# ChannelScope

- **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:** [Rajiv Sangle](https://cerebralvalley.ai/u/rajiv_sangle)
- **GitHub:** https://github.com/Rajiv-Sangle/channelscope
- **Demo video:** https://youtu.be/EW113Vq_CRk
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/149

ChannelScope builds standardized, provenance-tracked context-graph objects for multi-omic biology — the structured groundwork that purpose-specific "omic" models need but rarely have. Multi-omic data arrives unstandardized, unlinked, and unprovenanced; ChannelScope assembles it into one portable, gene-agnostic object where every modality — structure, sequence, population genetics, energetics, function, clinical evidence — attaches to shared, typed entities (a variant, a residue, a conformational state), with a citation and a confidence flag on every claim. Standardization is what lets these objects compose — across omic layers, sources, and proteins — and it is FAIR by design (Accessible / Interoperable / Reusable strong; Findable honestly partial).

We prove it on the hardest honest test: 
RYR1, the ~2.2 MDa calcium-release channel behind malignant hyperthermia (MH) and the congenital myopathies — a ~5,000-residue-per-protomer tetramer that gates between closed, primed, and open states, beyond de-novo folding servers and known only through cross-species cryo-EM. Given a gene and a missense variant, ChannelScope assembles the best-available experimental evidence per conformational state, maps the variant onto each, and emits the object + a human-readable report + a self-contained interactive 3D viewer. It assembles evidence; it does not fold de novo — every region carries its template, species, and a confidence flag.

Why this is different: 
A pathogenicity scalar (AlphaMissense) gives no where, no what-it-touches, no state; AlphaGenome reads regulation, not protein structure; and folding the mutant is no answer — one substitution barely moves a predicted backbone (Buel & Walters 2022; WT-vs-mutant Cα RMSD 0.1–0.6 Å). The sharp point: a pathogenicity scalar and a folding ΔΔG share the same gain-of-function blind spot — both track fold stability, which gating variants barely change. ChannelScope adds the conformational-state + interface layer that resolves exactly that class — e.g. T4826I, pathogenic for MH yet ΔΔG-stabilizing, which a scalar/stability tool miscalls.

Validated, and honest about scope: 
On an 18-variant literature benchmark — independently re-derived through a second code path (Biopython + Biotite; proximity matched ≤ 0.1 Å) — it reproduced 18/18 numbering and 14/14 pathogenic structural buckets and mechanism directions; the engine carries 92 passing tests. It runs on one canonical protein frame (UniProt P21817 SV3 / RefSeq NM_000540.3) with protein-HGVS input — auto-normalizing arbitrary clinical input is a scoped roadmap item, flagged deliberately. Gene-agnostic (RYR2, CACNA1S, titin are config nodes, not rewrites). We intend to put ChannelScope to use at the 5th Undiagnosed Hackathon (Wilhelm Foundation · Singapore · Sep 17–20, 2026). Research / interpretation-support tool — not a diagnostic device.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/149. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt