# sonrg

- **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:** [Ryeogang Son](https://cerebralvalley.ai/u/pulzang)
- **GitHub:** https://drive.google.com/file/d/1qDgJ2B9fEnjECtoG8DJhE80ZFUiTcuw8/view?usp=sharing
- **Demo video:** https://youtu.be/EoF65RKxpH8
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/33

Project: We benchmarked whether Boltz-2 (an AlphaFold-class structure predictor) can substitute for a binding measurement, testing two complementary questions across two systems: does its confidence score rank affinity (SH2 domains vs. measured KD panels for c-Src and Fyn), and does it detect a damaged interface (MDM2:PMI under site-saturation mutagenesis).

What we found: Boltz-2 reliably separates binders from non-binders — free two-peptide competition is the best readout (65% c-Src, 81% Fyn) — and maps which residues matter with anchor-localized, chemically-coherent sensitivity. But it cannot resolve fine affinity rank among true binders (the Fyn 1-log test collapses, residual pointing the wrong way), and the peptide-linker fusion trick does not transfer to a folded protein–protein interface (barnase–barstar fails at every linker length, even femtomolar wild-type).

Why it matters: It draws a clear, evidence-backed boundary for using structure-prediction confidence as an affinity proxy — sound for binder/non-binder triage and interface-hotspot reading, unreliable for quantitative ranking — so users know which question the score can actually answer before they trust it.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/33. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt