Viewer
See any protein.
Open any AlphaFold structure in your browser, coloured honestly by confidence.
One protein, one question. Every free, in-browser tool — filter by who you are, what kind, and status.
See any protein.
Open any AlphaFold structure in your browser, coloured honestly by confidence.
How close is the prediction to experiment?
Superpose an experimental structure and AlphaFold's prediction client-side for an honest RMSD diff.
Where could a drug bind?
A geometric binding-pocket finder — cavities ranked by a transparent heuristic. Geometry, not a druggability predictor.
Where do two chains touch?
Find the epitope/paratope interface of a complex from geometry. A geometric contact map, not an experimentally-mapped epitope.
What does this mutation change?
A phenotype → causal-gene → protein-structure explorer over well-established monogenic conditions. Not a predictor or diagnostic.
What does a real protein look like?
The same AlphaFold content in a Grade 5–7 voice, NGSS-aligned and COPPA-safe by design.
Can you sort every amino acid?
A COPPA-safe amino-acid sorting game. No accounts, no outbound links, nothing collected.
How is a cell built?
Guided, textbook-sourced tours of eight cell types. A small, curated starter set.
Which structures won the Nobel?
The structural-biology Nobel Prizes as one narrative timeline, feeding the Viewer and a collection.
How good has structure prediction become?
An honest, dated scoreboard of every CASP experiment (1994→2024) built from real published results.
Where is AI reaching the clinic?
A sourced, dated tracker of AI-in-clinical-trials milestones, with a strict no-causation posture.
Where is the money moving?
Public AI-biotech capital flows, every figure cited to a primary source, with an honest coverage gauge.
How do closed-loop labs discover molecules?
Landmark closed-loop discovery runs, each cited to its primary paper. A small, curated starter set.
What does the research say?
A reader over the Fold Commons research archive — search and read deep reports on AlphaFold and AI for biology.
Mechanism or purpose?
Call each protein claim mechanism or purpose under a shrinking timer — spot teleology and rewrite it as honest physics.
What gets through the pore?
Predict which particles pass real channels (aquaporin-1, GLUT4, CFTR), then see the live AlphaFold structure. A pore is not a wall with a doorman.
How does a motor really move?
Set the ATP level, predict the drift, and watch a jostled motor — a biased random walk, not a purposeful walk.
Do you trust where the domains sit?
A model can be crisp everywhere and still be unsure how its domains are arranged. Judge the arrangement, then read the real PAE.
Is that floppy bit broken or working?
Low-confidence regions are usually intrinsically disordered — and doing a job. Tag each region, then see the live UniProt annotation.
Does one changed letter matter?
Most changed amino acids change little; a few change everything. Draw a real variant, call its effect, and learn to calibrate.
Firm or fuzzy — where do the domains sit?
The gentle companion to HingeHunt: two crisp domains can still have a fuzzy relative place. Paint your prior, then reveal the PAE.
What does the model leave out?
A single-chain model is a starting point, not the whole machine. Spot the missing cofactor or partner, checked against live UniProt.
Rigid keyhole, or does it reshape?
Binding pockets are rarely rigid keyholes — many reshape on contact (induced fit). Predict rigid vs reshapes for real enzymes.
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