signal peptides showed that only GZMK-3 achieved efficient secretion and strong expression in HEK293F cells, while other constructs either failed to clone or exhibited very low secretion efficiency. This demonstrated that signal peptide selection plays a critical role in the success of secretory protein expression. We therefore identified GZMK-3 as the optimal construct and successfully obtained high-purity GZMK, laying a solid foundation for subsequent functional assays.

### Cycle 5: Enzymatic Cleavage to Obtain Active GZMK

#### Design

Considering the cytotoxicity and stability of active GZMK, we expressed GZMK with its propeptide. After purification, the propeptide was cleaved in vitro to activate GZMK, yielding enzymatically active GZMK.

#### Build

Due to the difficulty in obtaining Cathepsin C, the enzyme responsible for propeptide cleavage in humans, we modified our approach by using the commonly available tool enzyme Enterokinase (EK). During vector construction, the signal peptide was followed by the EK cleavage sequence (DDDDK), and then the mature GZMK sequence, ensuring that EK processing yields enzymatically active GZMK.

![](https://static.igem.wiki/teams/5553/engineer/2wetlabwiki-04.webp)

#### Test

The GZMK zymogen solution was dialyzed to exchange into the working buffer for enterokinase. Enterokinase was added according to the recommended dosage, and dialysis was performed at room temperature for 16 hours to obtain the cleaved solution. Enzyme activity was measured using an instrument, revealing significant activity compared to the control sample.

![](https://static.igem.wiki/teams/5553/engineer/image-6.webp)

GZMK Activity Curve

Absorbance Data Table (click to expand / collapse)

Tip: Scroll horizontally if the table is wide.

If the table did not load, please check whether `CSV_URL` is correct and the file is accessible.

#### Learn

By introducing an EK cleavage site into the construct, we successfully enabled in vitro activation of GZMK zymogen and observed the expected enzymatic activity after cleavage. This demonstrated the feasibility of our design, with all genetic elements (signal peptide, cleavage site, etc.) functioning as intended. The resulting active GZMK not only validated our experimental strategy but also provided a reliable protein source for downstream affinity measurements and inhibitor screening.

### Cycle 6: Optimized GZMK Expression

#### Design

The subsequent inhibitor screening and affinity measurements require a substantial amount of GZMK, necessitating large-scale expression. Based on previous experiments, we continuously optimized the experimental protocol and ultimately established a stable protocol for the expression and purification of GZMK.

#### Build

To distinguish GZMK from its binding proteins, we replaced the His tag with a Flag tag and removed the linker between GZMK and the tag to prevent artificial glycosylation. The final construct utilized the pcDNA3.1 plasmid, into which the IgK signal peptide, EK cleavage site, GZMK sequence, and Flag tag were inserted.

![](https://static.igem.wiki/teams/5553/engineer/image-7-1.webp)

Schematic Diagram of the Final GZMK Construct

#### Test

The aforementioned vector was transfected into HEK 293F cells, and the supernatant was collected after 72 hours. Affinity chromatography using Flag resin was performed, followed by concentration and EK cleavage. Post-cleavage, size-exclusion chromatography was conducted, with the elution peak corresponding to the size of GZMK. The protein at the peak fraction was aliquoted and cryopreserved. Subsequent activity testing revealed significant activity compared to the control.

![](https://static.igem.wiki/teams/5553/engineer/image-8.webp)

SEC Image and Enzyme Activity Curve of GZMK

#### Learn

Through iterative exploration and optimization in the previous cycles, we ultimately established a stable and efficient workflow for producing active GZMK. This system combined appropriate signal peptide selection, tag design, and cleavage strategy, ensuring both yield and functionality. With aliquoted cryopreservation, we secured sufficient and stable supplies of GZMK, providing a solid foundation for large-scale affinity measurements and inhibitor screening in subsequent experiments.

## Quantitative GZMK Activity Testing

### Cycle 1: Activity Assay of Prokaryotically Expressed GZMK Using Z-Lys-SBZL

#### Design

To evaluate the biological function of prokaryotically expressed GZMK, we designed this experiment to measure its protease activity following activation by the key enzyme, Cathepsin C (CTSC).

#### Build

We selected Z-Lys-SBZL as a specific chromogenic substrate for GZMK. Active GZMK can cleave this substrate to release a free thiol group (-SH), which in turn reacts with DTNB to produce a yellow product with maximum absorbance at 412 nm. Enzyme activity is quantified by monitoring the change in absorbance. Due to instrument limitations, we ultimately chose to perform the measurement at a wavelength of 405 nm.

![](https://static.igem.wiki/teams/5553/engineer/image-9.webp)

Structure of Z-Lys-SBZL

#### Test

We mixed CTSC-activated GZMK, the substrate Z-Lys-SBZL, and the chromogenic reagent DTNB in reaction buffer, and set up different control groups. The absorbance at 405 nm was measured using a microplate reader, and the results are shown below. Compared with the blank control, the absorbance of the GZMK+CTSC group increased significantly. However, the CTSC-only group also showed a similarly high signal, suggesting that fluorescence interference might originate from CTSC itself. Moreover, since the fluorescence intensity curve of the GZMK+CTSC group was a flat line rather than the expected sloped line, we concluded that GZMK exhibited no enzymatic activity.

![](https://static.igem.wiki/teams/5553/engineer/add/image-1.webp)

Activity Assay of GZMK Based on Z-Lys-SBZL

#### Learn

The GZMK+CTSC group showed an elevated baseline but a near-flat time course, while the CTSC-only control also produced strong signals. Given DTNB’s thiol sensitivity, we infer the interference arises from reducing agent DTT in the CTSC buffer, which react directly with DTNB, yielding a high background without kinetic slope. Therefore, these data do not establish whether the prokaryotic GZMK is active; the core issue is assay interference. Next, we switch to a DTT-free activator (EK in the next cycle) to restore kinetic interpretability.

### Cycle 2: Activity Assay of Eukaryotically Expressed GZMK Using Z-Lys-SBZL

#### Design

The objective of this experimental cycle was to verify whether the GZMK obtained from eukaryotic expression exhibits the expected protease activity after being cleaved and activated by Enterokinase.

#### Build

The experimental principle was the same as in the previous cycle: the assay quantifies enzyme activity by using GZMK to cleave the substrate Z-Lys-SBZL, which releases a thiol group. This thiol group then reacts with DTNB to produce a color signal detectable at 405 nm.

#### Test

We mixed the Enterokinase-activated GZMK solution with the substrate Z-Lys-SBZL and DTNB. To eliminate potential interference, we included both a blank control and a control group containing only Enterokinase. The results showed that while the absorbance of the GZMK experimental group increased significantly, the Enterokinase control group also exhibited a notable rise in absorbance.

![](https://static.igem.wiki/teams/5553/engineer/image-11.webp)

Validation of GZMK Activity Before and After Enterokinase Cleavage

#### Learn

After EK activation, the GZMK group increased markedly, suggesting catalytic activity; however, the EK-only control also rose, indicating persistent background. The hierarchy “GZMK+EK > EK-only > blank” implies a positive net GZMK effect (after subtracting EK-only), yet EK or its buffer contributes to background. To clarify the mechanism and reduce error, the next cycle modulates EK activity (Ca²⁺/EDTA) to test whether EK directly cleaves the substrate or indirectly affects the readout.

### Cycle 3: Investigation into the Influence of Enterokinase on Enzyme Activity Assay

#### Design

We hypothesized that Enterokinase (EK) itself might cleave the substrate, thus interfering with the accurate measurement of GZMK activity. This experiment was designed to test this hypothesis and to explore whether this interference could be eliminated by modulating EK activity (via Ca²⁺ concentration) to obtain more precise GZMK activity data.

#### Build

Our strategy involved using Ca² ⁺ as an activator and EDTA as an inhibitor of Enterokinase. By adding supplemental Ca² ⁺ (to enhance its activity) and EDTA (to inhibit its activity by chelating Ca²⁺) to separate reaction systems, we expected to observe corresponding changes in the background signal, thereby validating our hypothesis.

#### Test

The specific experimental groups were set up as follows: the final concentration of calcium ions was 2mM, and the final concentration of EDTA was 4mM.

![](https://static.igem.wiki/teams/5553/engineer/image-12.webp)

Investigation of the Effect of Enterokinase on GZMK Activity

#### Learn

If our hypothesis were correct, Ca²⁺ should elevate and EDTA should reduce EK-related background; the observed changes did not match these predictions, failing to support EK directly cleaving Z-Lys-SBZL as the sole cause. The background is likely multifactorial (e.g., sensitivity of the substrate/chromophore to protein impurities, ions, or trace reductants). Since we cannot reliably isolate the true signal, the Z-Lys-SBZL + DTNB system is unsuitable for quantitative analysis here. We therefore switch to a fluorogenic peptide substrate to achieve higher S/N and cleaner kinetics.

### Cycle4: Activity Assay of GZMK Based on Fluorescent Peptide Substrate

#### Design

Given that the Z-Lys-SBZL colorimetric assay could not provide precise quantitative data, a critical requirement for our subsequent inhibitor screening experiments, we decided to develop and establish a new activity assay system based on a specific fluorogenic peptide substrate.

#### Build

Based on literature research, we designed and synthesized a specific fluorogenic peptide substrate (DABCYL-GDGRSIMTE-EDANS). The peptide sequence is a specific cleavage site for GZMK and is flanked by a fluorophore (EDANS) and a quencher (DABCYL). Upon cleavage by GZMK, the two are separated, abolishing the quenching effect. The fluorophore then emits light at 490 nm upon excitation at 340 nm, with the fluorescence intensity being directly proportional to enzyme activity.

![](https://static.igem.wiki/teams/5553/engineer/add1.webp)

Principle of Fluorescent Substrate Color Development

#### Test

We performed the activity assay in a standard buffer system. The final concentration of GZMK was 0.42 μM, and the fluorogenic peptide substrate was tested at three different concentrations (250, 125, and 62.5 μM), with two replicates per group. A blank control group without GZMK was also included. We monitored the change in fluorescence signal continuously for 30 minutes. It should be noted that due to filter limitations, the actual emission wavelength we measured was 460 nm.

![](https://static.igem.wiki/teams/5553/engineer/image-13.webp)

Activity Assay of GZMK Based on Fluorescent Peptide Substrate

#### Learn

With a low, stable blank, all assay groups showed time-dependent increases that scaled with substrate concentration, yielding the expected kinetic slopes. Despite reading at 340/460 nm instead of the canonical 340/490 nm, the net signal remained strong, indicating robustness. We therefore confirm this peptide is a specific, efficiently cleaved substrate for GZMK, and the assay platform provides the reproducibility and sensitivity required for kinetic analysis and inhibitor screening.

### Cycle5: GZMK Activity Assay

#### Design

After successfully establishing and validating the fluorogenic peptide-based activity assay, we used this system to perform a systematic enzyme kinetics analysis of the GZMK obtained from our eukaryotic expression and purification pipeline.

#### Build

We treated GZMK as an enzyme that follows Michaelis-Menten kinetics. The experimental method involved measuring the initial reaction velocity (the rate of fluorescence increase at the start of the reaction) at a fixed GZMK concentration across a series of varying fluorogenic peptide substrate concentrations. Finally, the data was fitted to the Michaelis-Menten equation using GraphPad Prism software to calculate the Michaelis constant (Km) and maximum reaction velocity (Vmax).

#### Test

We set up a series of substrate concentrations ranging from 0.9375 μM to 500 μM, with three parallel replicates and one blank control for each concentration. To correct for spontaneous substrate hydrolysis, the data used for the final fitting was calculated by subtracting the control group values from the average of the replicate groups. During data processing, we excluded the data points for the 500 μM and 125 μM concentrations due to anomalies or contamination.

![](https://static.igem.wiki/teams/5553/engineer/image-14.webp)

Enzyme Kinetics Assay of GZMK

#### Learn

Through Michaelis-Menten fitting, we successfully obtained key enzyme kinetic parameters for GZMK: a Km value of 50.20 μM, indicating a relatively high affinity between GZMK and the fluorogenic substrate, and a Vmax of 437.5 RFU/min. These data provide a critical baseline for subsequent inhibitor screening experiments.

## High-Precision Inhibitor Screening

### Cycle1: Determination of Screening Conditions

#### Design

Before initiating large-scale screening, we need to establish various conditions for the screening process, including substrate concentration, protein concentration, and inhibitor concentration. Therefore, we plan to conduct preliminary experiments to investigate these factors.

#### Build

For substrate concentration, we set two final concentrations: 50 μM and 100 μM. For protein concentration, we set four final concentrations: 25 nM, 50 nM, 100 nM, and 200 nM. The total volume of each reaction system was set to 50 μL.

#### Test

Based on the conditions described above, we established 10 groups for preliminary experiments. The basic information, fluorescence intensity curves, and curve slopes are as follows:

![](https://static.igem.wiki/teams/5553/engineer/image-15.webp)

Preliminary Screening Experiment Results

#### Learn

The pilot runs showed that 100 μM substrate produced a larger initial-rate separation within the same time window while maintaining a clean linear range. At 100 nM enzyme, the slope difference versus control was already robust, and higher enzyme levels mainly increased consumption with limited gain in window size. Balancing signal-to-noise, substrate use, enzyme economy, and linear initial-rate stability, we selected 100 μM substrate with 100 nM enzyme for large-scale screening, which secures adequate kinetic slope while preserving throughput and cost efficiency.

### Cycle2: Large-Scale Screening

#### Design

We selected the L1000 compound library, which contains 1,813 approved drugs, all of which have been approved by regulatory agencies such as the FDA, EMA, and CFDA. We will screen the entire library.

#### Build

Each reaction system contains HEPES buffer, 84.23 nM GZMK protein, 100 μM fluorescent substrate, 1 mM inhibitor, 0.1 mg/mL BSA, and 0.01% Triton X-100. Fluorescence intensity was measured once per minute for 15 cycles.

#### Test

Due to the large volume of data, we cannot present all results here. Among the 1,813 compounds tested, three exhibited results consistent with expectations based on fluorescence intensity and excitation light curves.

![](https://static.igem.wiki/teams/5553/engineer/add/image-2.webp)

Results of Large-Scale Preliminary Screening

#### Learn

Under the chosen parameters, most compounds showed only background-level variation, while three compounds produced a stable and reproducible drop in initial rate across replicates with kinetics consistent with true inhibition. Together with BSA and low Triton X-100 suppressing nonspecific aggregation, this supports these as true positives rather than optical or aggregation artifacts. The next step is retesting and dose–response confirmation to rule out autofluorescence, inner-filter effects, and precipitation.

### Cycle 3: Determination of Precise Inhibitory Effects

#### Design

Based on the results from the previous large-scale screening, we selected Nafamostat mesylate, which exhibited the highest inhibition rate, for more precise characterization. The IC50 value, which indicates the concentration of a protease inhibitor required to inhibit 50% of the target enzyme’s activity, is a critical parameter for evaluating drug potency and selecting candidate compounds. The IC50 curve, by demonstrating the dose-response relationship, aids researchers in optimizing drug design, predicting effective concentrations, and assessing potential therapeutic efficacy. We aim to measure the IC50 value of Nafamostat mesylate.

#### Build

To determine the IC50 value of Nafamostat mesylate, we established a control curve of GZMK activity at varying concentrations of Nafamostat mesylate. Starting from 200 μM, we performed serial twofold dilutions to create 18 concentration gradients, with the lowest concentration being approximately 1.53 nM, for the GZMK activity assay.

#### Test

During the experiment, GZMK was first mixed with Nafamostat mesylate and incubated for 90 seconds. The substrate was then added and mixed, followed by immediate measurement of fluorescence intensity in a microplate reader, recorded once per minute for a total of 15 cycles.
After data processing, we obtained the Nafamostat mesylate concentration–GZMK activity inhibition rate curve. Curve fitting yielded an IC50 value of 0.1951 μM.

![](https://static.igem.wiki/teams/5553/engineer/add/image-3.webp)

Nafamostat Mesylate Concentration–GZMK Activity Inhibition Rate Curve

#### Learn

Dose–response fitting yielded an IC50 of 0.1951 μM for Nafamostat mesylate, indicating submicromolar potency and significant inhibition at low concentrations. This provides a quantitative baseline for selectivity profiling and mechanism assignment, and a reference for defining a standard control and refining hit thresholds. Next we will verify reproducibility, compare apparent IC50 stability across substrate levels, and extend to mechanism and reversibility assays.

## High-Throughput Expression of Binders

### Cycle 1: Positive Control Expression

#### Design

Subsequent experiments involving binders require a positive control, i.e., a pair of proteins confirmed to bind to each other. We also need such a pair to validate the entire experimental workflow. Therefore, before formally initiating binder expression, we will express a pair of positive control proteins.

#### Build

Our requirements for the positive control include simple expression (preferably via direct expression in Escherichia coli), moderate affinity (Kd in the μM range), and stable properties (to facilitate testing under extreme conditions). Ultimately, we identified a suitable protein pair: Ubc9 and SUMO1.

#### Test

We cloned Ubc9 and SUMO1 into plasmids and expressed them in Escherichia coli. Following affinity chromatography and size-exclusion chromatography, high-purity Ubc9 and SUMO1 solutions were obtained. Subsequently, surface plasmon resonance (SPR) was used to measure binding affinity, yielding a Kd of 8.267 μM.

![](https://static.igem.wiki/teams/5553/engineer/image-17.webp)

Affinity Assay of SUMO1 and Ubc9

In the figure above, the Raw Data section shows the real-time SPR response curves. Each colored line corresponds to a different concentration of binder injected over immobilized GZMK on the chip. As the concentration increases, the response signal rises accordingly, and decreases during the dissociation phase. This concentration-dependent response is a hallmark of specific binding. Minor negative values or sharp fluctuations observed in some curves are background noise or nonspecific signals, which do not affect the overall interpretation.

In the Fitted Data section, the x-axis represents sample concentration and the y-axis represents equilibrium response. Colored points indicate experimental data, while the black or gray line represents the fitted model. Good agreement between data points and the fitted curve indicates reliable results, from which the dissociation constant (Kd) can be calculated. The Kd value reflects the strength of molecular interaction: a lower Kd indicates tighter binding, whereas a higher Kd suggests weaker binding.

#### Learn

The workflow was successfully completed, with Ubc9 and SUMO1 expressed stably in E. coli and purified to high quality. SPR analysis yielded a binding affinity in the micromolar range, meeting our design criteria for a positive control. This confirms that our experimental pipeline is functional and provides a reliable protein pair. The positive control can be used not only for binder validation but also for testing hardware devices and colloidal gold test strips.

### Cycle 2: High-Throughput Protein Expression

#### Design

Due to the currently low success rate of computationally designed binders, we need to express a large number of proteins to ensure the identification of the target protein, necessitating high-throughput protein expression.

#### Build

Simply increasing the number of samples processed simultaneously in each experiment would reach a bottleneck, as many instruments have limited throughput and cannot handle multiple samples concurrently. Instead, we opted to overlap multiple rounds of experiments temporally, conducting different stages of multiple experiments simultaneously to maximize efficiency.

#### Test

The current protein purification cycle takes 4 days: Day 1 involves transformation and plating, Day 2 includes picking single colonies and small-scale culture, Day 3 entails scaling up to large-scale culture and induction, and Day 4 consists of cell harvesting, affinity chromatography, and size-exclusion chromatography. We conduct two rounds of expression simultaneously, with Days 3 and 4 of the first batch overlapping with Days 1 and 2 of the next batch.

![](https://static.igem.wiki/teams/5553/engineer/add/2wetlabwiki-05.webp)

#### Learn

By adopting a temporal overlapping strategy, we overcame the limitations of single-batch instrument throughput, increasing overall output while maintaining the same experimental cycle. Results confirmed that overlapping steps from different batches did not interfere with each other. This approach ensured consistent expression quality while significantly boosting efficiency, making it highly suitable for processing the large number of samples required in binder screening.

### Cycle 3: Rapid Protein Expression

#### Design

Under specific circumstances (e.g., completing expression within a single weekend), we require a shorter cycle than the original 4-day protocol to achieve rapid protein expression.

#### Build

The key factor limiting the experimental duration is several overnight steps exceeding 10 hours, including the overnight plate culture on Day 1, which is necessary for picking single colonies. Since the goal of our experiment is to obtain protein, whether single colonies are used does not impact the outcome, allowing this step to be omitted.

#### Test

The new experimental cycle spans two days plus one night: transformation and small-scale culture on the evening of Day 1, scale-up to large-scale culture and induction on Day 2, and cell harvesting, affinity chromatography, and size-exclusion chromatography on Day 3. Experimental validation confirmed that protein expression results are equivalent to those of the standard 4-day cycle.

#### Learn

By omitting non-essential overnight steps, we successfully shortened the cycle to two days plus one night, with protein yields equivalent to the standard four-day protocol. This demonstrates that certain steps can be streamlined according to experimental goals, allowing flexible adaptation to time-constrained situations. For instance, during weekends or urgent schedules, this method enables efficient completion of experiments within limited time.

### Cycle 4: Segmental Protein Expression

#### Design

During the semester, team members typically have classes from Monday to Friday, leaving substantial time for experiments over the two weekend days. Using the original experimental method, only one batch of experiments could be completed per week, resulting in low efficiency.

#### Build

We found that in the 3-day rapid expression protocol, the first two days involve minimal experimental work, which can be completed between classes. The final day requires over 10 hours of experimental time. Therefore, we pause the experiment on the morning of the third day and resume over the weekend, achieving higher efficiency.

#### Test

Segmental Protein Expression: Transformation and small-scale culture are performed on the evening of Day 1, followed by scale-up to large-scale culture and induction on Day 2. On Day 3, cells are harvested and the bacterial pellet is cryopreserved, pausing the experiment. Upon resumption, cell lysis, affinity chromatography, and size-exclusion chromatography are conducted, taking approximately 10 hours. Experimental validation confirmed that protein expression results are equivalent to those of the standard 4-day cycle.

![](https://static.igem.wiki/teams/5553/engineer/add/2wetlabwiki-06.webp)

#### Learn

By cryopreserving the bacterial pellet on Day 3 and resuming the process over the weekend, we effectively overcame the limitation of restricted weekday hours. Results showed that freezing did not impair protein expression or purification quality, and the outcomes were consistent with the full cycle. This method makes full use of spare time during the week and extended weekend hours, enabling more experimental batches per week and significantly enhancing overall efficiency.

## Optimized Binder Affinity Assay

### Cycle 1: Preliminary Experiment for Feasibility Validation

#### Design

Through preliminary in-depth research, we found that SPR technology can achieve high-throughput screening, while also offering advantages such as convenient operation, rapid detection, and low cost, which meet our needs. To verify its feasibility, we used the Biacore T200 to measure the affinity of positive control binding and compared it with its theoretical value.

#### Build

We coupled Ubc9 and YPet-Ubc9 to the chip as stationary phases, using SUMO1 and CyPet-SUMO1 as their corresponding mobile phase proteins, respectively. Since the isoelectric points of Ubc9 and YPet-Ubc9 are 7.73 and 6.77, respectively, the pH value for protein coupling should be less than the pI. Therefore, we ultimately determined the protein coupling pH to be 5.0.

#### Test

During chip coupling, concentration gradients of SUMO1 and CyPet-SUMO1 were prepared. The program was run to obtain the raw data of SUMO1\_Ubc9 affinity, and the fitted Kd value was 8.267 μM

![](https://static.igem.wiki/teams/5553/engineer/image-18.webp)

Affinity Assay of SUMO1 and Ubc9

![](https://static.igem.wiki/teams/5553/engineer/image-19.webp)

Affinity Assay of CyPet-SUMO1 and YPet-Ubc9

The raw data of CyPet-SUMO1\_YPet-Ubc9 affinity was obtained, and the fitted Kd value was 1.329μM

#### Learn

The measured Kd deviated significantly from the theoretical value, and curve fitting lacked sufficient reliability, indicating errors in the assay system. Based on the experimental details, the issues were likely caused by inappropriate buffer selection and inaccuracies in preparing the concentration gradient. Therefore, we concluded that reliable affinity data requires further optimization of buffer conditions and improvement of gradient preparation.

### Cycle 2: Preliminary Experiment Optimization

#### Design

There were certain errors in the previous round of experimental process, which resulted in lower credibility of the obtained affinity data. We repeated the experiment after optimizing the experimental operation to obtain a more accurate Kd value for this group of positive controls.

#### Build

We employed a gradient dilution method to establish a concentration gradient to minimize errors, and adjusted the highest concentration to more effectively cover the concentration range corresponding to Kd.

#### Test

Prepare a protein concentration gradient for the mobile phase, and keep the remaining operations consistent with the previous cycle. Run the program, and obtain the accurate affinity value from the chart data

![](https://static.igem.wiki/teams/5553/engineer/image-20.webp)

![](https://static.igem.wiki/teams/5553/engineer/image-21.webp)

Improved Affinity Assay of SUMO1 and Ubc9

![](https://static.igem.wiki/teams/5553/engineer/image-22.webp)

![](https://static.igem.wiki/teams/5553/engineer/image-23.webp)

Improved Affinity Assay of CyPet-SUMO1 and YPet-Ubc9

#### Learn

With improved gradient dilution and concentration range coverage, the quality control score increased significantly, and the affinity fitting results became more stable and reliable. This demonstrated that proper gradient design and careful operation are critical for obtaining accurate Kd values in SPR assays. Through this optimization, we determined the concentration gradients and operational procedures required for subsequent affinity assays, providing a solid reference for large-scale screening.

### Cycle 3: High-Throughput Screening for Affinity Binders

#### Design

We have verified the feasibility of determining affinity using SPR technology through the interaction between SUMO1 and Ubc9. Next, we need to preliminarily screen samples from the large number of binding proteins we have expressed, aiming to identify those with high affinity and specificity for GZMK.

#### Build

Based on the fundamental properties of GZMK and binding proteins, we chose GZMK as the stationary phase and various binding proteins as the mobile phase for affinity detection through the chip in sequence. Since the isoelectric point of GZMK protein is 9.47, we selected pH=5.0 as the pH for protein coupling.

#### Test

Couple GZMK protein on the new chip, prepare the purified 27 binding protein samples into mobile phases of the same concentration, and run the program. Among the 27 samples, the RU values of 1-6 and 1-24 are positive, and the kinetic data are highly reliable.

![](https://static.igem.wiki/teams/5553/engineer/image-24.webp)

Preliminary Screening Results for Binder 1–6

![](https://static.igem.wiki/teams/5553/engineer/image-25.webp)

Preliminary Screening Results for Binder 1–24

#### Learn

Among the 27 candidate proteins screened, only 1-6 and 1-24 exhibited clear response signals with reliable kinetic data, while the others showed no effective binding. These results demonstrated that SPR technology can efficiently identify potential binders from a large sample set, guiding subsequent precise affinity measurements.

### Cycle 4: Obtaining Accurate Affinity Values

#### Design

Binder 1–6 and 1–24 have been confirmed to possess notable affinity, necessitating the design of experiments to obtain more accurate Kd values to determine their suitability for colloidal gold test strip construction and to aid in optimizing subsequent protein design.

#### Build

The pH value of the coupling protein was set to 5.0, and GZMK was selected as the stationary phase protein. The concentration gradients of the two mobile phase proteins were prepared using a gradient dilution method, and the program was run.

#### Test

Configure the concentration gradient of the two protein samples according to the procedure and run the instrument.

The fitted Kd value of 1-6 was 1.329μM

![](https://static.igem.wiki/teams/5553/engineer/image-26.webp)

Accurate Affinity Measurement of Binder 1–6

The graph showed negative response units (RU) for samples 1-24, indicating invalid data.

![](https://static.igem.wiki/teams/5553/engineer/image-27.webp)

Accurate Affinity Measurement of Binder 1–24

#### Learn

For binder 1-6, the affinity measurement yielded a stable Kd of 6.863 μM with high reliability, indicating promising potential. In contrast, the measurement of binder 1-24 produced negative response signals, rendering the data invalid. Based on analysis, this anomaly was likely caused by nonspecific binding interference. Therefore, we decided to adjust buffer conditions in subsequent experiments to confirm and eliminate nonspecific effects.

### Cycle5: Elimination of Nonspecific Binding

#### Design

To optimize experimental results and obtain accurate affinity data, it is necessary to eliminate the influence of non-specific binding on instrumental analysis. We suspect that the occurrence of non-specific binding is related to the salt concentration of the buffer solution, hence the focus of our experimental design lies in varying the salt concentration of the buffer solution.

#### Build

The original SPR Running Buffer formulation consisted of 1X PBS, 3 mM EDTA, 0.05% Tween-20, and 1 M NaCl. We attempted to reduce the salt concentration of the buffer, designing a new buffer formulation: 1X PBS, 3 mM EDTA, 0.05% Tween-20, and 363 mM NaCl.

#### Test

Accurate numerical values for the binding affinity of 1-24 were obtained, with a high degree of reliability. Kd=0.4033μM

![](https://static.igem.wiki/teams/5553/engineer/image-28.webp)

Improved Affinity Assay of Binder 1–24

#### Learn

By reducing the salt concentration in the buffer, we eliminated the previously observed nonspecific binding signals and successfully obtained an accurate affinity value for binder 1-24, with Kd=0.4033 μM. This experiment demonstrated that buffer ionic strength is a key factor influencing specific binding versus background noise. We ultimately established a buffer system suitable for measuring GZMK binders, which will serve as the standard condition for all subsequent affinity assays.

## Innovative Colloidal Gold Test Strip Development

### Cycle 1: Determination of Optimal Coupling pH

#### Design

As mentioned above, we obtained a pair of interacting proteins as a positive control. We aim to use this protein pair to test the preparation process of colloidal gold test strips and to serve as a quality control line to assess the validity of the test strips.

#### Build

The first step in preparing colloidal gold test strips is to conjugate the target protein with colloidal gold, which requires determining the optimal coupling pH. A pH that is too high or too low can alter the charge, leading to unsuccessful conjugation.

#### Test

We took a small amount of colloidal gold and adjusted the pH to 6, 7, 8, and 9, respectively. Excess SUMO1 protein was added, followed by a specific concentration of NaCl to induce aggregation of unconjugated colloidal gold.

![](https://static.igem.wiki/teams/5553/engineer/image-29.webp)

Results for Determining the Optimal Coupling pH

It was observed that at pH 6 or 9, the colloidal gold turned blue and formed precipitates. At pH 7 or 8, the color remained nearly identical to that of the initial colloidal gold.

#### Learn

At pH 6 or 9 the solution turned blue with precipitates, indicating colloidal gold aggregation and insufficient protein adsorption. At pH 7 or 8 the color matched the starting colloid, indicating a dispersed state and successful coating. We therefore defined the optimal window as 7 to 8 and set pH 7.5 for subsequent work.

### Cycle2: Determination of Optimal Coupling Amount

#### Design

After determining the optimal coupling pH, the optimal coupling amount needs to be established to minimize the waste of protein and colloidal gold.

#### Build

We will take a small amount of colloidal gold, adjust it to the optimal pH, and observe whether the colloidal gold is fully conjugated under different coupling amounts.

#### Test

We took a small amount of colloidal gold, adjusted the pH to 7.5, and added 10, 20, 30, and 40 μg of SUMO1 protein, respectively. A specific concentration of NaCl was then added to induce aggregation of unconjugated colloidal gold.

![](https://static.igem.wiki/teams/5553/engineer/image-30.webp)

Results for Determining the Optimal Coupling Amount

It was observed that for all tested coupling amounts, the color of the colloidal gold remained nearly identical to the initial color.

#### Learn

In the salt challenge assay the color remained unchanged from the starting colloid across 10 to 40 micrograms, showing that all doses reached the protective threshold and that the lowest dose was still excessive. To balance cost and stability we selected the minimal effective amount with a small safety margin and set 11 micrograms SUMO1 per milliliter of colloidal gold.

### Cycle3: Conjugation of SUMO1 with Colloidal Gold

#### Design

After determining the optimal coupling pH and amount, we need to scale up the process for formal conjugation.

#### Build

We will scale up the volume of colloidal gold used for conjugation, following the optimal coupling pH and amount, to prepare test strips.

#### Test

We took 20 mL of colloidal gold, adjusted the pH to 7.5, and added 220 μg of SUMO1, stirring thoroughly. Excess BSA was then added to block unbound sites. After thorough centrifugation, the pellet was collected and resuspended in a mild buffer to obtain the conjugated colloidal gold.

![](https://static.igem.wiki/teams/5553/engineer/image-31.webp)

Prepared Colloidal Gold

#### Learn

After scale-up the solution retained a stable red color without visible precipitation, and resuspension after centrifugation and blocking was smooth, indicating uniform coating and stable colloidal gold. This confirms the small-to-large process is transferable and ready for strip assembly and functional testing.

### Cycle4: Testing the Quality Control Line

#### Design

Conventional colloidal gold test strips typically use antibodies to achieve protein-protein binding. We replaced antibodies with smaller binders and modified the binding logic from target protein–primary antibody–secondary antibody to two binders capable of simultaneously binding the target protein.

#### Build

The colloidal gold test strip consists of the following components: a substrate, a sample pad, a conjugate pad, an NC membrane (bearing the detection and quality control lines), and an absorbent pad. Having conjugated SUMO1 with colloidal gold, we immobilized Ubc9 on the quality control line of the NC membrane to create the detection line.

![](https://static.igem.wiki/teams/5553/engineer/add/image-4.webp)

#### Test

Two groups of materials were prepared, with 1 μL and 10 μL of SUMO1-colloidal gold, respectively. A 1 μL Ubc9 solution was applied as a thin line on the NC membrane. After assembling and drying the test strips, 40 μL of PBS was added to the sample pad, and results were observed after 10–15 minutes.

![](https://static.igem.wiki/teams/5553/finalddl/image.webp)

Results of Quality Control Line Testing for Colloidal Gold Test Strips

The leftmost strip, with 1 μL SUMO1-colloidal gold, showed a faint band. The middle strip, with 10 μL SUMO1-colloidal gold, displayed a distinct band. The rightmost strip was a commercially available COVID-19 test strip.

#### Learn

The low-dose strip showed a faint band and the high-dose strip showed a strong band, giving an input-dependent response. A visible line formed only at the Ubc9 position, indicating specific binding to SUMO1. The band clarity matched a commercial control, confirming that the control line design is reliable for validating strip performance.

### Cycle 5: Construction of Test Line and Testing of the Entire Test Strip

#### Design

We innovated on the existing principle of colloidal gold test strips, modifying the binding mode from target protein–primary antibody–secondary antibody to Binder 1–target protein–Binder 2. For the test line construction, we conjugated the screened Binders 1–6 with colloidal gold and immobilized another binder, Aprotinin, on the test line to enable colloidal gold aggregation and color development.

#### Build

Following the same steps as before, we first determined the optimal pH and coupling amount for conjugating Binders 1–6 with colloidal gold. Subsequently, we formally conjugated Binders 1–6 with colloidal gold and assembled the entire test strip using the same method as previously described.

#### Test

We prepared the substrate, sample pad, conjugate pad, NC membrane, and absorbent pad. On the conjugate pad, 4 μL of SUMO1-colloidal gold and 4 μL of Binders 1–6-colloidal gold were applied. On the NC membrane, 0.5 μL of Ubc9 solution was applied at the control line (C line) position, and 0.5 μL of Aprotinin solution was applied at the test line (T line) position, ensuring application as a thin line. The test strip was assembled, taking care to maintain the correct layering. For the blank sample, 30 μL of PBS was applied to the sample pad. For the test sample, 30 μL of GZMK solution was applied to the sample pad. After waiting 10 minutes, bands were observed.

![](https://static.igem.wiki/teams/5553/engineer/add3.webp)

Colloidal Gold Test Strip Results

The left strip, representing the blank sample, showed only a single control line. The right strip, representing the test sample, displayed both a test line and a control line, indicating normal test strip functionality and results consistent with expectations.

#### Learn

The blank sample produced only the control line, while the test sample produced both test and control lines, indicating that in the presence of GZMK the bridge between Binder 1 and Aprotinin forms via the target and drives nanoparticle aggregation and color development. The contrast with the control shows the bands arise from specific recognition. This validates the antibody-free binder design on a full strip and provides a foundation for further optimization of sensitivity and specificity.

## Structure Prediction

### Cycle 1

#### Design

Our primary goal was to obtain the active conformation of GZMK, as an accurate active-site structure is critical for studying inhibitor binding modes and ensuring the success of virtual screening. The only published crystal structure is of a pro-granzyme K S195A variant, designed to prevent autocatalytic cleavage. This conformation exhibits extremely low catalytic efficiency and fails to represent the native active state. Therefore, we decided to use the AlphaFold Server to predict the structure of wild-type GZMK, aiming to obtain a model that more closely resembles its physiological conformation.

#### Build

We submitted the amino acid sequence of GZMK to the AlphaFold Server and obtained five candidate models. According to the provided confidence metrics, the best model achieved a pTM (predicted TM-score) of 0.92, indicating high confidence in its global topology. We exported this predicted structure for subsequent alignment, visualization, and preparation for docking.

#### Test

We used Maestro to perform a structural alignment of the AlphaFold model with the reported pro-enzyme crystal structure. The alignment revealed a high degree of overlap in the backbone trace and secondary structure arrangement, demonstrating strong overall topological consistency. This result structurally validates the reliability of our AlphaFold model.

![](https://static.igem.wiki/teams/5553/engineer/model/af-gzmk.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/protein-align.webp)

#### Learn

Through this cycle, we successfully obtained an active GZMK protein structure model with high global confidence. This model provides a reasonable initial definition of the active site's geometry and serves as a solid structural foundation for subsequent pocket definition, grid generation, and ligand screening.

### Cycle 2

#### Design

Considering that post-translational modifications, particularly glycosylation, could alter the protein's surface chemical environment and interfere with the selection of binding hotspots, we planned to first predict potential N-glycosylation sites on GZMK. This step aimed to avoid mistakenly selecting regions that might be modified or conformationally unstable as key interaction sites.

#### Build

We used the Re-Glyco online tool from the GlycoShape platform to perform an in silico prediction of N-glycosylation sites on the AlphaFold-generated GZMK structure. To ensure reproducibility and standardization, we uploaded the PDB model and ran the prediction workflow using default parameters.

* Ives, C.M., Singh, O. et al. Restoring protein glycosylation with GlycoShape. Nat Methods (2024).

#### Test

After the computation, we reviewed the output, focusing on the prediction scores and solvent accessibility of potential N-X-S/T motifs. The results detected no reliable N-glycosylation sites on the mature GZMK model (with the signal peptide removed), indicating that our selection of surface hotspots would not be significantly constrained by N-glycosylation.

#### Learn

The prediction ruled out interference from N-glycosylation in our target site selection. This allowed us to focus on hotspot identification strategies driven by structure and energetics, thereby streamlining the subsequent definition of small-molecule pockets and protein binding interfaces while reducing uncertainty from potential modifications.

### Cycle 3

#### Design

While AlphaFold is highly reliable for backbone topology prediction, the side-chain orientations and local flexible regions in its models often lack high-resolution accuracy. To obtain a conformation that more closely represents the true physiological state, we decided to introduce Molecular Dynamics (MD) simulation for further structural relaxation and refinement.

#### Build

We performed a 100 ns MD simulation on the AlphaFold-predicted GZMK model using the Desmond module from the Schrödinger suite under near-physiological conditions. The simulation parameters were as follows:

* Temperature: 310 K (near physiological temperature)
* Ionic strength: 0.15 M NaCl (with Na⁺ ions to neutralize the system)
* Force field: S-OPLS
* Simulation time: 100 ns

After the simulation, we performed conformational clustering on the trajectory and selected the representative structure from the most stable cluster as the final model for all subsequent computational work.

#### Test

Root Mean Square Deviation (RMSD) analysis showed that the GZMK backbone fluctuation remained within 3.8 Å throughout the 100 ns simulation, indicating a highly stable overall conformation. Furthermore, key active site residues did not undergo significant displacement, preserving the critical geometry of the initial model.

![](https://static.igem.wiki/teams/5553/engineer/model/gzmk-rmsd-2.webp)

#### Learn

Through MD simulation, we obtained a GZMK structure that is dynamically more stable and conformationally more reasonable. This model is not only globally stable but also features local side-chain arrangements and pocket geometries that better represent its native state. This fully refined structure provided a solid and reliable foundation for subsequent ligand docking, pocket energetic analysis, and inhibitor screening.

## Protein Binder Design

### Cycle 1

#### Design

We employed a hotspot-guided de novo protein design strategy. This approach aims to ensure high geometric complementarity at the binder-GZMK interface and to provide potential for future affinity maturation. The primary goal of this cycle was to generate binders with detectable affinity, laying the groundwork for developing a dual-epitope pair required for a sandwich assay.

#### Build

Our computational workflow was as follows: First, we identified and selected residue clusters on the GZMK surface as hotspots by evaluating their Spatial Aggregation Propensity (SAP). Next, we used RFdiffusion to generate candidate protein backbones complementary to these hotspots, followed by sequence design using ProteinMPNN. Finally, we performed iterative rounds of structure prediction and energy scoring with AlphaFold2 and Rosetta to filter for the most stable designs. This pipeline yielded 30 binder candidates targeting 8 distinct hotspots.

![](https://static.igem.wiki/teams/5553/engineer/model/025d85dae877e3e776ad3c80ea0ee63e.webp)

#### Test

Wet-lab validation successfully identified two binders with detectable affinity for GZMK. Although both showed promising predicted binding modes, they were found to target the exact same hotspot. This prevented them from forming the non-competing, dual-epitope pair required for a sandwich assay.

#### Learn

This cycle validated the feasibility of our de novo design pipeline but also highlighted a critical limitation: the lack of epitope diversity. To meet the requirements of a sandwich assay, it became imperative to design a stable binder targeting a second, distinct hotspot on GZMK.

### Cycle 2

#### Design

To explore ways of increasing the design success rate, we attempted a template-based strategy. The plan was to use a known natural inhibitor protein as a structural scaffold and re-engineer its interface to confer specificity for GZMK. This approach aimed to balance the inherent stability of a natural protein with engineered target specificity.

#### Build

During backbone generation, we used a partial-diffusion approach to preserve key structural fragments of the native inhibitor protein. In the sequence design phase, we used ProteinMPNN to replace native cysteine (Cys) residues, aiming to mitigate the risks of misfolding and sample heterogeneity caused by unintended disulfide bond formation during in vitro expression.

#### Test

The computational evaluation was discouraging. The predicted interface PAE scores from AlphaFold were generally above 20 Å, indicating significant uncertainty in the binding interface geometry. We inferred that the native disulfide bonds of the template protein were crucial for maintaining its overall scaffold rigidity and stability; simply removing them compromised the designs' foldability. Furthermore, Molecular Dynamics (MD) simulations revealed that these binders contained excessive flexible loops and exhibited large-scale motions over 100 ns, failing to maintain a stable conformation.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/ver2-binder-rmsd.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/ver2-interface-rmsd.webp)

(Comparison: MD simulation of a successful, stable binder)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/294-binder-rmsd.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/294-interface-rmsd.webp)

#### Learn

This strategy failed to produce stable binder structures in the absence of their native disulfide bonds. This led us to revert to the proven de novo pipeline from Cycle 1 and focus our efforts on targeting a second, distinct hotspot to ensure the final delivery of a functional dual-site binding solution for the sandwich assay.

### Cycle 3

#### Design

Drawing lessons from Cycle 1, we adopted a more refined four-residue combination strategy for hotspot definition. We carefully selected four new hotspots that were spatially distinct from the first successful one, aiming to create entirely new interfaces with different geometric fits and solvent accessibility profiles.

#### Build

We replicated the established workflow from Cycle 1: generating backbones with RFdiffusion, designing sequences with ProteinMPNN, and performing rigorous structural confidence and energy-based filtering using AlphaFold2 and Rosetta. This process yielded a new set of candidate binders targeting the newly defined hotspots.

#### Test

The computational screening results were highly encouraging. The overall distribution of key metrics (such as pAE, ΔΔG, and SASA) for the new candidate pool was very similar to that of the successful batch from Cycle 1. This filtering process yielded a set of binders with excellent scores across all computational criteria.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/output.webp)

#### Learn

At the computational level, we have successfully generated a pool of promising binder candidates for a second, distinct hotspot. This provides the crucial molecular components needed for a sandwich assay pair. The next critical step will be to validate their binding affinity, epitope independence, and complex stability through biophysical and functional experiments, such as SPR.

## Small-Molecule Screening

### Cycle 1

#### Design

As GZMK is a serine protease, our core hypothesis was that its enzymatic activity could be effectively inhibited by small molecules that competitively occupy the active site pocket housing the catalytic triad (H41–D90–S188). Therefore, the objective of this cycle was to identify potential competitive inhibitors through an active-site-based virtual screening campaign.

#### Build

We established a docking workflow using the Schrödinger Glide module. The prepared GZMK structure (at a simulated pH of 7.6±0.5) was used to screen two compound libraries: Specs-21 (~20,000 compounds) and the L1000 library of approved drugs (1813 compounds). A docking grid was centered on the catalytic triad. Finally, the top 20 hits from each library were re-ranked using MM/GBSA binding free energy calculations (OPLS4 force field, VSGB solvent model).

