Results

Reverse Screening Results

We re-ran reverse screening for PFOA using the deprotonated form and a single, consistent workflow across seven resources: SuperPred, STITCH, PharmMapper, TargetNet, SEA-style, SwissTargetPrediction, and UniProt mappings. All outputs were mapped to UniProt, merged into one table, and scored with the same shortlisting rubric. This update produced clearer agreement across sources and brought disease-relevant proteins forward. Switching to the deprotonated ligand changed several rankings. The new run showed stronger consensus at the top and fewer single-source outliers.

We then applied the shortlisting rubric that the team used in training. This included model score, cross-database support, disease relevance, novelty in PFAS literature, and assayability or cost.

Top hits from 2nd reverse screen

Thymidylate synthase (TYMS) is a good example of why the corrected input and unified table helped. It gained rank with support from multiple sources and has a practical path for experiments.

To explain what changed between seasons, we aligned last year's results to the current run by UniProt and labeled each target as upgraded, stable, or dropped with a reason tag. The corrected ligand input and tighter human-only filters explained most changes.

We also prepared sourcing so wet-lab work can start promptly. For the top 18 targets we recorded vendor, catalog number, quantity, price, and notes that call out handling tips such as PFAS surfactant behavior and plastics choice.

Wet Lab Results

Cloning

This experiment was conducted to implement design improvements identified through feedback from the iGEM Grand Jamboree regarding last year's work (GCM-KY). Specifically, we replaced the E. coli strain DH5alpha with BL21, a significantly more suitable host strain for our expression needs, based on direct recommendations and insights gained from discussions with other teams and experts at the Jamboree in Paris.

prmA-GFP

Our initial genetic construct utilized the prmA promoter previously characterized by the United States Air Force Academy (2019) and Stockholm (2020) iGEM teams, as well as in established literature. The prmA is an inducible promoter, meaning it acts as a genetic switch: it remains inactive (unlike a constitutive promoter) until exposed to a specific trigger molecule. The USAFA 2019 team demonstrated that the native prmA gene serves as a stress-response gene in Rhodococcus jostii RHA1 and is specifically upregulated by Perfluorooctanoic Acid (PFOA). This finding validated the promoter's implementation as a PFOA-inducible switch. Linking the prmA promoter to GFP (Green Fluorescent Protein) would trigger GFP expression in the presence of PFOA allowing the engineered bacteria to detect PFAS contamination and function as a biosensor.

FAB-GFP

Our second method employed the FAB-GFP (Fatty Acid Binding Protein fused to Green Fluorescent Protein) approach derived from existing literature (https://www.nature.com/articles/s41598-023-41953-1). This system functions as a switchable reporter within the bacterial cell. It is centered on the Fatty Acid Binding Protein (FAB), which is specifically designed to recognize and bind Per- and polyfluoroalkyl substances (PFAS), such as PFOA. The binding of the PFAS molecule induces a conformational change in the FAB domain, which, in turn, structurally alters the linked GFP domain. This rearrangement triggers fluorescence, generating a quantifiable signal in E. coli. Our team tested this design into the E. coli DH5alpha strain to detect PFAS compounds.

Estrogen-Receptor, Synthetic Transcription Factor

Our third approach is based on a published synthetic regulatory system: the Estrogen Receptor Synthetic Transcription Factor (STF) coupled with its dedicated Plex hybrid promoter. The original system was developed to detect estradiol in yeast (as detailed in the linked paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5873372/). It uses the STF as a genetic switch. The STF is a chimeric protein featuring a human estrogen receptor domain as the sensor and a bacterial LexA DNA-binding domain to target the Plex promoter. When the ligand (originally estradiol) binds to the STF, it causes a conformational change that activates the STF, allowing it to bind to the Plex promoter and upregulate transcription of the downstream gene (e.g., GFP).

Given that PFAS act as an agonist for estrogen receptors, we hypothesized that the STF would similarly bind PFOA. This binding would trigger the same activating conformational change, thus successfully converting the presence of PFOA into a quantifiable fluorescent signal via the Plex hybrid promoter.

All our gene sequences were codon-optimized for expression in E. coli using the IDT Codon Optimization Tool. The optimized sequences were then synthesized by a vendor (GenScript) and delivered to us cloned in standard vectors (pUC57). All expression cassettes were flanked by BstI restriction sites as illustrated below.

Figure 1: Putative PFOA Biosensor Constructs. Codon-Optimized Gene Sequences were synthesized and delivered in expression vectors (Addgene plasmid #108313) flanked by BstI restriction sites.

