Proof of Concept

We set out to answer the question: “Do our enzymes work, and under what conditions?” To test this, we performed enzymatic assays on our TfCut2 5ZOA wildtype by measuring pNPB hydrolysis and monitoring p-nitrophenol (pNP) release at 405 nm in a plate reader. Our goals were to confirm enzyme activity, map activity across pH and temperature, and compare wildtype to six engineered mutants.

Experimental Setup

• Enzymes: TfCut2 Wildtype (5ZOA) and Variants 1–6, equal molar concentration (1:9 with buffer)
• Substrate: pNPB stock in acetonitrile, diluted into Tris-HCl (2:7)
• Buffers: Tris-HCl, pH range 5–9
• Temperatures: 35–70 °C
• Readout: A405 absorbance
• Replicates: n = 3 per condition
• Controls: buffer + pNPB (no enzyme)

Assay Workflow

1. Add enzyme alongside pNPB substrate
2. Equilibrate buffer to target temperature
3. Incubate for 30 minutes
4. Measure kinetic readings

After the basic set up of the experiments, we developed a mechanism to analyze the data we acquired from the earlier assays.

Analysis

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques employed for modeling and analyzing problems in which a response of interest is influenced by several quantitative input variables. The primary objective of RSM is to optimize this response by identifying the optimal levels of the input factors.

In our case, RSM is employed to find the optimal condition for our pNPB assays. Our initial plan was to use the result we obtained from RSM as reference for PET degradation , due to the fact that the optimal condition for PET degradation is challenging to obtain. However, we later found that pNPB assay conditions could not be applied directly to PET degradation assays.

Although pNPB cannot directly predict PET degradation, it provides a useful reference to compare mutant activity against wildtype under identical conditions, especially for variants 4–6. Variants that performed well in the pNPB assay will also be prioritized in the PET degradation assays as we anticipated better degradation results.

Results

The graphs below were the RSM graphs that we generated via RStudio. By graphing activity across various temperatures and pH into a surface, we were able to predict the optimal conditions in which our enzymes should function.

Wild Type
Variant 1
Variant 2
Variant 3
Variant 4
Variant 5
Variant 6

With wildtype as the reference, the variants’ activities were being compared. From above, V3 is the best performing of all mutants with an activity of 2.976, followed closely by V2 at 2.955. Coming after wildtype were variants V1 and V5, with activities close to 2.8. V5 is also the best performing mutant that we obtain from machine learning. Its fellow generated mutants V4 performed also decently well, close to 2.2. V6 is the worst performing of the bunch, having activities below 0.3, which indicated almost no degradation.

This 2-D graph was derived from the combined results of wildtype and variants 1-6. By lining the activities of individual mutants, we were able to compare their effects to the wildtype. The chart shows alignment to our RSM results. V3 is still the best performing enzymes, with V6 being the worst.

Conclusion

We used pNPB assays as a preliminary test for our enzymes and their activities before moving on to later degradation. Although pNPBS do not decide our conditions for later tests, we still use the results as the key indication of how well the enzymes function. From our pNPB tests we are able to show that most of the variants, excluding variant 6, has functioned similarly well to our wildtype enzymes.

Extra Information

Enzymatic activity of TfCut2 was assessed in Tris-HCI buffer using the p-nitrophenyl butyrate (pNPB) assay across a range of pH values (6,7,8,9) and temperatures (45°C, 50°C, 55°C). The highest activity was observed at pH 8 and 45°C, with activity dropped sharply after pH 8 (Figure 1).

Subsequent experiments were conducted accordingly, focusing on a narrower range between pH 7 to 8.5 (7, 7.5, 8, 8.5) and lower temperatures (35°C, 45°C, 55°C). Optimal conditions across variants were determined to be pH 7.5 and 35°C. Under these conditions, Variant 1 and Variant 5 exhibited 2.7% and 10.9% higher activity than the wild-type 5ZOA, respectively. Variants 2, 3, and 4 showed 7.3%, 12.3%, and 12.5% lower activity, respectively. Variant 6 exhibited negligible activity across all pHs and temperatures (Figure 2).

Figure 1: pNPB Assay Results across pHs 6, 7, 8, 9 and temperatures 45°C, 50°C, 55°C. Absorbance was determined in a microplate reader at 405 nm. Error bars represent standard error.

Figure 2: Compiled pNPB Assay results from 6/28 to 8/3 across pHs 7, 7.5, 8, 8.5 and temperatures 35°C, 45°C, 55°C.

