Design mCherry–Nanobody Fusion

Inspiration

My Project Story: mCherry–Nanobody Fusion for Rapid HER2+ Cancer Cell Labeling

What Inspired Us

Cancer remains one of the most challenging diseases to diagnose and treat. In particular, HER2+ breast cancer requires rapid and accurate detection. Traditional antibody-based detection methods can be slow and expensive. I was inspired to design a smaller, faster, and more precise solution using nanobodies fused to a fluorescent protein. The idea was to combine the targeting precision of nanobodies with the visualization power of mCherry, a red fluorescent protein.

What We Learned

Throughout this project, I learned how computational biology tools like RFdiffusion, AlphaFold, and pLDDT analysis can guide protein design:

• RFdiffusion helped generate candidate protein backbones.
  
• AlphaFold predicted protein folding structures.
  
• pLDDT scores gave insight into model reliability.

I also learned about the importance of protein stability, binding affinity, and fluorescence performance in creating a practical fusion protein.

How We Built Our Project

The workflow was systematic and iterative:

1. Problem Definition
   Create a compact, effective fluorescent labeling system for HER2+ cancer cells.

2. Design Phase

   • Designed nanobody-mCherry fusions in silico.
     
   • Modeled structural compatibility.

3. Modeling Tools

   # Example pipeline
   rfdiffusion --input nanobody_sequence.fasta --output designs/
   alphafold --fasta designs/fusion.fasta --output predictions/
   plddt_extractor predictions/
   

What It Does

This project creates a fusion protein that binds specifically to HER2+ cancer cells and emits a bright red fluorescent signal. By combining a nanobody with mCherry, the system can:

• Quickly label HER2+ cells for imaging.
  
• Improve detection sensitivity compared to traditional antibodies.
  
• Enable real-time visualization of cancer cells in lab experiments.

Built With

• github