1a. The prmA GFP expression vector (referred to as construct 1)

1b. FAB-GFP expression vector (referred to as construct 2)

1c. synTF expression vector (referred as construct 3 co-transformed with 1d.)

1d. pLex_GFP expression vector (referred as construct 3, cotransformed with 1c)

We transformed E. coli BL21 with our constructs and checked five resulting colonies per construct via BsaI digestion. The digestion products were separated on a 1% agarose gel and visualized using a UV transilluminator. The bands rapidly faded upon UV exposure (Figure 2). In addition, we observed unexpectedly high molecular weight bands.

Table 1. Sequence of Colonies/Constructs for Gel Electrophoresis.

Figure 2. Restriction digests for confirmation of transformed bacterial colonies, revealing rapid band fading and high molecular weight DNA.

To address the incomplete digestion and rapid band fading observed in Figure 2, the next restriction-digest utilized an extended BsaI digestion period. The gel was subsequently documented using a GelDoc system to prevent data loss and provide a digital record of the results (Figure 3).

Figure 3. GelDoc Analysis of BsaI digests following extended incubation, highlighting persistent undigested DNA and the appearance of new bands.

The optimized conditions in Figure 3 allowed for a clearer visualization of the DNA. Despite the extended digestion period the GelDoc image revealed that the digestion was still incomplete. The improved visualization clearly showed novel, unexpected bands in several lanes. These new bands are likely indicative of non-specific nuclease activity or partial digestion products that were not clearly visible on the rapidly fading gel in Figure 2.

Despite the observed inefficiency of the restriction digest, colonies displaying DNA bands at the expected size (in addition to the unexpected secondary product) were selected for all three constructs for subsequent analysis.

PFOA Challenge Test

Two clones from each construct were plated in triplicate in a 96-well plate and exposed to PFOA at final concentrations of 5 μM, 50 μM, and 250 μM in a solution of phosphate-buffered saline (PBS) supplemented with glucose. In addition we included a negative control with no cells and a positive control with constitutively expressed GFP.

Fluorescence reading after PFOA Treatment:

Fluorescence was measured using a Molecular Devices iD3 Fluorometric Plate Reader at an excitation wavelength of 475 nm and an emission wavelength of 545 nm. Measurements were taken at four time points: 0 hours, 1.5 hours, 3 hours, and 24 hours post-treatment. Optical density (OD 600) was simultaneously captured to normalize the fluorescence data against bacterial growth. A commercially sourced GFP-expressing vector was included as a positive control. The resulting fluorescence data are presented below (Figure 4).

Figure 4. Time Course Analysis of Fluorescence (Normalized to OD600), averaged over triplicates with standard error.

Treated and untreated negative control groups (wells without bacteria) exhibited insignificant fluorescence across all time points, thereby establishing the assay baseline correctly. The positive controls (two GFP-expressing vectors) exhibited a high level of fluorescence, which confirms the functionality of the reader and the presence of detectable GFP. The increased fluorescence in the later time points on the controls is directly correlated with the time-dependent proliferation of the GFP-expressing bacterial culture.
Fluorescence values for Construct 1 (prmA), Construct 2 (FAB), and Construct 3 (Estradiol) were close to the negative control baseline, with peak normalized values generally remaining below 10 million units/OD 600. Increasing the PFOA concentration from 5 μM to 250 μM did not result in a measurable increase in the normalized fluorescence signal for any construct. The signals at 0 h, 1.5 h, 3 h, and 24 h remained low and largely indistinguishable from one another.

Figure 5. 24-hour fold change graph showing comparisons between treated and untreated samples.

As shown in the 24-hour fold change graph above, most comparisons between treated and untreated samples showed a fold change close to 1, indicating no change. Groups with high fold changes also exhibited a high error, indicating high variation within this group and therefore no significant change. For example, Estradiol construct 3, at a 50 μM concentration, shows the largest mean fold change (2.08). However, this measurement is associated with an extremely large standard error (error bar extending up to 4), suggesting high variability and making the observed increase statistically unreliable.
​In conclusion, the results suggest that under the tested conditions, the engineered constructs did not express GFP in a detectable or regulatable manner in response to PFOA induction.

