Jake Lawrence · AI Systems / Structural Biology / STS · AI Systems theme
AlphaFold made the whole protein universe legible, and it ships a per-residue confidence that tells you exactly where to trust it. Rotate a real prediction of p53 and watch its certain core give way to tails the model honestly cannot place.
An interactive essay on AlphaFold, DeepMind's protein-structure prediction system, read through the site's legibility lens. For fifty years a protein's amino-acid sequence was easy to read and its folded shape was not, so shapes were measured one at a time; decades of that work left on the order of 200,000 experimental structures against hundreds of millions of known sequences. In 2020 AlphaFold2 reached a median 92.4 GDT at the CASP14 blind test, near experimental accuracy, and the open AlphaFold Protein Structure Database now covers over 214 million sequences, free and CC-BY. The signature interaction is a real AlphaFold prediction of human p53, rotatable and colored by the model's own per-residue confidence (pLDDT): a compact high-confidence core flanked by disordered tails the model correctly marks as unplaceable, not failed. The reading, in the series' idiom: AlphaFold is the rare instrument that renders something newly legible and, in the same breath, publishes the borders of its own legibility. Content-as-code figures re-derive live and in CI; the structure parses from AlphaFold DB entry AF-P04637-F1. Covers AlphaFold 3 and the 2024 Nobel Prize in Chemistry, with the standing caveat that a prediction is not an experiment.
Classification as Infrastructure names the pattern where a category system does the quiet work of deciding what is visible while staying invisible itself. AlphaFold inverts it: a legibility instrument that ships a per-residue confidence, drawing the borders of what it can and cannot see onto the map itself instead of hiding them.
Seeing Like an AI Company reads a frontier lab's proposal to classify the AI industry from the inside and finds the problems it will not name. AlphaFold is the constructive counterpoint from the same industry: an AI system that renders something newly legible and publishes, residue by residue, exactly where not to trust it.
Two readings of legibility from opposite directions. The Legibility Gap measures what a wartime state cannot see about its own damaged homes; AlphaFold measures the protein universe into view and then marks, in low confidence, the regions where no single structure exists to be seen.
An interactive reading of a real prediction, colored by the model's own confidence. Subscribe and you hear when the next interactive essay ships. It is the one list for the whole site, so you will get the rest of the work too. No schedule, easy out.
Back to the release table · See it on the network · Machine-readable catalog
I build the AI tools and automations a team adopts, and the full-stack apps and data pipelines under them, and ship them to production. Tell me the problem in a sentence and I'll give you an honest read on fit within a day.
Work with me →