In a study regarding effects of various buffers on polyester hydrolase activity, Schmidt et al. (2016) found that Tris competitively inhibits aminopeptidases, aminopeptidases, cholinesterases, and hydrolases such as TfCut2 when used as a buffer. The activity of TfCut2 decreased when placed under the Tris buffer, especially as the molarity of Tris increased. Hence, we changed from using Tris buffer for pNPB and PET degradation assays to using HEPES buffer to prevent inaccurate results and improve TfCut2 ability.

Esterases such as TfCut1 or TfCut2 are not sufficiently thermostable at high temperatures, which are required for efficient PET degradation. Since mutagenesis and MD simulations show that the Ca²⁺-binding residues Asp204 and Gly205 are positioned within the loop that also contains His208 (a key member of the catalytic triad), adding Ca²⁺ cations to the enzymes reduces unwanted loop mobility. Because of this, thermostability is induced through the addition of Ca²⁺, enabling the melting point of the enzymes to increase 10.8 °C and 12.5 °C at most from the usual 71.2 °C (Then et al., 2014). Thus, we added Ca²⁺ to our HEPES buffer, which will allow TfCut2 to efficiently degrade PET substrates under elevated temperatures.

Overview

Polyester and cotton–polyester blends have regions of high crystallinity, where polymer chains are tightly packed in an ordered structure. These crystalline regions are highly resistant to enzymatic attack because enzymes cannot easily access or penetrate the compact arrangement of fibers. As a result, crystallinity poses one of the main barriers to efficient textile degradation.

Method

We performed an enzymatic assay based on the protocol that shows alkaline pretreatment combined with autoclaving at 121°C for 15 min greatly improves downstream hydrolysis of cotton-PET blends. Systematically, autoclave steam damages the matrix, letting NaOH depolymerize PET (forming terephthalate in sodium form), thus reducing crystallinity and roughening fibers.

Results

Visual changes of the textile were observed after pretreatment with 15% NaOH and autoclave for 15 min, followed by drying at 50°C for two days (Figure 1-3).

Figure 1: 100% PET textile. The left tube is after pretreatment and the right tube is before pretreatment.

Figure 2: Cotton and PET blend textile (Cotton:PET=65:35)

Figure 3: Cotton and PET blend textile (Cotton:PET=55:45)

Conclusion

This approach weakens the crystalline structure of the fibers, reduces resistance to enzymatic hydrolysis, and ultimately makes degradation easier for our engineered enzymes

Methods

To evaluate the effectiveness of our pretreatment and enzymatic degradation, we utilized four complementary analytical techniques. SEM and DSC were first applied to confirm pretreatment success, while FTIR and HPLC were used to track chemical and molecular changes during degradation. Together, these methods provide a comprehensive view of how our process impacts textile fibers.

SEM (Scanning Electron Microscopy): Visualizes fiber surfaces at high resolution, allowing us to observe physical changes such as cracks, roughness, and erosion.

DSC (Differential Scanning Calorimetry): Measures thermal transitions to assess crystallinity and stability, confirming whether pretreatment effectively reduces resistance to degradation.

FTIR (Fourier Transform Infrared Spectroscopy): Detects chemical bonds and functional groups, confirming chemical modifications during pretreatment and degradation.

HPLC (High-Performance Liquid Chromatography): Separates and quantifies breakdown products, enabling detection of monomers released from PET and cellulose.

To evaluate the effectiveness of our pretreatment strategy and enzymatic degradation, pretreated pure PET textiles were incubated individually with the seven enzymes under controlled conditions for defined durations. The resulting textiles were analyzed using SEM, FTIR and HPLC to respectively monitor the changes in film morphology, chemical structure and the formation of degradation products following enzymatic treatment.

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We further applied our analytical workflow to cotton–PET blended textiles, which pose a greater challenge than pure PET due to the interweaving of natural cotton fibers with PET, creating a complex structure that increases crystallinity and limits enzymatic accessibility. To assess pretreatment success and subsequent degradation, the pretreated cotton-PET blends were first incubated with our engineered cellulase cocktail to selectively remove cellulose, followed by incubation with our TfCut2 enzymes. This allows us to further assess their degradation efficiency on the remaining PET within the blended textiles. Complementary techniques including SEM, FTIR and HPLC were used to monitor morphological, chemical and molecular changes throughout the process.

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