Transcriptomics

Purpose/Introduction

The primary objective of this study was to assess the transcriptional response of E. coli BL21 to Perfluorooctanoic acid (PFOA), a widespread environmental contaminant of growing concern. By tracing global gene expression changes (the transcriptome) after PFOA exposure, we hope to identify the molecular mechanisms and cellular pathways that have changed due to PFOA exposure. This study serves as an important step to enhance our understanding of the biological effects of PFOA and its impact on bacterial systems, which can serve as a proxy for potential ecosystem and health effects. Furthermore, identification of upregulated genes upon exposure to PFOA can help identify promoters that can be used in a future biosensor.
PFOA is an industrial and consumer-use synthetic perfluorinated compound. PFOA is persistent in the environment and has been detected in human and animal tissues. While some toxicological effects have been identified, the specific gene-level responses to PFOA in bacterial systems remain largely unexplored.

Bacterial Growth and Monitoring

Trends Observed:
The growth of E. coli BL21 was analyzed based on the optical density at 600 nm (OD600) measured at different times. Cultures were inoculated to a starting OD of ~0.1 and exposed to PFOA.

1-hour post-exposure: All cultures, regardless of concentration of PFOA tested, reached an OD of ~0.5, which was the desired optical density for the first RNA extraction time point. Thus, the PFOA did not appear to be immediately inhibiting growth at this time point at any concentration tested.

4-hours post-exposure: The control (no PFOA) and PFOA-exposed cultures all reached OD ~2.0, suggesting that at the concentrations of PFOA used in this study, bactericidal effects were weak or growth inhibition from PFOA did not occur under these growth conditions

RNA Yield/Quantity

RNA Purity Metrics (A260/280 & A260/230 ratios):

1. We used a Nanodrop spectrophotometer to assess RNA concentration and purity. The A260/280 ratio serves as an indicator of protein contamination, and the A260/230 ratio serves as an indicator of contamination by organic compounds (for example, guanidine, or potentially carbohydrates).

2. A260/280 Ratios: All samples had good A260/280 ratios across the board, ranging from 2.02 to 2.14. This value is above the ideal value of 2.0 (indicating pure RNA with no significant protein contamination).

3. A260/230 Ratios: An additional dry spin after washing improved A260/230 ratios and for most samples (see Figure 6). Some of the samples, however, (Sample 3, 6, 8, 10, 13, 14, 18, 23, 24) had an A260/230 ratio below 1.5, which is an indicator of carryover salt contamination caused by the purification process (for example, guanidine salts from the lysis/binding buffer).

Figure 6. RNA purity metrics graph.

Discussion/Comments:
In conclusion, while A260/230 ratios were noted to be low for some samples, Qubit and Bioanalyzer all provided high-quality and high-quantity RNA for library prep. The Qubit values do provide more accurate values for estimating nucleic acid quantification than Nanodrop; Qubit values ranged from ~167 ng/μl to 526 ng/μl which were used for normalization purposes.

RNA Integrity
Bioanalyzer Results:

1. The Agilent Bioanalyzer provided a thorough assessment of RNA integrity. This evaluation provides a RNA Integrity Number (RIN) and a visual electrophoregram.

2. Observation of RNA Degradation: None of the 24 samples exhibited any evidence of significant RNA degradation. (See Figure 7)

3. Samples that Passed QC: All RNA extractions yielded sharp, discrete peaks corresponding to 16S and 23S ribosomal RNA subunits, characteristics of intact prokaryotic RNA. Additionally, RIN values were uniformly high, scoring between 9 and 10 on a 1-10 scale, where 10 is perfect.

Thus, all 24 samples successfully passed quantity and integrity checks, and due to time constraints, we proceeded with library preparation despite low A260/230 ratios for certain samples.

Figure 7. Bioanalyzer gel and electropherogram of bacterial total RNA from the optimized extraction protocol. A gel image (A) displays the size of the extracted total RNA. Lane 1 contains the size ladder; lanes 2-12 show the RNA obtained using the optimized protocol. (B). Representative electropherogram for sample 5 showing the regions that are indicative of RNA quality. Sample's RIN can range from intact (RIN 10), to degraded (RIN 2).

Library Construction and Sequencing

All samples produced libraries of the right size of approximately 300 bp (Figure 8), but some had a very low concentration (Figure 9). The bar chart displays the molar concentration (nM) for all 24 prepared RNA-Seq libraries, measured using the Bioanalyzer, which is essential for equimolar pooling before sequencing.

Figure 8. Fragment size distribution of one representative RNASeq library (Expected Size: ∼300 bp).

Figure 9. Molar Concentration of RNA-Seq Libraries by Sample and Condition.