![](https://static.igem.wiki/teams/5553/engineer/model/p1/e55949556fd802302485e0c24c6c9cdd.webp)

#### Test

The docking results were promising, with the top-scoring molecule from the Specs-21 library achieving a docking score of −8.574, and the best from the approved drug library scoring −7.915. The MM/GBSA calculations provided effective re-ranking, revealing that several top candidates formed reasonable hydrogen bonds and hydrophobic interactions within the active site.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/123.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/docking-result1.webp)

| Title | Docking Score (kcal/mol) | MMGBSA ΔG Bind (kcal/mol) |
| --- | --- | --- |
| T0138 | -6.905 | -62.44 |
| T0179 | -6.613 | -62.37 |
| T0138 | -7.457 | -59.91 |
| T0125 | -6.634 | -59.31 |
| T1020 | -6.634 | -59.31 |
| T2815 | -6.674 | -58.66 |
| T0795 | -7.915 | -57.72 |
| T0346 | -6.651 | -55.05 |
| T0795 | -6.806 | -51.69 |
| T0138 | -6.618 | -49.89 |
| T0827 | -6.637 | -48.62 |
| T1218 | -6.703 | -46.51 |
| T0966 | -6.683 | -44.97 |
| T0453 | -7.007 | -43.59 |
| T0279 | -7.315 | -41.00 |
| T0307 | -6.826 | -40.11 |
| T1339 | -6.914 | -39.02 |
| T0469 | -7.234 | -36.97 |
| T1320 | -7.566 | -35.40 |
| T1026 | -6.935 | -29.92 |

| Title | Docking Score (kcal/mol) | MMGBSA ΔG Bind (kcal/mol) |
| --- | --- | --- |
| T0795 | -7.638 | -64.20 |
| T1218 | -6.738 | -60.95 |
| T0827 | -5.639 | -59.53 |
| T0138 | -8.139 | -59.49 |
| T2815 | -6.855 | -57.92 |
| T0138 | -7.769 | -52.72 |
| T2391 | -4.016 | -52.50 |
| T0795 | -6.009 | -49.69 |
| T0346 | -3.831 | -47.35 |
| T2392 | -3.722 | -45.44 |
| T1339 | -7.203 | -39.69 |
| T0966 | -4.910 | -39.52 |
| T0453 | -5.296 | -32.16 |
| T0279 | -5.552 | -32.13 |
| T3359 | -2.199 | -28.09 |
| T0307 | -4.940 | -24.38 |
| T1320 | -5.114 | -22.64 |
| T0469 | -4.590 | -21.37 |
| T1026 | -4.815 | -15.30 |

#### Learn

This initial screen validated the feasibility of our active-site-based virtual screening approach and yielded a set of potential lead compounds. However, the fact that the experimentally validated hits did not rank highly in this screen highlighted the potential limitations of rigid docking in accurately capturing the true binding mode and accounting for receptor flexibility.

### Cycle 2

#### Design

Given the low docking scores of the experimental hits, we proposed an "allosteric regulation" hypothesis: these molecules might inhibit enzyme activity indirectly by binding to a pocket outside the active site. To test this, the goal of this cycle was to systematically identify potential allosteric pockets on the GZMK surface and evaluate the binding of our experimental hits within them. (Note: Due to cost and time constraints, all subsequent computational validation focused on the L1000 drug library.)

#### Build

We used the Schrödinger SiteMap tool to probe the surface of GZMK, which identified three potential binding pockets. We then re-docked the experimental hits into each of these newly identified sites to assess their binding potential and pose stability.

#### Test

One pocket identified by SiteMap was located adjacent to the catalytic triad. Although the experimental hits achieved their best allosteric poses in this pocket, their docking scores were only around −4.016, significantly weaker than their scores in the active site. While their MM/GBSA energies were comparable to some active site binders, the binding poses were not convincing.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/sitemap.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/pasted-image-20250917005304.webp)

#### Learn

The results did not support the existence of a functional allosteric pocket. This negative finding guided our attention toward another critical factor: the intrinsic flexibility of the protein itself likely plays a dominant role in ligand binding. We concluded that the dynamic nature of the receptor must be taken into account.

### Cycle 3

#### Design

Building on the insights from the first two cycles, we formulated a new core hypothesis: "induced fit." We posited that the experimental hits achieve their potent binding by inducing a conformational change in the active site, a phenomenon that rigid docking cannot capture. The goal of this cycle was to test this hypothesis using Induced Fit Docking (IFD).

#### Build

We selected the top 15 virtual hits from Cycle 1 and the 3 experimental hits for IFD analysis. The docking grid was centered on the pose of a high-scoring virtual hit (T0138), and the side chains of residues within the pocket were allowed to move flexibly during the docking process.

#### Test

The IFD results were striking. The docking scores of two experimental hits dramatically improved to −9.545 and −9.476. By allowing receptor flexibility, the resulting binding poses were far more reasonable, forming a stable network of interactions including hydrogen bonds, a salt bridge, and π-π stacking.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/t2392-fit.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/t2392-ori.webp)

(Comparison shows the binding pose after IFD (left) is more favorable than with rigid docking (right))

#### Learn

The results strongly supported the induced fit hypothesis. However, IFD provides a static optimal snapshot. We still needed to validate the stability of this induced-fit complex in a dynamic environment using molecular dynamics simulation.

### Cycle 4

#### Design

To definitively confirm the reliability and stability of the binding mode predicted by IFD, this cycle was designed to use Molecular Dynamics (MD) simulation to examine the dynamic behavior of the protein-ligand complex in a simulated aqueous environment.

#### Build

Using Schrödinger Desmond (S-OPLS force field), we ran 100 ns MD simulations for the top IFD poses of the virtual hit T0138 and the two experimental hits. Crucially, we also simulated the initial rigid-docking pose of Nafamostat (T2392) as a control. To ensure robustness, the simulation of the Nafamostat IFD pose was repeated multiple times.

#### Test

Trajectory analysis showed that all complexes formed from IFD poses remained stable. The most critical finding was that the initial rigid-docking pose of Nafamostat (T2392) spontaneously transitioned during the relaxation phase into a stable conformation that was nearly identical to the IFD-predicted pose (forming a key hydrogen bond with H41 and a salt bridge with D182). This stable pose was maintained for the remainder of the simulation.

#### Learn

The MD simulations provided definitive evidence that the experimental hits act via an induced fit mechanism. This explains why traditional rigid docking failed to identify them effectively. We concluded that while the combined IFD+MD approach is powerful, its high computational overhead makes it impractical for large-scale primary screening.

### Cycle 5

#### Design

Given GZMK's induced fit nature and the high cost of the IFD+MD protocol, we designed a novel workflow to balance efficiency and accuracy: a "Similarity-Search-Driven IFD-MD" pipeline. The goal was to create an efficient process for discovering potent inhibitors from large chemical databases.

#### Build

The new two-step workflow is as follows: First, using known serine protease inhibitors (e.g., Nafamostat, PMSF) as query "seeds," we perform a rapid chemical similarity search using our tool, GeminiMol, to enrich for high-potential candidates from a large library. Second, only this small, enriched subset of top-scoring molecules is subjected to the computationally expensive IFD+MM/GBSA+MD pipeline for rigorous validation and ranking.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/geminimolyuanli.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/047f26cfcc170f4b6315e41695ae779b.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/siansuan.webp)

* 1.0 → PMSF: Known to completely inhibit GZMK.
* 0.8 → AEBSF: Water-soluble, irreversible inhibitor.
* 0.7 → Nafamostat: Clinical drug, potent serine protease inhibitor.
* 0.6 → Camostat, Benzamidine: Weaker, but reported to inhibit GZMK.
* 0.5 → Gabexate, 3,4-DCI: Broad-spectrum protease inhibitors with some relevance.

| SMILES | Label | Inhibitor | Notes |
| --- | --- | --- | --- |
| O=S(F)(=O)Cc1cccc1 | 1.0 | PMSF | Known to completely inhibit GZMK |
| FS(=O)(=O)c1ccc(cc1)CCN | 0.8 | AEBSF | Water-soluble, irreversible inhibitor |
| NC(=N)c1ccccc1 | 0.6 | Camostat / Benzamidine | Weak activity, but literature suggests inhibition of GZMK |
| ClC1=C(Cl)c2ccccc2C(=O)O1 | 0.5 | Gabexate / 3,4-DCI | Broad-spectrum protease inhibitor, partial relevance |
| N=C(N)c1ccc2cc(OC(=O)c3ccc(N=C(N)N)cc3)ccc2c1 | 0.7 | Nafamostat | Clinical injection drug, strong serine protease inhibitor |
| CN(C)C(=O)COC(=O)CC1=CC=CC=C1OC(=O)C2=CC=CC=C2N=C(N)N | 0.6 | Camostat / Benzamidine | Weak activity, but literature suggests inhibition of GZMK |
| O=C(Oc1ccc(cc1)C(=O)OCC)CCCCC/N=C(N)N | 0.5 | Gabexate / 3,4-DCI | Broad-spectrum protease inhibitor, partial relevance |

#### Test

We applied this new workflow to the L1000 drug library. The result was a resounding success: we correctly identified two of the experimentally validated hits within our top-10 ranked predictions. This demonstrated the high efficiency and accuracy of our new pipeline. We subsequently applied the workflow to the much larger Specs library and performed IFD and MM/GBSA calculations on the top 30 hits to generate a further list of candidates.

![](https://static.igem.wiki/teams/5553/engineer/model/p1/gemini-result-l1000.webp)

![](https://static.igem.wiki/teams/5553/engineer/model/p1/gemini-result-spec.webp)

| SMILES | ID | w\_1.0 | w\_0.8 | w\_0.6 | w\_0.5 | w\_0.7 | Score |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Cl.NCC1=CC=C(C=S1)S(N)(=O)=O | T1026 | 0.41429 | 0.62460 | 0.38088 | 0.26535 | 0.43794 | 2.1230 |
| NS(=O)(=O)C1=CC=C(C=C1)C(O)=O | T0398 | 0.48743 | 0.54954 | 0.35754 | 0.28610 | 0.43834 | 2.1189 |
| NCC1=CC=C(C=C1)C(O)=O | T1252 | 0.45990 | 0.62544 | 0.40630 | 0.23729 | 0.37817 | 2.1071 |
| CS(O)(=O)=O.CN(C)C(=O)COC(=O)CC1=CC=CC=C1 | T2391 | 0.27359 | 0.39438 | 0.44614 | 0.43391 | 0.54197 | 2.0900 |
| O.[Na].CC(C)NS(=O)(=O)C1=CC=C(C(N)C=C1 | T0087 | 0.48172 | 0.59416 | 0.32486 | 0.28470 | 0.39247 | 2.0779 |
| OC(=O)C1=CC=C(C=C1)S(=O)(=O)N(C)C | T0037 | 0.60851 | 0.60449 | 0.22529 | 0.28336 | 0.31356 | 2.0352 |
| NC1=CC=C(C=C1)S(N)(=O)=O | T0123 | 0.36788 | 0.56466 | 0.37540 | 0.27302 | 0.45233 | 2.0333 |
| CS(O)(=O)=O.CS(O)(=O)=O.NC(N)=NC1=CC=CC=C1 | T2392 | 0.22591 | 0.39310 | 0.37731 | 0.35836 | 0.66217 | 2.0168 |
| CS(=O)(=O)C1=CC=C(C=C1)C(O)C(O)NC(=O) | T1550 | 0.52616 | 0.58203 | 0.28635 | 0.31276 | 0.29499 | 2.0023 |
| COC(CN)=O | T0051 | 0.47927 | 0.42985 | 0.38996 | 0.32663 | 0.37379 | 1.9995 |
| OCCS(O)(=O)=O.OCCS(O)(=O)=O.NC(=O)C1=CC=CC=C1 | T1654 | 0.32289 | 0.40699 | 0.40953 | 0.37532 | 0.45566 | 1.9704 |
| CC(CN(C)C)OC.CC(CN(C)C)OC.CC(CN(C)C)OC | T3614 | 0.56355 | 0.51427 | 0.35565 | 0.24139 | 0.28466 | 1.9595 |
| NC(C)NS(=O)C1=CC=CC(=C1)C(N)C | T0241 | 0.24465 | 0.50061 | 0.37088 | 0.32622 | 0.51583 | 1.9538 |
| Cl.CC(N)NCC(O)C1=CC=C(NS(C)(=O)=O)C=C1 | T0483 | 0.53746 | 0.60055 | 0.22115 | 0.26553 | 0.31785 | 1.9425 |
| CC(C)NS(=O)(=O)C1=CC=CC(=NC(=O)C2=CC=CC=C2)C1 | T0409 | 0.38723 | 0.39565 | 0.38357 | 0.30095 | 0.46789 | 1.9335 |
| OC(=O)C1N+[I-]O=C=C1 | T0067 | 0.67609 | 0.40260 | 0.39212 | 0.23904 | 0.21825 | 1.9281 |
| CC(=O)NC1=CC=CC(C(CO)=O)C=C1 | T0310 | 0.56403 | 0.51920 | 0.33687 | 0.18753 | 0.24850 | 1.9141 |
| COC(C=O)C1=CC=CC(=N)C=C1 | T0924 | 0.53666 | 0.53666 | 0.28377 | 0.28365 | 0.32323 | 1.8777 |
| NC(N)=NS(=O)C1=CC=CC(=C1)C(N)C | T0510 | 0.46683 | 0.36503 | 0.33043 | 0.33475 | 0.54416 | 1.8761 |
| NC(C1=CC=CC=C1)C(O)=O | T0689 | 0.51580 | 0.49026 | 0.38748 | 0.19998 | 0.28168 | 1.8752 |

#### Learn

We successfully developed a novel screening workflow that intelligently combines prior chemical knowledge with rigorous physics-based models. This pipeline can efficiently and accurately identify potent inhibitors from large databases, laying a methodological foundation for future large-scale drug discovery. Importantly, this integrated strategy is not limited to GZMK; it represents a highly promising screening paradigm for other protein targets that also feature flexible active sites.

## Software

### Cycle 1: Traditional Protein Design Methods

#### Usage

We employed a **protein design strategy based on RFdiffusion** with the goal of obtaining high-quality binders. Specifically, RFdiffusion was used to generate backbones, ProteinMPNN was applied to design sequences, and subsequent screening was performed using AlphaFold and PyRosetta. The top candidates identified through this computational pipeline were then subjected to experimental validation, with the ultimate aim of discovering binders exhibiting superior affinity and structural stability.

![](https://static.igem.wiki/teams/5553/engineer/software/cycle-1.webp)

#### Learn

In applying this workflow, we found that it is not strictly binding-oriented but instead relies on generating large numbers of candidates followed by high-throughput dry/wet screening. During sequence design, the method primarily ensures the structural integrity of the binder while insufficiently addressing interface construction and backbone potential. As a result, even binders with measurable affinity retain considerable room for improvement in both backbone and sequence. To address this, our goal is to develop a tool that can fully exploit and optimize the potential of each candidate protein to further enhance binding performance.

### Cycle 2: BetterEvoDiff

#### Design

We aim to develop a tool capable of fully exploiting the potential of screened proteins. We recognized that existing optimization methods are largely restricted to single-point mutations, which fail to capture the interdependence among amino acid residues and thus limit further performance improvement. To address this, our objective is to leverage coordinated multi-point mutations to unlock greater optimization potential in candidate proteins. Specifically, the goals are: (1) to establish rational relationships among different mutation sites; and (2) to collect and learn from the effects of mutations to guide more effective designs. To achieve this, we combined discrete autoregressive modeling with reinforcement learning to construct a framework for multi-point mutation and evolution.

#### Build

Based on this design, we developed a protein optimization tool that integrates EvoDiff with the Group Relative Policy Optimization (GRPO) algorithm. The tool takes any input sequence and iteratively performs the following cycle until convergence: random masking of residues, denoising with EvoDiff to generate mutated sequences, property evaluation in an environment to obtain rewards, parameter optimization of EvoDiff using GRPO, and subsequent regeneration of sequences with the updated model. Through repeated iterations, the model gradually learns from mutation outcomes and converges to a state where it can reliably produce near-optimal solutions under random perturbations. Finally, the tool enables batch generation of candidate sequences, followed by dry-lab screening and wet-lab validation to identify binders with improved performance.

![](https://static.igem.wiki/teams/5553/software/ezgif-72b805ecaa2e7b.webp)

#### Test

We first carried out dry-lab validation, where a reward function was constructed using AF output metrics to optimize proteins with affinity generated by classical methods. The results showed that our tool was able to achieve directed optimization of the protein binding interface under our reward function, converging at the highest reward state and reaching the theoretical optimum. We then randomly selected a subset of optimized sequences from the dry-lab experiments for wet-lab validation and obtained affinity data. Disappointingly, the optimized proteins did not exhibit significant improvements in affinity, and in some cases, proteins with seemingly better binding interfaces after optimization displayed lower affinity than their unoptimized counterparts.

#### Learn

We found that our tool can optimize protein properties toward specific targets, demonstrating the effectiveness of the proposed framework. However, the core challenge lies in aligning dry-lab rewards with wet-lab rewards. Even though AlphaFold provides highly accurate predictions, it still cannot fully match wet-lab results, which is beyond our ability to resolve. Therefore, in subsequent work, we will continue to use AF3 as the reward function, as it can at least verify whether our framework is capable of converging toward defined objectives. Current results also highlight that the backbone is a crucial starting point, playing an even more decisive role in affinity than the side chains themselves—a poor backbone cannot be optimized into a high-affinity binder. This motivates us to develop a de novo protein design framework capable of both selecting superior backbones and fully exploiting their potential, thereby enabling more accurate protein design.

### Cycle 3: BetterMPNN

#### Design

In current protein design, the upper limit of performance is determined more by prediction models than by generative models, as the reliability of prediction results significantly exceeds that of generative outputs. Against this background, our work focuses on optimizing generative models so that they can distill structural knowledge from prediction models. At the same time, RFdiffusion, as the most effective backbone generation tool available, remains our best choice for backbone design—even though only a small fraction of its outputs prove usable in experimental validation, when combined with our framework it can still quickly provide effective starting points. Based on these backbones, ProteinMPNN has classically been used only to generate sequences that can fold into the target structure, but we aim to further extend its capability so that it not only ensures structural foldability but also learns to generate sequences with superior binding interfaces. To achieve this, we integrate RFdiffusion, ProteinMPNN, and GRPO into a unified high hit-rate (one-shot) protein design approach. Within this optimization loop, ProteinMPNN is continually refined through reinforcement feedback, enabling rapid convergence from backbone generation to high-quality binders.

#### Build

For implementation, we first use RFdiffusion to generate dozens to hundreds of backbones, each of which is subjected to an independent optimization loop (parallelized when resources permit). Within each loop, ProteinMPNN generates multiple sequences for a given backbone, which are then evaluated in an environment to compute rewards. These reward signals are used to optimize ProteinMPNN parameters via the GRPO algorithm, progressively teaching it to generate better binders. The updated ProteinMPNN is then reused for sequence design, and this cycle continues until convergence. During training, metrics such as loss, KL divergence, and reward values allow us to quickly assess backbone reliability: reliable backbones are optimized to convergence, while unreliable ones are discarded and replaced. Once converged, the optimized ProteinMPNN is used for batch design on high-quality backbones, and the resulting sequences, together with those collected during optimization, are advanced to dry-lab screening. A subset of high-confidence sequences is further selected for wet-lab validation.

![](https://static.igem.wiki/teams/5553/software/dongtu1.webp)

#### Test

Through dry-lab experiments, we demonstrated several key capabilities of our method: the design of protein inhibitors directly targeting the GZMK active pocket within 20 hours (from backbone to binder), the design of binders targeting distinct GZMK hot-spots, the ability to design protease cleavage sites, autonomous selection of optimal hot-spots, and rapid evaluation of RFdiffusion backbones within minutes to hours. Under dry-lab conditions, the method shows strong potential for one-shot design, provided that dry-lab and wet-lab conditions can be correctly aligned. At present, our wet-lab experiments and validations on additional targets are still ongoing, and progress updates will be available on our GitLab.

#### Learn

Throughout this work, we have gained several important insights. First, reinforcement learning has proven to be highly suitable for biological applications, as it naturally simulates the process of biological evolution and allows knowledge to be accumulated and refined through iterative feedback. Second, the critical importance of the backbone was reaffirmed: it serves as the essential starting point and largely determines whether a binder can achieve high affinity, since a poor backbone cannot be rescued by subsequent sequence optimization. At present, the reward obtained from our evaluations is applied only to optimize the sequence design model, while the backbone remains unchanged. If we could design a mechanism whereby the reward signal is first broadcast to the side chains and then further broadcast to the backbone, it would enable the simultaneous optimization of both backbone and side chains, thereby yielding more rational and higher-quality binders. This will also be the direction of our future work.

### Cycle 4: Future Plans

We are continuing to pursue the above idea with the goal of developing a more precise and efficient protein design tool. This work is still in progress—stay tuned, and future updates will be available on our GitLab.

## Construction and Optical Performance Optimization for the Micro-Volume Liquid Detection Platform

### Cycle 1: Detection Platform Construction

#### Design

Laboratory-scale cell-free systems typically feature small expression volumes and high unit production costs. The expression volume of a single system is nearly equivalent to the sample amount required for one read on a microplate reader. This severely limits the throughput of protein expression in cell-free systems and the reproducibility of subsequent detection experiments. To address this need, our team constructed a microchannel configuration with reduced detection volume, which significantly decreased the sample amount required per measurement; fully transparent materials were adopted to facilitate subsequent fluorescence detection.

#### Build

In this version, the chip uses soda-lime glass as the substrate. A flexible PDMS channel is obtained by negative photolithography. Plasma cleaning is applied to remove organic contaminants and activate surface oxygen radicals on both materials, followed by lamination and overnight heating to form a stable covalent bond between them. The main channel section includes an inlet, a waste outlet, and a detection window, and is connected to the external injection system via P20 tubing. Prior to use, Aquapel is applied to hydrophobize the inner walls of the channel, preventing adsorption of proteinaceous substances by PDMS that would introduce detection errors and cause channel blockage, while also mitigating capillary effects that can compromise flow stability. A ballast component is introduced at the detection window, which, together with an external Harvard pump, ensures flow stability at the detection window.

![](https://static.igem.wiki/teams/5553/engineer/hardware/img-2099-jpeg.webp)

The first-generation chip

#### Test

The FCS detection results showed weak excitation signals, low photon flux density, and a poor signal-to-noise ratio (SNR).

![](https://static.igem.wiki/teams/5553/engineer/hardware/sumo.webp)

FCS detection results of the first-generation chip

#### Learn

Based on the above results, it is hypothesized that the relatively thick soda-lime glass moderately absorbs excitation photons; additionally, the increased optical path length introduced by the thicker glass leads to spherical aberration in high-angle rays that cannot be compensated by the objective design, resulting in a substantial reduction in photon flux density. Meanwhile, surface-adhered organics also reduce transmissivity.

### Cycle 2: Substrate Optical Performance Improvement

#### Design

Targeted optimizations were made to the previous chip version, particularly by improving the glass substrate materials in the optical path, exploring thinner materials with lower absorption at the specific wavelengths used. Surface treatment processes were also refined.

#### Build

In testing, microfluidic chips were constructed using substrates of ultra-clear glass, borosilicate glass, and fused silica with different thicknesses (2 mm and 0.15 mm). All substrate surfaces were treated by plasma at 30 W for 130 s to remove surface contaminants and were stored in clean Petri dishes after treatment.

#### Test

The results showed that 0.15 mm fused silica exhibited a distinct advantage as an optical medium for laser-based detection, representing a clear improvement over the previous-generation chip, though still falling short of the control group.

#### Learn

A new issue emerged: the 0.15 mm fused silica exhibited observable deformation at the objective interface, introducing an additional refractive surface in the optical path and thereby affecting focusing to some extent. Meanwhile, the small contact area at the objective face resulted in low friction, and the chip frequently slipped due to physical perturbations.

