Proteins are essential to life, and understanding their structure can facilitate 
a mechanistic understanding of their function. Through an enormous experimental 
effort1-4, the structures of around 100,000 unique proteins have been 
determined5, but this represents a small fraction of the billions of known 
protein sequences6,7. Structural coverage is bottlenecked by the months to years 
of painstaking effort required to determine a single protein structure. Accurate 
computational approaches are needed to address this gap and to enable 
large-scale structural bioinformatics. 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 years9. Despite recent 
progress10-14, existing methods fall far short of atomic accuracy, especially 
when no homologous structure is available. 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. 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. Underpinning the latest version 
of AlphaFold is a novel machine learning approach that incorporates physical and 
biological knowledge about protein structure, leveraging multi-sequence 
alignments, into the design of the deep learning algorithm.