# Duck-Kyun Yoo (Team DK)

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Duck-Kyun Yoo](https://cerebralvalley.ai/u/dk93js)
- **GitHub:** https://github.com/dk93js/gc_project, https://drive.google.com/drive/folders/19S4tkwlr4iTsHV2KyxBpoCAm1Cy821XO?usp=sharing
- **Demo video:** https://youtu.be/UPEfdLzsHrE
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/165

Germinal-center (GC) selection is almost always modeled along a single axis —
antigen affinity. But B cells are also selected on a second axis: tolerance —
how polyreactive/self-reactive an antibody is. That axis has been hard to use
because it was not measurable at repertoire scale. We make it measurable
directly from structure. We fold paired antibody Fv *monomers* from sequence
(Boltz-2, ESMFold-v1, Protenix) — deliberately avoiding antibody–antigen
complex prediction, which is still unreliable — and read a physicochemical
tolerance axis, surface-displayed CDR charge, off the fold. The score is
folder-invariant, agrees with crystallographic geometry, and transfers without
retraining to an autoimmune (type-1-diabetes) repertoire and to a
tissue-resolved human GC repertoire, where the axis is significantly
constrained in GC B cells. We report the result honestly, including the null
(as a black-box predictor, folded structure does not beat a charge baseline out
of distribution) and the one surviving charge-orthogonal signal. The output is
a mechanistic, reproducible coordinate that single-axis accounts of GC
selection have lacked.

Results (attached again just in case):
https://github.com/dk93js/gc_project, https://drive.google.com/drive/folders/19S4tkwlr4iTsHV2KyxBpoCAm1Cy821XO?usp=sharing

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