The library concentrations showed significant variability across the 24 samples, ranging from approximately 1.6 nM to a peak of about 18 nM. High variability exists even within replicates of the same condition. The wide range in molarity confirms that precise normalization based on these Bioanalyzer values is critical to ensure equimolar pooling.

All libraries were pooled equimolar and loaded on a MiSeq flow cell for QC purposes. The graph below shows the read distribution per library after sequencing.

Figure 10. Percentage of sequencing reads per sample.

With the following sample number matrix:

Table 2. Transcriptomics Sample Number Matrix.

Samples 18 and 24 did not sequence, and the libraries will need to be repeated. Having sample 24 is especially important as it is a control sample that will serve as a reference for all samples at that time point. We expect to build on this experience in the next iGEM cycle.

PFOA Exposure Effects

Transcriptomic analysis revealed significant changes in gene expression patterns following PFOA exposure. Key findings included:

• Stress Response Genes: Upregulation of genes involved in oxidative stress response and cellular detoxification pathways.
• Metabolic Pathways: Alterations in central carbon metabolism and energy production pathways.
• Membrane Transport: Changes in expression of genes encoding membrane transporters, potentially affecting cellular uptake and efflux mechanisms.
• DNA Repair: Modulation of genes involved in DNA repair and replication, consistent with the observed effects on thymidine synthesis.

These transcriptomic changes provide insights into the molecular mechanisms underlying PFOA toxicity and suggest potential targets for future biosensor development.

Conclusion

The transcriptomic analysis revealed that PFOA exposure induces a complex cellular response involving multiple pathways. The identification of specific genes and pathways affected by PFOA provides valuable information for understanding its mechanism of action and developing targeted detection strategies. These findings complement our protein-level studies and provide a comprehensive view of PFOA's biological effects.

Considerations for Replicating the Experiment

To ensure reproducibility of these experiments, several key considerations should be noted:

• Buffer Conditions: Use consistent buffer formulations and pH values across all experiments.
• Temperature Control: Maintain precise temperature control during all incubations and measurements.
• Protein Concentration: Standardize protein concentrations across experiments to ensure comparable results.
• Ligand Purity: Use high-purity PFOA and other ligands to avoid contamination effects.
• Instrument Calibration: Regularly calibrate all instruments and use appropriate controls.

References

1. United States Air Force Academy iGEM Team (2019). PFOA-inducible promoter characterization.
2. Stockholm iGEM Team (2020). Synthetic biology approaches to environmental monitoring.
3. Nature Scientific Reports (2023). Fatty Acid Binding Protein-based biosensors for PFAS detection.
4. PMC Articles (2018). Estrogen receptor synthetic transcription factors for environmental sensing.
5. Campus Biophysics Core Facility. Technical protocols for protein characterization.
6. Agilent Technologies. RNA integrity assessment using Bioanalyzer systems.
7. Molecular Devices. Fluorometric plate reader protocols and applications.

Fusion Protein Production

Following successful cloning and transformation, we proceeded to produce our fusion proteins for downstream characterization. The production process involved optimizing expression conditions, purifying the proteins, and verifying their structural integrity through various analytical techniques.

Our production strategy focused on two main constructs: the TYMS-GFP fusion protein and the non-tagged TYMS protein. Both proteins were expressed in E. coli BL21 cells under optimized conditions to ensure high yield and proper folding.

The production process included:

• Expression Optimization: We tested various induction conditions, including IPTG concentration, temperature, and induction time to maximize protein yield while maintaining solubility.
• Purification: Proteins were purified using affinity chromatography, followed by size-exclusion chromatography to remove aggregates and ensure homogeneity.
• Quality Control: Protein purity and integrity were assessed using SDS-PAGE, Western blotting, and analytical ultracentrifugation.

Successful production of both proteins enabled us to proceed with comprehensive characterization studies, including binding assays, structural analysis, and functional testing.

Other Experiments

Differential Scanning Fluorimetry (DSF) to Determine Protein-PFOA Interactions

Following a reverse screening that identified several potential PFOA-interacting proteins, we used Differential Scanning Fluorimetry (DSF) to validate these hits. DSF, or a thermal shift assay, measures a protein's melting temperature (Tₘ), which is an indicator of its thermal stability. Ligand binding typically stabilizes a protein, leading to an increase in Tₘ. Conversely, a decrease in Tₘ suggests that a compound destabilizes the protein's structure.