### Cycle 3: Lens Coupling Improvement

#### Design

To address the above issues, our team first aimed to increase the hardness of the channel layer; in parallel, a support structure was designed for the chip. The chip layout was also redesigned to ensure exact coincidence of the detection center with the geometric center, preventing a tendency toward lateral tilting.

#### Build

Specifically, the PDMS layer thickness was increased, the crosslinker ratio was raised, and the curing time was extended, making the chip less prone to elastic deformation. In addition, drawing on common fixation methods in XRD measurements, moldable butyl plasticine was used to provide structural support for the chip. A cutting registration at 60 mm × 24 mm was introduced to fit the quartz thin glass used. The chip’s tubing layout was redesigned, placing the detection window at the geometric center of the cutting region.

#### Test

![](https://static.igem.wiki/teams/5553/engineer/hardware/yanmoban.webp)

Design of the third-generation chip

Now, the detection window (the area within the red circle) represents the geometric center of the chip. The results show that these measures significantly reduce chip deformation, especially preventing elongation along the long axis. The addition of butyl plasticine both supports the chip against deformation and prevents slipping. Moreover, with the geometric center aligned vertically with the working fulcrum, the chip’s operational stability and anti-interference performance (anti-vibration) were improved.

#### Learn

Construction goals achieved.

## Performance Optimization for Mixing in High-Reynolds-Number Differential Systems

### Cycle 1: Mixing Ratio

#### Design

Empirically, relying solely on off-chip mixing followed by reinjection is labor-intensive and time-intensive and prone to operator-dependent variability that compromises mixing uniformity, thereby limiting system throughput and reproducibility. By contrast, integrating mixing functionality directly on-chip can substantially shorten workflows and time costs, while achieving more efficient and controllable mixing under stable flow, thus laying the foundation for subsequent high-throughput heterogeneous-system detection.

In the specific design, different mixing modes were explored: simple two-layer co-flow, droplet-based dispersion, and a jetting mode in which a sheath-confined jet forms a lamella.

To prevent channel clogging by particulates in cell-free systems, this generation of chips introduced a weir structure; the inlet channel for the cell-free stream was also made relatively wider to reduce the risk of blockage.

#### Build

In the current chip design, while preserving the optimizations achieved in the previous generation, a cross-shaped mixer was introduced to evaluate both droplet-based and jetting-based mixing strategies. It is noteworthy that these two modes are realized by adjusting the channel width to modulate the sheath flow rate, thereby ensuring the stable formation of droplets and jets. Consequently, it can be inferred that the sheath flow velocity remains constant throughout the experiments.

#### Test

Multiple mixing schemes were evaluated in the comparative design study, and the stability of jetting at different flow rates was compared.

#### Learn

![](https://static.igem.wiki/teams/5553/engineer/hardware/image-42.webp)

Jetting

![](https://static.igem.wiki/teams/5553/engineer/hardware/image-43.webp)

Droplet

The results indicate that, relative to droplet-based dispersion or simple two-layer co-flow, the jetting mode—which forms a laminar liquid layer through a sheath-confined jet—achieves a more uniform and stable compositional distribution on shorter timescales. This conclusion is drawn primarily from microscopic observations of stable jetting and droplet generation rates; in two-layer co-flow, the interface was found to be the least stable due to differing wall-induced viscous drag on the two liquids.

It must be acknowledged, however, that jetting requires a narrow range of flow-rate ratios; stable jets are obtained only within appropriate velocity windows. Solutions to this constraint will be proposed in the work on “high-throughput detection.”

### Cycle2: Mixing Modes

#### Design

In essence, liquid mixing must be both “stable” and “unstable”: stability is needed prior to mixing to maintain a constant ratio—hence robust self-sustained flow stability—whereas instability is desired at the interface under specific engineered perturbations to promote mixing. The key lies in designing suitable mixer structures. Because our team is mixing a high-Reynolds-number stream with a low-Reynolds-number stream, existing microfluidic designs for such conditions often focus on active mixing, which necessarily involves acoustic, thermal, magnetic, or other fields—undesirable here due to the potential to interfere with stable protein affinity measurements under the microscope. Therefore, passive mixing was chosen as the primary strategy. Yet for systems with large Reynolds-number disparities, studies on passive mixing remain limited; such extreme conditions pose heightened challenges for both mixing efficiency and structural design.

In light of this, our team initially selected a passive micromixer containing triangular obstacles. This design performs well across a broad Reynolds-number range. Its operating principle is the canonical split-and-recombine (SAR): upon encountering triangular baffles, the flow is compelled to split and subsequently recombine downstream, repeatedly stretching and folding the fluid layers and enlarging interfacial area. Under low-Re conditions, such geometric perturbations markedly shorten diffusive path lengths; under higher Re, they also induce transverse velocity components that further promote convective mixing. Under the present experimental conditions, splitting/recombination and diffusion are coupled, allowing the triangular baffles to achieve efficient mixing within a short channel length.

#### Build

The mixing structure was positioned downstream and adjacent to the cross-mixer, with the same fabrication workflow as above.

#### Test

When the mixture was examined with an optical microscope, it was found that the mixture was not thorough and the luminous intensity was insufficient.

#### Learn

Mixing was found to be incomplete—only a weak signal—yet an initial degree of homogenization was achieved, and the Reynolds numbers are relatively closer. In the next-generation chip, we plan to incorporate a mixer tailored for low-Re conditions with small Re contrasts.

### Cycle 3: Composite Gradient Mixing

#### Design

Given the relatively successful mixing in the prior stage and the need to further blend the two liquids, the operating regime now transitions to mixing a low-Re stream with another low-Re stream. Therefore, a serpentine micromixer was introduced downstream to ensure thorough mixing. Its principle leverages Dean vortices generated by continuous bends: as fluid traverses a curved segment, centrifugal effects drive a pair of counter-rotating secondary flows across the cross-section; with alternating bends along the channel, these vortices flip correspondingly, continuously stretching, folding, and redistributing fluid layers. This enhances interfacial perturbations across the cross-section and markedly improves mixing efficiency.

#### Build

Two sets of serpentine micromixers were designed to ensure complete mixing, and sufficiently sized ballast structures were placed downstream to stabilize the flow and allow diffusion to reach equilibrium.

#### Test

FCS detection experiments were conducted.

![](https://static.igem.wiki/teams/5553/engineer/hardware/image-44.webp)

FCS results of SUMO-mGFP

![](https://static.igem.wiki/teams/5553/engineer/hardware/image-45.webp)

G(t)-t graph of SUMO-mGFP

#### Learn

FCS measurements of the SUMO-mGFP protein solution yielded well-defined G(t)-t curves, with fluorescence intensity falling within the normal range. Notably, a small number of particles were observed to exhibit internal vortex circulation, which in turn affected the surrounding surface flow and further accelerated the mixing process.

The experimental results demonstrated that the objectives of this generation’s chip design were successfully achieved, thereby paving the way for the development of the next-generation chip.

## Construction and Improvement of High-throughput Protein Affinity Detection Platform

### Cycle 1: Vertically Variable Concentration System

#### Design

The project targets multiple proteins; thus, high-throughput detection is crucial not only for this work but also for its broad future applicability. The primary goal of high throughput is to construct complete affinity curves for a given protein across different concentrations, thereby obtaining continuous concentration-dependent affinity data. However, in second-generation chip tests we found that the jetting mode imposes clear constraints on the allowable flow-rate window, which in turn limits the accessible detection range. For this reason, subsequent designs adopted a pre-mixing approach to expand the measurement window.

#### Build

A Y-mixer was introduced upstream of the cross-mixer, with a ballast to stabilize the flow and a 45° angle to prevent backflow.

#### Test

The design is as follows:

![](https://static.igem.wiki/teams/5553/engineer/hardware/ymb.webp)

Design of the third-generation chip

#### Learn

The pre-dilution region lacked sufficient space, allowing only a Y-mixer, whose performance is relatively limited—this can be addressed in the next generation.

### Cycle 2: Multi-concentration Gradient Configuration

#### Design

Given the need to detect multiple proteins and switch between them, as well as the requirement for concentration-gradient measurements in cross-correlation modes on fluorescence-related spectrometers, integrating on-chip optical pumping to generate gradients is highly challenging and unstable. We therefore adopted an off-chip modular design, employing a primary–secondary chip architecture to produce different gradients.

#### Build

The N-to-1 miniature chip with a ballast incorporates an improved tubing connection scheme, delivering higher connection stability and reliability and supporting repeated plug-and-play without damage. The secondary chip enables pre-dilution and facilitates subsequent automation integration; it can be expanded with different secondary chip according to experimental needs, enhancing stability and scalability.

#### Test

Using a 2-to-1 chip as an example, the operational stability of the secondary chip was evaluated.

#### Learn

The results show that this primary–secondary chip strategy mixes and dilutes different liquids—such as cell-free systems and protein solutions—effectively.

![](https://static.igem.wiki/teams/5553/engineer/hardware/mixing-huang-jpg.webp)

Cell-free protein solution with yellow ink

![](https://static.igem.wiki/teams/5553/engineer/hardware/mixing-lan-jpg.webp)

Protein solution with blue ink

![](https://static.igem.wiki/teams/5553/engineer/hardware/mixing-lv-jpg.webp)

Cell-free protein solution with yellow ink and protein solution with blue ink mixing to green solution

### Cycle 3: FCCS Detection

#### Design

FCCS (Fluorescence Cross-Correlation Spectroscopy) enables the detection of fluorescence intensity fluctuations at the single-molecule level within an extremely small observation volume, allowing the determination of molecular diffusion times and complex formation. Compared with traditional methods that rely on purification and multi-concentration titration, FCCS can simultaneously track protein synthesis and binding processes in situ within cell-free systems, thereby enabling faster construction of affinity curves.

#### Build

In implementation, we employed Fluorescence Cross-Correlation Spectroscopy (FCCS). Fluorescent molecules in solution undergo Brownian motion, randomly diffusing in and out of the confocal detection volume. This results in statistical fluctuations of the instantaneous number of fluorescent molecules, N(t), around its time-averaged value ⟨N⟩. Consequently, the detected fluorescence intensity, I(t), exhibits random fluctuations proportional to the number of molecules present within the observation volume. The instrument simultaneously monitors two fluorescent fusion proteins with distinct spectral properties, SUMO-mGFP and Ubc-mRuby2, and records fluorescence signals in two independent channels. By maintaining a constant concentration of SUMO-mGFP while gradually increasing the concentration of Ubc-mRuby2, a series of G\_cross(0) values under different conditions can be obtained. Since G\_cross(0) is proportional to the concentration of the complex [AB], a binding curve can be plotted and nonlinearly fitted to a binding model based on the law of mass action to determine the dissociation constant (Kd), thereby achieving precise and quantitative characterization of protein–protein interactions.

#### Test

Using FCCS to analyze the two fluorescent protein samples, we observed that although the 561 nm channel and the cross-correlation curve exhibited some noise, the overall profiles remained within the normal range. From the analysis, the binding data between the two proteins was determined.

![](https://static.igem.wiki/teams/5553/engineer/hardware/image-46.webp)

tFCCS results of 488/561 standard sample

![](https://static.igem.wiki/teams/5553/finalddl/ss.webp)

Analyzed FCCS results of 488/561 standard sample

#### Learn

The experimental results collectively demonstrate that FCCS, owing to its high sensitivity and temporal resolution, enables the rapid acquisition of protein–protein binding data within an extremely short time.

### Cycle 4: Integration of Automated Equipment

#### Design

In traditional protein affinity assays, injection and sample loading operations rely heavily on manual handling. When the scale of microfluidic screening expands to a large number of parallel assays, it becomes difficult to accurately perform multi-gradient dilution manually, and the timing of sample introduction at different concentrations cannot be precisely controlled. This not only incurs substantial labor costs but also introduces human-induced variability, leading to poor reproducibility and reduced data reliability.

To address these challenges, the present design incorporates programmable automated equipment—specifically syringe pumps and peristaltic pumps—to replace manual operations. This setup enables automated generation of concentration gradients and precise temporal control of sample injection, thereby laying the foundation for high-throughput and high-precision operation of the integrated microfluidic system.

#### Build

The system integrates two types of automated fluid-driving devices. First, the YHPLC0100S micro syringe pump, which employs a stepper motor coupled with a lead-screw transmission system, converts motor rotation into linear injection motion to achieve uniform and quantitative liquid delivery. It is used for the precise dispensing of protein solutions, ensuring the accuracy of concentration gradient generation within the secondary chip.

![](https://static.igem.wiki/teams/5553/engineer/hardware/zhushebeng.webp)

YHPLC0100S Micro-syringe Pump

Second, the *Longer* BT100-2J peristaltic pump supports multi-pump-head configurations and a wide adjustable flow-rate range, making it suitable for delivering buffer solutions with lower precision requirements and fulfilling the needs of high-throughput experiments.

![](https://static.igem.wiki/teams/5553/engineer/hardware/rudongbeng.webp)

*Longer* BT100-2J Peristaltic Pump

Both devices allow pre-setting of parameters such as flow rate, injection duration, and operation mode, enabling precise control over the concentration gradient generation process in the secondary chip. This ensures that samples of varying concentrations enter the detection inlet in accordance with the timing requirements of cross-correlation measurements, substantially reducing manual workload and improving experimental consistency.

#### Test

We conducted preliminary tests to evaluate the performance and compatibility of the automated devices. First, we examined the precision of gradient generation to ensure that the coordinated operation of the devices met experimental requirements. Second, we verified the accuracy with which samples of different concentrations entered the detection inlet according to the preset timing, confirming compliance with the temporal requirements of cross-correlation measurements. Third, repeated experiments were performed to assess operational stability and data reliability, while also confirming a significant reduction in manual labor.

The test results demonstrated that both types of automated equipment successfully executed the predefined operations and fully met the integration requirements of the system.

![](https://static.igem.wiki/teams/5553/engineer/hardware/zongjiagou.webp)

Preliminary Test of the Automated Devices

#### Learn

To accommodate the differing requirements of protein solutions and buffer delivery, we adopted a differentiated configuration: the micro syringe pump for high-precision control (maintaining a concentration deviation of less than 5%) and the peristaltic pump for cost efficiency and high-throughput adaptability. This approach meets our goal of minimizing cost while ensuring precision, and it reduces manual labor by over 80% compared to traditional manual operation.

However, current programming control still requires manual adjustment of pump parameters. Future iterations should enhance the integration between the pumps and the host software to enable automated parameter updates based on experimental needs. In addition, prolonged use of peristaltic pump tubing leads to a decline in flow-rate accuracy, and millisecond-level synchronization errors have been observed between the syringe and peristaltic pumps.

To address these issues, a scheduled replacement and calibration mechanism for wear-prone components should be established, and software algorithms should be introduced to compensate for timing discrepancies, thereby improving the long-term reliability and precision consistency of the system.

### Cycle 5: Intuitive Analysis and Characterization

#### Design

In traditional protein affinity assays, data analysis and interpretation largely rely on manual processing. FCCS raw data must be manually reformatted, binding curves require third-party software for fitting, and experimental progress as well as device operation status must be recorded and correlated by hand. This workflow is not only inefficient but also prone to data-matching errors, which can compromise the reliability of analytical conclusions.

To address these limitations, our team developed an integrated analysis platform that enables fully automated management across the entire workflow—covering experiment design, process execution, data parsing, and iterative result optimization. This system provides intuitive analytical visualization and rapid optimization capabilities, supporting high-throughput experimentation with improved efficiency and accuracy.

#### Build

The software was developed based on the Streamlit framework, featuring three core functional modules:

(1) Experiment Design Module — This module supports parameter configuration for three dedicated pumps (protein solution, buffer A, and buffer B), allowing customizable flow rates and operation durations. It also provides a five-step standardized workflow (including pump operation, mixing reaction, and data acquisition) with an intuitive visual interface, thereby lowering the operational threshold through structured user interaction.

(2) Process Execution Module — Built upon session\_state, this module enables real-time management of pump operation status (start/stop), step progression (completion flags and progress percentage), and emergency stop signals. It automatically records detailed system logs containing timestamps and event descriptions, ensuring full traceability throughout the experimental process.

(3) Data Parsing and Visualization Module — This component integrates a custom parse\_fcs\_data function for automatic validation and extraction of FCS data in CSV format, including columns such as protein, concentration, and affinity. The binding curve is fitted to the model

\[
Y = \frac{B\_{\max} \cdot C}{K\_d + C}
\]

using the curve\_fit function from SciPy, and the resulting data are visualized via Plotly, enabling direct transformation from raw measurements to interpretable conclusions.

#### Test

Based on the software’s design logic, the completeness and coordination of the core functional modules were verified through simulation. First, the experiment design module was tested by simulating various pump parameters—such as flow rates ranging from 50 to 100 μL/min and operation times from 10 to 30 seconds—to ensure that parameter modifications were instantly synchronized with the process execution module, thereby maintaining consistency between design inputs and execution commands.

Second, the process execution module was evaluated by simulating the triggering of all five experimental steps, verifying the logical coherence of pump state transitions, step progress updates, and system log recording. For instance, after Pump 1 completed its operation, the system was expected to automatically flag Step 1 as finished.

Finally, the data parsing and fitting module was validated using a constructed standard CSV dataset to confirm the completeness of data extraction, ensuring correct identification of required columns and accurate format conversion, as well as the logical reliability of the curve-fitting process.

#### Learn

Through an integrated design, the software achieves end-to-end connectivity from experimental setup to data visualization. Its structured interface and automated logic minimize manual intervention, theoretically improving both analytical efficiency and the accuracy of data correlation.

From a design perspective, however, there remains room for further optimization. First, the current data format support is limited to CSV files, expanding compatibility to include direct parsing of native FCS instrument formats would enhance applicability. Second, the curve-fitting function currently relies on a fixed binding model, incorporating additional models such as competitive or cooperative binding would better accommodate complex experimental scenarios. Third, the logging functionality is relatively basic, integrating visual statistical modules—such as pump runtime distributions and step duration analyses—would strengthen data traceability and provide greater support for workflow optimization.

Future iterations can build upon these directions to enhance the software’s flexibility and analytical depth.

## Microfluidic Hardware Integration: 3D-Printed Chip Enclosure Design

### Cycle1: Preliminary Design and Build

#### Design

Functional requirements: Design a unified enclosure that houses one primary chip and one auxiliary chip. The primary chip measures 75×25 mm. The auxiliary chip has a PDMS layer measuring 25×30 mm with a thickness of 3～5 mm; its microchannels are 50×100 μm. The substrate glass slide is 75×25×1 mm, and all access ports are 1.4 mm in diameter.

Design concept: The enclosure should integrate sample injection on the auxiliary chip with protein mixing on the primary chip. Specifically, capillary tubing is connected to the top port of the auxiliary chip, and its outlet is routed via tubing to the inlet of the primary chip; proteins are mixed on the primary chip and discharged from its outlet, thereby enabling high-throughput sample loading. Two different auxiliary chips are used to introduce multiple proteins, so the system must allow effortless hot-swapping of the auxiliary chip during input. Mechanically, the device should provide a reliable clamping fixture that secures the chips without compressing the PDMS, and incorporate basic leak-prevention and a visual inspection window for operational transparency.

#### Build

During assembly, 3D-printing materials should be selected by function: the outer housing is fabricated from PLA, which offers low warpage, dimensional stability, and low cost; the transparent cover uses acrylic (PMMA) to provide optical visibility while maintaining adequate mechanical strength; fasteners are stainless-steel M4 screws; and the connecting lines are medical-grade silicone tubing.

![](https://static.igem.wiki/teams/5553/engineer/hardware/a1b1dd8b0479341a3ef85e3640549a6f.webp)

Design Blueprint of the First Version of 3D Printed Chip Cartridge

![](https://static.igem.wiki/teams/5553/engineer/hardware/ezgif-6df8a6dd3cdea6.webp)

Exploded View of the First Version of 3D Printed Chip Cartridge

![](https://static.igem.wiki/teams/5553/finalddl/hardware/hardwareadd/4.avif)

Physical Image of the First Version of 3D Printed Chip Cartridge

#### Test

The testing phase comprised three key evaluations—leak testing, optical testing, and mechanical stability testing. For the static-pressure leak test, deionized water was injected using a peristaltic pump to check for seepage at all interfaces and housing ports. The optical test assessed field-of-view occlusion and lid glare; if glare was pronounced, the cover needed to be replaced with an anti-reflective alternative. Mechanical stability was evaluated by moving the enclosure and applying light vibrations to verify that tubing connections did not loosen. All tests were passed.

However, during experiments using the primary and auxiliary chips, we observed that when the auxiliary chip was laid flat, the input liquid did not enter the channels smoothly and frequently exited from the other loading port; additionally, the interconnecting capillary between the two chips tended to exhibit liquid hold-up.

#### Learn

Material upgrades: If higher resistance to organic solvents or stronger cleaning capability is required, the housing should be switched to SLS material.

Tubing specification: If insertion or removal causes excessive resistance, replace with tubing of 1.3 mm or 1.5 mm outer diameter to match different needle bore sizes, or apply a small amount of silicone-free lubricant to the PDMS inlet before insertion.

Dimensions and tolerances: Based on the initial 3D printing and assembly experience, adjust the geometry slightly to ensure the PDMS chip remains flat and is not compressed.

### Cycle 2: Optimization for Liquid Flow

#### Design

To address the liquid flow and retention issues observed in version 1.0 during experiments involving the primary and auxiliary chips, as well as to accommodate the updated dimensions of the primary chip, the 3D-printed housing was optimized. The redesign aims to fit the new chip size while ensuring smooth fluid flow within the auxiliary chip and minimizing liquid retention inside the connecting tubing.

#### Build

Slot adaptation: As the primary chip’s dimensions were updated in the new iteration, the slot structure was accordingly modified to ensure a precise fit and secure placement of the chip.

Trapezoidal platform design: A trapezoidal platform was introduced to position the auxiliary chip on an elevated, slanted plane above the primary chip. This adjustment optimizes the spatial relationship between the two chips and allows the auxiliary chip to be tilted, so that gravity assists the smooth flow of liquid from the auxiliary chip into the connecting tubing.

![](https://static.igem.wiki/teams/5553/engineer/hardware/e51dab0204dc16684350045a4ee6c8ce.webp)

Design Blueprint of the Second Version of 3D Printed Chip Cartridge

![](https://static.igem.wiki/teams/5553/engineer/hardware/ezgif-17d363787bdeee.webp)

Exploded View of the second Version of 3D Printed Chip Cartridge

#### Test

We subsequently conducted the same series of tests on version 2.0, focusing on verifying the fitting accuracy of the primary chip slot, assessing the smoothness of liquid flow within the auxiliary chip, and checking whether any fluid retention persisted within the connecting tubing.

#### Learn

Although version 2.0 resolved several issues, software-based simulation tests revealed that the auxiliary chip, due to its inclined placement and insufficient slot fit, was not securely fixed in position. In addition, the solid trapezoidal platform consumed a considerable amount of 3D printing material, indicating the need for further structural optimization.

### Cycle3: Optimization for mechanical stability and reduction of material loss

#### Design

Building upon the issues identified in version 2.0—namely insufficient fixation of the auxiliary chip and excessive material consumption in the trapezoidal platform—we further optimized the 3D-printed housing. The goal was to enhance the fixation of the auxiliary chip, reduce material usage, and maintain overall mechanical stability.

