Highly accurate protein structure prediction with AlphaFold

Abstract Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. 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.

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• 1 Through an enormous experimental effort 1–4 , 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 .
• 2 Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure.
• 3 Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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