The problem. Predicting a protein’s 3D structure from its sequence was a 50-year open problem. Physics-based folding didn’t scale; earlier ML got close-ish. Then at CASP14, AlphaFold2 predicted structures at roughly experimental accuracy for most targets, and the field’s central question changed overnight.

The idea. Two coupled representations: a multiple-sequence-alignment track (evolutionary covariation — which residues mutate together, hinting at contacts) and a pairwise track over residue pairs, refined together in the Evoformer by repeated attention. A final structure module places atoms directly in 3D, end-to-end, and the whole thing recycles its own output to iterate. It emits a per-residue confidence (pLDDT) that turns out to be trustworthy — you can tell where to believe it.

Why it matters. For someone whose day job is analysis, the underappreciated angle is that AlphaFold2 is as much a data and evaluation win as a modeling one: decades of curated PDB structures, a ruthless blind benchmark (CASP), and a well-posed loss. The architecture matters, but the scaffolding around it is what made the result real and reproducible — the same lesson that governs any serious pipeline. Downstream, predicted structures and the confidence score are now infrastructure: inputs to design (ProteinMPNN, RFdiffusion), annotation, and drug work.

Verdict. Genuinely field-defining, and the caveats are well-known and honestly relevant: it predicts a static structure, leans on deep MSAs (weak for orphan/designed sequences), and says little about dynamics, complexes, or the effect of point mutations. Read it for the architecture, but steal the meta-lesson about data and evaluation. Pairs naturally with ESMFold as “with MSA vs. without.”