#### Build

Clamp addition: Because the auxiliary chip is placed at an angle and the slot size still does not perfectly match, we added clamping components to secure the chip more effectively and prevent loosening.

Platform optimization: The lower portion of the solid trapezoidal platform was hollowed out and reinforced with support structures, minimizing 3D printing material consumption while maintaining mechanical stability.

![](https://static.igem.wiki/teams/5553/finalddl/hardware1/23333.avif)

Application scenarios of the Third Version of 3D Printed Chip Cartridge

#### Test

We conducted another round of hardware and software testing on version 3.0, examining the firmness of auxiliary chip fixation, the structural stability of the trapezoidal platform, and the effectiveness of material reduction in 3D printing.

#### Learn

Version 3.0 showed significant improvements in both auxiliary chip fixation and material efficiency. After verification, we obtained a well-refined 3D-printed chip enclosure suitable for integration into the hardware system, which has now been deployed in the project. Further refinements can be made based on continued user feedback and real-world performance.

×
![preview]()

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## Protocol

### DNA Cloning Methods

#### PCR

PCR (Polymerase Chain Reaction) is a technique used to amplify a template DNA by cycling through denaturation, annealing, and extension phases with a thermostable DNA polymerase.

1. Set up assembly reactions in PCR-tubes for a total volume of 50µL as follows:

   **Typical composition of a PCR reaction.**

   | Component | Volume/Amount |
   | --- | --- |
   | Template DNA | 1 µL |
   | Forward Primer | 2 µL |
   | Reverse Primer | 2 µL |
   | High-Fidelity DNA Polymerase | 0.5 µL |
   | 2x phanta buffer | 5 µL |
   | dNTP Solution Mix (10 mM) | 1 µL |
   | Nuclease-free water | to 50 µL |
2. Mix gently and spin down.
3. Place PCR-tubes in thermocycler and run the following program:

   **Table 8: Thermocyler program for a PCR.**

   | Step | Temperature | Time | Return to step | Passes total |
   | --- | --- | --- | --- | --- |
   | Initial Denaturation | 98°C | 30 s |  | 1 |
   | Denaturation | 98°C | 10 s |  | 25 - 40 |
   | Annealing | 60 - 72 °C | 15 s |  | 25 - 40 |
   | Extension | 72°C | 60 s/kb | 2.0 | 25 - 40 |
   | Final Extension | 72°C | 5 min |  | 1 |
   | Hold | 4°C | ∞ |  | 1 |
4. Store at 4 °C until further use.

#### Agarose Gel Electrophoresis

Agarose gel electrophoresis is used to separate DNA fragments of different nucleotide lengths from each other.

1. Depending on the nucleotide lengths of the DNA fragments, gels with a different agarose concentration should be used:

   **Agarose concentrations for different nucleotide lengths.**

   | Nucleotide Length | Agarose Concentration |
   | --- | --- |
   | 600 - 50000 bp | < 1% |
   | 400 - 8000 bp | 1% |
   | 100 - 2000 bp | 2% |
   | 25 - 1000 bp | 3% |
2. Add agarose to TAE buffer in the desired agarose concentration and heat in the microwave until the agarose is completely dissolved (be careful to not let the agarose boil over).
3. Pour gel and wait for 15 - 30 min until the gel becomes firm.
4. Mix the product with 5 µL 10xDNA Loading Buffer and load each mixture per tube
5. Run gel at 180 volts for 30-45 min
6. Visualize gel under UV or blue light (depending on the staining agent used).

#### Purification of DNA fragments from agarose gels

The procedures should strictly follow the instructions of PureLink Quick Gel
Extraction Kit (ThermoFisher Scientific) provided as bellow.

#### Gibson assembly

Gibson assembly is a molecular cloning strategy used to assemble multiple DNA fragments with overlapping overhangs. In contrast to classical restriction cloning, gibson assembly does not require restriction sites for assembly.

1. Design DNA fragments with 15 - 20 bp overhangs.
2. Amplify DNA fragments via PCR.
3. Set up Gibson assembly reaction mix:

   **Typical composition of a gibson assembly reaction.**

   | Component | Single Fragment Assembly |
   | --- | --- |
   | vector | final concentration 3 pmol |
   | fragment | final concentration 6 pmol |
   | Gibson Assembly Master Mix (2X) | 5 µL |
   | ddH2O | to 10 µL |
4. Incubate the reaction mix at 50 °C for 10 min.

#### Transformation of BL21 (DE3) competent E.coli cells

1. Thaw cells on ice for 5 min.
2. Add 1 - 2 µL of plasmid DNA to each tube of cells and incubate for 30 min on ice. Do not mix.
3. Heat-shock cells at 42 °C for 60 sec.
4. Place on ice for 3 min.
5. Add 900µL LB liquid medium and recover cells for 45 min at 37 °C while shaking.
6. Centrifuge for 4000 rpm for 4 min.
7. Discard supernatant by decanting and resuspend cell pellet in remaining supernatant.
8. Plate 75 µL supernatant on LB agar plates containing 50 µg/mL kanamycin.
9. Incubate the cells in an incubator at 37℃ for 15 hours.

### Standard Binder Expression & Analysis

#### Protein Expression

1. Select specific monoclonal colony and shake it in 5 ml LB liquid medium containing 50 µg/mL kanamycin overnight
2. Inoculate 500 mL LB liquid medium containing 50 µg/mL kanamycin with basically all the bacteria solution
3. The cell culture was grown at 37 °C while shaking until the optical density at 600 nm reached 0.6.
4. Expression was induced by adding isopropyl-β-D-thiogalactopyranoside (IPTG) to a final concentration of 0.5 mM at 16◦C overnight

#### Cell Lysis

1. Transfer the bacteria solution to 500ml centrifuge tube and harvest cells by centrifugation (4500rpm, 15min, 16°C).
2. Discard the supernatant and resuspend the sediment with 35mL lysis buffer(50mM Tris-HCl (pH=8), 300mM NaCl).
3. The bacteria solution is lysed by high pressure homogeniser at 800 bar for 2.5min at 4 °C.
4. Clear the lysate by centrifugation at 18000 rpm for 45 min at 4 °C, then retain the supernatant

#### Purification of His-tagged Binders

##### 1. Buffer Preparation

Prepare the following buffers and store them at 4°C. The concentrations provided are typical starting points and may require optimization for the specific protein of interest.

* Lysis/Binding Buffer: 50mM Tris-HCl (pH=8), 300mM NaCl
* Wash Buffer: 20 mM imidazole solution
* Elution Buffer: 300 mM imidazole solution

##### 2. Column Preparation

1. Gently swirl the bottle of Ni-NTA agarose slurry to ensure it is fully resuspended.
2. Transfer an appropriate volume of the slurry to an empty gravity-flow column. The amount of resin depends on the expected yield of the protein; a 1 mL bed volume can typically bind 5-10 mg of a His-tagged protein, but this is protein-dependent.
3. Allow the storage buffer (typically 20% ethanol) to drain from the column by gravity.
4. Equilibrate the resin by washing it with 5-10 column volumes (CV) of Lysis/Binding Buffer. Let the buffer drain completely through the column. This step adjusts the pH and prepares the resin for protein binding.

##### 3. Protein Binding

1. Carefully apply the clarified lysate to the top of the equilibrated Ni-NTA resin bed.
2. After sufficient resuspending, transfer the mixture to a new 50 ml centrifuge tube and incubate the lysate with the resin by mixing gently on a rocker for 60 minutes at 4°C before being loaded onto the column.
3. Allow the lysate to flow through the column by gravity.

##### 4. Washing

1. Once the entire lysate has passed through the column, wash the resin with 8-15 CV of Wash Buffer.
2. This step removes non-specifically bound proteins. Continue washing until the absorbance of the flow-through at 280 nm (A280) returns to baseline.
3. Collect the wash fractions for analysis by SDS-PAGE to confirm that the target protein is not being prematurely eluted.

##### 5. Elution

1. Apply the Elution Buffer to the column. The high concentration of imidazole will compete with the His-tag for binding to the Ni-NTA resin, thereby displacing and eluting the target protein.
2. Collect the eluate in separate fractions (e.g., 0.5-1.0 CV per fraction). This allows for the collection of the most concentrated and pure protein fractions, which typically elute first.
3. Analyze the collected fractions for protein content using a protein assay (e.g., Bradford) and for purity using SDS-PAGE. Pool the fractions that contain the purified protein of interest.

#### SDS-PAGE

##### Gel and Apparatus Preparation

* Remove the precast gel cassette from its packaging.
* Rinse the cassette with deionized (DI) water.
* Carefully remove the comb from the top of the gel cassette.
* Remove the sealing tape from the bottom of the cassette to allow ion flow during electrophoresis.
* Install the cassette into the electrophoresis tank (cassette dam may be required for a single gel setup) according to the manufacturer's instructions.
* Fill the inner (upper) and outer (lower) buffer chambers with 1x MOPS Running Buffer. Ensure the buffer in the inner chamber covers the wells completely.
* Use a pipette to gently rinse the wells with running buffer to remove any residual storage buffer and dislodge air bubbles.

##### Sample Preparation

* Combine 20 μL of the protein sample with 5 μL 5x SDS-PAGE Sample Loading Buffer.
* Heat the mixture at 100°C for 10 minutes for complete denaturation and reduction.
* Centrifuge the samples for 1-2 minutes at high speed to pellet any insoluble material.

##### Electrophoresis

* Carefully load the prepared samples and a molecular weight marker into the wells of the precast gel. The maximum loading volume for a 15-well GenScript gel is typically 25 µL.
* Secure the lid on the electrophoresis tank and connect the electrodes to the power supply (connect red to red and black to black).
* Run the electrophoresis. For ExpressPlus™ gels, a constant voltage of 120-140 V is recommended.
* Continue the run until the bromophenol blue dye front reaches the bottom of the gel, which typically takes 40-50 minutes.
* Once the run is complete, turn off the power supply and disconnect the electrodes.

##### Staining and Destaining

* Disassemble the electrophoresis unit and carefully open the gel cassette using a gel knife or spatula.
* Transfer the gel into a clean container with ddH2O and rinse briefly.
* Submerge the gel in coomassie brilliant blue dye and incubate with gentle agitation for 30-60 minutes.
* Pour off the stain and add Destaining Solution.
* Agitate gently, replacing the destaining solution periodically until the protein bands are sharp against a clear background.

### Mammalian Cell Protocols

#### Cell Maintenance

This is a basic protocol for splitting Human Embryonic Kidney 293T (HEK293T) cells to maintain the culture throughout the week, with excess cells reserved for future experiments and plating. Make sure to work in a sterile environment to avoid contamination of the cells.

1. Warm reagents to 37°C and prepare complete DMEM supplemented with 10% FCS and 1% P/S:

   **Table 15: Composition of complete DMEM supplemented with 10% FCS and 1% P/S.**

   | Reagent | Volume [mL] |
   | --- | --- |
   | Dulbecco's Modified Eagle Medium (DMEM) | 445.0 |
   | Fetal Calf Serum (FCS) | 50.0 |
   | Penicillin/Streptomycin (P/S) | 5.0 |
2. Take cells out of the incubator and aspirate the old media from the plate or flask.
3. Wash with Dulbecco's Phosphate Buffered Saline (DPBS).
4. Add trypsin and incubate at 37°C until cells start to detach.
5. Add DMEM to neutralize the trypsin.
6. Count cells or decide on the appropriate dilution based on confluence.
7. Dilute with DMEM until the desired concentration is reached.
8. Seed cells onto a new plate or flask, gently shuffle to ensure even dispersal, and return cells to the incubator.

#### Transfection

Transfection refers to the uptake of exogenous DNA into eukaryotic cells.

1. Ensure a 300 mL suspension cell culture is ready for transfection, with a target density of 2.4–2.5 x 10⁶ viable cells/mL
2. Prepare Solution A (DNA): add 300 µg of plasmid DNA to a sterile 15 mL conical tube and adjust the total volume to 5.0 mL with serum-free medium. Mix gently by inverting the tube.
3. Prepare Solution B (PEI): add 950 µL of PEI reagent to a separate sterile 15 mL conical tube and adjust the total volume to 5.0 mL with serum-free medium. Mix gently by inverting the tube.
4. Incubate both Solution A and Solution B at room temperature for 3 minutes.
5. Add the 5.0 mL of Solution B (PEI) to Solution A (DNA). Immediately mix by gentle pipetting.
6. Incubate the final mixture at room temperature for 30 minutes to allow for the formation of transfection complexes.
7. Aseptically transfer the entire DNA-PEI complex solution into the cell culture flask while gently swirling the flask.
8. Place the flask in an orbital shaker incubator and incubate cells at 37 °C and 5% CO2 for 2 - 3 days.

## Materials & Methods

### GZMK expression and purification

The gene encoding GZMK was cloned into the pHL-sec vector with a flag tag on the C terminal and a secretion signal tag fused to an enterokinase cleavage site at the N terminus according to previous researches. For GZMK production, the expression vector was transiently transfected into FreeStyle 293-F cells using PEI. One day after transfection, add sodium butyrate to the cell culture system to a final concentration of 5 mM. Fpur days after transfection, the GZMK proenzyme was purified from filtered cell supernatants by affinity chromatography based on anti-Flag affinity beads. The eluent was subject to enterokinase cleavage at 16℃ for 24h(add 1μL enterokinase for 10μg target protein, also add calcium chloride to a final concentration of 2 mM). Finally, GZMK in its active form is purified through gel filtration chromatography.

### Determination of GZMK activity

**1. Enzymatic Activity Assay Using Z-Lys-SBZL**

The enzymatic activity of GZMK was measured in a 100 μL reaction volume. The reaction mixture consisted of 0.1 μM GZMK, 1.0 mM Z-Lys-SBZL substrate, and 1.1 mM DTNB in a buffer containing 50 mM Tris-HCl (pH 7.6), 150 mM NaCl, and 0.01% (v/v) Triton X-100. The substrate and DTNB were pre-dissolved in DMSO, ensuring the final concentration in the assay remained below 5% (v/v). Following reaction initiation, the change in absorbance at 405 nm was monitored every 20 seconds for 30 minutes using a microplate reader.

**2. Enzymatic Activity Assay Using a FRET Peptide Substrate**

This assay was performed in a 50 μL reaction volume with buffer and GZMK concentrations identical to the previous method. The reaction was initiated by adding the FRET peptide substrate (DABCYL-GDGRSIMTE-EDANS) to a final concentration of 500 μM, with the final DMSO concentration kept below 5% (v/v). Fluorescence was monitored for 30 minutes (read every 20 seconds) on a microplate reader, with excitation and emission wavelengths set to 340 nm and 490 nm, respectively.

**3. GZMK Enzyme Kinetics Analysis**

To determine enzyme kinetic parameters, the FRET assay was performed with a range of substrate concentrations (500, 250, 125, 60, 30, 15, 7.5, 3.75, 1.875, and 0.9375 μM). The initial velocity (v0) at each concentration was determined from the slope of a linear regression of the data points from 4 to 20 minutes. Finally, the initial velocities were plotted against substrate concentrations and fitted to the Michaelis-Menten equation using non-linear regression in GraphPad Prism to calculate Km and Vmax.

### Surface plasmon resonance (SPR) experiments

SPR experiments were performed on the Biacore 8K instrument. GZMK in its active form was immobilized using the Series S Sensor protein A chip (Cytiva). Increasing concentrations (0.156 μM, 0.3125 μM, 0.625 μM, 1.25 μM, 2.5 μM, 5 μM, 10 μM, 20 μM) of various binders were flowed over the surface for single-cycle kinetic experiments. The surface was regenerated in 10mM glycine pH 1.0. The experiments were performed at 4°C, using a running buffer containing 1xPBS, 500mM NaCl, 0.05% v/v Tween-20, and 3mM EDTA. For affinity screen experiment, 32 binder solution was adjusted to the same concentration (6.7 μmol) and then repeated the above procedures sequentially to screen for binders with obvious affinity.

### Screening of small molecule inhibitors

Small molecule screening experiments were conducted in 50 μL reaction systems, each comprising three components: Protein Buffer, substrate solution, and small molecule solution.

**Protein Buffer (Pro):** 47.5 μL was added per 50 μL reaction system. GZMK solution (22 tubes) was pooled, centrifuged, and the supernatant concentration was determined to be 0.14 mg/mL. Each 40 mL of Protein Buffer contained 680 μL GZMK solution, 8 mL 5x HEPES, 40 μL 100 mg/mL BSA aqueous solution, 40 μL 10% Triton X-100 aqueous solution, and 31.24 mL ddH₂O. For screening six 384-well plates, approximately 110 mL of Protein Buffer was required, and 120 mL (3 × 40 mL) was prepared to ensure complete pipetting. The GZMK concentration in the Protein Buffer was 88.66 nM, resulting in a final GZMK concentration of 84.23 nM in the reaction system.

**Substrate Solution (Sub):** 2 μL was added per 50 μL reaction system. A 2.5 mM substrate solution in DMSO was prepared by dissolving 10 mg of substrate in 2.731 mL DMSO per tube, with five tubes combined to yield 13.655 mL of solution.

**Small Molecule Solution:** 0.5 μL of 1 mM small molecule solution was added per 50 μL reaction system.

Experiments were performed using the Bravo liquid handling system for pipetting, mixing, and dispensing. First, 47.5 μL Protein Buffer was mixed with 0.5 μL small molecule solution and incubated for 90 seconds. Then, 2 μL substrate solution was added and mixed, followed by immediate fluorescence intensity measurement in a microplate reader, recorded every minute for 15 cycles.

### Binders expression and purification

DNA sequences encoding the proteins of interest were purchased from Atantares and incorporated into plasmids directly. The plasmids were then transformed into BL21(DE3) competent *E. coli*. The transformation reactions were used to inoculate starter cultures in 5 ml of LB broth, supplemented with 50 mg/L kanamycin. After shaking overnight at 37 °C, the starter cultures were diluted into 5 500 ml of LB with 50 mg/L kanamycin. These cultures were incubated at 37 °C, shaking, until the optical density reached 0.6–0.8, at which point protein expression was induced by the addition of IPTG. The cultures were then further incubated overnight at 16 °C. Cells were harvested by centrifugation for 15 min at 4500 rpm, pellets were resuspended in lysis buffer (50mM Tris-HCl，300mM NaCl, pH 8.0), the cells were lysed by high pressure homogenization and the lysate was clarified by further centrifugation for 45 min at 18000g. The supernatant was passed through Ni-NTA resin in a gravity column and then the resin was washed with 20 column volumes of 20 mM imidazole. Either the His-tagged protein was eluted with 1ml 300mM imidazole twice. The first 1ml of eluent was loaded onto a Cytiva Superdex 75 Increase 10/300 GL gel filtration column equilibrated in 1x PBS buffer. Fractions containing the desired binders were collected and concentrations were estimated spectroscopically by absorbance at 280 nm. For proteins with no tryptophan, tyrosine or cysteine residues, concentrations were approximated by Bradford reagent absorbance at 470 nm in comparison to BSA standards of known concentration.

### Preparation of colloidal gold test strips

1. **Optimization of Protein-Colloidal Gold Conjugation**

Prior to conjugation, the optimal conditions for protein adsorption onto colloidal gold nanoparticles were determined. First, the optimal pH was identified by adjusting 1 mL aliquots of colloidal gold solution to pH values ranging from 6.0 to 9.0 with 1 M Na2CO3. An excess of protein (≥50 µg) was added to each aliquot, followed by a 15-minute incubation at room temperature. The stability of the mixture was then challenged by the addition of 100 µL of 200 mM NaCl solution. The optimal pH was determined as the lowest pH value that prevented salt-induced aggregation, indicated by the retention of the solution's original red color.

Subsequently, using the optimized pH, the minimum protein concentration required for stabilization was determined. A titration series was performed by adding increasing amounts of protein (5-45 µg) to 1 mL aliquots of the pH-adjusted colloidal gold. After a 15-minute incubation, 100 µL of 200 mM NaCl was added to each tube. The lowest protein concentration that successfully stabilized the nanoparticles against aggregation was identified. For all subsequent conjugations, a working concentration corresponding to this minimum amount plus a 10% surplus was used to ensure robust and complete nanoparticle coating.

2. **Preparation and Purification of Colloidal Gold-Protein Conjugates**

For large-scale conjugate preparation, 20 mL of colloidal gold solution was adjusted to the predetermined optimal pH under constant stirring. The optimized mass of protein was added, and the mixture was allowed to react for 30 minutes at room temperature. To block any remaining active sites on the gold surface and to stabilize the conjugates, 10% (w/v) Bovine Serum Albumin (BSA) was added to a final concentration of 1% (v/v), followed by an additional 30-minute incubation. The final conjugate solution was stored at 4°C overnight.

The conjugates were purified via centrifugation at 12,000 rpm for 20 minutes. The supernatant was discarded, and the pellet was washed sequentially by resuspension and centrifugation, first in a 2% (w/v) BSA solution, and subsequently in a solution of 1% (w/v) BSA in 0.02 M Tris-Buffered Saline (TBS), pH 8.2. The final purified pellet was resuspended in a storage buffer and stored at 4°C until use.

3. **Fabrication and Evaluation of the Lateral Flow Assay Strip**

The lateral flow assay (LFA) strips were constructed on an adhesive backing card. The purified colloidal gold-protein conjugate solution was dispensed onto the conjugate pad (10 µL per strip) and dried. A solution of capture protein (1 µL) was immobilized onto a nitrocellulose membrane to form the test line. Finally, a sample pad and an absorbent pad were laminated onto the card at the proximal and distal ends of the nitrocellulose membrane, respectively, to complete the assembly. The functionality of the fabricated LFA strips was verified by applying 40 µL of Phosphate-Buffered Saline (PBS) to the sample pad. The results were visually interpreted after a 15-minute development period at room temperature.

