OPUS-Bio

OPUS-ET-AGENT: agent-conducted cryo-electron tomography Cryo-electron tomography (cryo-ET) is the only technique that resolves macromolecules at near-molecular detail inside intact cells. But turning raw tilt-series into in-cell 3D maps normally takes a week of hand-driving several specialist packages (WARP, AreTomo2, PyTOM, OPUS-ET, M), with expert judgment gating nearly every step — which is why in-cell structural biology remains labor-intensive and low-throughput. In this project, we put Claude Code in the driver's seat as a supervised-autonomy "conductor." It discovers the toolchain, configures and submits cluster jobs, runs QC at each stage, and then stops at each scientific checkpoint ("gate") to hand the decision to a human — backed by the evidence it has just computed. We defined five gates that guard processing quality: alignment QC, particle-picking QC, compositional-state selection, resolution, and joint refinement. On a real dataset (EMPIAR-10988), the agent drove two molecular species to high resolution at once: a ribosome to 7.76 Å and the far rarer fatty-acid synthase (FAS) to 13.88 Å. It did this by importing both into a single refinement population, so multi-particle refinement solves the shared tilt-series geometry using every particle from both species — the abundant ribosomes anchor the model while the sparse FAS rides along, gaining roughly 12 Å. Both maps were then placed back at every particle's pose inside the tomogram, reconstructing molecular sociology straight off the data. The key challenge: template matching is run for recall, so its picks are noisy and heavily over-picked. Claude Code leverages OPUS-ET's per-particle heterogeneity analysis to separate signal from junk, clustering each species' candidates into compositional states where the genuine high-resolution population stands out. For the sparse FAS, most of its 30 latent states are noise; only the clean D3 barrels — identifiable by their three-fold central pore — emerge as the real population. That per-particle separation of true species from a noisy template-matching set is what lets a rare complex resolve at all. Why it matters Operationally: a weeks-long, expert-only workflow becomes reproducible, test-covered (187 tests), and agent-driven. The agent explores, runs experiments, and presents the evidence, while the scientist keeps command of every call that matters. Along the way it even caught two silent, crash-free scientific bugs — a CTF pixel-size mis-scaling and a missing-wedge sign error — that all-green tests would never have surfaced. Biologically: placing every molecule back at its true position turns isolated structures into a map of who sits next to whom — molecular sociology. The crowding and neighbor relationships revealed here can never be seen in purified single-particle structures. By determining and interpreting how molecules pack in space, subtler in-cell mechanisms become discoverable. Where we're headed OPUS-ET already resolves each particle's conformational and compositional state. Next, by drawing on Claude Code's visualization abilities, we can integrate per-particle state directly into the in-cell map — labeling each placed molecule not just by position but by the functional state it occupies there. This would let OPUS-ET-AGENT deliver state-resolved molecular sociology: far richer biological context than structure or position alone, and a route to molecular-level mechanisms you can only see when you know both where a molecule is and what it's doing.

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