Wenn 80.000 Blutproben auf virtuelle Realität treffen: Mein Weg vom Proteomik-Paper zum VR-Moleküllabor

On 6:47 AM, an email from GEN arrived in my inbox. The subject: Alamar Biosciences and Nightingale Health, 80,000 samples from the UK Biobank, NULISA platform, and hundreds of blood proteins. I clicked the link, still clutching my coffee, feeling that familiar itch as a bioinformatician: the data was there, but the structure? I'm not a structural biologist.

My background is in bioinformatics, pipeline engineering, and the occasional Docker container for AlphaFold inference. Yet, I had stared at enough Ramachandran plots to know that a list of differentially expressed proteins is only an index. The book was in 3D. As the paper wasn't enough, I sat in my home office, an Advance Print of the Alamar Study open on my second monitor, a half-eaten apple beside the keyboard.

I put on the Meta Quest 3, launched NexoPeek, and there it was: TREM2 in the Professional PDB Viewer. No tutorial, no onboarding — straight into the viewer. TREM2's extracellular domain appeared before me, 2.1 Å in Röntgen resolution, room view enabled, passthrough transparent. I reached out, bare-hand tracking, scaled it to 3 meters.

I rotated it around the Y-axis with a hand gesture flip. The Ig-like folding, β-sandwich, glycosylated loop — spatially, not as a ribbon diagram in PyMOL. The viewer can do what it does (and what it can't) is technically nuanced. NexoPeek is not a molecular dynamics package. It's a networked visualization client with on-board analytics querying RCSB PDB, UniProtKB, AlphaFold DB, SWISS-MODEL, and PDBe.

Formats include .pdb, .cif, .mol2, .gro, .xyz, .dx, .xtc, .py. Local files are moved to the headset directory via SideQuest or USB transfer and appear under Local. The Mol* presets (Cartoon, Ball-and-Stick, Spacefill CPK, Molecular Surface, B-Factor Heat, Tube Trace) are one-tap switches. No scripting. For publication graphics, there's a screenshot function with a transparent background, 4K render in the headset.

It's not ChimeraX quality, but adequate for quick figures in grant applications. The most time-consuming feature I explored was AI Pocket Prediction. Two engines: PrankWeb/ P2Rank (cloud, GPU-accelerated, ~3-5 seconds per structure) and the built-in offline engine (runs on Snapdragon XR2 Gen 2, ~15-20 seconds, no internet connection needed).

I tested both on 2V7Q. The cloud result gave three pockets, with a top score of 0.87 at the ligand-binding site (known from literature). The offline result gave two pockets, with a top score of 0.81 and the same region. The difference lies in the sensitivity for flatter pockets — the cloud model had seen more training data. I take the docs seriously: analysis aids support, never replace, validated scientific pipelines.

I use pocket prediction as a hypothesis generator, not a lead optimizer. Structural alignment with Foldseek is the next step: TREM2 vs. TREML1, a paralog with 35% sequence identity. The Foldseek pipeline aligns the two structures, and the RMSD over 180 Cα is 1.2 Å. The differences in the loop region (residues 87-94) are immediately visualized as a halo — a red ring around the divergent region.

This is where VR shows its value: I walk around the overlay, see how the β-folds cover each other, where sidechains drift apart. In PyMOL, you would rotate with the mouse. Here, I stand beside it. The twelve tours — not just for students — surprised me. The first six pillars cover biotechnology fundamentals, hemoglobin (O₂ cooperativity), ATP synthase (rotary catalysis), glycolysis, trypsin (proteolytic mechanism), and medications like aspirin, caffeine, and penicillin.

Each tour runs 8-12 minutes in German or English, with real PDB structures (not cartoons). During narration, you can pause, rotate residues, and inspect them in 3D. Granular sequence control allows per-chain amino acid scrolling, residue pills with 3D halo projections, and pinning user-defined atoms. Isolating chains and visualizing divergent regions becomes intuitive.

For me, a first encounter with CRISPR-Cas9 structure (5F9R), this was more illuminating than three review articles. The Ligand Inspector and PubChem feature accelerated workflow, but TREM2 had no ligands. I dragged-and-dropped CID 12345678 (an example) to visualize its agonist. The distance calculator showed distances in angstroms (Distance calculator: trigger two atoms). This is not a replacement for AutoDock Vina or Glide.