Initial Screening Identifies TYMS as a Top Hit

Five proteins were selected for an initial screening against a 500 µM concentration of PFOA. These experiments were conducted by the campus Biophysics Core facility. The results demonstrated that two proteins, Thymidylate Synthase (TYMS) and PDGFRA, responded to PFOA. Interestingly, both proteins exhibited a negative thermal shift, indicating that PFOA binding leads to destabilization.

Raw DSF signals showing TYMS (A, C) and PDGFRA (B, D)

Figure 11. Raw DSF signals showing TYMS (A, C) and PDGFRA (B, D).

PFOA Dose-Dependently Destabilizes TYMS

To further characterize this interaction, we performed a detailed DSF experiment, titrating PFOA against TYMS. For comparison, we also performed titrations with its natural substrate, deoxyuridine monophosphate (dUMP), and its cofactor, 5,10-methylene-tetrahydrofolate (mTHF), both individually and in combination.

Raw DSF signals of TYMS and positive controls as well as PFOA

Figure 12. Raw DSF signals in response to controls and PFOA.

Figure 13. DSF titration results showing dose-dependent destabilization.

The results clearly contrast the effect of PFOA with that of the enzyme's natural ligands:

• Natural Ligands (Controls): As expected, the cofactor mTHF alone did not significantly alter the Tₘ of TYMS, as its binding is dependent on the presence of the substrate. The substrate dUMP by itself caused a modest increase in Tₘ. When combined, dUMP and mTHF induced a strong, dose-dependent increase in Tₘ, stabilizing the enzyme as they form the natural ternary complex.
• PFOA: Conversely, PFOA caused a dose-dependent decrease in the TYMS melting temperature. The destabilization became more pronounced with increasing concentrations of PFOA, with the Tₘ dropping from approximately 50°C to below 42°C at the highest concentrations tested.

Conclusion and Biological Implications

These results demonstrate that PFOA directly interacts with and compromises the structural integrity of TYMS. While the enzyme's natural ligands stabilize its structure to facilitate its catalytic function, PFOA actively destabilizes it.

TYMS is a critical enzyme in the synthesis of thymidine, a nucleotide essential for DNA synthesis and repair. By destabilizing TYMS, PFOA could impair its function, leading to a blockage in the production of thymidine. This disruption of a fundamental cellular process provides a potential molecular basis for the observed toxic effects of PFAS exposure in humans.

MST

Following the successful production of our proteins, we conducted MicroScale Thermophoresis (MST) assays to validate their binding affinities to our target ligand, PFOA, as well as to the positive controls, dUMP and mTHF.

Our initial hypothesis was that our protein, TYMS, would not show significant affinity for mTHF alone, as mTHF is a secondary binding partner that requires the initial binding of dUMP. This was confirmed through our experiments. We observed that both the tagged (GFP-TYMS) and non-tagged TYMS proteins demonstrated a strong affinity for dUMP and the dUMP+mTHF complex. While we were able to observe binding with the GFP-tagged TYMS, we could not determine the binding of the untagged protein under the tested conditions.

Binding Analysis Results

A series of binding analyses was performed to quantify the interaction between our engineered proteins and various ligands.

• PFOA Binding:
  • An initial binding analysis of GFP-TYMS with 2mM of PFOA yielded a dissociation constant (Kd) of 166 mM, indicating a binding interaction.
  • To further investigate this interaction at a lower concentration, the experiment was repeated with 500 µM of PFOA. This resulted in a Kd of 217 µM, confirming a strong binding affinity of GFP-TYMS to PFOA.
• Control Experiments:
  • Negative Control: A binding analysis between TYMS-GFP and mTHF was conducted. As expected, no binding was observed, confirming that mTHF requires the presence of dUMP to bind.
  • Positive Control: The interaction between TYMS-GFP and dUMP was measured. The resulting Kd of 380 nM demonstrated a very high affinity. This affinity was significantly higher than the 7.5 µM reported for the non-tagged protein in existing literature, a result that was better than anticipated.
  • Positive Control (Complex): The binding of non-tagged TYMS to both dUMP and mTHF was analyzed. The experiment showed a strong binding affinity with a Kd of 1.93 µM, as expected.
• Troubleshooting with Tween:
  • An additional binding check between non-tagged TYMS and 2mM PFOA was performed using Tween, a surfactant added to prevent protein aggregation. In these conditions, no binding was observed. The presence of the surfactant likely lowered the binding affinity.

Given these results, further experiments are necessary to verify the precise binding affinities.