## Sequence

### GZMK expression

#### Plasmid construction for prokaryotic expression

![](https://static.igem.wiki/teams/5553/seq/image-27.webp)

#### Plasmid construction for eukaryotic expression

![](https://static.igem.wiki/teams/5553/seq/image-28.webp)

![](https://static.igem.wiki/teams/5553/seq/image-29.webp)

![](https://static.igem.wiki/teams/5553/seq/image-30.webp)

![](https://static.igem.wiki/teams/5553/seq/image-31.webp)

![](https://static.igem.wiki/teams/5553/seq/image-32.webp)

#### Primer sequences related to plasmid construction

[primer-sequence-for-gzmk.csv](https://static.igem.wiki/teams/5553/seq/primer-sequence-for-gzmk.csv)

▶primer-sequence-for-gzmk.csv

| primer | sequence |
| --- | --- |
| GZMK-1 | tgatgacgatgacaaaATTATTGGTGGTAAAGAAGTAAGCCCTCAC |
| GZMK-2 | ttggtaccgttggtgtGGGGCGGCA |
| GZMK-3 | acaccaacggtaccaaGCACCACCATCACC |
| GZMK-4 | tttgtcatcgtcatcaCCGGTTTCAGCTACGCAAC |
| GZMK-5 | ggatgacgatgacaaaATTATTGGTGGTAAAGAAGTAAGCCCTCAC |
| GZMK-6 | ttggtgccgttggtgtGGGGCGGCA |
| GZMK-7 | acaccaacggcaccaaGCACCATCACC |
| GZMK-8 | tttgtcatcgtcatccACCAAGGCCAAGATAAGTGCGA |
| GZMK-9 | cgatgacgatgacaaaATTATTGGTGGTAAAGAAGTAAGCCCTCAC |
| GZMK-10 | ttggtgccgttggtgtGGGGCGGCA |
| GZMK-11 | acaccaacggcaccaaGCACCATCACC |
| GZMK-12 | tttgtcatcgtcatcgTCACCAGTGGAACCTGGA |
| GZMK-Flag | CTTATCGTCGTCATCCTTGTAATCGTTGGTGTGGGGC |
| pcDNA-Flag | GATTACAAGGATGACGACGATAAGTGACTCGAGtctagagggc |
| pHLcx\_FOR | CTAGAGCCTCTGCTAACC |
| Vector.REV | TTTTGTCATCGTCATCACCGGTTTCAGCTACGC |
| pHL-R1 | TTTGTCATCGTCATCACCGGTTTCAGCTACGC |
| GZMK-F1 | CCGGTGATGACGATGACAAAATTATTGGTGGTAAAGAAGTAAGCCC |
| Fragment.FOR | CGGTGATGACGATGACAAAATTATTGGTGGTAAAG |
| Fragment.REV | TTGGTACCGTTGGTGTGGGGCGGCAC |
| GZMK-R1 | CTTATCGTCGTCATCCTTGTAATCGTTGGTGTGGGGCGG |
| pHL-F1 | GATTACAAGGATGACGACGATAAGTAATGATCACTCGAGACTAGTATCGC |
| pHLcx\_REV | CAAGGGGCTTCATGATGT |
| pET-28a-3 | ATACCAACCACCACCACCACCACCACTGAGATCC |
| vector-F | CTGCGCATATGGCTGCCGCGCG |
| pET-28a-4 | ATTTCCATGGTATATCTCCTTCTTAAAGTTAAACAAAATTATTTCTAGAGG |
| pET-28a-1 | GATATACCATGGAAATTATTGGTGGCAAAGAAGTGAG |
| pET-28a-2 | TGGTGGTGGTTGGTATGCGGCGGCAC |

#### GZMK sequence

[gzmk-sequence.csv](https://static.igem.wiki/teams/5553/seq/gzmk-sequence.csv)

▶gzmk-sequence.csv

| Protein | sequence |
| --- | --- |
| DDDDK-mature GZMK-6His | ETGDDDDKIIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTNGTKHHHHHH |
| GZMK-6His | MEIIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTNLEHHHHHH |
| GZMK-Flag before enzyme digestion | METDTLLLWVLLLWVPGSTGDDDDDKIIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTNDYKDDDDK |
| GZMK-Flag after enzyme digestion | IIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTNDYKDDDDK |
| precursor of GZMK | MTKFSSFSLFFLIVGAYMTHVCFNMEIIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTN |
| mature GZMK-6His | IIGGKEVSPHSRPFMASIQYGGHHVCGGVLIDPQWVLTAAHCQYRFTKGQSPTVVLGAHSLSKNEASKQTLEIKKFIPFSRVTSDPQSNDIMLVKLQTAAKLNKHVKMLHIRSKTSLRSGTKCKVTGWGATDPDSLRPSDTLREVTVTVLSRKLCNSQSYYNGDPFITKDMVCAGDAKGQKDSCKGDSGGPLICKGVFHAIVSGGHECGVATKPGIYTLLTKKYQTWIKSNLVPPHTNLEHHHHHH |

### Binder expression

[binder-sequence.csv](https://static.igem.wiki/teams/5553/seq/binder-sequence.csv)

▶binder-sequence.csv

| Binder | sequence |
| --- | --- |
| 1-1 | EEHKKKAEQLLEDAKKFKEEGDKEDAKYAAESAIAEAKKLPEYYEEVEKKAKEILS\* |
| 1-2 | EKEKEVKEYEEDTKKYAEGSSEAAKEDDPSKAEEAKKAAEEQGKKDQENISK\* |
| 1-3 | MEELKKEAKEIAENGTPEQAAIASSAYWAVEKYKKDPKKAKEYYNYLKSQI\* |
| 1-4 | SSKKEAEELKKKSEEYKKESEEYKEEAKEYEKEGNTKAAESANKLSEEYEKESEKYEKKAEELEK\* |
| 1-5 | SEDSKEELEFTKKLAKHYDKETQKAILESLAEQYKELGDEETAKKAEEEAKKAYE\* |
| 1-6 | ERAKKVREEIERTYKEKTKEEAVDKAYELATKAYQEGDEEAAKEAEREAEKYSK\* |
| 1-7 | NELKEKTKEYSEKAKEYKKKGDEEKSEAYLQKSESYAWLGGEESEKEYEKIKKEIE\* |
| 1-8 | KEEEVKELKEKSELAKEYSKEVKEEAEKIYKETGNKQALEKGKELAEDAEKSYKEYEKKAKELEE\* |
| 1-9 | KIIDDEELAETNKKIEELEKKDEKEAQKLAEELGEKIKENEKKRKEEKEK\* |
| 1-10 | KLEEGLKWLEEKTAEAIEKGDEENARKYSLAGWESSEKGWDKEETLKYAEELIEN\* |
| 1-11 | SEEIKKEWKEKVLKGIKEGDEAYQKVIDEALEKDESGNDEETTAIEEGIYEAKKEYK\* |
| 1-12 | QKELAEKAAEYSIKSYELEKEGKEEESLKYIEEEAKLQRKMDQEGIEEYEEILKEKT\* |
| 1-13 | SKEESYKEADKAIEEAKKKNYEEAQEIINKQTAEHLEKGDEETARYTTIKGQEVI\* |
| 1-14 | EKEREEAEEVGRRIREASEEEATKIAGESWQRFLDKGWYKAAELVREYYEEWLKSR\* |
| 1-15 | DETAKKGEEYYEKAKEYYKEGKEDEATEYYSKAHKYYAKTGNKENQDKVIDDYEKN\* |
| 1-16 | SSERRQRRLDRLKESLKNAPKDLEELEKEHPELVKKHKSYEELKKELEKGVKKIESGELTDEEAKELTSKITEHMTSIYTYIETTLEKKK\* |
| 1-17 | SSEYYTEKAKEYSKKGEENKDNPYLAYSYYSRASTYADVAGDKELSEEAEKKAKEAE\* |
| 1-18 | SYREYAEEAIKKAEESKDEKESEKILEEALEKLEKKYEEYKKEEEKKKEEEE\* |
| 1-19 | LEEKAKEYYKKGEEYYKKGNKEEAARYYAKAGNEYSKAGKKEEAEKILKKADEVDE\* |
| 1-20 | EKKKKAEEYKKKSEEYKKKSEELEKKSKEYEEKGDEENSSKSLEESAKYYRESYKYELESEKLEE\* |
| 1-21 | SEGEKYYKKGKEYEKKGDYKKSSEYYKKSSLKYLEEGNEDNAEKSYEESLKSEEKYK\* |
| 1-22 | DEKKEEAKEYYEDAKEAKKVGNYKTAADYAKAAEELAKEAGDKETAEKAKKLEKEAK\* |
| 1-23 | STEKGKEYYEKGKEYYKEGNKNEALKYYGKSASYYATSGDDEGYNKSYENAEKTEE\* |
| 1-24 | SAVEKAKKAAEKLSEKGYEGAGKSGVELAKDAEKAGDTETAEEIAKDTEELAKE\* |
| 1-25 | SEVEKAKEAAEELNKKGYTGAGNSGYKLAKDAEKAGDTETAKEIAEDTKELAKE\* |
| 1-26 | LKEKADEYYEKGEKYEKEGNKDEAAEYYLQASNLYSDYGDDENAEKANQKSLDVAK\* |
| 1-27 | SSSTWSEAVKYSLKLSEKDPEEATELLSKAYEAKEKGDTETIEEQRKKAKEKVKEL\* |
| 1-28 | SDEEKANEKVVDYGEKKGASTEAIVAGDAEAGKAIEKGKSAEEAAKIGIDKINELT\* |
| 1-29 | SKRIEEQKKNIEKSKKATEELIKNKEELTEEELEGVKEYSKEVEKAEKELEKEK\* |
| 1-30 | SAEEYKKKGDEYEKKGDKENAQEYYLEAANKYYEEGKREESRKYAKKAEEL\* |
| 1-31 | DDVEYLKKEAKETLEISEKYEEKGKKYEKEGDKEKAYLELGLSERYKEAAKRLEESAKEAE\* |
| 1-32 | EKEKKAKEYKKKSDEYLKKSGEYNDEASKYYKEGKTEEGDKYDEKSKEYEDKSTEYLEKSSELEN\* |
| 2-1 | MGSSHHHHHHSSGLVPRGSHMSDRIELQKKNLEKSKKATEELIRQKEELTQEELEGVIEYSKVVEKAEKELEKTK |
| 2-2 | MGSSHHHHHHSSGLVPRGSHMSKKIKEQKKHLEKSKEALEELIKNKEELTEEDLEGVKRYSKEVEKAAKELEKEK |
| 2-3 | MGSSHHHHHHSSGLVPRGSHMSKRIERQKKNIEKSKKATEELIKNKENLTKEELEGVKEYSKIIEKAEKELEKEK |
| 2-4 | MGSSHHHHHHSSGLVPRGSHMMKRINEQMKNIEKSKKATEELIENKEELTEEELEGVKEYSKLVEKAEKELLKEV |
| 2-5 | MGSSHHHHHHSSGLVPRGSHMEKAREEQKKNIEKSKKATEELIKNKEELTEEELEGVKEYAKTVEKAKKELEKEK |
| 2-6 | MGSSHHHHHHSSGLVPRGSHMSKRVEERLKNIEKSKKATEELIKNKEEMTEEELDGVIEYSKIIEKAEKGLLKEK |
| 2-7 | MGSSHHHHHHSSGLVPRGSHMSKTIEEQKKNIEESKKATEDLIKNKEELSEAELEGVKEYSKEVENAERELEEEK |
| 2-8 | MGSSHHHHHHSSGLVPRGSHMSARIEEQKKNIEVSKKATEELIKNKEILTEEELEGLKEYSKELAKAEKELAKEK |
| 2-9 | MGSSHHHHHHSSGLVPRGSHMSKRIENAKKNIEKSKIAEEELIKNKEELTQEEIEGVKEYSKEVEKAEKELEKEK |
| 3-1 | MGSSHHHHHHSSGLVPRGSHMEEPEKIYLKRKTLESTDTDPYNSGTSRTSRLRYYYNHETGTVEEYVYGGNKPNKNTYETAEEAAKKYGSTIKTSDLEEYLRNI |
| 3-2 | MGSSHHHHHHSSGLVPRGSHMETEREYRTEKEKKKNKNEEIKDGYSNTYRERWAYNNKKGKIEKHVFGGSDQNGNRWNSEEEYKKASI |
| 3-3 | MGSSHHHHHHSSGLVPRGSHMWDKSYINEPIVTGYLNTYYRRYAYDKEKGKIVEYTYGGSDTNGNRWDTKEEAEKKTGITI |
| 3-4 | MGSSHHHHHHSSGLVPRGSHMERKPDPTEPITQGYSKTYRERWAYNTEKGRWEKVVFGGSDENGNRWETKEEAEKASI |
| 3-5 | MGSSHHHHHHSSGLVPRGSHMKKKVERKKTTPRKQGYTKTYRRRWSYNTETGKWEQYIYGGTDENGNRWETKEEAEKKNKEEGKTYYEYTEEI |
| 3-6 | MGSSHHHHHHSSGLVPRGSHMMSEELAKLHLKRAKEAKEKGDKENAINYAKAALDSAKEAGDEETEKEAKELIKEVKI |

#### Proteins for Hardware and preparation of colloidal gold test strips

##### Plasmids Construction

![](https://static.igem.wiki/teams/5553/seq/image-33.webp)

![](https://static.igem.wiki/teams/5553/seq/image-34.webp)

![](https://static.igem.wiki/teams/5553/seq/image-35.webp)

![](https://static.igem.wiki/teams/5553/seq/image-36.webp)

##### Sequence

[protein-for-hardware.csv](https://static.igem.wiki/teams/5553/seq/protein-for-hardware.csv)

▶protein-for-hardware.csv

| Protein | sequence |
| --- | --- |
| CyPet-SUMO1 | MVSKGEELFGGIVPILVELEGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTWGVQCFSRYPDHMKQHDFFKSVMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYISHNVYITADKQKNGIKANFKARHNITDGSVQLADHYQQNTPIGDGPVILPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYKGSGMSDQEAKPSTEDLGDKKEGEYIKLKVIGQDSSEIHFKVKMTTHLKKLKESYCQRQGVPMNSLRFLFEGQRIADNHTPKELGMEEEDVIEVYQEQTGGHSTV |
| Ypet-Ubc9 | MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKLLCTTGKLPVPWPTLVTTLGYGVQCFARYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYITADKQKNGIKANFKIRHNIEDGGVQLADHYQQNTPIGDGPVLLPDNHYLSYQSALFKDPNEKRDHMVLLEFLTAAGITEGMNELYKGSGMSGIALSRLAQERKAWRKDHPFGFVAVPTKNPDGTMNLMNWECAIPGKKGTPWEGGLFKLRMLFKDDYPSSPPKCKFEPPLFHPNVYPSGTVCLSILEEDKDWRPAITIKQILLGIQELLNEPNIQDPAQAEAYTIYCQNRVEYEKRVRAQAKKFAPS |
| mRuby2-Ubc9 | MGSSHHHHHHMVSKGEELIKENMRMKVVMEGSVNGHQFKCTGEGEGNPYMGTQTMRIKVIEGGPLPFAFDILATSFMYGSRTFIKYPKGIPDFFKQSFPEGFTWERVTRYEDGGVVTVMQDTSLEDGCLVYHVQVRGVNFPSNGPVMQKKTKGWEPNTEMMYPADGGLRGYTHMALKVDGGGHLSCSFVTTYRSKKTVGNIKMPGIHAVDHRLERLEESDNEMFVVQREHAVAKFAGLGGGMDELYKGSGMSGIALSRLAQERKAWRKDHPFGFVAVPTKNPDGTMNLMNWECAIPGKKGTPWEGGLFKLRMLFKDDYPSSPPKCKFEPPLFHPNVYPSGTVCLSILEEDKDWRPAITIKQILLGIQELLNEPNIQDPAQAEAYTIYCQNRVEYEKRVRAQAKKFAPS\* |
| mEGFP-SUMO1 | MGSSHHHHHHMVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSKLSKDPNEKRDHMVLLEFVTAAGITLGMDELYKGSGMSDQEAKPSTEDLGDKKEGEYIKLKVIGQDSSEIHFKVKMTTHLKKLKESYCQRQGVPMNSLRFLFEGQRIADNHTPKELGMEEEDVIEVYQEQTGGHSTV\* |

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}Present the results of your project, along with a detailed analysis and discussion of their significance. Also outline future plans and reflections on the impact of your project.{% endblock %}
{% block bg %}url('https://static.igem.wiki/teams/shanghaitech-china/img/headerbg.webp'){% endblock %}
{% block page\_content %}
{% from 'hero.html' import fancy\_hero %}
{{ fancy\_hero(
'https://static.igem.wiki/teams/5553/finalddl/1012.avif',
'Results',
'A presentation of core results in our diagnostic, therapeutic, software, and hardware components.'
) }}

## Diagnosis of Rhinosinusitis

![](https://static.igem.wiki/teams/5553/finalddl/result/image.webp)

Workflow of Rhinosinusitis Diagnostic Test Strip

### Preface

In the following sections, we summarize the key experimental findings from the diagnostic module. This part focuses on the development of a rapid GZMK-based detection system using de novo designed binders and colloidal gold test strips. Detailed experimental procedures are documented in Notebook, while optimization cycles are presented in Engineering. This structure highlights the essential results of binder design, expression, and detection, while allowing readers to trace the complete workflow when needed.

### Principle and Summary

Chronic rhinosinusitis (CRS) is a prevalent inflammatory disease in which patients often suffer from persistent nasal obstruction, purulent discharge, and reduced olfaction, with approximately one-quarter developing comorbid nasal polyps. Due to its complex etiology and high recurrence rate, conventional diagnostic approaches—such as imaging or symptom scoring—usually provide information only at advanced stages, lacking sensitive and convenient early diagnostic methods. Recent studies have identified GZMK (Granzyme K), a serine protease secreted by CD8+ T cells, as a key factor in nasal inflammation. Receiver operating characteristic (ROC) curve analysis demonstrates that the AUC value for GZMK detection (0.667) is significantly higher than that of eosinophil counts (0.556) and IL-5 levels (0.504), highlighting its potential as a predictive biomarker for CRS and nasal polyps.

The aim of this project was to develop a rapid GZMK-based diagnostic tool to enable early detection and disease monitoring of CRS. To achieve this, we employed the principle of lateral flow immunochromatography and, innovatively, replaced traditional antibodies with de novo designed binding proteins (binders), establishing a specific “Binder1–GZMK–Binder2” recognition model. This design allows for a simple, rapid molecular diagnostic workflow without the need for sophisticated instrumentation.

The experimental workflow comprised three major stages. First, in the binder design stage, approximately 130,000 candidate sequences targeting GZMK hotspot regions were generated, and several dozen promising designs were selected. Second, during expression and affinity validation, 47 binders were expressed in *Escherichia coli*, with 37 purified to high quality. Among these, 8 exhibited measurable binding affinity, and the best-performing binder achieved a Kd of 0.403 μM as determined by SPR. Finally, in the colloidal gold test strip construction stage, high-affinity binders were conjugated to gold nanoparticles, while Aprotinin served as the capture reagent on the test line and SUMO1/Ubc9 as the control system, enabling optimized strip assembly.

The results demonstrated that in the presence of GZMK, the test strip produced clear red bands on both the test line and control line within approximately 5 minutes, in sharp contrast to the negative control. These findings confirm not only the wet-lab effectiveness of de novo designed binders but also their feasibility as antibody substitutes in colloidal gold lateral flow assays, providing a novel solution for the early diagnosis of rhinosinusitis.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-1.webp)

Experimental Workflow for Rhinosinusitis Diagnosis

### Binder Design

#### Introduction

Our ultimate goal is to develop a colloidal gold-based test strip for the rapid detection of Granzyme K (GZMK). Traditional methods rely on specific antibodies, the development of which is time-consuming, costly, and resource-intensive, placing it beyond the scope of our project. Therefore, we turned to the cutting-edge field of computational protein design to create a *de novo* protein binder that can specifically bind to GZMK, serving as a functional substitute for antibodies in the assay.

#### Aim

To meet the requirements for a future sandwich assay, the objective of this phase is to computationally design non-competing, high-affinity protein binders that target multiple distinct hotspots on the surface of GZMK. Given that the success rate of *de novo* computational design is generally low, our strategy is to generate a vast library containing tens of thousands of protein sequences and structures. From this pool, we will apply a rigorous filtering process to select a few dozen of the most promising designs for subsequent wet-lab expression and validation.

#### Result

We generated approximately 130,000 initial binder designs targeting various hotspots on the GZMK surface. From this extensive pool, we selected a few dozen candidates that simultaneously satisfied a stringent set of computational metrics: a predicted Aligned Error for the interaction domain below 10 Å (pAE\_interaction < 10), favorable energy and shape complementarity (Buns\_heavy\_ball\_1.1D ≤ 1), a strong predicted binding free energy (ΔΔG < -30.0 kcal/mol), and a high binding efficiency per unit of surface area (ΔΔG per SASA < -0.09 kcal/mol/Å²).

* A “nine-grid” distribution plot of the evaluation metric

  ![](https://static.igem.wiki/teams/5553/finalddl/result/output.webp)

  Nine-grid distribution plot of the evaluation metric
* A predicted complex structure of a selected candidate binder (blue) bound to the target protein GZMK (red), showing a tightly packed interaction interface between the two.

  ![](https://static.igem.wiki/teams/5553/finalddl/result/gzmk-complex.webp)

  Predicted complex structure of binder (blue) bound to GZMK (red)

#### Discussion

In this process, I developed an semi-automated workflow by writing batch-processing scripts for repetitive tasks such as sequence design (ProteinMPNN), structure relaxation, and multi-metric scoring. This significantly enhanced our computational throughput. However, the process remains computationally intensive. We also observed that the initial choice of hotspots critically influences the success rate, as some selected target sites failed to yield any viable binders, indicating a key area for methodological improvement.

#### Outlook

Looking ahead, we plan to incorporate AI-driven methods for more accurate hotspot prediction, which could substantially increase the design success rate. Furthermore, pending time and funding, we aim to perform a second round of *in silico* affinity maturation on the most promising binders. This could potentially enhance their binding affinity by one to two orders of magnitude, making them competitive with or even superior to natural binder for our diagnostic application.

### Binder Expression and Affinity Assay

#### Introduction

According to literature data, most binders generated by de novo protein design fail to exhibit detectable affinity in wet-lab experiments. Thus, it is crucial to express and purify the designed sequences, followed by affinity assays, to identify those with practical binding capacity. This step validates computational predictions and supplies essential reagents for subsequent test strip development.

#### Aim

To express and purify approximately 50 binders and perform affinity assays, determining precise Kd values for binders exhibiting affinity.

#### Result

We expressed binders using *Escherichia coli*. Due to the large number of proteins, we innovated the protein purification method to maximize throughput. Ultimately, we expressed 47 proteins, each purified through a two-step process involving affinity chromatography and size-exclusion chromatography. Ten proteins failed to yield high-purity solutions due to inclusion body formation or degradation.

SPR assays of the 37 purified binders revealed 8 with measurable binding affinity. Among them, binders 1–6 and 1–24 demonstrated the most promising performance, with fitted Kd values of 6.863 μM and 0.4033 μM, respectively. These results highlight their potential as strong candidates for downstream applications.

