Coding

Part:BBa_K5151019

Designed by: KUO, YUN-HSIN   Group: iGEM24_NYCU-Formosa   (2024-09-01)

Lpp-OmpA 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.
Finally, we used the model to identify the top ten diseases with high relevance and selected leukemia as the target disease for rapid detection to validate the feasibility of our strategy. Based on the results generated by the model, we chose CD97 as the biomarker for leukemia from all the proteins associated with the disease. Subsequently, we aim to use an electrochemical sensor to detect the expression levels of CD97 to determine whether the patient has the disease[2].
To detect the expression levels of CD97, we need to find its receptor. In our project, we will attach the receptor to the Lpp-OmpA-GS linker sequence and transform a plasmid containing the Lpp-OmpA-GS linker-receptor sequence into E. coli for expression, allowing the receptor to be displayed on the surface of the bacteria. When it binds to CD97 in the sample, we will obtain an electrochemical signal from the binding event. Finally, we will integrate and analyze these binding electrochemical signals to determine whether the patient has the disease[3].


More application of this part

Our team has developed a series of Lpp-OmpA platforms for the detection of leukemia-associated diseases.

  1. Lpp-OmpA-GS Linker(BBa_K5151007)
  2. Lpp-OmpA-GS Linker-GFP-6x His(BBa_K5151009)
  3. Lpp-OmpA-GS Linker-Protein G(BBa_K5151012)
  4. Lpp-OmpA-GS Linker-CD55(BBa_K5151010)
  5. Lpp-OmpA-GS Linker-2BOU-2(BBa_K5151006)
  6. Lpp-OmpA-GS Linker-2BOU-3(BBa_K5151015)

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
    INCOMPATIBLE WITH RFC[25]
    Illegal NgoMIV site found at 388
  • 1000
    COMPATIBLE WITH RFC[1000]


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Parameters
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