Highly accurate protein structure prediction with AlphaFold

The landmark Nature 2021 paper describing AlphaFold2 — the system that solved the 50-year-old protein folding problem. Hassabis and team trained a neural network on the Protein Data Bank to predict 3D protein structure from amino acid sequence alone, achieving median accuracy comparable to experimental methods. AlphaFold uses a novel architecture combining multiple sequence alignments with residue pair representations and an iterative refinement module ('Evoformer') to capture both local and global structural constraints simultaneously. The result was GDT scores above 90 on the CASP14 benchmark — a score previously considered unattainable by computational methods. The paper represents the first time AI demonstrably solved a problem that had resisted the entire biological community for half a century, and triggered an immediate cascade of downstream applications: drug discovery, enzyme design, and fundamental biological research. Within two years, the AlphaFold Protein Structure Database contained predicted structures for virtually every known protein — over 200 million entries made freely available to the scientific community.