AlphaFold Catalogues 200 Million Protein Fold Predictions

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AlphaFold, an AI system by DeepMind, predicts protein 3D structures from amino acid sequences with competitive accuracy to experimental methods. Its latest database release features over 200 million entries, significantly enhancing coverage of UniProt, the primary repository for protein sequences and annotations. The importance of AlphaFold's predictions hinges on their accuracy, though a comprehensive quantitative assessment of this accuracy is lacking. The database does provide confidence levels for individual protein segments, but an overall accuracy metric remains elusive. This development is considered a major breakthrough in protein research, with media outlets like Financial Times and Ars Technica highlighting its significance.
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The protein predictions are based on AI models. Overall quantitative accuracy does not seem to be reported.
In their (AlphaFold) words:
AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment. ... The latest database release contains over 200 million entries, providing broad coverage of UniProt (the standard repository of protein sequences and annotations).

The value of this as a news story is directly proportional to the accuracy of the predictions.
The AlphaFold Database includes information about their confidence for each protein segment, but I have not found an overall quantitativ assessment of its accuracy.
 
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Overall quantitative accuracy does not seem to be reported.
An average is probably not that meaningful as different proteins will have different importance to research.

This looks like a big breakthrough if the typical accuracy is good enough.
 

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