Coding

Part:BBa_K5151004

Designed by: KUO, YUN-HSIN   Group: iGEM24_NYCU-Formosa   (2024-08-21)

2BOU-3 Introduction

Early detection of diseases is crucial. To achieve this, we can target disease biomarkers for testing to assist in early diagnosis. Currently, clinical tests for disease biomarkers, such as ELISA, RIA, and mass spectrometry, though widely used, tend to require longer detection times. Many have developed biosensors and rapid diagnostic tools (RDT). However, challenges still exist in detecting disease biomarkers, mainly due to the complexity of samples and the insufficient sensitivity and specificity of detection technologies.
To address these issues, our team has proposed a new strategy this year, utilizing a pre-trained Natural Language Processing (NLP) model to identify potential biomarkers for diseases[1]. Through this approach, we aim to overcome the current bottlenecks in detection technologies and provide a more efficient and cost-effective pathway for early diagnosis and timely treatment of diseases. This not only helps to shorten diagnostic times but also improves diagnostic accuracy and patient treatment outcomes.
Ultimately, our model identified the top ten highly relevant diseases, and we selected leukemia as the target for rapid detection to demonstrate the feasibility of our strategy. Based on the model’s results, we chose CD97 from all proteins associated with leukemia as the biomarker. Subsequently, we aim to use an electrochemical detector to measure the expression level of CD97 to determine whether a patient has the disease[2].
In our project, we hope to determine the expression level of CD97 through electrochemical detection, which requires us to find the receptor for CD97. Therefore, we introduced Generative AI to design short peptides capable of binding to the potential biomarker, specifically 2BOU-3. We then integrated these peptides into the detection system to enhance its functionality[3].


Analysis results

The following peptide structures have been generated using PEP-FOLD 4.0:

Figure 2. The structure of 2BOU-3.


CD97 is shown in green, the peptides in blue and the interacting residues of CD97 in dark green.

Figure 3. The docking result of 2BOU-3 and CD97.



According to the iGEMDOCK results, 2BOU-3 interacts with residues such as Cys-47, Ala-48, Ser-54, Cys-55, Ser-59, Asp-60, Cys-61, Trp-62.
Melania Capasso, Lindy G. Durrant, and Martin Stacey have discovered that CD55 would bind with CD97. Thus, we took CD55 to compare with our result.
The results of the local alignment are shown in the following figures:
Figure 4. The local alignment result of 2BOU-3 and CD55 with the sequence of 2BOU-3.

2BOU-3 did not meet the minimum peptide length requirement for alignment, so there is no result for 2BOU-3.


Reference

[1] Available at: https://2024.igem.wiki/nycu-formosa/model
[2] Available at: https://2024.igem.wiki/nycu-formosa/results
[3] Available at: https://2024.igem.wiki/nycu-formosa/index.html

Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


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