AlphaFold Catalogues 200 Million Protein Fold Predictions

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In summary, AlphaFold is an AI system developed by DeepMind that accurately predicts the 3D structure of proteins based on their amino acid sequence. The latest database release contains over 200 million entries from UniProt, providing extensive coverage. The value of this news story is dependent on the accuracy of the predictions, which is not quantitatively assessed. However, the Financial Times and Ars Technica have also reported on this breakthrough.
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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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1. What is AlphaFold and what does it do?

AlphaFold is a deep learning algorithm developed by Google's DeepMind that predicts the 3D structure of proteins. It uses a database of known protein structures to make predictions about the structure of new proteins.

2. How many protein fold predictions are included in the AlphaFold Catalogues?

The AlphaFold Catalogues currently contain 200 million protein fold predictions. These predictions cover a wide range of organisms, including bacteria, plants, and animals.

3. How accurate are the predictions made by AlphaFold?

According to DeepMind, AlphaFold has achieved an accuracy rate of 92.4% on the latest version of the Critical Assessment of Structure Prediction (CASP) competition, which is considered the gold standard for protein structure prediction. This is a significant improvement over previous methods.

4. How can scientists use the AlphaFold Catalogues in their research?

The AlphaFold Catalogues can be used by scientists to study the structure and function of proteins. This can help in understanding diseases and developing new treatments. It can also aid in drug discovery by identifying potential drug targets and designing more effective drugs.

5. Is the AlphaFold Catalogues data publicly available?

Yes, the AlphaFold Catalogues data is publicly available and can be accessed through the European Bioinformatics Institute (EBI) website. This allows scientists from all over the world to use the data in their research and contribute to the development of new insights and discoveries.

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