A reference manager
for the AI age

Highly accurate protein structure prediction with AlphaFold

Proteins are essential to life, and understanding their structure can help us understand their function. Through an enormous experimental effort, the structures of around one hundred thousand unique proteins have been determined — but this is a small fraction of the billions of known protein sequences. Structural coverage is bottlenecked by the months to years of painstaking work required to determine a single protein structure. 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 — has been an important open research problem for more than fifty years. Despite recent progress, 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 where no similar structure is known. We validated an entirely redesigned version of our neural-network-based model, AlphaFold, in the challenging fourteenth Critical Assessment of Protein Structure Prediction, 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 builds physical and biological knowledge about protein structure, and multiple-sequence alignments, into the design of the deep-learning algorithm.