Binder expression and affinity assay results

[Binder\_Results\_CSV.csv](https://static.igem.wiki/teams/5553/finalddl/result/binder-results-csv.csv)

| Number | Serial Number | Molecular Weight/Da (Befire/After Enzyme Digestion) | Absorbance | Cryopreservation Concentration/μM | Affinity with GZMK | Kd Value/μM |
| --- | --- | --- | --- | --- | --- | --- |
| 1-1 | 1-1070 | 3008.72 | 0.34 | 650.11 | No | — |
| 1-2 | 1-131 | 8185.8 | 0.36 | — | — | — |
| 1-3 | 1-39 | 8779.79 | 0.51 | 21.64 | No | — |
| 1-4 | 1-1947 | 8440.19 | 1.24 | 26.07 | No | — |
| 1-5 | 1-559 | 8307.29 | 0 | — | — | — |
| 1-6 | 1-1883 | 8759.8 | 0.17 | 244.3 | Yes | 6.863 |
| 1-7 | 1-1824 | 8784.42 | 1.19 | 68.3 | No | — |
| 1-8 | 1-1875 | 8101.9 | 0.18 | — | — | — |
| 1-9 | 1-1855 | 8699.66 | 0.97 | 77 | No | — |
| 1-10 | 1-1296 | 10294.26 | 1.01 | 84.6 | No | — |
| 1-11 | 1-1094 | 8838.51 | 1.35 | 84.7 | No | — |
| 1-12 | 1-1004 | 8667.49 | 0.52 | 103.61 | No | — |
| 1-13 | 1-1728B | 8040.77 | 0.37 | 46.76 | No | — |
| 1-14 | 1-1983 | 10130.96 | 1.18 | 76 | No | — |
| 1-15 | 1-1728A | 7934.69 | 0.19 | 54.44 | No | — |
| 1-16 | 1-461 | 9018.93 | 1.44 | 68 | No | — |
| 1-17 | 1-1697 | 8811.4 | 1.69 | 81.49 | No | — |
| 1-18 | 1-293 | 8703.56 | 0.51 | 85.6 | No | — |
| 1-19 | 1-1632 | 8706.61 | 0.68 | 70.6 | No | — |
| 1-20 | 1-507 | 9993.09 | 0.6 | 32.72 | No | — |
| 1-21 | 1-723 | 8916.79 | 1.12 | — | — | — |
| 1-22 | 1-1050 | 9051.75 | 1.48 | 39.4 | No | — |
| 1-23 | 1-1277 | 8864.81 | 1.18 | 159.06 | No | — |
| 1-24 | 1-1320 | 9176.01 | 1.46 | 156.6 | Yes | 0.4033 |
| 1-25 | 1-345 | 8678.52 | 0.69 | 42.52 | No | — |
| 1-26 | 1-199 | 10038.9 | 0.74 | 14.84 | No | — |
| 1-27 | 1-1031 | 9146.05 | 2.29 | 30.94 | No | — |
| 1-28 | 1-1962 | 9498.51 | 0.78 | 12.1 | No | — |
| 1-29 | 1-1180 | 8725.56 | 0.68 | 12.61 | No | — |
| 1-30 | 1-612 | 8726.55 | 2.23 | 6.3 | No | — |
| 1-31 | 1-183 | 8207.24 | 1.58 | 13.77 | No | — |
| 1-32 | 1-738 | 9137.13 | 0.65 | — | — | — |
| 2-1 | 4-7 | 8698.8/6816.74 | 0.17/0.22 | 66.01 | Yes | — |
| 2-2 | 132-0 | 8721.88/6839.82 | 0.17/0.22 | 194.01 | No | — |
| 2-3 | 98-6 | 8768.99/6886.93 | 0.17/0.22 | 232.98 | No | — |
| 2-4 | 124-4 | 8731.92/6849.86 | 0.17/0.22 | — | — | — |
| 2-5 | 46-7 | 8714.8/6832.74 | 0.17/0.22 | 75.71 | Yes | — |
| 2-6 | 179-2 | 8657.86/6775.8 | 0.17/0.22 | 20.07 | Yes | 14.76 |
| 2-7 | 173-7 | 8634.45/6752.39 | 0.17/0.22 | — | — | — |
| 2-8 | 58-6 | 8569.68/6687.63 | 0.17/0.22 | 76.71 | Yes | — |
| 2-9 | 76-2 | 8699.74/6817.69 | 0.17/0.22 | — | — | — |
| 3-1 | aprotinin-1337 | 11995.06/10113 | 1.24/1.47 | 5.043 | No | — |
| 3-2 | bikunin-1170 | 10442.37/8560.32 | 1.77/2.16 | 112.15 | Yes | 99.61 |
| 3-3 | bikunin-2010 | 9388.32/7506.26 | 2.44/3.05 | 4.93 | No | — |
| 3-4 | bikunin-3264 | 9030.83/7148.78 | 2.32/2.93 | — | — | — |
| 3-5 | bikunin-369 | 11220.33/9338.27 | 2.53/3.04 | — | — | — |
| 3-6 | hs11-1088 | 8865.76/6783.70 | 0.17/0.22 | 125.3 | Yes | — |

![](https://static.igem.wiki/teams/5553/finalddl/result/image-2.webp)

SPR binding results of Binder 1-6 and 1-24

#### Discussion

By continuously optimizing expression and purification protocols, we achieved our primary objective of obtaining sufficient binders and identifying those with strong binding affinity. The best-performing binder reached submicromolar affinity (0.403 μM), demonstrating the feasibility of computational design in experimental settings. However, limitations remain, including insolubility in ~20% of constructs and low expression yields, which constrained replicates in affinity measurements.

#### Outlook

In the future, we aim to further optimize the workflow by adopting smaller-scale systems to increase speed and parallelization. Additionally, we anticipate integrating commercially available high-throughput protein expression and detection platforms to fully realize the potential of de novo designed binders.

### Colloidal Gold Test Strips

#### Introduction

To establish a rapid and user-friendly diagnostic method for rhinosinusitis, we developed colloidal gold test strips based on the principle of lateral flow immunochromatography. Gold nanoparticles conjugated to specific binders form complexes with GZMK in the sample. These complexes migrate via capillary action along the strip and are captured at the test line (T line) by another immobilized binder, producing a visible red band that indicates a positive result. A control line (C line) confirms the validity of the strip using a positive control system. This method enables rapid diagnosis within minutes, without the need for additional equipment.

#### Aim

To construct colloidal gold test strips employing two validated binders capable of binding GZMK, enabling reliable detection within 5 minutes and demonstrating feasibility for early rhinosinusitis diagnosis.

#### Result

We innovated on the conventional "target protein–antibody–antibody" mode by adopting a "Binder1–GZMK–Binder2" strategy, aligning with the workflow of de novo protein design. Binder 1–6 was conjugated to gold nanoparticles, while Aprotinin was immobilized on the T line. The C line incorporated SUMO1-gold conjugates and immobilized Ubc9 as the positive control.

Optimization experiments established the best conjugation conditions (pH and protein dosage), after which conjugated gold nanoparticles were purified and assembled onto the conjugate pad. Proteins were coated onto the NC membrane, and the strips were manually cut and assembled. Upon applying GZMK-containing samples, results were visible within 5 minutes, with clear double bands (T and C lines), in sharp contrast to the negative control, thereby confirming the validity and sensitivity of the test strip.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-3.webp)

Design and Validation of the GZMK Diagnostic Test Strip

#### Discussion

The final results demonstrated proper functionality of the test strip and reliable indication of GZMK presence, achieving the intended goal. Nonetheless, limitations were observed: the bands produced were not fully uniform, likely due to manual pipetting instead of automated spraying, and manual assembly lacked precision, which impacted the aesthetic quality and reproducibility of the strips.

#### Outlook

With professional fabrication equipment such as automated sprayers and precision cutters, the aesthetic quality and reproducibility of the strips could be significantly improved. In the future, higher-affinity and more specific binders could replace current materials to enhance sensitivity and specificity. This platform also holds potential for multiplex detection, broadening its application in both clinical diagnostics and home-based early screening.

## Treatment of Rhinosinusitis

![](https://static.igem.wiki/teams/5553/finalddl/lese-4.svg)

Small-Molecule Inhibition of GZMK Activity

### Preface

In the following sections, we summarize the main experimental results from the therapeutic module. This part focuses on the expression of active GZMK, establishment of the enzyme activity assay, and high-throughput screening of inhibitors. Detailed procedures are provided in Notebook, and iterative optimizations are described in Engineering. This arrangement allows us to present key validated outcomes clearly while preserving traceability of all experimental details.

### Principle and Summary

Inhibiting GZMK activity has been identified as a potential therapeutic strategy for rhinosinusitis. GZMK, a serine protease secreted by memory CD8+ T cells, can cleave complement components C2, C3, C4, and C5, thereby activating the complement cascade at multiple levels. This process leads to the release of potent inflammatory mediators such as C3a, which drive eosinophil infiltration, inflammation, and tissue damage. In mouse models, either genetic ablation or pharmacological inhibition of GZMK markedly reduced inflammatory cell infiltration, improved lung function, and ameliorated histopathological features, providing direct experimental evidence for its therapeutic potential. Collectively, these findings highlight GZMK as a key molecular target for controlling chronic rhinosinusitis recurrence.

Building on this rationale, our project aimed to identify and validate small-molecule inhibitors of GZMK activity. The workflow consisted of three main stages: first, constructing a recombinant human GZMK plasmid, followed by expression and purification in HEK293F cells to obtain high-purity, zymogen-form GZMK that could be activated in vitro; second, establishing a FRET-based assay using the fluorescent substrate DABCYL-GDGRSIMTE-EDANS, which successfully confirmed enzymatic activity and yielded kinetic parameters (Vmax = 437.5 RFU/min, Km = 50.20 μM); and third, conducting high-throughput screening with the L1000 compound library, from which three approved drugs were identified with inhibition rates exceeding 90%.

Among these, Nafamostat mesylate showed the strongest inhibitory effect, with an IC50 value of 0.1951 μM. This result not only validates the robustness of our detection and screening system but also provides a promising lead compound for the development of novel GZMK-targeted therapies for rhinosinusitis.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-5.webp)

Experimental Workflow for Rhinosinusitis Treatment

### Expression of GZMK

#### Introduction

Subsequent experiments, including inhibitor screening and binder–GZMK affinity assays, require substantial amounts of high-purity GZMK protein. As a human-derived serine protease, GZMK must be correctly folded to retain enzymatic activity, and its intracellular and extracellular toxicity poses significant challenges. Therefore, multiple expression strategies were explored to achieve functional recombinant GZMK.

#### Aim

To express and purify human GZMK in a manner that preserves its native folding and activity, ultimately yielding a stable, high-purity protein solution for downstream experiments.

#### Result

We initially attempted expression in *E. coli*, but difficulties in proper folding and significant differences from native human GZMK led us to switch to a mammalian expression system. A recombinant human GZMK construct was designed for secretion in HEK293F cells as an inactive zymogen, avoiding cytotoxicity, and subsequently activated in vitro via enterokinase cleavage.

The workflow included plasmid cloning in *E. coli* DH5α, transfection into HEK293F cells, 72-hour culture, collection of supernatant, Flag affinity purification, enterokinase cleavage, and final purification by size-exclusion chromatography (SEC). Both SEC profiles and SDS-PAGE confirmed successful isolation of high-purity GZMK suitable for downstream applications.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-6.webp)

GZMK Purification Results

#### Discussion

GZMK expression serves as the cornerstone of the entire project. The process involved multiple rounds of trial and error, as detailed in the Engineering Cycle. We are pleased to report the successful expression, purification, and processing of GZMK, with verification of its expected enzymatic activity (described in the subsequent section), laying a robust foundation for downstream experiments.

#### Outlook

In the future, we plan to optimize the expression system further by testing alternative signal peptides, improved plasmid vectors, and expression timing. These improvements are expected to yield higher quantities and greater consistency of GZMK for large-scale applications.

### GZMK Activity Assay

#### Introduction

To confirm that the expressed and purified GZMK retains enzymatic activity, and to establish a methodological foundation for inhibitor screening, we developed an in vitro assay system. A robust assay not only validates protein activity but also allows determination of kinetic parameters, providing technical support for high-throughput applications.

#### Aim

To validate and optimize existing in vitro enzyme activity assay methods from the literature and to obtain GZMK enzyme kinetics data.

#### Result

We initially employed a spectrophotometric thioester substrate (Z-Lys-SBZL), but low sensitivity and interference from buffer components and enterokinase prevented accurate results. We therefore switched to a FRET-based assay using the fluorescent substrate DABCYL-GDGRSIMTE-EDANS.

This optimized system successfully measured GZMK activity, confirming functional protein. Kinetic analysis using varying substrate concentrations yielded Vmax = 437.5 RFU/min and Km = 50.20 μM, demonstrating that we established a quantifiable and reproducible activity assay.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-7.webp)

GZMK Activity Assay Results

#### Discussion

In developing the assay, spectrophotometric methods were limited by interference and low sensitivity. The switch to a FRET-based assay overcame these issues and provided robust results. However, constraints remained: the use of a 460 nm filter instead of the optimal 490 nm likely reduced sensitivity, and the lack of calibration between RFU and substrate concentration meant Vmax could only be expressed in RFU/min rather than conventional enzymatic units.

#### Outlook

In the future, we can further optimize the enzyme activity assay system by acquiring suitable filters to enhance experimental sensitivity and by establishing the correlation between RFU and substrate concentration for more precise enzyme kinetics characterization. These improvements will make our assay system more sensitive, accurate, and suitable for inhibitor screening applications.

### Inhibitor Screening

#### Introduction

GZMK is a serine protease, and previous studies have demonstrated that inhibiting its activity can significantly alleviate airway inflammation such as rhinosinusitis. To identify potential inhibitors efficiently, we employed a high-throughput compound library screening approach, aiming to rapidly discover strong inhibitors of GZMK and assess their potential as lead compounds for drug development.

#### Aim

To screen for small molecules with strong inhibitory effects on GZMK activity and determine their IC50 values through concentration–response experiments, thereby providing candidate compounds for drug development.

#### Result

Before screening, we optimized substrate and enzyme concentrations to balance sensitivity and protein consumption. Using the L1000 compound library, which contains 1813 approved drugs, we conducted a high-throughput screen. Three compounds showed >90% inhibition, with Nafamostat mesylate standing out.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-8.webp)

Results of Large-Scale Preliminary Screening

Subsequent concentration–response experiments with 18 gradient concentrations yielded an IC50 of 0.1951 μM for Nafamostat mesylate, confirming it as a potent inhibitor of GZMK with strong potential as a drug lead.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-9.webp)

Nafamostat Mesylate Concentration–GZMK Activity Inhibition Rate Curve

#### Discussion

Through this high-throughput screen, we not only successfully identified Nafamostat mesylate as a potent GZMK inhibitor but also precisely determined its IC50 value. The significance of this achievement is twofold: it provides a highly promising lead compound for subsequent drug development, and importantly, the experimental validation of an active molecule offers a critical reference for exploring the binding mode of the GZMK-ligand complex. This insight will enable us to refine and calibrate our virtual screening models, paving the way for more efficient and accurate drug discovery in the future.

#### Outlook

Based on the successful experience of this screening, we have outlined a clear roadmap for the subsequent development of GZMK inhibitors, aiming to systematically advance the current findings toward the stage of preclinical candidates.

1. Expansion of Lead Compound Library & Mechanistic Studies

Guided by the latest virtual screening results, we will conduct a new round of screening against a larger and more diverse compound library, with the goal of identifying inhibitors with novel chemical scaffolds. This will enrich our candidate pool and reduce the risk of relying on a single structure type. In parallel, we will perform in-depth enzymological studies on Nafamostat mesylate to determine its precise mode of inhibition (e.g., competitive, non-competitive, etc.).

2. Lead Optimization & Preclinical Profiling

Once multiple scaffolds of lead compounds are obtained, we will initiate systematic lead optimization. Leveraging quantitative structure–activity relationship (QSAR) models and AI-assisted drug design, we will carry out rational modifications of existing molecules to generate derivatives with improved potency and selectivity. At this stage, essential preclinical profiling will be conducted in parallel, including:

* Selectivity analysis: Evaluating inhibitory activity against other homologous serine proteases to ensure high specificity for GZMK.
* ADMET prediction and assessment: Combining computational models with in-vitro assays to systematically evaluate absorption, distribution, metabolism, excretion, and toxicity, thereby filtering out molecules with poor drug-like properties at an early stage.

3. Validation in Cell and Animal Models

Optimized candidates with the best overall profiles will advance into biological validation. We will test their ability to inhibit GZMK downstream signaling pathways and alleviate inflammation in relevant cellular models of rhinosinusitis. Ultimately, we aim to demonstrate efficacy and safety in animal models, thereby providing robust experimental evidence to initiate true preclinical studies and achieving the complete transition from an initial “hit compound” to a “drug candidate.”

## Software

### Preface

In the following sections, we focus on presenting the core achievements obtained based on the computational protein design tools BetterMPNN and BetterEvoDiff. We concentrate on the key iterative process of method development and the performance of the models in dry-lab experiments, aiming to clearly demonstrate how we achieve efficient and precise de novo protein design through a strategy combining reinforcement learning with generative models. Detailed model architectures, training parameters, and workflows are documented in the project GitLab; while the optimization ideas and development processes are systematically summarized in the Software section. Through this arrangement, we hope to present the main findings in a structured manner while ensuring readers can trace back to complete methodological details and data support when needed.

### Principle and Summary

De novo protein design represents a cutting-edge frontier in both synthetic biology and biomedicine. During our attempts to design GZMK-binding proteins, we found that classical approaches — such as backbone generation using RFdiffusion combined with sequence design via ProteinMPNN — achieved some success but still relied heavily on large-scale experimental screening, resulting in low efficiency and high costs. In recent years, reinforcement learning (RL) has demonstrated great potential in complex decision-making tasks, making it particularly suitable for modeling the iterative “generate–evaluate–optimize” loop in protein sequence design.

The goal of this project is to develop an intelligent design framework integrating reinforcement learning with protein generative models to achieve efficient, high-hit-rate de novo protein design. We constructed two tools: BetterMPNN and BetterEvoDiff. The former iteratively optimizes ProteinMPNN using the GRPO algorithm, enabling it to learn to generate high-affinity sequences on fixed backbones; the latter implements multi-site synergistic mutations based on EvoDiff for the directed evolution of existing proteins.

The experimental workflow includes three key phases: First, in the model training phase, we established a reward function based on AlphaFold metrics (ipTM, pTM, inter-chain PAE) and rapidly evaluated and screened a large number of candidate backbones through a parallelized training framework. Second, in the protein design phase, we obtained various functional proteins including active pocket inhibitors, multi-mode binding proteins, and cleavage sites. Finally, in the validation phase, we systematically evaluated the structural rationality and binding reliability of the designed results through dry-lab experiments and performed wet-lab validation on some sequences.

The results show that BetterMPNN can complete the entire design process from backbone to high-affinity inhibitors within 20 hours, achieving ipTM scores above 0.9 in dry-lab experiments. Simultaneously, we established criteria for rapidly assessing backbone potential early in training, significantly improving the resource efficiency of the design pipeline. Although BetterEvoDiff faces challenges in aligning dry/wet rewards during wet-lab validation, its optimization capability in dry-lab experiments still demonstrates the potential of the multi-site synergistic mutation strategy. These achievements not only validate the effectiveness of reinforcement learning in protein design but also provide a feasible technical path towards achieving one-shot design.

### Introduction

Proteins are the executors of life functions, and enhancing our ability to design them has profound implications for biomedicine, enzyme engineering, and materials science. Traditional protein design methods primarily rely on physical force fields, structural simulations, and expert knowledge, resulting in cumbersome processes and limited success rates. With the development of deep learning technologies, particularly the breakthroughs of the AlphaFold series in structure prediction, new opportunities have emerged for protein design. Generative models like RFdiffusion can design protein backbones de novo, while ProteinMPNN can generate foldable amino acid sequences based on these backbones, together forming the current mainstream "backbone-first" design pipeline.

However, this pipeline has clear limitations: Firstly, the generation process lacks explicit functional guidance and heavily depends on subsequent high-throughput screening. Secondly, sequence design primarily considers foldability, not optimizing for functional indicators like binding affinity. Thirdly, the screening process is resource-intensive and time-consuming. These issues severely constrain the efficiency and scalability of protein design.

To overcome these challenges, we explored introducing reinforcement learning into the protein generation process. Reinforcement learning, through the "agent-environment" interaction mechanism, allows generative models to learn how to produce sequences that meet target properties iteratively. This project aims to develop protein design tools capable of autonomously optimizing binding interfaces and systematically evaluate their performance in tasks such as inhibitor design and binding protein optimization.

### Aim

The core objective of this phase is to establish and validate two protein design frameworks—BetterMPNN and BetterEvoDiff—and, based on this, achieve the following specific tasks:

* **Develop a reinforcement learning-driven sequence generation pipeline:** Use ProteinMPNN as the agent, GRPO as the optimization algorithm, and the environment providing rewards based on AlphaFold predictions and interface scoring to construct an “exploration–evaluation–optimization” loop.
* **Achieve rapid screening of high-potential backbones:** Establish evaluation criteria for backbone potential using metrics like reward dynamics and sequence diversity early in training to enhance overall design efficiency.
* **Automatically select the optimal Hot-spot:** Without pre-defining binding sites, the model can autonomously determine and converge to the optimal binding region during exploration.
* **Validate the one-shot generation capability of the model:** Test the potential of the converged model to generate high-performance sequences in a single attempt, reducing the scale of experimental screening.

By achieving the above goals, we hope to demonstrate the effectiveness of the reinforcement learning framework in protein design and provide a solid foundation for subsequent wet-lab validation and tool iteration.

### Results

#### **Two Novel Protein Design Tools: BetterMPNN & BetterEvoDiff**

#### BetterMPNN

We proposed a novel design approach: treating the protein generation model as an intelligent agent within an "exploration-evaluation-optimization" loop, where reinforcement learning enabled it to progressively learn how to generate proteins with target properties, ultimately converging to stable, high-quality candidate solutions.

Based on this concept, we designed and developed BetterMPNN—a groundbreaking one-shot protein design tool. By integrating RFdiffusion, ProteinMPNN, and GRPO through an optimized iterative cycle, we continuously enhanced ProteinMPNN's generation capability, achieving rapid convergence from protein backbone structures to high-quality binders.

![](https://static.igem.wiki/teams/5553/software/dongtu1.webp)

The Workflow of BetterMPNN

To validate the model's one-shot potential, we directly invoked the trained model for a single round of generation and obtained the following results. These demonstrate that the trained model exhibits one-shot capability potential, achieving high dry-lab scores for the generated sequences through just a single inference pass.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-11.webp)

Results Generated by BetterMPNN at One Time after Training

#### BetterEvoDiff

We also developed BetterEvoDiff as an optimization tool for existing proteins. Based on the discrete autoregressive model EvoDiff-OADM and integrated with the GRPO algorithm, this tool enables multi-site cooperative mutations that systematically explore and optimize protein properties within the sequence space, making it suitable for binding affinity enhancement and functional evolution of proteins.

![](https://static.igem.wiki/teams/5553/software/ezgif-72b805ecaa2e7b.webp)

The Workflow of BetterEvoDiff

We directly called the trained model for one round of generation, and the reward values of the three obtained sequences were 0.9038, 0.8828, and 0.8645. This indicates that our model has acquired the one-shot capability after training, meaning it can generate sequences with excellent scores in a single attempt.

#### Rapid Screening of High-Potential Backbones

Through our designed workflow, the early-stage training curves of the model can indicate whether the initial backbone generated for a given task possesses potential for further optimization. The figure below displays the total reward trajectories from three representative tasks during training, revealing distinct differentiation as early as 30-40 steps.

![](https://static.igem.wiki/teams/5553/finalddl/result/image-13.webp)

Total Reward Trend for Different Potential Backbones

Through extensive testing, observation, and analysis, we have identified that "high-potential" backbones typically exhibit these characteristics:

* Early emergence of high-scoring results during initial training phases, when the agent hasn't undergone substantial learning, indicating the backbone's inherent capacity to generate sequences with favorable structural predictions
* Clear learning trends in early stages, where total reward progression follows the expected upward trajectory, demonstrating the agent's effective optimization toward generating higher-quality outcomes
* Absence of premature convergence at low performance plateaus, suggesting the backbone possesses sufficient optimization headroom for continued improvement

By analyzing early-stage training curves, we can rapidly assess a backbone's potential for further optimization. This enables early exclusion of numerous low-potential backbones during initial training phases, thereby significantly enhancing training efficiency while conserving computational resources and costs.