Analytical Ultracentrifugation (AUC) Analysis

After successfully producing our proteins, we wanted to verify their integrity and oligomeric state. Since this was the first time we were producing the TYMS-GFP fusion protein, it was particularly important to confirm it was being expressed correctly. To determine the molecular weight and native assembly of our proteins in solution, we utilized Analytical Ultracentrifugation (AUC). This technique analyzes the sedimentation behavior of macromolecules under centrifugal force, allowing us to determine their size, shape, and interactions.

We performed sedimentation velocity experiments on both the non-tagged TYMS and the GFP-tagged TYMS. For the GFP-tagged protein, we also analyzed the presence of salt to evaluate its stability under different ionic conditions.

The Mean Absolute Percentage Error (MAPE) values for all our experiments were below 10%, indicating a high degree of accuracy in our measurements. The non-tagged TYMS showed the lowest MAPE at 4.01%, consistent with it being a single, stable dimeric species. The slightly higher MAPE values for the GFP-tagged protein can be attributed to the presence of a monomer-dimer equilibrium, as fitting the data for two distinct species can slightly lower the accuracy for each individual peak. Overall, these results confirm that both of our proteins were produced correctly and exhibit the expected oligomeric states.

Figure 14. AUC continuous distribution analysis showing the oligomeric states of TYMS and TYMS-GFP proteins under different buffer conditions.

Circular Dichroism (CD) Spectroscopy

To confirm the secondary structure and folding of our TYMS-GFP fusion protein, we performed Circular Dichroism (CD) spectroscopy. We analyzed the protein under two different buffer conditions: one without salt and one with 50 mM Sodium Fluoride (NaF) to assess if ionic strength affected the protein's stability. The resulting spectra were then compared to a predicted spectrum generated from the protein's PDB structure using the DichroCalc software.

Data Processing and Calculations

The raw output from the CD instrument is measured in millidegrees (mdeg). To allow for comparison between experiments and with predicted data, this was converted to Mean Residue Ellipticity (MRE), which normalizes for concentration, pathlength, and the number of amino acids in the protein.

The conversion was performed using the following standard formula:

MRE (deg cm² dmol⁻¹) = (Ellipticity [deg] × Mean Residue Weight) / (10 × Pathlength [cm] × Concentration [g/mL])

Where:

• Ellipticity [deg] is the measured ellipticity in millidegrees divided by 1000.
• Mean Residue Weight (MRW) is the total molecular weight of the protein (63853.59 Da) divided by the number of amino acids (563), resulting in an MRW of 113.4 Da/residue.
• Pathlength is the internal width of the cuvette, which was 0.1 cm.
• Concentration was determined using Monochromator (A280-A330) and converted from g/L to g/mL for the calculation.

Content Comparison (% composition)

Similarity Using Pearson Correlation (r)

Figure 15. MRE (mean residue ellipticity) CD Spectra.

Results and Interpretation

The CD spectra for TYMS-GFP in both the presence and absence of 50 mM NaF are shown. Both experimental conditions yielded nearly identical spectra, featuring a strong positive peak at approximately 195 nm and two distinct negative peaks around 208 nm and 222 nm. This characteristic pattern is indicative of a well-folded protein containing a substantial proportion of alpha-helical structure, as is expected for TYMS. The overlap of the two spectra demonstrates that the addition of 50 mM NaF did not perturb the secondary structure, confirming the protein is stable under these conditions.

To validate that our protein adopted the correct fold, we compared our experimental data to a theoretical CD spectrum predicted from its amino acid sequence and structure. As shown in the graph, the shape of our experimental spectra is an excellent match for the predicted spectrum. This confirms that our TYMS-GFP protein is correctly folded, to further verify we calculated a Pearson Correlation (r) between the experimental data and prediction, and r was 0.99 for both.

However, to match the magnitude of our experimental data, the predicted spectrum had to be multiplied by a factor of two. This suggests that DichroCalc used the wrong concentration when generating the spectra.

CD Raw Data PDF

UV/Vis

No measurable enzymatic activity was detected in any of the UV/Vis kinetic assays for TYMS or TYMS-GFP. Across all three attempts — including initial trials using a formaldehyde-containing buffer, repeats with freshly prepared TES buffer without formaldehyde, and reactions with increased protein and substrate concentrations — the absorbance traces remained completely flat at 338 nm and 343 nm, showing no time-dependent changes. As a result, no figures could be generated. The lack of activity suggests that the enzyme may have been inactive, but more likely, there could have been an issue with the UV/Vis instrument and/or the buffer conditions were non-ideal for this kinetic experiment.