We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. Despite recent progress 10, 11, 12, 13, 14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the ‘protein folding problem’ 8-has been an important open research problem for more than 50 years 9. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure.

Through an enormous experimental effort 1, 2, 3, 4, the structures of around 100,000 unique proteins have been determined 5, but this represents a small fraction of the billions of known protein sequences 6, 7. Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Nature volume 596, pages 583–589 ( 2021) Cite this article Highly accurate protein structure prediction with AlphaFold

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