#### Proteins Designed

Using BetterMPNN, we successfully designed a protein inhibitor capable of directly inserting into the active site pocket of granzyme K (GZMK) within 20 hours. This inhibitor forms a short peptide-like structure at its C-terminus, with its key residue (arginine) inserting into GZMK's catalytic pocket in a manner resembling natural substrates. Since its terminal residue is valine (Val), it theoretically avoids cleavage, thereby achieving stable binding. Dry lab evaluations demonstrated both ipTM and pTM scores exceeding 0.9, indicating its potential for high binding affinity.

Notably, although the model received no prior knowledge about GZMK's natural substrates or active site during training, it spontaneously converged to a structural pattern analogous to natural protease-inhibitor interactions. This computationally validates the significant potential of our function-oriented design approach.

Below we present the predicted structure and computational scores of a representative "inhibitor-type" protein obtained through our methodology.

![](https://static.igem.wiki/teams/5553/finalddl/result/r1-2.webp)

Predicted Structure and Computational Scores of the "Inhibitor-type" Protein

* **ipTM:** A scalar in the range 0-1 indicating predicted interface TM-score (confidence in the predicted interfaces) for all interfaces in the structure. Values higher than 0.8 represent confident high-quality predictions, while values below 0.6 suggest likely a failed prediction.
* **pTM:** A scalar in the range 0-1 indicating the predicted TM-score for the full structure. A pTM score above 0.5 means the overall predicted fold for the complex might be similar to the true structure.
* **∆G\_binding**: The free energy change of multiple chains forming a complex, in the unit kcal/mol. The more negative ∆G\_binding , the higher affinity between proteins.

During model training and one-shot potential validation, we also obtained numerous designed proteins with diverse binding modes.

![](https://static.igem.wiki/teams/5553/finalddl/node/r-4.avif)

Predicted Structure and Computational Scores of Proteins with Other Binding Modes

Note: For more specific data analysis and result presentation, please read the [Software](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%20%7D%7D) page.

### Summary and Outlook

In summary, our developed tools, BetterMPNN and BetterEvoDiff, demonstrate the following advantages in de novo protein design tasks:

* BetterMPNN enables near one-shot efficient design, widely producing high-affinity inhibitors and binding proteins.
* Through dynamic assessment early in training, we can rapidly judge the optimization potential of backbones, significantly enhancing the resource efficiency of the overall design pipeline.
* BetterEvoDiff demonstrates strong sequence optimization capability in dry-lab experiments, although its performance in wet-lab settings still requires improvement.

Looking ahead, we will continue to optimize the reward function to better align dry and wet-lab results and explore mechanisms for jointly optimizing both side chains and backbones by broadcasting reward signals to both. We believe that with continuous algorithmic iteration and the accumulation of validation data, this framework holds promise for achieving broader and more precise applications in the field of protein design.

## Hardware

### Overview

During the progression of our project, we observed that the success rate of currently available de novo designed proteins remains relatively low, requiring extensive expression and screening to obtain Binders with the desired functions. However, traditional prokaryotic or eukaryotic expression systems are time-consuming and labor-intensive. Furthermore, most candidate Binders exhibit low affinity, and large-scale reliance on Surface Plasmon Resonance (SPR) measurements is not only inefficient but also results in resource wastage. Based on this, we have developed the Nexus System — a high-throughput affinity screening platform — which aims to overcome the limitations of traditional protein affinity measurement methods in terms of cost, throughput, and physiological environment simulation. It enables large-scale preliminary affinity screening with moderate precision, followed by high-precision instrumental measurements for Binders with high affinity. This platform can not only be applied to our own project but also serve as a universal tool to provide a novel approach for binding protein affinity measurement.

This platform utilizes Fluorescence Cross-Correlation Spectroscopy (FCCS) to monitor intermolecular interactions in real time within the nanoliter-volume reaction units of a microfluidic chip. The technology directly measures complexes formed between two proteins, each labeled with a distinct fluorophore. A significant cross-correlation signal is generated only when these fluorescence-labeled proteins bind and simultaneously pass through the overlapping confocal volume of two lasers (488 nm and 561 nm). By analyzing the amplitude of this cross-correlation curve, we can directly quantify the concentration of the bound complexes, thereby enabling precise calculation of the binding dissociation constant (Kd).

To achieve the aforementioned functions, the entire platform primarily consists of four core components:

* Microfluidic Chip Design and Control
* FCCS Detection
* 3D-printed Chip Cartridge
* Integrated Architecture

A comprehensive description of the platform can be found in the “[Hardware](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27hardware%27%29%20%7D%7D)” page.

![](https://static.igem.wiki/teams/5553/finalddl/fc/2.avif)

Overall Design Entity of Protein Affinity Detection Platform

### Microfluidic Chip Driving and Control Module

### Microfluidic Chip Design and Control

#### Introduction

In traditional high-throughput protein affinity measurement processes, 96-well plates are commonly used for sample preparation and detection. However, this method fails to meet the project’s requirements in terms of sample consumption and automation level. Additionally, the open environment of 96-well plates makes samples prone to volatilization and contamination. To address these bottlenecks, our team ultimately selected microfluidic chips as the carrier for reactions and detection. After multiple iterations and optimizations, the microfluidic chips now possess core advantages including high throughput, low sample consumption, efficient mixing, high integration, and strong portability.

We optimized the primary chip in terms of optical performance and mixing efficiency under high-Reynolds-number differential flow conditions, enabling highly efficient sample mixing. In conjunction with the secondary chip, the system achieves precise and efficient sample injection. During the experiment, only a single sample loading is required for the samples to become fully mixed within the chip, after which FCCS (Fluorescence Cross-Correlation Spectroscopy) detection can be directly performed without any additional operation steps.

The control of the microfluidic system is crucial for realizing accurate sample manipulation and high-throughput analysis: it uses peristaltic pumps and syringe pumps to precisely control the flow, mixing, and reaction processes of liquids within the chip. Meanwhile, our team has designed standardized chip interfaces and fixing devices, which enable rapid assembly, disassembly and precise positioning of the chip, ensuring that the lasers can accurately focus on the detection window on the chip. Through multiple iterations and optimizations, the adaptability of the microfluidic system to measurement samples and its detection performance have been further improved.

![](https://static.igem.wiki/teams/5553/finalddl/add/xiaohexin.svg)

Schematic diagram of the small core of our integrated architecture

#### Aim

The goal of this module is to ensure, through accurate manipulation and high-throughput analysis of protein samples, that the microfluidic chip can efficiently perform functions such as sample mixing, efficient sample injection, and automatic concentration gradient generation. Ultimately, it provides stable fluorescent protein samples for subsequent FCCS detection.

#### Result

We have completed the construction and verification of the microfluidic chip design and control module. At flow rates on the order of milliliters per minute, the flow field at the detection site is stable, and the focus can be aligned—meeting the stability requirements for FCCS/FCS detection. We have designed Y-shaped mixers (45 degrees on both sides), T-shaped mixers, and cross-shaped mixers: the first two are used to test the double-layer co-flow method, while the latter is used to test droplet-based and jetting-based mixing strategies. Two sets of Serpentine micromixers have been designed to ensure sufficient mixing, and a flow stabilizer of appropriate size has been installed downstream of the Serpentine micromixers to balance stable liquid flow and adequate diffusion.

![](https://static.igem.wiki/teams/5553/finalddl/hardware1/3.avif)

Physical image of the primary chip

Experimental verification shows that the microfluidic chip can handle the perfusion and flow of various sample solutions, including ink and protein samples, without clogging. Simultaneously, the auxiliary chip generates concentration gradients through sample injection, while the main chip is used for mixing buffer and protein solutions—realizing the organic integration of sample injection and mixing functions.

The detection window on the main chip is used for fluorescence detection; the integration of FCCS has significantly optimized the detection platform’s ability to measure affinity in micro-volume systems.

This platform not only simplifies the experimental workflow and eliminates manual protein purification, but also, owing to its high sensitivity and temporal resolution, enables the acquisition of affinity data within an extremely short time.

![](https://static.igem.wiki/teams/5553/finalddl/hardware1/image.avif)

Physical image of the secondary chip

![](https://static.igem.wiki/teams/5553/finalddl/result/image-15.webp)

Cell-free protein solution with yellow ink

![](https://static.igem.wiki/teams/5553/finalddl/result/329bc95131bcc80ecd4dd7661e58b5b2.webp)

Protein solution with blue ink

![](https://static.igem.wiki/teams/5553/finalddl/result/image-14.webp)

Cell-free protein solution with yellow ink and protein solution with blue ink mixing to green solution

#### Discussion

Regarding the problems encountered in the experiment, we hypothesize that the issues of weak excitation signals, insufficient photon flux density, and poor signal-to-noise ratio (SNR) stem from the soda-lime glass used: on one hand, the soda-lime glass absorbs a moderate amount of excitation photons; on the other hand, the relatively thick glass slide increases the optical path length, causing spherical aberration of high-angle light to be uncompensated by the objective lens design—resulting in severe insufficient photon flux density. At the same time, organic substances adhering to the surface also reduce light penetration. Therefore, we improved the material of the glass substrate in the optical path and attempted to replace it with a thinner material that has lower absorption values for light of specific wavelengths. After testing, the detected light intensity was significantly improved.

During the testing of the second-generation chip, we found that the jetting mode had clear limitations in its applicable flow-rate range, thereby limiting the concentration range that could be reliably generated and detected. To overcome this constraint, subsequent designs employed liquid pre-mixing, which reduced dependence on precise flow-rate control and expanded the measurable range.

#### Outlook

Looking ahead, we plan to implement automated gradient dilution on the secondary chip and connect it to the priamry chip, thereby integrating sample loading, dilution, mixing, detection, and output within a whole platform. Building upon this architecture, we will further conduct characterization of proteins, ultimately developing a scalable high-throughput protein interaction detection platform.

### FCCS Detection

#### Introduction

There are various detection methods for measuring the affinity of binding proteins, such as Surface Plasmon Resonance (SPR), Enzyme-Linked Immunosorbent Assay (ELISA), and Bio-Layer Interferometry (BLI). However, these methods either suffer from excessively high costs, insufficient accuracy under low-concentration conditions, or excessive demands on the amount of protein samples, making them unsuitable for our project. After comparison, we ultimately selected Fluorescence Cross-Correlation Spectroscopy (FCCS) as the method for affinity determination.

#### Aim

The aim of FCCS Detection is to achieve efficient fluorescence signal detection and data acquisition based on the existing fluorescence correlation spectroscopy analyzer, thereby obtaining reliable binding affinity constant (Kd).

#### Result

We first measured the 488 nm standard sample for routine calibration of the instrument parameters, followed by the measurement of SUMO-mGFP, which was expressed using a cell-free expression system and excited with 488 nm laser light. The recorded data confirmed that the fluorescence spectroscopy analyzer is capable of performing autocorrelation analysis on our fluorescent protein samples.

![](https://static.igem.wiki/teams/5553/finalddl/result/2025-09-29-171623.webp)

FCS results of 488 standard sample

![](https://static.igem.wiki/teams/5553/finalddl/result/2025-09-29-171240.webp)

FCS results of Sumo-1 Protein

To test the analysis of cross-correlation data using the fluorescence correlation spectroscopy analyzer, we mixed the 488 nm standard sample and the 561 nm standard sample, placed a small volume of the mixture on a cover slip, and positioned it on the water-immersion objective of the FCCS detection module. The detection window was then aligned with the dual-color confocal focus, and both detector channels simultaneously recorded the temporal fluorescence fluctuations. The detection duration and switching interval were set for the spectrometer at each concentration point. By analyzing the cross-correlation curves obtained at different target protein concentrations, we determined the relative fraction of dual-color fluorescent complexes—directly reflected by the amplitude of the cross-correlation curve. As the target protein concentration increased, more dual-labeled proteins formed complexes, resulting in higher cross-correlation amplitudes. Fitting the cross-correlation amplitudes at different concentrations using a standard binding equilibrium model yielded a binding curve, from which the dissociation constant (Kd), a key indicator of binding affinity, was calculated. The result demonstrated that FCCS method sensitively reflects the binding interactions between proteins.

![](https://static.igem.wiki/teams/5553/finalddl/result/2025-09-29-164948.webp)

FCCS results of 488/561 standard sample

![](https://static.igem.wiki/teams/5553/finalddl/result/2025-09-29-172512.webp)

Analyzed FCCS results of 488/561 standard sample

Unfortunately, when we mixed SUMO-mGFP and Ubc-mRuby2 within the microfluidic chip and excited the fluorescent molecules simultaneously with 488 nm and 561 nm lasers, we were unable to obtain satisfactory cross-correlation curves and binding data.

#### Discussion

Through our investigation, we found that one of the major factors affecting the measurement results was signal crosstalk between the 488 nm and 561 nm excitation channels.

To address this issue, we applied a correction strategy: by pre-measuring samples containing only a single fluorescent label, we calculated the proportion of signal leakage from one channel to the other. During the subsequent analysis of dual-labeled samples, we used a crosstalk correction formula to subtract this leakage signal from the raw data, thereby ensuring the authenticity and accuracy of the cross-correlation signal.

In addition, other potential factors may also have influenced measurement accuracy, for example, insufficient purity or quality of the Ubc-mRuby2 protein.

#### Outlook

Looking ahead, we plan to implement systematic optimization in two key areas — microfluidic chip structure and fluorescent protein selection — to significantly increase the effective photon count without increasing molecular occupancy, and to more thoroughly eliminate dual-channel crosstalk.

Specifically, in terms of microfluidic chip design, we will further optimize the geometric dimensions of the detection region to achieve better matching between the spectrometer’s detection volume and the chip’s channel size; in terms of fluorescent protein selection, we will increase the difference in excitation wavelengths between the two fluorescent proteins, thereby minimizing crosstalk between the two optical signals and fundamentally improving data accuracy and reliability.

### 3D-Printed Chip Cartridge

#### Introduction

To ensure the stability, portability, and operational convenience of the system, we integrated and fixed all microfluidic components in a customized 3D-printed chip cartridge. The interior of the device features modular partitioning for the main and auxiliary chips, with necessary shock absorption and light shielding measures implemented to eliminate environmental interference with high-precision optical measurements.

#### Aim

Our design requirement was to create a housing for one main chip and one auxiliary chip to achieve unified packaging. The main chip has dimensions of 75×25mm, while the PDMS layer of the auxiliary chip measures 25×30mm with a thickness of 3-5mm; the channel depth and width are 50×100μm, the substrate glass slide has specifications of 75×25×1mm, and the drilled hole size is 1.4mm. Since two different auxiliary chips are used to inject multiple proteins, the design must enable easy replacement of the auxiliary chip during sample input. Mechanically, the device needs to provide reliable clamping fixtures that do not compress the PDMS chip, along with basic leak-proof and visual design features.

![](https://static.igem.wiki/teams/5553/finalddl/add/3ddayinhe.svg)

Design of 3D Printed Chip Cartridge

#### Result

We constructed a chip cartridge comprising a PLA material housing, a plexiglass cover, and M4 fasteners. A trapezoidal platform was designed inside the cartridge, with the auxiliary chip placed diagonally above the main chip to prevent liquid stagnation in thin tubes and avoid clogging or leakage from the chip holes in the auxiliary chip channels. This integration combines the sample injection function of the auxiliary chip with the protein mixing function of the main chip. In experiments, thin tubes were connected to the top holes of the auxiliary chip, and their end holes were linked to the main chip's sample inlet via thin tubes. Proteins are mixed in the main chip and output through the main chip's sample outlet, enabling high-throughput injection, mixing, and output.

Tests confirmed that the chip cartridge passed the static pressure leakage test: by injecting clean water via a peristaltic pump, no leakage was observed at any interfaces or housing openings. Additionally, the cartridge passed optical tests, demonstrating that the viewing window enables clear and distinct observation.

Furthermore, the chip cartridge was connected to a peristaltic pump and a syringe pump via hoses, forming a multi-functional hardware integrated device. The peristaltic pump is used to deliver buffer solutions, and the syringe pump for protein solutions. We selected the laboratory micro-syringe pump model YHPLC0100S, which includes three key components: the syringe pump structure, power adapter, and controller. The syringe pump utilizes a stepper motor and lead screw drive system to convert motor rotation into linear pushing motion, operating according to set speed and timing to achieve uniform and quantitative output of liquids or gases. We also chose the Baoding Longer BT100-2J peristaltic pump, which provides a flow range of 0.002–380 ml/min (single tube) and can be equipped with various pump heads. These two devices are connected to different holes of the auxiliary chip inside the cartridge via hoses for sample input; the main chip receives and mixes the samples, and the end hose outputs the mixture outside the cartridge. Verification confirmed that this hardware integrated device realizes the functions of input, mixing, and output of sample proteins.

![](https://static.igem.wiki/teams/5553/finalddl/hardware/hardwareadd/5.avif)

Design Blueprint of the Third Version of 3D Printed Chip Cartridge

![](https://static.igem.wiki/teams/5553/finalddl/hardware/hardwareadd/6.avif)

Physical Image of the Third Version of 3D Printed Chip Cartridge

![](https://static.igem.wiki/teams/5553/finalddl/hardware/hardwareadd/7.avif)

Application scenarios of the Third Version of 3D Printed Chip Cartridge

#### Discussion

In response to changes in design requirements, we iterated to version 2.0, which includes two improvements over version 1.0: first, due to the iteration of the main chip, its dimensions changed, and accordingly, the main chip slot was adjusted to adapt to this change; second, during experiments using the main and auxiliary chips, we found that when the auxiliary chip was placed flat, on one hand, the liquid input into the auxiliary chip could not flow smoothly into the channels and often leaked from another sample hole, and on the other hand, the thin tubes connecting the two chips were prone to liquid stagnation. Therefore, we designed a trapezoidal platform to adjust the positional relationship between the two chips, placing the auxiliary chip on a platform diagonally above the main chip to prevent liquid stagnation in the tubes; meanwhile, tilting the auxiliary chip ensured that the input liquid flows smoothly through the tubes due to gravity.

After version 2.0, we conducted software simulation tests and proposed version 3.0 based on identified issues, with two further improvements: first, since the auxiliary chip could not be well fixed due to its tilted placement and mismatched slot dimensions, a clamping component was added; second, the solid trapezoidal platform consumed excessive 3D printing material, so the lower part of the platform was hollowed out and support components were added to minimize material consumption while ensuring mechanical stability.

#### Outlook

Future optimization directions mainly involve three aspects: in terms of material upgrading, if the device needs to resist organic solvents or have stronger cleaning capabilities, the housing material can be replaced with SLS material; regarding hose specification optimization, if plugging and unplugging resistance is excessive, hoses with an outer diameter of 1.3mm or 1.5mm can be selected and matched with different drill bit diameters, or a small amount of silicone-free lubricant can be applied to the PDMS hole openings before inserting the hoses; in terms of size and tolerance adjustment, the device shape should be fine-tuned based on the assembly experience of the first print to ensure that the PLA material is not warped under pressure.

### Integrated Architecture

#### Introduction

Our integrated architecture takes the microfluidic chip as the core component, with pipetting pumps serving as the medium for transporting liquids to be reacted. Inside the microfluidic chip, a fluorescence detection device enables the measurement of protein affinity, and all devices are integrated within a 3D-printed housing. We have also developed a supporting detection platform on the host computer, which allows for the control of the entire device and the output of result curves.

![](https://static.igem.wiki/teams/5553/finalddl/fc/1.avif)

The overall structure of the integrated architecture

#### Aim

Designed with Fluorescence Cross-Correlation Spectroscopy (FCCS) as the core detection principle, this integrated architecture aims to integrate fluid driving, high-throughput detection, and software control functions, constructing an integrated system with hardware-software synergy. It addresses the issues of low manual operation efficiency, high sample consumption, and poor data reproducibility in traditional protein affinity detection. Specific objectives include:

* Balancing detection accuracy and cost through the differentiated selection of fluid driving equipment (syringe pumps for protein solution delivery, peristaltic pumps for buffer delivery)
* Achieving high-throughput screening capabilities via the collaborative architecture of the main and auxiliary chips, which enables "single sample loading for multi-concentration gradient generation + sequential automatic detection"
* Developing supporting control software to realize the integration of device control, experimental process management, and data analysis. It also adapts to scenarios such as precise laboratory detection and on-site demonstration, reducing the operation threshold while ensuring data reliability.

#### Result

In terms of the fluid driving system, we finally confirmed the division of labor scheme: "YHPLC0100S micro-syringe pump + *Longer* BT100-2J peristaltic pump". The syringe pump, with a flow rate range of 0.01–98 mL/min and a stepper motor precision of 0.0003125 mm, achieves low shear force and pulse-free delivery of protein solutions. The peristaltic pump, featuring a flow rate range of 0.002–380 mL/min and multi-pump head compatibility, meets the requirements for low-cost and high-throughput buffer delivery. The two work in synergy to ensure the accuracy and efficiency of fluid driving.

For high-throughput detection technology, based on the collaborative architecture of the main and auxiliary chips, the auxiliary chip’s dendritic dilution network enables automatic generation of multi-gradient concentrations with a single sample loading. Combined with the sequential measurement protocol, the software can automatically trigger signal acquisition and data calculation. Compared with manual control, the introduction of automation in our system significantly improves experimental efficiency and reduces sample consumption.

The software system is developed based on the Streamlit framework, implementing global state management through the session\_state mechanism. It integrates device control, experimental process automation, real-time data analysis, and visualization functions, with built-in emergency stop and state reset modules. Non-professionals can quickly master the operation.

Tools: <https://github.com/Quanyuyuyu/iGEM2025-Hardware.git>

#### Discussion

The current integrated architecture still faces several challenges:

* The single-channel design of the syringe pump limits multiple sets of parallel experiments;
* The hoses of the peristaltic pump are prone to wear after long-term use, affecting stability;
* The software only supports fixed experimental processes and lacks custom protocols and complex data mining functions;
* Detection of low-concentration samples is easily interfered by background signals, requiring further algorithm optimization to improve the signal-to-noise ratio (SNR).

#### Outlook

Future efforts will advance the optimization of the architecture from three aspects: hardware upgrading, software expansion, and scenario extension.

* **Hardware end**: Plan to develop multi-channel integrated syringe pumps to reduce equipment footprint; explore intelligent peristaltic pumps with hose wear monitoring; introduce micro-flow sensors to build a "pump-sensor-software" closed-loop control system, aiming to minimize flow rate errors to the greatest extent.
* **Software end**: Intend to develop a custom experimental protocol module; integrate machine learning algorithms to realize automatic parameter optimization and abnormal data identification; add cloud-based data management functions to support collaboration and data security.
* **Application scenarios**: Adapt to antibody-target screening in the biopharmaceutical field, rapid sample analysis in clinical diagnosis, and technology demonstration needs in scientific research and teaching. This will promote the development of the architecture toward multi-functionality and high adaptability.

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